dataset stringlengths 0 82 | model_name stringlengths 0 150 | paper_title stringlengths 19 175 | paper_date timestamp[ns] | paper_url stringlengths 32 35 | code_links sequencelengths 1 1 | prompts stringlengths 105 331 | answer stringlengths 1 67 |
|---|---|---|---|---|---|---|---|
GSM8K | AlphaLLM (with MCTS) | Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing | 2024-04-18T00:00:00 | https://arxiv.org/abs/2404.12253v2 | [
"https://github.com/yetianjhu/alphallm"
] | In the paper 'Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing', what Accuracy score did the AlphaLLM (with MCTS) model get on the GSM8K dataset
| 92 |
Wildtrack | TrackTacular (Bilinear Sampling) | Lifting Multi-View Detection and Tracking to the Bird's Eye View | 2024-03-19T00:00:00 | https://arxiv.org/abs/2403.12573v1 | [
"https://github.com/tteepe/tracktacular"
] | In the paper 'Lifting Multi-View Detection and Tracking to the Bird's Eye View', what IDF1 score did the TrackTacular (Bilinear Sampling) model get on the Wildtrack dataset
| 95.3 |
ActivityNet-QA | TESTA (ViT-B/16) | TESTA: Temporal-Spatial Token Aggregation for Long-form Video-Language Understanding | 2023-10-29T00:00:00 | https://arxiv.org/abs/2310.19060v1 | [
"https://github.com/renshuhuai-andy/testa"
] | In the paper 'TESTA: Temporal-Spatial Token Aggregation for Long-form Video-Language Understanding', what Accuracy score did the TESTA (ViT-B/16) model get on the ActivityNet-QA dataset
| 45 |
Youtube-VIS 2022 Validation | CTVIS (ResNet-50) | CTVIS: Consistent Training for Online Video Instance Segmentation | 2023-07-24T00:00:00 | https://arxiv.org/abs/2307.12616v1 | [
"https://github.com/kainingying/ctvis"
] | In the paper 'CTVIS: Consistent Training for Online Video Instance Segmentation', what mAP_L score did the CTVIS (ResNet-50) model get on the Youtube-VIS 2022 Validation dataset
| 39.4 |
HIDE | ALGNet-B | Learning Enriched Features via Selective State Spaces Model for Efficient Image Deblurring | 2024-03-29T00:00:00 | https://arxiv.org/abs/2403.20106v2 | [
"https://github.com/Tombs98/ALGNet"
] | In the paper 'Learning Enriched Features via Selective State Spaces Model for Efficient Image Deblurring', what PSNR score did the ALGNet-B model get on the HIDE dataset
| 31.68 |
fake | Trompt + OpenAI embedding | PyTorch Frame: A Modular Framework for Multi-Modal Tabular Learning | 2024-03-31T00:00:00 | https://arxiv.org/abs/2404.00776v1 | [
"https://github.com/pyg-team/pytorch-frame"
] | In the paper 'PyTorch Frame: A Modular Framework for Multi-Modal Tabular Learning', what AUROC score did the Trompt + OpenAI embedding model get on the fake dataset
| 0.979 |
CIFAR-100 (partial ratio 0.05) | ILL | Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations | 2023-05-22T00:00:00 | https://arxiv.org/abs/2305.12715v4 | [
"https://github.com/hhhhhhao/general-framework-weak-supervision"
] | In the paper 'Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations', what Accuracy score did the ILL model get on the CIFAR-100 (partial ratio 0.05) dataset
| 74.58 |
VoxCeleb1 | ReDimNet-B4-LM (6.3M) | Reshape Dimensions Network for Speaker Recognition | 2024-07-25T00:00:00 | https://arxiv.org/abs/2407.18223v2 | [
"https://github.com/IDRnD/ReDimNet"
] | In the paper 'Reshape Dimensions Network for Speaker Recognition', what EER score did the ReDimNet-B4-LM (6.3M) model get on the VoxCeleb1 dataset
| 0.51 |
WebApp1K-React | llama-v3p1-405b-instruct | Insights from Benchmarking Frontier Language Models on Web App Code Generation | 2024-09-08T00:00:00 | https://arxiv.org/abs/2409.05177v1 | [
"https://github.com/onekq/webapp1k"
] | In the paper 'Insights from Benchmarking Frontier Language Models on Web App Code Generation', what pass@1 score did the llama-v3p1-405b-instruct model get on the WebApp1K-React dataset
| 0.302 |
ImageNet | GTP-DeiT-B/P8 | GTP-ViT: Efficient Vision Transformers via Graph-based Token Propagation | 2023-11-06T00:00:00 | https://arxiv.org/abs/2311.03035v2 | [
"https://github.com/ackesnal/gtp-vit"
] | In the paper 'GTP-ViT: Efficient Vision Transformers via Graph-based Token Propagation', what Top 1 Accuracy score did the GTP-DeiT-B/P8 model get on the ImageNet dataset
| 81.5% |
COCO-Stuff Labels-to-Photos | SCDM | Stochastic Conditional Diffusion Models for Robust Semantic Image Synthesis | 2024-02-26T00:00:00 | https://arxiv.org/abs/2402.16506v3 | [
"https://github.com/mlvlab/scdm"
] | In the paper 'Stochastic Conditional Diffusion Models for Robust Semantic Image Synthesis', what mIoU score did the SCDM model get on the COCO-Stuff Labels-to-Photos dataset
| 38.1 |
GA1457 | DiffAug | DiffAug: Enhance Unsupervised Contrastive Learning with Domain-Knowledge-Free Diffusion-based Data Augmentation | 2023-09-10T00:00:00 | https://arxiv.org/abs/2309.07909v2 | [
"https://github.com/zangzelin/code_diffaug"
] | In the paper 'DiffAug: Enhance Unsupervised Contrastive Learning with Domain-Knowledge-Free Diffusion-based Data Augmentation', what Classification Accuracy score did the DiffAug model get on the GA1457 dataset
| 92.7 |
GoPro | M3SNet | A Mountain-Shaped Single-Stage Network for Accurate Image Restoration | 2023-05-09T00:00:00 | https://arxiv.org/abs/2305.05146v1 | [
"https://github.com/Tombs98/M3SNet"
] | In the paper 'A Mountain-Shaped Single-Stage Network for Accurate Image Restoration', what PSNR score did the M3SNet model get on the GoPro dataset
| 33.74 |
ChEBI-20 | MolReGPT (GPT-4-0413) | Empowering Molecule Discovery for Molecule-Caption Translation with Large Language Models: A ChatGPT Perspective | 2023-06-11T00:00:00 | https://arxiv.org/abs/2306.06615v2 | [
"https://github.com/phenixace/molregpt"
] | In the paper 'Empowering Molecule Discovery for Molecule-Caption Translation with Large Language Models: A ChatGPT Perspective', what Text2Mol score did the MolReGPT (GPT-4-0413) model get on the ChEBI-20 dataset
| 59.3 |
QM9 | PAMNet | A Universal Framework for Accurate and Efficient Geometric Deep Learning of Molecular Systems | 2023-11-19T00:00:00 | https://arxiv.org/abs/2311.11228v1 | [
"https://github.com/XieResearchGroup/Physics-aware-Multiplex-GNN"
] | In the paper 'A Universal Framework for Accurate and Efficient Geometric Deep Learning of Molecular Systems', what MAE score did the PAMNet model get on the QM9 dataset
| 0.136 |
Amazon Computers | GraphSAGE | Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification | 2024-06-13T00:00:00 | https://arxiv.org/abs/2406.08993v2 | [
"https://github.com/LUOyk1999/tunedGNN"
] | In the paper 'Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification', what Accuracy score did the GraphSAGE model get on the Amazon Computers dataset
| 93.25±0.14 |
CULane | CLRmatchNet (Enhancing curved lane, Resnet-101) | CLRmatchNet: Enhancing Curved Lane Detection with Deep Matching Process | 2023-09-26T00:00:00 | https://arxiv.org/abs/2309.15204v2 | [
"https://github.com/sapirkontente/clrmatchnet"
] | In the paper 'CLRmatchNet: Enhancing Curved Lane Detection with Deep Matching Process', what F1 score score did the CLRmatchNet (Enhancing curved lane, Resnet-101) model get on the CULane dataset
| 80.00 |
CocoGlide | Late Fusion | MMFusion: Combining Image Forensic Filters for Visual Manipulation Detection and Localization | 2023-12-04T00:00:00 | https://arxiv.org/abs/2312.01790v2 | [
"https://github.com/idt-iti/mmfusion-iml"
] | In the paper 'MMFusion: Combining Image Forensic Filters for Visual Manipulation Detection and Localization', what Average Pixel F1(Fixed threshold) score did the Late Fusion model get on the CocoGlide dataset
| .574 |
MassSpecGym | FraGNNet | MassSpecGym: A benchmark for the discovery and identification of molecules | 2024-10-30T00:00:00 | https://arxiv.org/abs/2410.23326v1 | [
"https://github.com/pluskal-lab/massspecgym"
] | In the paper 'MassSpecGym: A benchmark for the discovery and identification of molecules', what Cosine Similarity score did the FraGNNet model get on the MassSpecGym dataset
| 0.52 |
Replica | OpenIns3D | OpenIns3D: Snap and Lookup for 3D Open-vocabulary Instance Segmentation | 2023-09-01T00:00:00 | https://arxiv.org/abs/2309.00616v5 | [
"https://github.com/Pointcept/OpenIns3D"
] | In the paper 'OpenIns3D: Snap and Lookup for 3D Open-vocabulary Instance Segmentation', what mAP score did the OpenIns3D model get on the Replica dataset
| 15.4 |
MVBench | LongVU (7B) | LongVU: Spatiotemporal Adaptive Compression for Long Video-Language Understanding | 2024-10-22T00:00:00 | https://arxiv.org/abs/2410.17434v1 | [
"https://github.com/Vision-CAIR/LongVU"
] | In the paper 'LongVU: Spatiotemporal Adaptive Compression for Long Video-Language Understanding', what Avg. score did the LongVU (7B) model get on the MVBench dataset
| 66.9 |
UCF101 | PromptKD | PromptKD: Unsupervised Prompt Distillation for Vision-Language Models | 2024-03-05T00:00:00 | https://arxiv.org/abs/2403.02781v5 | [
"https://github.com/zhengli97/promptkd"
] | In the paper 'PromptKD: Unsupervised Prompt Distillation for Vision-Language Models', what Harmonic mean score did the PromptKD model get on the UCF101 dataset
| 86.10 |
THuman2.0 Dataset | SiTH | SiTH: Single-view Textured Human Reconstruction with Image-Conditioned Diffusion | 2023-11-27T00:00:00 | https://arxiv.org/abs/2311.15855v2 | [
"https://github.com/SiTH-Diffusion/SiTH"
] | In the paper 'SiTH: Single-view Textured Human Reconstruction with Image-Conditioned Diffusion', what CLIP Similarity score did the SiTH model get on the THuman2.0 Dataset dataset
| 0.8978 |
SMAC 26m_vs_30m | QPLEX | A Unified Framework for Factorizing Distributional Value Functions for Multi-Agent Reinforcement Learning | 2023-06-04T00:00:00 | https://arxiv.org/abs/2306.02430v1 | [
"https://github.com/j3soon/dfac-extended"
] | In the paper 'A Unified Framework for Factorizing Distributional Value Functions for Multi-Agent Reinforcement Learning', what Median Win Rate score did the QPLEX model get on the SMAC 26m_vs_30m dataset
| 78.12 |
CACD | ResNet-50-DLDL | A Call to Reflect on Evaluation Practices for Age Estimation: Comparative Analysis of the State-of-the-Art and a Unified Benchmark | 2023-07-10T00:00:00 | https://arxiv.org/abs/2307.04570v3 | [
"https://github.com/paplhjak/facial-age-estimation-benchmark"
] | In the paper 'A Call to Reflect on Evaluation Practices for Age Estimation: Comparative Analysis of the State-of-the-Art and a Unified Benchmark', what MAE score did the ResNet-50-DLDL model get on the CACD dataset
| 3.96 |
NTU RGB+D 120 | CHASE(CTR-GCN) | CHASE: Learning Convex Hull Adaptive Shift for Skeleton-based Multi-Entity Action Recognition | 2024-10-09T00:00:00 | https://arxiv.org/abs/2410.07153v1 | [
"https://github.com/Necolizer/CHASE"
] | In the paper 'CHASE: Learning Convex Hull Adaptive Shift for Skeleton-based Multi-Entity Action Recognition', what Accuracy (Cross-Subject) score did the CHASE(CTR-GCN) model get on the NTU RGB+D 120 dataset
| 91.3 |
Pittsburgh-30k-test | AnyLoc-VLAD-DINOv2 | AnyLoc: Towards Universal Visual Place Recognition | 2023-08-01T00:00:00 | https://arxiv.org/abs/2308.00688v2 | [
"https://github.com/AnyLoc/AnyLoc"
] | In the paper 'AnyLoc: Towards Universal Visual Place Recognition', what Recall@1 score did the AnyLoc-VLAD-DINOv2 model get on the Pittsburgh-30k-test dataset
| 87.66 |
MVTec AD | Dinomaly ViT-L (model-unified multi-class) | Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detection | 2024-05-23T00:00:00 | https://arxiv.org/abs/2405.14325v4 | [
"https://github.com/guojiajeremy/dinomaly"
] | In the paper 'Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detection', what Detection AUROC score did the Dinomaly ViT-L (model-unified multi-class) model get on the MVTec AD dataset
| 99.77 |
MixATIS | BiSLU | Joint Multiple Intent Detection and Slot Filling with Supervised Contrastive Learning and Self-Distillation | 2023-08-28T00:00:00 | https://arxiv.org/abs/2308.14654v1 | [
"https://github.com/anhtunguyen98/bislu"
] | In the paper 'Joint Multiple Intent Detection and Slot Filling with Supervised Contrastive Learning and Self-Distillation', what Accuracy score did the BiSLU model get on the MixATIS dataset
| 81.5 |
VoiceBank + DEMAND | aTENNuate | Real-time Speech Enhancement on Raw Signals with Deep State-space Modeling | 2024-09-05T00:00:00 | https://arxiv.org/abs/2409.03377v2 | [
"https://github.com/Brainchip-Inc/aTENNuate"
] | In the paper 'Real-time Speech Enhancement on Raw Signals with Deep State-space Modeling', what PESQ score did the aTENNuate model get on the VoiceBank + DEMAND dataset
| 3.27 |
Eynsham | BoQ | BoQ: A Place is Worth a Bag of Learnable Queries | 2024-05-12T00:00:00 | https://arxiv.org/abs/2405.07364v3 | [
"https://github.com/amaralibey/bag-of-queries"
] | In the paper 'BoQ: A Place is Worth a Bag of Learnable Queries', what Recall@1 score did the BoQ model get on the Eynsham dataset
| 92.2 |
UBody | Multi-HMR | Multi-HMR: Multi-Person Whole-Body Human Mesh Recovery in a Single Shot | 2024-02-22T00:00:00 | https://arxiv.org/abs/2402.14654v2 | [
"https://github.com/naver/multi-hmr"
] | In the paper 'Multi-HMR: Multi-Person Whole-Body Human Mesh Recovery in a Single Shot', what PVE-All score did the Multi-HMR model get on the UBody dataset
| 56.4 |
VietMed | GMM-HMM Tri | VietMed: A Dataset and Benchmark for Automatic Speech Recognition of Vietnamese in the Medical Domain | 2024-04-08T00:00:00 | https://arxiv.org/abs/2404.05659v2 | [
"https://github.com/leduckhai/multimed"
] | In the paper 'VietMed: A Dataset and Benchmark for Automatic Speech Recognition of Vietnamese in the Medical Domain', what Dev WER score did the GMM-HMM Tri model get on the VietMed dataset
| 61.3 |
UCR Anomaly Archive | DAGMM | Explainable Time Series Anomaly Detection using Masked Latent Generative Modeling | 2023-11-21T00:00:00 | https://arxiv.org/abs/2311.12550v5 | [
"https://github.com/ml4its/timevqvae-anomalydetection"
] | In the paper 'Explainable Time Series Anomaly Detection using Masked Latent Generative Modeling', what accuracy score did the DAGMM model get on the UCR Anomaly Archive dataset
| 0.061 |
FER2013 | VGG based | IdentiFace : A VGG Based Multimodal Facial Biometric System | 2024-01-02T00:00:00 | https://arxiv.org/abs/2401.01227v2 | [
"https://github.com/MahmoudRabea13/IdentiFace"
] | In the paper 'IdentiFace : A VGG Based Multimodal Facial Biometric System', what 5-class test accuracy score did the VGG based model get on the FER2013 dataset
| 66.13% |
USNA-Cn2 (long-term) | Air-Water Temperature Difference | Effective Benchmarks for Optical Turbulence Modeling | 2024-01-07T00:00:00 | https://arxiv.org/abs/2401.03573v1 | [
"https://github.com/cdjellen/otbench"
] | In the paper 'Effective Benchmarks for Optical Turbulence Modeling', what RMSE score did the Air-Water Temperature Difference model get on the USNA-Cn2 (long-term) dataset
| 1.046 |
SOD4SB Public Test | DL method (YOLOv8 + Ensamble) | MVA2023 Small Object Detection Challenge for Spotting Birds: Dataset, Methods, and Results | 2023-07-18T00:00:00 | https://arxiv.org/abs/2307.09143v1 | [
"https://github.com/iim-ttij/mva2023smallobjectdetection4spottingbirds"
] | In the paper 'MVA2023 Small Object Detection Challenge for Spotting Birds: Dataset, Methods, and Results', what AP50 score did the DL method (YOLOv8 + Ensamble) model get on the SOD4SB Public Test dataset
| 73.1 |
4D-OR | ORacle | ORacle: Large Vision-Language Models for Knowledge-Guided Holistic OR Domain Modeling | 2024-04-10T00:00:00 | https://arxiv.org/abs/2404.07031v1 | [
"https://github.com/egeozsoy/Oracle"
] | In the paper 'ORacle: Large Vision-Language Models for Knowledge-Guided Holistic OR Domain Modeling', what F1 score did the ORacle model get on the 4D-OR dataset
| 0.91 |
CSL-Daily | XmDA | Cross-modality Data Augmentation for End-to-End Sign Language Translation | 2023-05-18T00:00:00 | https://arxiv.org/abs/2305.11096v4 | [
"https://github.com/atrewin/signxmda"
] | In the paper 'Cross-modality Data Augmentation for End-to-End Sign Language Translation', what BLEU-4 score did the XmDA model get on the CSL-Daily dataset
| 21.58 |
SOTS Outdoor | OneRestore | OneRestore: A Universal Restoration Framework for Composite Degradation | 2024-07-05T00:00:00 | https://arxiv.org/abs/2407.04621v4 | [
"https://github.com/gy65896/onerestore"
] | In the paper 'OneRestore: A Universal Restoration Framework for Composite Degradation', what PSNR score did the OneRestore model get on the SOTS Outdoor dataset
| 35.58 |
ogbl-citation2 | GraphGPT(SMTP) | GraphGPT: Graph Learning with Generative Pre-trained Transformers | 2023-12-31T00:00:00 | https://arxiv.org/abs/2401.00529v1 | [
"https://github.com/alibaba/graph-gpt"
] | In the paper 'GraphGPT: Graph Learning with Generative Pre-trained Transformers', what Test MRR score did the GraphGPT(SMTP) model get on the ogbl-citation2 dataset
| 0.9055 ± 0.0016 |
REDDIT-BINARY | GIN + PANDA | PANDA: Expanded Width-Aware Message Passing Beyond Rewiring | 2024-06-06T00:00:00 | https://arxiv.org/abs/2406.03671v2 | [
"https://github.com/jeongwhanchoi/panda"
] | In the paper 'PANDA: Expanded Width-Aware Message Passing Beyond Rewiring', what Accuracy score did the GIN + PANDA model get on the REDDIT-BINARY dataset
| 91.055 |
Weather (336) | TSMixer | TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series Forecasting | 2023-06-14T00:00:00 | https://arxiv.org/abs/2306.09364v4 | [
"https://github.com/ibm/tsfm"
] | In the paper 'TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series Forecasting', what MSE score did the TSMixer model get on the Weather (336) dataset
| 0.243 |
CAMELYON16 | Snuffy (DINO Exhaustive) | Snuffy: Efficient Whole Slide Image Classifier | 2024-08-15T00:00:00 | https://arxiv.org/abs/2408.08258v2 | [
"https://github.com/jafarinia/snuffy"
] | In the paper 'Snuffy: Efficient Whole Slide Image Classifier', what AUC score did the Snuffy (DINO Exhaustive) model get on the CAMELYON16 dataset
| 0.987 |
Ego4D | EgoVideo | EgoVideo: Exploring Egocentric Foundation Model and Downstream Adaptation | 2024-06-26T00:00:00 | https://arxiv.org/abs/2406.18070v4 | [
"https://github.com/opengvlab/egovideo"
] | In the paper 'EgoVideo: Exploring Egocentric Foundation Model and Downstream Adaptation', what R@1 IoU=0.3 score did the EgoVideo model get on the Ego4D dataset
| 28.05 |
EQ-Bench | meta-llama/Llama-2-70b-chat-hf | EQ-Bench: An Emotional Intelligence Benchmark for Large Language Models | 2023-12-11T00:00:00 | https://arxiv.org/abs/2312.06281v2 | [
"https://github.com/eq-bench/eq-bench"
] | In the paper 'EQ-Bench: An Emotional Intelligence Benchmark for Large Language Models', what EQ-Bench Score score did the meta-llama/Llama-2-70b-chat-hf model get on the EQ-Bench dataset
| 51.56 |
ScanNet200 | OneFormer3D | OneFormer3D: One Transformer for Unified Point Cloud Segmentation | 2023-11-24T00:00:00 | https://arxiv.org/abs/2311.14405v1 | [
"https://github.com/oneformer3d/oneformer3d"
] | In the paper 'OneFormer3D: One Transformer for Unified Point Cloud Segmentation', what val mIoU score did the OneFormer3D model get on the ScanNet200 dataset
| 30.1 |
CelebA-HQ | WavePaint | WavePaint: Resource-efficient Token-mixer for Self-supervised Inpainting | 2023-07-01T00:00:00 | https://arxiv.org/abs/2307.00407v1 | [
"https://github.com/pranavphoenix/WavePaint"
] | In the paper 'WavePaint: Resource-efficient Token-mixer for Self-supervised Inpainting', what FID score did the WavePaint model get on the CelebA-HQ dataset
| 5.53 |
RAF-DB | ARBEx | ARBEx: Attentive Feature Extraction with Reliability Balancing for Robust Facial Expression Learning | 2023-05-02T00:00:00 | https://arxiv.org/abs/2305.01486v5 | [
"https://github.com/takihasan/arbex"
] | In the paper 'ARBEx: Attentive Feature Extraction with Reliability Balancing for Robust Facial Expression Learning', what Overall Accuracy score did the ARBEx model get on the RAF-DB dataset
| 92.47 |
PCQM-Contact | NeuralWalker | Learning Long Range Dependencies on Graphs via Random Walks | 2024-06-05T00:00:00 | https://arxiv.org/abs/2406.03386v2 | [
"https://github.com/borgwardtlab/neuralwalker"
] | In the paper 'Learning Long Range Dependencies on Graphs via Random Walks', what MRR-ext-filtered score did the NeuralWalker model get on the PCQM-Contact dataset
| 0.4707 ± 0.0007 |
Stanford Cars | DePT | DePT: Decoupled Prompt Tuning | 2023-09-14T00:00:00 | https://arxiv.org/abs/2309.07439v2 | [
"https://github.com/koorye/dept"
] | In the paper 'DePT: Decoupled Prompt Tuning', what Harmonic mean score did the DePT model get on the Stanford Cars dataset
| 77.79 |
MBPP | GPT-3.5 Turbo (ChatGPT) + AgentCoder | AgentCoder: Multi-Agent-based Code Generation with Iterative Testing and Optimisation | 2023-12-20T00:00:00 | https://arxiv.org/abs/2312.13010v3 | [
"https://github.com/huangd1999/AgentCoder"
] | In the paper 'AgentCoder: Multi-Agent-based Code Generation with Iterative Testing and Optimisation', what Accuracy score did the GPT-3.5 Turbo (ChatGPT) + AgentCoder model get on the MBPP dataset
| 89.9 |
FGVC Aircraft | TURTLE (CLIP + DINOv2) | Let Go of Your Labels with Unsupervised Transfer | 2024-06-11T00:00:00 | https://arxiv.org/abs/2406.07236v1 | [
"https://github.com/mlbio-epfl/turtle"
] | In the paper 'Let Go of Your Labels with Unsupervised Transfer', what Accuracy score did the TURTLE (CLIP + DINOv2) model get on the FGVC Aircraft dataset
| 36.5 |
SportsMOT | MeMOTR | MeMOTR: Long-Term Memory-Augmented Transformer for Multi-Object Tracking | 2023-07-28T00:00:00 | https://arxiv.org/abs/2307.15700v3 | [
"https://github.com/mcg-nju/memotr"
] | In the paper 'MeMOTR: Long-Term Memory-Augmented Transformer for Multi-Object Tracking', what HOTA score did the MeMOTR model get on the SportsMOT dataset
| 70.0 |
Automatic Cardiac Diagnosis Challenge (ACDC) | MIST | MIST: Medical Image Segmentation Transformer with Convolutional Attention Mixing (CAM) Decoder | 2023-10-30T00:00:00 | https://arxiv.org/abs/2310.19898v1 | [
"https://github.com/rahman-motiur/mist"
] | In the paper 'MIST: Medical Image Segmentation Transformer with Convolutional Attention Mixing (CAM) Decoder', what Avg DSC score did the MIST model get on the Automatic Cardiac Diagnosis Challenge (ACDC) dataset
| 92.56 |
Falko-MERLIN | Llama + 1M BT + gold | To Err Is Human, but Llamas Can Learn It Too | 2024-03-08T00:00:00 | https://arxiv.org/abs/2403.05493v2 | [
"https://github.com/TartuNLP/gec-llm"
] | In the paper 'To Err Is Human, but Llamas Can Learn It Too', what F0.5 score did the Llama + 1M BT + gold model get on the Falko-MERLIN dataset
| 76.75 |
FLIR | MiPa | MiPa: Mixed Patch Infrared-Visible Modality Agnostic Object Detection | 2024-04-29T00:00:00 | https://arxiv.org/abs/2404.18849v2 | [
"https://github.com/heitorrapela/mipa"
] | In the paper 'MiPa: Mixed Patch Infrared-Visible Modality Agnostic Object Detection', what AP 0.5 score did the MiPa model get on the FLIR dataset
| 0.813 |
IMDb-B | Graph-JEPA | Graph-level Representation Learning with Joint-Embedding Predictive Architectures | 2023-09-27T00:00:00 | https://arxiv.org/abs/2309.16014v2 | [
"https://github.com/geriskenderi/graph-jepa"
] | In the paper 'Graph-level Representation Learning with Joint-Embedding Predictive Architectures', what Accuracy score did the Graph-JEPA model get on the IMDb-B dataset
| 73.68% |
Charades-STA | UVCOM | Bridging the Gap: A Unified Video Comprehension Framework for Moment Retrieval and Highlight Detection | 2023-11-28T00:00:00 | https://arxiv.org/abs/2311.16464v1 | [
"https://github.com/easonxiao-888/uvcom"
] | In the paper 'Bridging the Gap: A Unified Video Comprehension Framework for Moment Retrieval and Highlight Detection', what R@1 IoU=0.5 score did the UVCOM model get on the Charades-STA dataset
| 59.25 |
Haze4k | CasDyF-Net | CasDyF-Net: Image Dehazing via Cascaded Dynamic Filters | 2024-09-13T00:00:00 | https://arxiv.org/abs/2409.08510v1 | [
"https://github.com/dauing/casdyf-net"
] | In the paper 'CasDyF-Net: Image Dehazing via Cascaded Dynamic Filters', what PSNR score did the CasDyF-Net model get on the Haze4k dataset
| 35.73 |
ColonINST-v1 (Unseen) | MobileVLM-1.7B
(w/ LoRA, w/ extra data) | MobileVLM : A Fast, Strong and Open Vision Language Assistant for Mobile Devices | 2023-12-28T00:00:00 | https://arxiv.org/abs/2312.16886v2 | [
"https://github.com/meituan-automl/mobilevlm"
] | In the paper 'MobileVLM : A Fast, Strong and Open Vision Language Assistant for Mobile Devices', what Accuray score did the MobileVLM-1.7B
(w/ LoRA, w/ extra data) model get on the ColonINST-v1 (Unseen) dataset
| 78.03 |
Atari 2600 Gravitar | ASL DDQN | Train a Real-world Local Path Planner in One Hour via Partially Decoupled Reinforcement Learning and Vectorized Diversity | 2023-05-07T00:00:00 | https://arxiv.org/abs/2305.04180v3 | [
"https://github.com/xinjinghao/color"
] | In the paper 'Train a Real-world Local Path Planner in One Hour via Partially Decoupled Reinforcement Learning and Vectorized Diversity', what Score score did the ASL DDQN model get on the Atari 2600 Gravitar dataset
| 760 |
AudioSet | DASS-Small (Audio-only, single) | DASS: Distilled Audio State Space Models Are Stronger and More Duration-Scalable Learners | 2024-07-04T00:00:00 | https://arxiv.org/abs/2407.04082v1 | [
"https://github.com/Saurabhbhati/DASS"
] | In the paper 'DASS: Distilled Audio State Space Models Are Stronger and More Duration-Scalable Learners', what Test mAP score did the DASS-Small (Audio-only, single) model get on the AudioSet dataset
| 0.472 |
Atari 2600 Kung-Fu Master | ASL DDQN | Train a Real-world Local Path Planner in One Hour via Partially Decoupled Reinforcement Learning and Vectorized Diversity | 2023-05-07T00:00:00 | https://arxiv.org/abs/2305.04180v3 | [
"https://github.com/xinjinghao/color"
] | In the paper 'Train a Real-world Local Path Planner in One Hour via Partially Decoupled Reinforcement Learning and Vectorized Diversity', what Score score did the ASL DDQN model get on the Atari 2600 Kung-Fu Master dataset
| 85182 |
NBMOD | RA-GraspNet (GraspNet with Rotation Anchor) | NBMOD: Find It and Grasp It in Noisy Background | 2023-06-17T00:00:00 | https://arxiv.org/abs/2306.10265v1 | [
"https://github.com/kmittle/grasp-detection-nbmod"
] | In the paper 'NBMOD: Find It and Grasp It in Noisy Background', what Acc score did the RA-GraspNet (GraspNet with Rotation Anchor) model get on the NBMOD dataset
| SSS: 99.2%, NSS: 98.8% |
Weather2K850 (192) | MoLE-DLinear | Mixture-of-Linear-Experts for Long-term Time Series Forecasting | 2023-12-11T00:00:00 | https://arxiv.org/abs/2312.06786v3 | [
"https://github.com/rogerni/mole"
] | In the paper 'Mixture-of-Linear-Experts for Long-term Time Series Forecasting', what MSE score did the MoLE-DLinear model get on the Weather2K850 (192) dataset
| 0.484 |
SPair-71k | GMT-BBGM | GMTR: Graph Matching Transformers | 2023-11-14T00:00:00 | https://arxiv.org/abs/2311.08141v2 | [
"https://github.com/jp-guo/gm-transformer"
] | In the paper 'GMTR: Graph Matching Transformers', what matching accuracy score did the GMT-BBGM model get on the SPair-71k dataset
| 0.8296 |
NYU Depth v2 | SGACNet (R18-NBt1D) | Spatial-information Guided Adaptive Context-aware Network for Efficient RGB-D Semantic Segmentation | 2023-08-11T00:00:00 | https://arxiv.org/abs/2308.06024v1 | [
"https://github.com/mvme-hbut/sgacnet"
] | In the paper 'Spatial-information Guided Adaptive Context-aware Network for Efficient RGB-D Semantic Segmentation', what Mean IoU score did the SGACNet (R18-NBt1D) model get on the NYU Depth v2 dataset
| 48.2% |
ETTm2 (192) Multivariate | PRformer | PRformer: Pyramidal Recurrent Transformer for Multivariate Time Series Forecasting | 2024-08-20T00:00:00 | https://arxiv.org/abs/2408.10483v1 | [
"https://github.com/usualheart/prformer"
] | In the paper 'PRformer: Pyramidal Recurrent Transformer for Multivariate Time Series Forecasting', what MSE score did the PRformer model get on the ETTm2 (192) Multivariate dataset
| 0.219 |
Traffic (336) | TSMixer | TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series Forecasting | 2023-06-14T00:00:00 | https://arxiv.org/abs/2306.09364v4 | [
"https://github.com/ibm/tsfm"
] | In the paper 'TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series Forecasting', what MSE score did the TSMixer model get on the Traffic (336) dataset
| 0.385 |
MM-Vet | Ferret-v2-13B | Ferret-v2: An Improved Baseline for Referring and Grounding with Large Language Models | 2024-04-11T00:00:00 | https://arxiv.org/abs/2404.07973v1 | [
"https://github.com/apple/ml-ferret"
] | In the paper 'Ferret-v2: An Improved Baseline for Referring and Grounding with Large Language Models', what GPT-4 score score did the Ferret-v2-13B model get on the MM-Vet dataset
| 35.7 |
Human3.6M | ZeDO (S=50) | Back to Optimization: Diffusion-based Zero-Shot 3D Human Pose Estimation | 2023-07-07T00:00:00 | https://arxiv.org/abs/2307.03833v3 | [
"https://github.com/ipl-uw/ZeDO-Release"
] | In the paper 'Back to Optimization: Diffusion-based Zero-Shot 3D Human Pose Estimation', what Average MPJPE (mm) score did the ZeDO (S=50) model get on the Human3.6M dataset
| 51.4 |
PASCAL-5i (1-Shot) | HDMNet (DifFSS, ResNet-50) | DifFSS: Diffusion Model for Few-Shot Semantic Segmentation | 2023-07-03T00:00:00 | https://arxiv.org/abs/2307.00773v3 | [
"https://github.com/TrinitialChan/DifFSS"
] | In the paper 'DifFSS: Diffusion Model for Few-Shot Semantic Segmentation', what Mean IoU score did the HDMNet (DifFSS, ResNet-50) model get on the PASCAL-5i (1-Shot) dataset
| 70.2 |
FMB Dataset | StitchFusion (RGB-Infrared) | StitchFusion: Weaving Any Visual Modalities to Enhance Multimodal Semantic Segmentation | 2024-08-02T00:00:00 | https://arxiv.org/abs/2408.01343v1 | [
"https://github.com/libingyu01/stitchfusion-stitchfusion-weaving-any-visual-modalities-to-enhance-multimodal-semantic-segmentation"
] | In the paper 'StitchFusion: Weaving Any Visual Modalities to Enhance Multimodal Semantic Segmentation', what mIoU score did the StitchFusion (RGB-Infrared) model get on the FMB Dataset dataset
| 63.30 |
PCQM4Mv2-LSC | EGT+SSA+Self-ensemble | The Information Pathways Hypothesis: Transformers are Dynamic Self-Ensembles | 2023-06-02T00:00:00 | https://arxiv.org/abs/2306.01705v1 | [
"https://github.com/shamim-hussain/ssa"
] | In the paper 'The Information Pathways Hypothesis: Transformers are Dynamic Self-Ensembles', what Validation MAE score did the EGT+SSA+Self-ensemble model get on the PCQM4Mv2-LSC dataset
| 0.0865 |
IllusionVQA | CogVLM | IllusionVQA: A Challenging Optical Illusion Dataset for Vision Language Models | 2024-03-23T00:00:00 | https://arxiv.org/abs/2403.15952v3 | [
"https://github.com/csebuetnlp/illusionvqa"
] | In the paper 'IllusionVQA: A Challenging Optical Illusion Dataset for Vision Language Models', what Accuracy score did the CogVLM model get on the IllusionVQA dataset
| 38.16 |
WikiTableQuestions | Chain-of-Table | Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding | 2024-01-09T00:00:00 | https://arxiv.org/abs/2401.04398v2 | [
"https://github.com/google-research/chain-of-table"
] | In the paper 'Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding', what Accuracy (Dev) score did the Chain-of-Table model get on the WikiTableQuestions dataset
| / |
This is not a Dataset | Vicuna13B v1.1 | This is not a Dataset: A Large Negation Benchmark to Challenge Large Language Models | 2023-10-24T00:00:00 | https://arxiv.org/abs/2310.15941v1 | [
"https://github.com/hitz-zentroa/this-is-not-a-dataset"
] | In the paper 'This is not a Dataset: A Large Negation Benchmark to Challenge Large Language Models', what Accuracy score did the Vicuna13B v1.1 model get on the This is not a Dataset dataset
| 95.7 |
BDD100K val | TwinLiteNetPlus-Nano | TwinLiteNet: An Efficient and Lightweight Model for Driveable Area and Lane Segmentation in Self-Driving Cars | 2023-07-20T00:00:00 | https://arxiv.org/abs/2307.10705v5 | [
"https://github.com/chequanghuy/TwinLiteNet"
] | In the paper 'TwinLiteNet: An Efficient and Lightweight Model for Driveable Area and Lane Segmentation in Self-Driving Cars', what mIoU score did the TwinLiteNetPlus-Nano model get on the BDD100K val dataset
| 87.3 |
GTA-to-Avg(Cityscapes,BDD,Mapillary) | tqdm (EVA02-CLIP-L) | Textual Query-Driven Mask Transformer for Domain Generalized Segmentation | 2024-07-12T00:00:00 | https://arxiv.org/abs/2407.09033v1 | [
"https://github.com/ByeongHyunPak/tqdm"
] | In the paper 'Textual Query-Driven Mask Transformer for Domain Generalized Segmentation', what mIoU score did the tqdm (EVA02-CLIP-L) model get on the GTA-to-Avg(Cityscapes,BDD,Mapillary) dataset
| 66.05 |
CLUSTER | NeuralWalker | Learning Long Range Dependencies on Graphs via Random Walks | 2024-06-05T00:00:00 | https://arxiv.org/abs/2406.03386v2 | [
"https://github.com/borgwardtlab/neuralwalker"
] | In the paper 'Learning Long Range Dependencies on Graphs via Random Walks', what Accuracy score did the NeuralWalker model get on the CLUSTER dataset
| 78.189 ± 0.188 |
CIFAR-100 | resnet8x4
(T: resnet32x4 S: resnet8x4) | Logit Standardization in Knowledge Distillation | 2024-03-03T00:00:00 | https://arxiv.org/abs/2403.01427v1 | [
"https://github.com/sunshangquan/logit-standardardization-kd"
] | In the paper 'Logit Standardization in Knowledge Distillation', what Top-1 Accuracy (%) score did the resnet8x4
(T: resnet32x4 S: resnet8x4) model get on the CIFAR-100 dataset
| 78.28 |
QVHighlights | LLMEPET | Prior Knowledge Integration via LLM Encoding and Pseudo Event Regulation for Video Moment Retrieval | 2024-07-21T00:00:00 | https://arxiv.org/abs/2407.15051v3 | [
"https://github.com/fletcherjiang/llmepet"
] | In the paper 'Prior Knowledge Integration via LLM Encoding and Pseudo Event Regulation for Video Moment Retrieval', what mAP score did the LLMEPET model get on the QVHighlights dataset
| 44.05 |
Elliptic Dataset | Node2vec | Network Analytics for Anti-Money Laundering -- A Systematic Literature Review and Experimental Evaluation | 2024-05-29T00:00:00 | https://arxiv.org/abs/2405.19383v2 | [
"https://github.com/B-Deprez/AML_Network"
] | In the paper 'Network Analytics for Anti-Money Laundering -- A Systematic Literature Review and Experimental Evaluation', what AUPRC score did the Node2vec model get on the Elliptic Dataset dataset
| 0.0594 |
DialogSum | InstructDS | Instructive Dialogue Summarization with Query Aggregations | 2023-10-17T00:00:00 | https://arxiv.org/abs/2310.10981v3 | [
"https://github.com/BinWang28/InstructDS"
] | In the paper 'Instructive Dialogue Summarization with Query Aggregations', what Rouge1 score did the InstructDS model get on the DialogSum dataset
| 47.8 |
TAP-Vid-DAVIS | LocoTrack-B | Local All-Pair Correspondence for Point Tracking | 2024-07-22T00:00:00 | https://arxiv.org/abs/2407.15420v1 | [
"https://github.com/ku-cvlab/locotrack"
] | In the paper 'Local All-Pair Correspondence for Point Tracking', what Average Jaccard score did the LocoTrack-B model get on the TAP-Vid-DAVIS dataset
| 69.4 |
ImageNet | SeCu | Stable Cluster Discrimination for Deep Clustering | 2023-11-24T00:00:00 | https://arxiv.org/abs/2311.14310v1 | [
"https://github.com/idstcv/secu"
] | In the paper 'Stable Cluster Discrimination for Deep Clustering', what NMI score did the SeCu model get on the ImageNet dataset
| 79.4 |
MSCOCO | LP-OVOD (OWL-ViT Proposals) | LP-OVOD: Open-Vocabulary Object Detection by Linear Probing | 2023-10-26T00:00:00 | https://arxiv.org/abs/2310.17109v2 | [
"https://github.com/vinairesearch/lp-ovod"
] | In the paper 'LP-OVOD: Open-Vocabulary Object Detection by Linear Probing', what AP 0.5 score did the LP-OVOD (OWL-ViT Proposals) model get on the MSCOCO dataset
| 44.9 |
CrowdPose | BUCTD-W48 (w/cond. input from PETR, and generative sampling) | Rethinking pose estimation in crowds: overcoming the detection information-bottleneck and ambiguity | 2023-06-13T00:00:00 | https://arxiv.org/abs/2306.07879v2 | [
"https://github.com/amathislab/BUCTD"
] | In the paper 'Rethinking pose estimation in crowds: overcoming the detection information-bottleneck and ambiguity', what mAP @0.5:0.95 score did the BUCTD-W48 (w/cond. input from PETR, and generative sampling) model get on the CrowdPose dataset
| 78.5 |
Set14 - 4x upscaling | CAMixerSR | CAMixerSR: Only Details Need More "Attention" | 2024-02-29T00:00:00 | https://arxiv.org/abs/2402.19289v2 | [
"https://github.com/icandle/camixersr"
] | In the paper 'CAMixerSR: Only Details Need More "Attention"', what PSNR score did the CAMixerSR model get on the Set14 - 4x upscaling dataset
| 28.82 |
FRMT (Portuguese - Portugal) | PaLM 2 | PaLM 2 Technical Report | 2023-05-17T00:00:00 | https://arxiv.org/abs/2305.10403v3 | [
"https://github.com/eternityyw/tram-benchmark"
] | In the paper 'PaLM 2 Technical Report', what BLEURT score did the PaLM 2 model get on the FRMT (Portuguese - Portugal) dataset
| 78.3 |
Kvasir-SEG | Yolo-SAM 2 | Self-Prompting Polyp Segmentation in Colonoscopy using Hybrid Yolo-SAM 2 Model | 2024-09-14T00:00:00 | https://arxiv.org/abs/2409.09484v1 | [
"https://github.com/sajjad-sh33/yolo_sam2"
] | In the paper 'Self-Prompting Polyp Segmentation in Colonoscopy using Hybrid Yolo-SAM 2 Model', what mean Dice score did the Yolo-SAM 2 model get on the Kvasir-SEG dataset
| 0.866 |
PACS | PromptStyler (CLIP, ViT-B/16) | PromptStyler: Prompt-driven Style Generation for Source-free Domain Generalization | 2023-07-27T00:00:00 | https://arxiv.org/abs/2307.15199v2 | [
"https://github.com/zhanghr2001/promptta"
] | In the paper 'PromptStyler: Prompt-driven Style Generation for Source-free Domain Generalization', what Average Accuracy score did the PromptStyler (CLIP, ViT-B/16) model get on the PACS dataset
| 97.2 |
Human3.6M | SoloPose | SoloPose: One-Shot Kinematic 3D Human Pose Estimation with Video Data Augmentation | 2023-12-15T00:00:00 | https://arxiv.org/abs/2312.10195v1 | [
"https://github.com/Santa-Clara-Media-Lab/SoloPose"
] | In the paper 'SoloPose: One-Shot Kinematic 3D Human Pose Estimation with Video Data Augmentation', what Average MPJPE (mm) score did the SoloPose model get on the Human3.6M dataset
| 38.9 |
MSR-VTT | vid-TLDR (UMT-L) | vid-TLDR: Training Free Token merging for Light-weight Video Transformer | 2024-03-20T00:00:00 | https://arxiv.org/abs/2403.13347v2 | [
"https://github.com/mlvlab/vid-tldr"
] | In the paper 'vid-TLDR: Training Free Token merging for Light-weight Video Transformer', what text-to-video R@1 score did the vid-TLDR (UMT-L) model get on the MSR-VTT dataset
| 58.1 |
MBPP | DeepSeek-Coder-Instruct 1.3B (few-shot) | DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence | 2024-01-25T00:00:00 | https://arxiv.org/abs/2401.14196v2 | [
"https://github.com/deepseek-ai/DeepSeek-Coder"
] | In the paper 'DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence', what Accuracy score did the DeepSeek-Coder-Instruct 1.3B (few-shot) model get on the MBPP dataset
| 49.4 |
ogbn-arxiv | LD+REVGAT | Label Deconvolution for Node Representation Learning on Large-scale Attributed Graphs against Learning Bias | 2023-09-26T00:00:00 | https://arxiv.org/abs/2309.14907v1 | [
"https://github.com/MIRALab-USTC/LD"
] | In the paper 'Label Deconvolution for Node Representation Learning on Large-scale Attributed Graphs against Learning Bias', what Test Accuracy score did the LD+REVGAT model get on the ogbn-arxiv dataset
| 0.7726 ± 0.0017 |
MassSpecGym | MIST | MassSpecGym: A benchmark for the discovery and identification of molecules | 2024-10-30T00:00:00 | https://arxiv.org/abs/2410.23326v1 | [
"https://github.com/pluskal-lab/massspecgym"
] | In the paper 'MassSpecGym: A benchmark for the discovery and identification of molecules', what Hit rate @ 1 score did the MIST model get on the MassSpecGym dataset
| 9.57 |
NExT-QA | Vamos | Vamos: Versatile Action Models for Video Understanding | 2023-11-22T00:00:00 | https://arxiv.org/abs/2311.13627v3 | [
"https://github.com/brown-palm/Vamos"
] | In the paper 'Vamos: Versatile Action Models for Video Understanding', what Accuracy score did the Vamos model get on the NExT-QA dataset
| 77.3 |
BoolQ | OPT-125M | Achieving Dimension-Free Communication in Federated Learning via Zeroth-Order Optimization | 2024-05-24T00:00:00 | https://arxiv.org/abs/2405.15861v3 | [
"https://github.com/ZidongLiu/DeComFL"
] | In the paper 'Achieving Dimension-Free Communication in Federated Learning via Zeroth-Order Optimization', what Test Accuracy score did the OPT-125M model get on the BoolQ dataset
| 61.6% |
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