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 listlengths 1 1 | prompts stringlengths 105 331 | answer stringlengths 1 67 |
|---|---|---|---|---|---|---|---|
GOT-10k | ODTrack-B | ODTrack: Online Dense Temporal Token Learning for Visual Tracking | 2024-01-03T00:00:00 | https://arxiv.org/abs/2401.01686v1 | [
"https://github.com/gxnu-zhonglab/odtrack"
] | In the paper 'ODTrack: Online Dense Temporal Token Learning for Visual Tracking', what Average Overlap score did the ODTrack-B model get on the GOT-10k dataset
| 77.0 |
ImageNet 64x64 | GDD | Diffusion Models Are Innate One-Step Generators | 2024-05-31T00:00:00 | https://arxiv.org/abs/2405.20750v2 | [
"https://github.com/Zyriix/GDD"
] | In the paper 'Diffusion Models Are Innate One-Step Generators', what FID score did the GDD model get on the ImageNet 64x64 dataset
| 1.42 |
PASCAL-5i (1-Shot) | MIANet (ResNet-50) | MIANet: Aggregating Unbiased Instance and General Information for Few-Shot Semantic Segmentation | 2023-05-23T00:00:00 | https://arxiv.org/abs/2305.13864v1 | [
"https://github.com/aldrich2y/mianet"
] | In the paper 'MIANet: Aggregating Unbiased Instance and General Information for Few-Shot Semantic Segmentation', what Mean IoU score did the MIANet (ResNet-50) model get on the PASCAL-5i (1-Shot) dataset
| 68.72 |
DUT-OMRON | BiRefNet (HRSOD, UHRSD) | Bilateral Reference for High-Resolution Dichotomous Image Segmentation | 2024-01-07T00:00:00 | https://arxiv.org/abs/2401.03407v6 | [
"https://github.com/zhengpeng7/birefnet"
] | In the paper 'Bilateral Reference for High-Resolution Dichotomous Image Segmentation', what MAE score did the BiRefNet (HRSOD, UHRSD) model get on the DUT-OMRON dataset
| 0.040 |
Wisconsin | MGNN + Hetero-S (6 layers) | The Heterophilic Snowflake Hypothesis: Training and Empowering GNNs for Heterophilic Graphs | 2024-06-18T00:00:00 | https://arxiv.org/abs/2406.12539v1 | [
"https://github.com/bingreeky/heterosnoh"
] | In the paper 'The Heterophilic Snowflake Hypothesis: Training and Empowering GNNs for Heterophilic Graphs', what Accuracy score did the MGNN + Hetero-S (6 layers) model get on the Wisconsin dataset
| 88.77 |
MSVD-QA | MA-LMM | MA-LMM: Memory-Augmented Large Multimodal Model for Long-Term Video Understanding | 2024-04-08T00:00:00 | https://arxiv.org/abs/2404.05726v2 | [
"https://github.com/boheumd/MA-LMM"
] | In the paper 'MA-LMM: Memory-Augmented Large Multimodal Model for Long-Term Video Understanding', what Accuracy score did the MA-LMM model get on the MSVD-QA dataset
| 0.606 |
SVTP | CLIP4STR-L | CLIP4STR: A Simple Baseline for Scene Text Recognition with Pre-trained Vision-Language Model | 2023-05-23T00:00:00 | https://arxiv.org/abs/2305.14014v3 | [
"https://github.com/VamosC/CLIP4STR"
] | In the paper 'CLIP4STR: A Simple Baseline for Scene Text Recognition with Pre-trained Vision-Language Model', what Accuracy score did the CLIP4STR-L model get on the SVTP dataset
| 97.4 |
CIFAR-100-LT (ρ=50) | LIFT (ViT-B/16, CLIP) | Long-Tail Learning with Foundation Model: Heavy Fine-Tuning Hurts | 2023-09-18T00:00:00 | https://arxiv.org/abs/2309.10019v3 | [
"https://github.com/shijxcs/lift"
] | In the paper 'Long-Tail Learning with Foundation Model: Heavy Fine-Tuning Hurts', what Error Rate score did the LIFT (ViT-B/16, CLIP) model get on the CIFAR-100-LT (ρ=50) dataset
| 16.9 |
ETTh2 (96) Multivariate | DiPE-Linear | Disentangled Interpretable Representation for Efficient Long-term Time Series Forecasting | 2024-11-26T00:00:00 | https://arxiv.org/abs/2411.17257v1 | [
"https://github.com/wintertee/dipe-linear"
] | In the paper 'Disentangled Interpretable Representation for Efficient Long-term Time Series Forecasting', what MSE score did the DiPE-Linear model get on the ETTh2 (96) Multivariate dataset
| 0.275 |
KIT Motion-Language | ParCo | ParCo: Part-Coordinating Text-to-Motion Synthesis | 2024-03-27T00:00:00 | https://arxiv.org/abs/2403.18512v2 | [
"https://github.com/qrzou/parco"
] | In the paper 'ParCo: Part-Coordinating Text-to-Motion Synthesis', what FID score did the ParCo model get on the KIT Motion-Language dataset
| 0.453 |
SICK | Rematch | Rematch: Robust and Efficient Matching of Local Knowledge Graphs to Improve Structural and Semantic Similarity | 2024-04-02T00:00:00 | https://arxiv.org/abs/2404.02126v1 | [
"https://github.com/osome-iu/Rematch-RARE"
] | In the paper 'Rematch: Robust and Efficient Matching of Local Knowledge Graphs to Improve Structural and Semantic Similarity', what Spearman Correlation score did the Rematch model get on the SICK dataset
| 0.6772 |
CIFAR-100 | ZLaP | Label Propagation for Zero-shot Classification with Vision-Language Models | 2024-04-05T00:00:00 | https://arxiv.org/abs/2404.04072v1 | [
"https://github.com/vladan-stojnic/zlap"
] | In the paper 'Label Propagation for Zero-shot Classification with Vision-Language Models', what Accuracy score did the ZLaP model get on the CIFAR-100 dataset
| 74 |
ImageNet-1k vs iNaturalist | NAC-UE (ResNet-50) | Neuron Activation Coverage: Rethinking Out-of-distribution Detection and Generalization | 2023-06-05T00:00:00 | https://arxiv.org/abs/2306.02879v3 | [
"https://github.com/bierone/ood_coverage"
] | In the paper 'Neuron Activation Coverage: Rethinking Out-of-distribution Detection and Generalization', what AUROC score did the NAC-UE (ResNet-50) model get on the ImageNet-1k vs iNaturalist dataset
| 96.52 |
CHILI-100K | EdgeCNN | CHILI: Chemically-Informed Large-scale Inorganic Nanomaterials Dataset for Advancing Graph Machine Learning | 2024-02-20T00:00:00 | https://arxiv.org/abs/2402.13221v2 | [
"https://github.com/UlrikFriisJensen/CHILI"
] | In the paper 'CHILI: Chemically-Informed Large-scale Inorganic Nanomaterials Dataset for Advancing Graph Machine Learning', what F1-score (Weighted) score did the EdgeCNN model get on the CHILI-100K dataset
| 0.572 +/- 0.017 |
CDD Dataset (season-varying) | SGSLN/256 | Exchanging Dual Encoder-Decoder: A New Strategy for Change Detection with Semantic Guidance and Spatial Localization | 2023-11-19T00:00:00 | https://arxiv.org/abs/2311.11302v1 | [
"https://github.com/walking-shadow/Semantic-guidance-and-spatial-localization-network"
] | In the paper 'Exchanging Dual Encoder-Decoder: A New Strategy for Change Detection with Semantic Guidance and Spatial Localization', what F1-Score score did the SGSLN/256 model get on the CDD Dataset (season-varying) dataset
| 96.24 |
COCO-Stuff | OTSeg+ | OTSeg: Multi-prompt Sinkhorn Attention for Zero-Shot Semantic Segmentation | 2024-03-21T00:00:00 | https://arxiv.org/abs/2403.14183v2 | [
"https://github.com/cubeyoung/OTSeg"
] | In the paper 'OTSeg: Multi-prompt Sinkhorn Attention for Zero-Shot Semantic Segmentation', what Transductive Setting hIoU score did the OTSeg+ model get on the COCO-Stuff dataset
| 49.8 |
MSP-Podcast (Activation) | wav2small-Teacher | Wav2Small: Distilling Wav2Vec2 to 72K parameters for Low-Resource Speech emotion recognition | 2024-08-25T00:00:00 | https://arxiv.org/abs/2408.13920v4 | [
"https://github.com/dkounadis/wav2small"
] | In the paper 'Wav2Small: Distilling Wav2Vec2 to 72K parameters for Low-Resource Speech emotion recognition', what CCC score did the wav2small-Teacher model get on the MSP-Podcast (Activation) dataset
| 0.7620181 |
Traffic (96) | 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 (96) dataset
| 0.356 |
Charades-STA | SG-DETR (w/ PT) | Saliency-Guided DETR for Moment Retrieval and Highlight Detection | 2024-10-02T00:00:00 | https://arxiv.org/abs/2410.01615v1 | [
"https://github.com/ai-forever/sg-detr"
] | In the paper 'Saliency-Guided DETR for Moment Retrieval and Highlight Detection', what R@1 IoU=0.5 score did the SG-DETR (w/ PT) model get on the Charades-STA dataset
| 71.10 |
LEVIR-CD | C2FNet | C2F-SemiCD: A Coarse-to-Fine Semi-Supervised Change Detection Method Based on Consistency Regularization in High-Resolution Remote Sensing Images | 2024-04-22T00:00:00 | https://arxiv.org/abs/2404.13838v1 | [
"https://github.com/chengxihan/c2f-semicd-and-c2f-cdnet"
] | In the paper 'C2F-SemiCD: A Coarse-to-Fine Semi-Supervised Change Detection Method Based on Consistency Regularization in High-Resolution Remote Sensing Images', what F1 score did the C2FNet model get on the LEVIR-CD dataset
| 91.83 |
RLBench | RVT | RVT: Robotic View Transformer for 3D Object Manipulation | 2023-06-26T00:00:00 | https://arxiv.org/abs/2306.14896v1 | [
"https://github.com/NVlabs/RVT"
] | In the paper 'RVT: Robotic View Transformer for 3D Object Manipulation', what Succ. Rate (18 tasks, 100 demo/task) score did the RVT model get on the RLBench dataset
| 62.9 |
D4RL | Primal.+DT | Primal-Attention: Self-attention through Asymmetric Kernel SVD in Primal Representation | 2023-05-31T00:00:00 | https://arxiv.org/abs/2305.19798v2 | [
"https://github.com/yingyichen-cyy/PrimalAttention"
] | In the paper 'Primal-Attention: Self-attention through Asymmetric Kernel SVD in Primal Representation', what Average Reward score did the Primal.+DT model get on the D4RL dataset
| 77.5 |
SAFIM | deepseek-coder-33b-base | Evaluation of LLMs on Syntax-Aware Code Fill-in-the-Middle Tasks | 2024-03-07T00:00:00 | https://arxiv.org/abs/2403.04814v3 | [
"https://github.com/gonglinyuan/safim"
] | In the paper 'Evaluation of LLMs on Syntax-Aware Code Fill-in-the-Middle Tasks', what Algorithmic score did the deepseek-coder-33b-base model get on the SAFIM dataset
| 60.78 |
IFEval | AutoIF (Qwen2 72B) | Self-play with Execution Feedback: Improving Instruction-following Capabilities of Large Language Models | 2024-06-19T00:00:00 | https://arxiv.org/abs/2406.13542v3 | [
"https://github.com/QwenLM/AutoIF"
] | In the paper 'Self-play with Execution Feedback: Improving Instruction-following Capabilities of Large Language Models', what Prompt-level strict-accuracy score did the AutoIF (Qwen2 72B) model get on the IFEval dataset
| 80.2 |
SEPE 8K | DiQP on AV1 with QP 255 | Reversing the Damage: A QP-Aware Transformer-Diffusion Approach for 8K Video Restoration under Codec Compression | 2024-12-12T00:00:00 | https://arxiv.org/abs/2412.08912v1 | [
"https://github.com/alimd94/DiQP"
] | In the paper 'Reversing the Damage: A QP-Aware Transformer-Diffusion Approach for 8K Video Restoration under Codec Compression', what Average PSNR (dB) score did the DiQP on AV1 with QP 255 model get on the SEPE 8K dataset
| 34.868 |
ZINC | CIN++-500k | CIN++: Enhancing Topological Message Passing | 2023-06-06T00:00:00 | https://arxiv.org/abs/2306.03561v1 | [
"https://github.com/twitter-research/cwn"
] | In the paper 'CIN++: Enhancing Topological Message Passing', what MAE score did the CIN++-500k model get on the ZINC dataset
| 0.077 |
SMAP | ContextFlow++ (Glow-based) | ContextFlow++: Generalist-Specialist Flow-based Generative Models with Mixed-Variable Context Encoding | 2024-06-02T00:00:00 | https://arxiv.org/abs/2406.00578v1 | [
"https://github.com/gudovskiy/contextflow"
] | In the paper 'ContextFlow++: Generalist-Specialist Flow-based Generative Models with Mixed-Variable Context Encoding', what Precision score did the ContextFlow++ (Glow-based) model get on the SMAP dataset
| 88.64 |
MS COCO | BUCTD (PETR, with 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 APM score did the BUCTD (PETR, with generative sampling) model get on the MS COCO dataset
| 74.2 |
NYU Depth v2 | CAINet (MobileNet-V2) | Context-Aware Interaction Network for RGB-T Semantic Segmentation | 2024-01-03T00:00:00 | https://arxiv.org/abs/2401.01624v1 | [
"https://github.com/yinglv1106/cainet"
] | In the paper 'Context-Aware Interaction Network for RGB-T Semantic Segmentation', what Mean IoU score did the CAINet (MobileNet-V2) model get on the NYU Depth v2 dataset
| 52.6% |
CUB 200 5-way 1-shot | PT+MAP+SF+BPA (transductive) | The Balanced-Pairwise-Affinities Feature Transform | 2024-06-25T00:00:00 | https://arxiv.org/abs/2407.01467v1 | [
"https://github.com/danielshalam/bpa"
] | In the paper 'The Balanced-Pairwise-Affinities Feature Transform', what Accuracy score did the PT+MAP+SF+BPA (transductive) model get on the CUB 200 5-way 1-shot dataset
| 95.80 |
Pittsburgh-30k-test | 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 Pittsburgh-30k-test dataset
| 93.7 |
BDD100K val | DSNet-head64 | DSNet: A Novel Way to Use Atrous Convolutions in Semantic Segmentation | 2024-06-06T00:00:00 | https://arxiv.org/abs/2406.03702v1 | [
"https://github.com/takaniwa/dsnet"
] | In the paper 'DSNet: A Novel Way to Use Atrous Convolutions in Semantic Segmentation', what mIoU score did the DSNet-head64 model get on the BDD100K val dataset
| 62.6(172.2FPS 4090) |
MBPP | DeepSeek-Coder-Base 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-Base 1.3B (few-shot) model get on the MBPP dataset
| 46.2 |
COCO minival | GLEE-Pro | General Object Foundation Model for Images and Videos at Scale | 2023-12-14T00:00:00 | https://arxiv.org/abs/2312.09158v1 | [
"https://github.com/FoundationVision/GLEE"
] | In the paper 'General Object Foundation Model for Images and Videos at Scale', what box AP score did the GLEE-Pro model get on the COCO minival dataset
| 62.0 |
Refer-YouTube-VOS (2021 public validation) | EPCFormer (ViT-H) | EPCFormer: Expression Prompt Collaboration Transformer for Universal Referring Video Object Segmentation | 2023-08-08T00:00:00 | https://arxiv.org/abs/2308.04162v1 | [
"https://github.com/lab206/epcformer"
] | In the paper 'EPCFormer: Expression Prompt Collaboration Transformer for Universal Referring Video Object Segmentation', what J&F score did the EPCFormer (ViT-H) model get on the Refer-YouTube-VOS (2021 public validation) dataset
| 65 |
OTB-2015 | PiVOT-L | Improving Visual Object Tracking through Visual Prompting | 2024-09-27T00:00:00 | https://arxiv.org/abs/2409.18901v1 | [
"https://github.com/chenshihfang/GOT"
] | In the paper 'Improving Visual Object Tracking through Visual Prompting', what Precision score did the PiVOT-L model get on the OTB-2015 dataset
| 0.946 |
ETTm2 (720) Multivariate | 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 ETTm2 (720) Multivariate dataset
| 0.399 |
LM-KBC 2023 | VE-BERT | Expanding the Vocabulary of BERT for Knowledge Base Construction | 2023-10-12T00:00:00 | https://arxiv.org/abs/2310.08291v1 | [
"https://github.com/MaastrichtU-IDS/LMKBC-2023"
] | In the paper 'Expanding the Vocabulary of BERT for Knowledge Base Construction', what F1 score did the VE-BERT model get on the LM-KBC 2023 dataset
| 0.362 |
MS-COCO (30-shot) | RISF (Resnet-101) | Re-Scoring Using Image-Language Similarity for Few-Shot Object Detection | 2023-11-01T00:00:00 | https://arxiv.org/abs/2311.00278v1 | [
"https://github.com/INFINIQ-AI1/RISF"
] | In the paper 'Re-Scoring Using Image-Language Similarity for Few-Shot Object Detection', what AP score did the RISF (Resnet-101) model get on the MS-COCO (30-shot) dataset
| 24.4 |
DAVIS 2017 (val) | HyperSeg | HyperSeg: Towards Universal Visual Segmentation with Large Language Model | 2024-11-26T00:00:00 | https://arxiv.org/abs/2411.17606v2 | [
"https://github.com/congvvc/HyperSeg"
] | In the paper 'HyperSeg: Towards Universal Visual Segmentation with Large Language Model', what J&F 1st frame score did the HyperSeg model get on the DAVIS 2017 (val) dataset
| 71.2 |
CUHK-PEDES | RDE | Noisy-Correspondence Learning for Text-to-Image Person Re-identification | 2023-08-19T00:00:00 | https://arxiv.org/abs/2308.09911v3 | [
"https://github.com/QinYang79/RDE"
] | In the paper 'Noisy-Correspondence Learning for Text-to-Image Person Re-identification', what R@1 score did the RDE model get on the CUHK-PEDES dataset
| 75.94 |
SUN-RGBD val | Point-GCC+TR3D+FF | Point-GCC: Universal Self-supervised 3D Scene Pre-training via Geometry-Color Contrast | 2023-05-31T00:00:00 | https://arxiv.org/abs/2305.19623v2 | [
"https://github.com/asterisci/point-gcc"
] | In the paper 'Point-GCC: Universal Self-supervised 3D Scene Pre-training via Geometry-Color Contrast', what mAP@0.25 score did the Point-GCC+TR3D+FF model get on the SUN-RGBD val dataset
| 69.7 |
RST-DT | Bottom-up Llama 2 (7B) | Can we obtain significant success in RST discourse parsing by using Large Language Models? | 2024-03-08T00:00:00 | https://arxiv.org/abs/2403.05065v1 | [
"https://github.com/nttcslab-nlp/rstparser_eacl24"
] | In the paper 'Can we obtain significant success in RST discourse parsing by using Large Language Models?', what Standard Parseval (Span) score did the Bottom-up Llama 2 (7B) model get on the RST-DT dataset
| 78.2 |
EvalCrafter Text-to-Video (ECTV) Dataset | Show-1 | Show-1: Marrying Pixel and Latent Diffusion Models for Text-to-Video Generation | 2023-09-27T00:00:00 | https://arxiv.org/abs/2309.15818v2 | [
"https://github.com/showlab/show-1"
] | In the paper 'Show-1: Marrying Pixel and Latent Diffusion Models for Text-to-Video Generation', what Visual Quality score did the Show-1 model get on the EvalCrafter Text-to-Video (ECTV) Dataset dataset
| 53.74 |
CNRPark+EXT | EfficientNet-P | Revising deep learning methods in parking lot occupancy detection | 2023-06-07T00:00:00 | https://arxiv.org/abs/2306.04288v3 | [
"https://github.com/eighonet/parking-research"
] | In the paper 'Revising deep learning methods in parking lot occupancy detection', what F1-score score did the EfficientNet-P model get on the CNRPark+EXT dataset
| 0.9683 |
Caltech-101 | ProMetaR | Prompt Learning via Meta-Regularization | 2024-04-01T00:00:00 | https://arxiv.org/abs/2404.00851v1 | [
"https://github.com/mlvlab/prometar"
] | In the paper 'Prompt Learning via Meta-Regularization', what Harmonic mean score did the ProMetaR model get on the Caltech-101 dataset
| 96.16 |
MLT17 | MRM | MRN: Multiplexed Routing Network for Incremental Multilingual Text Recognition | 2023-05-24T00:00:00 | https://arxiv.org/abs/2305.14758v3 | [
"https://github.com/simplify23/MRN"
] | In the paper 'MRN: Multiplexed Routing Network for Incremental Multilingual Text Recognition', what Acc score did the MRM model get on the MLT17 dataset
| 78.4 |
S3DIS Area5 | SuperCluster | Scalable 3D Panoptic Segmentation As Superpoint Graph Clustering | 2024-01-12T00:00:00 | https://arxiv.org/abs/2401.06704v2 | [
"https://github.com/drprojects/superpoint_transformer"
] | In the paper 'Scalable 3D Panoptic Segmentation As Superpoint Graph Clustering', what PQ score did the SuperCluster model get on the S3DIS Area5 dataset
| 50.1 |
MCubeS | ShareCMP (B2 RGB-A-D) | ShareCMP: Polarization-Aware RGB-P Semantic Segmentation | 2023-12-06T00:00:00 | https://arxiv.org/abs/2312.03430v2 | [
"https://github.com/lefteyex/sharecmp"
] | In the paper 'ShareCMP: Polarization-Aware RGB-P Semantic Segmentation', what mIoU score did the ShareCMP (B2 RGB-A-D) model get on the MCubeS dataset
| 50.99% |
MM-Vet | LLaVA-1.5-7B (VG-S) | ProVision: Programmatically Scaling Vision-centric Instruction Data for Multimodal Language Models | 2024-12-09T00:00:00 | https://arxiv.org/abs/2412.07012v2 | [
"https://github.com/jieyuz2/provision"
] | In the paper 'ProVision: Programmatically Scaling Vision-centric Instruction Data for Multimodal Language Models', what GPT-4 score score did the LLaVA-1.5-7B (VG-S) model get on the MM-Vet dataset
| 40.4 |
MELD | ConCluGen | Multi-Task Multi-Modal Self-Supervised Learning for Facial Expression Recognition | 2024-04-16T00:00:00 | https://arxiv.org/abs/2404.10904v2 | [
"https://github.com/tub-cv-group/conclugen"
] | In the paper 'Multi-Task Multi-Modal Self-Supervised Learning for Facial Expression Recognition', what Weighted Accuracy score did the ConCluGen model get on the MELD dataset
| 60.03 |
WDC Products-80%cc-seen-medium | Llama3.1_8B_structured_explanations | Fine-tuning Large Language Models for Entity Matching | 2024-09-12T00:00:00 | https://arxiv.org/abs/2409.08185v1 | [
"https://github.com/wbsg-uni-mannheim/tailormatch"
] | In the paper 'Fine-tuning Large Language Models for Entity Matching', what F1 (%) score did the Llama3.1_8B_structured_explanations model get on the WDC Products-80%cc-seen-medium dataset
| 74.13 |
FSC147 | CounTX (uses text descriptions instead of visual exemplars) | Open-world Text-specified Object Counting | 2023-06-02T00:00:00 | https://arxiv.org/abs/2306.01851v2 | [
"https://github.com/niki-amini-naieni/countx"
] | In the paper 'Open-world Text-specified Object Counting', what MAE(val) score did the CounTX (uses text descriptions instead of visual exemplars) model get on the FSC147 dataset
| 17.10 |
UCF101 | VFIMamba | VFIMamba: Video Frame Interpolation with State Space Models | 2024-07-02T00:00:00 | https://arxiv.org/abs/2407.02315v2 | [
"https://github.com/mcg-nju/vfimamba"
] | In the paper 'VFIMamba: Video Frame Interpolation with State Space Models', what PSNR score did the VFIMamba model get on the UCF101 dataset
| 35.45 |
Atari 2600 Assault | 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 Assault dataset
| 14372.8 |
nuScenes LiDAR only | LION | LION: Linear Group RNN for 3D Object Detection in Point Clouds | 2024-07-25T00:00:00 | https://arxiv.org/abs/2407.18232v1 | [
"https://github.com/happinesslz/LION"
] | In the paper 'LION: Linear Group RNN for 3D Object Detection in Point Clouds', what NDS score did the LION model get on the nuScenes LiDAR only dataset
| 73.9 |
Wikidata5M | KGT5 + Description | Friendly Neighbors: Contextualized Sequence-to-Sequence Link Prediction | 2023-05-22T00:00:00 | https://arxiv.org/abs/2305.13059v2 | [
"https://github.com/uma-pi1/kgt5-context"
] | In the paper 'Friendly Neighbors: Contextualized Sequence-to-Sequence Link Prediction', what MRR score did the KGT5 + Description model get on the Wikidata5M dataset
| 0.381 |
UCSD Ped2 | SD-MAE | Self-Distilled Masked Auto-Encoders are Efficient Video Anomaly Detectors | 2023-06-21T00:00:00 | https://arxiv.org/abs/2306.12041v2 | [
"https://github.com/ristea/aed-mae"
] | In the paper 'Self-Distilled Masked Auto-Encoders are Efficient Video Anomaly Detectors', what AUC score did the SD-MAE model get on the UCSD Ped2 dataset
| 95.4% |
ImageNet | KD++(T: ViT-S, S:resnet18) | Improving Knowledge Distillation via Regularizing Feature Norm and Direction | 2023-05-26T00:00:00 | https://arxiv.org/abs/2305.17007v1 | [
"https://github.com/wangyz1608/knowledge-distillation-via-nd"
] | In the paper 'Improving Knowledge Distillation via Regularizing Feature Norm and Direction', what Top-1 accuracy % score did the KD++(T: ViT-S, S:resnet18) model get on the ImageNet dataset
| 71.46 |
HIDE (trained on GOPRO) | CAPTNet | Prompt-based Ingredient-Oriented All-in-One Image Restoration | 2023-09-06T00:00:00 | https://arxiv.org/abs/2309.03063v2 | [
"https://github.com/Tombs98/CAPTNet"
] | In the paper 'Prompt-based Ingredient-Oriented All-in-One Image Restoration', what PSNR (sRGB) score did the CAPTNet model get on the HIDE (trained on GOPRO) dataset
| 31.86 |
CATH 4.2 | StructGNN | Knowledge-Design: Pushing the Limit of Protein Design via Knowledge Refinement | 2023-05-20T00:00:00 | https://arxiv.org/abs/2305.15151v4 | [
"https://github.com/A4Bio/OpenCPD"
] | In the paper 'Knowledge-Design: Pushing the Limit of Protein Design via Knowledge Refinement', what Sequence Recovery %(All) score did the StructGNN model get on the CATH 4.2 dataset
| 35.91 |
ScanNet200 | OpenIns3D (3d only) | 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 (3d only) model get on the ScanNet200 dataset
| 8.8 |
GSM8K | DART-Math-DSMath-7B-Prop2Diff (0-shot CoT, w/o code) | DART-Math: Difficulty-Aware Rejection Tuning for Mathematical Problem-Solving | 2024-06-18T00:00:00 | https://arxiv.org/abs/2407.13690v1 | [
"https://github.com/hkust-nlp/dart-math"
] | In the paper 'DART-Math: Difficulty-Aware Rejection Tuning for Mathematical Problem-Solving', what Accuracy score did the DART-Math-DSMath-7B-Prop2Diff (0-shot CoT, w/o code) model get on the GSM8K dataset
| 86.8 |
VideoInstruct | SlowFast-LLaVA-34B | SlowFast-LLaVA: A Strong Training-Free Baseline for Video Large Language Models | 2024-07-22T00:00:00 | https://arxiv.org/abs/2407.15841v2 | [
"https://github.com/apple/ml-slowfast-llava"
] | In the paper 'SlowFast-LLaVA: A Strong Training-Free Baseline for Video Large Language Models', what mean score did the SlowFast-LLaVA-34B model get on the VideoInstruct dataset
| 3.32 |
ScanNetV2 | 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 PQ score did the OneFormer3D model get on the ScanNetV2 dataset
| 71.2 |
AudioCaps | EnCLAP-large | EnCLAP: Combining Neural Audio Codec and Audio-Text Joint Embedding for Automated Audio Captioning | 2024-01-31T00:00:00 | https://arxiv.org/abs/2401.17690v1 | [
"https://github.com/jaeyeonkim99/enclap"
] | In the paper 'EnCLAP: Combining Neural Audio Codec and Audio-Text Joint Embedding for Automated Audio Captioning', what CIDEr score did the EnCLAP-large model get on the AudioCaps dataset
| 0.8029 |
Financial PhraseBank | FiLM | Exploring the Impact of Corpus Diversity on Financial Pretrained Language Models | 2023-10-20T00:00:00 | https://arxiv.org/abs/2310.13312v1 | [
"https://github.com/deep-over/film"
] | In the paper 'Exploring the Impact of Corpus Diversity on Financial Pretrained Language Models', what Accuracy score did the FiLM model get on the Financial PhraseBank dataset
| 86.25 |
RefCOCOg-val | VATEX | Vision-Aware Text Features in Referring Image Segmentation: From Object Understanding to Context Understanding | 2024-04-12T00:00:00 | https://arxiv.org/abs/2404.08590v2 | [
"https://github.com/nero1342/VATEX_RIS"
] | In the paper 'Vision-Aware Text Features in Referring Image Segmentation: From Object Understanding to Context Understanding', what mIoU score did the VATEX model get on the RefCOCOg-val dataset
| 69.73 |
SARDet-100K | MSFA (GFL+R50) | SARDet-100K: Towards Open-Source Benchmark and ToolKit for Large-Scale SAR Object Detection | 2024-03-11T00:00:00 | https://arxiv.org/abs/2403.06534v2 | [
"https://github.com/zcablii/sardet_100k"
] | In the paper 'SARDet-100K: Towards Open-Source Benchmark and ToolKit for Large-Scale SAR Object Detection', what box mAP score did the MSFA (GFL+R50) model get on the SARDet-100K dataset
| 53.7 |
DomainNet | 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 DomainNet dataset
| 59.4 |
AgeDB | ResNet-50-SORD | 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-SORD model get on the AgeDB dataset
| 5.81 |
Wildtrack | EarlyBird | EarlyBird: Early-Fusion for Multi-View Tracking in the Bird's Eye View | 2023-10-20T00:00:00 | https://arxiv.org/abs/2310.13350v1 | [
"https://github.com/tteepe/EarlyBird"
] | In the paper 'EarlyBird: Early-Fusion for Multi-View Tracking in the Bird's Eye View', what IDF1 score did the EarlyBird model get on the Wildtrack dataset
| 92.3 |
Atari 2600 Atlantis | 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 Atlantis dataset
| 947275 |
ChartQA | UniChart | UniChart: A Universal Vision-language Pretrained Model for Chart Comprehension and Reasoning | 2023-05-24T00:00:00 | https://arxiv.org/abs/2305.14761v3 | [
"https://github.com/vis-nlp/unichart"
] | In the paper 'UniChart: A Universal Vision-language Pretrained Model for Chart Comprehension and Reasoning', what 1:1 Accuracy score did the UniChart model get on the ChartQA dataset
| 66.24 |
Office-Home | GMDG (ResNet-50) | Rethinking Multi-domain Generalization with A General Learning Objective | 2024-02-29T00:00:00 | https://arxiv.org/abs/2402.18853v1 | [
"https://github.com/zhaorui-tan/GMDG_cvpr2024"
] | In the paper 'Rethinking Multi-domain Generalization with A General Learning Objective', what Average Accuracy score did the GMDG (ResNet-50) model get on the Office-Home dataset
| 70.7 |
MVTec LOCO AD | ComAD+RD4AD | Component-aware anomaly detection framework for adjustable and logical industrial visual inspection | 2023-05-15T00:00:00 | https://arxiv.org/abs/2305.08509v1 | [
"https://github.com/liutongkun/comad"
] | In the paper 'Component-aware anomaly detection framework for adjustable and logical industrial visual inspection', what Avg. Detection AUROC score did the ComAD+RD4AD model get on the MVTec LOCO AD dataset
| 88.2 |
CIFAR-10-LT (ρ=100) | GCL | Long-tailed Visual Recognition via Gaussian Clouded Logit Adjustment | 2023-05-19T00:00:00 | https://arxiv.org/abs/2305.11733v1 | [
"https://github.com/keke921/gclloss"
] | In the paper 'Long-tailed Visual Recognition via Gaussian Clouded Logit Adjustment', what Error Rate score did the GCL model get on the CIFAR-10-LT (ρ=100) dataset
| 17.32 |
PACS | Crafting-Shifts(ResNet18) | Crafting Distribution Shifts for Validation and Training in Single Source Domain Generalization | 2024-09-29T00:00:00 | https://arxiv.org/abs/2409.19774v1 | [
"https://github.com/nikosefth/crafting-shifts"
] | In the paper 'Crafting Distribution Shifts for Validation and Training in Single Source Domain Generalization', what Accuracy score did the Crafting-Shifts(ResNet18) model get on the PACS dataset
| 70.37 |
Occluded-DukeMTMC | BoT+UFFM+AMC | Enhancing Person Re-Identification via Uncertainty Feature Fusion and Auto-weighted Measure Combination | 2024-05-02T00:00:00 | https://arxiv.org/abs/2405.01101v4 | [
"https://github.com/chequanghuy/Enhancing-Person-Re-Identification-via-UFFM-and-AMC"
] | In the paper 'Enhancing Person Re-Identification via Uncertainty Feature Fusion and Auto-weighted Measure Combination', what mAP score did the BoT+UFFM+AMC model get on the Occluded-DukeMTMC dataset
| 61.0 |
ScanNetV2 | Metric3Dv2 (g2, In-domain) | Metric3Dv2: A Versatile Monocular Geometric Foundation Model for Zero-shot Metric Depth and Surface Normal Estimation | 2024-03-22T00:00:00 | https://arxiv.org/abs/2404.15506v3 | [
"https://github.com/yvanyin/metric3d"
] | In the paper 'Metric3Dv2: A Versatile Monocular Geometric Foundation Model for Zero-shot Metric Depth and Surface Normal Estimation', what % < 11.25 score did the Metric3Dv2 (g2, In-domain) model get on the ScanNetV2 dataset
| 77.8 |
Weather (192) | DiPE-Linear | Disentangled Interpretable Representation for Efficient Long-term Time Series Forecasting | 2024-11-26T00:00:00 | https://arxiv.org/abs/2411.17257v1 | [
"https://github.com/wintertee/dipe-linear"
] | In the paper 'Disentangled Interpretable Representation for Efficient Long-term Time Series Forecasting', what MSE score did the DiPE-Linear model get on the Weather (192) dataset
| 0.187 |
PASTIS | Exchanger+Unet+PaPs | Revisiting the Encoding of Satellite Image Time Series | 2023-05-03T00:00:00 | https://arxiv.org/abs/2305.02086v2 | [
"https://github.com/TotalVariation/Exchanger4SITS"
] | In the paper 'Revisiting the Encoding of Satellite Image Time Series', what SQ score did the Exchanger+Unet+PaPs model get on the PASTIS dataset
| 80.3 |
KonIQ-10k | UNIQA | You Only Train Once: A Unified Framework for Both Full-Reference and No-Reference Image Quality Assessment | 2023-10-14T00:00:00 | https://arxiv.org/abs/2310.09560v2 | [
"https://github.com/barcodereader/yoto"
] | In the paper 'You Only Train Once: A Unified Framework for Both Full-Reference and No-Reference Image Quality Assessment', what SRCC score did the UNIQA model get on the KonIQ-10k dataset
| 0.926 |
ColonINST-v1 (Unseen) | Bunny-v1.0-3B
(w/ LoRA, w/ extra data) | Efficient Multimodal Learning from Data-centric Perspective | 2024-02-18T00:00:00 | https://arxiv.org/abs/2402.11530v3 | [
"https://github.com/baai-dcai/bunny"
] | In the paper 'Efficient Multimodal Learning from Data-centric Perspective', what Accuray score did the Bunny-v1.0-3B
(w/ LoRA, w/ extra data) model get on the ColonINST-v1 (Unseen) dataset
| 75.08 |
PeMS04 | Cy2Mixer | Enhancing Topological Dependencies in Spatio-Temporal Graphs with Cycle Message Passing Blocks | 2024-01-29T00:00:00 | https://arxiv.org/abs/2401.15894v2 | [
"https://github.com/leemingo/cy2mixer"
] | In the paper 'Enhancing Topological Dependencies in Spatio-Temporal Graphs with Cycle Message Passing Blocks', what 12 Steps MAE score did the Cy2Mixer model get on the PeMS04 dataset
| 18.14 |
COCO 2017 | DAT-S++ | DAT++: Spatially Dynamic Vision Transformer with Deformable Attention | 2023-09-04T00:00:00 | https://arxiv.org/abs/2309.01430v1 | [
"https://github.com/leaplabthu/dat"
] | In the paper 'DAT++: Spatially Dynamic Vision Transformer with Deformable Attention', what AP score did the DAT-S++ model get on the COCO 2017 dataset
| 50.2 |
Stackoverflow | HP-CDE | Hawkes Process Based on Controlled Differential Equations | 2023-05-09T00:00:00 | https://arxiv.org/abs/2305.07031v2 | [
"https://github.com/kookseungji/Hawkes-Process-Based-on-Controlled-Differential-Equations"
] | In the paper 'Hawkes Process Based on Controlled Differential Equations', what Accuracy score did the HP-CDE model get on the Stackoverflow dataset
| 0.452±0.001 |
BorealTC | Mamba | Proprioception Is All You Need: Terrain Classification for Boreal Forests | 2024-03-25T00:00:00 | https://arxiv.org/abs/2403.16877v2 | [
"https://github.com/norlab-ulaval/BorealTC"
] | In the paper 'Proprioception Is All You Need: Terrain Classification for Boreal Forests', what Accuracy (5-fold) score did the Mamba model get on the BorealTC dataset
| 93.68 |
HRSOD | BiRefNet (HRSOD, UHRSD) | Bilateral Reference for High-Resolution Dichotomous Image Segmentation | 2024-01-07T00:00:00 | https://arxiv.org/abs/2401.03407v6 | [
"https://github.com/zhengpeng7/birefnet"
] | In the paper 'Bilateral Reference for High-Resolution Dichotomous Image Segmentation', what S-Measure score did the BiRefNet (HRSOD, UHRSD) model get on the HRSOD dataset
| 0.956 |
WikiTableQuestions | SynTQA (Oracle) | SynTQA: Synergistic Table-based Question Answering via Mixture of Text-to-SQL and E2E TQA | 2024-09-25T00:00:00 | https://arxiv.org/abs/2409.16682v2 | [
"https://github.com/siyue-zhang/SynTableQA"
] | In the paper 'SynTQA: Synergistic Table-based Question Answering via Mixture of Text-to-SQL and E2E TQA', what Test Accuracy score did the SynTQA (Oracle) model get on the WikiTableQuestions dataset
| 77.5 |
AIDA-CoNLL | SpEL-base (2023) | SpEL: Structured Prediction for Entity Linking | 2023-10-23T00:00:00 | https://arxiv.org/abs/2310.14684v1 | [
"https://github.com/shavarani/spel"
] | In the paper 'SpEL: Structured Prediction for Entity Linking', what Micro-F1 strong score did the SpEL-base (2023) model get on the AIDA-CoNLL dataset
| 88.1 |
ANLI test | PaLM 2-M (one-shot) | 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 A1 score did the PaLM 2-M (one-shot) model get on the ANLI test dataset
| 58.1 |
URMP | YourMT3+ (YPTF.MoE+M) | YourMT3+: Multi-instrument Music Transcription with Enhanced Transformer Architectures and Cross-dataset Stem Augmentation | 2024-07-05T00:00:00 | https://arxiv.org/abs/2407.04822v3 | [
"https://github.com/mimbres/yourmt3"
] | In the paper 'YourMT3+: Multi-instrument Music Transcription with Enhanced Transformer Architectures and Cross-dataset Stem Augmentation', what Onset F1 score did the YourMT3+ (YPTF.MoE+M) model get on the URMP dataset
| 81.79 |
Hawkins | CLIP | 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 CLIP model get on the Hawkins dataset
| 33.05 |
Persona-Chat | P5 | P5: Plug-and-Play Persona Prompting for Personalized Response Selection | 2023-10-10T00:00:00 | https://arxiv.org/abs/2310.06390v1 | [
"https://github.com/rungjoo/plug-and-play-prompt-persona"
] | In the paper 'P5: Plug-and-Play Persona Prompting for Personalized Response Selection', what R20@1 score did the P5 model get on the Persona-Chat dataset
| 0.875 |
ACMPS | MobileNetV2 | Revising deep learning methods in parking lot occupancy detection | 2023-06-07T00:00:00 | https://arxiv.org/abs/2306.04288v3 | [
"https://github.com/eighonet/parking-research"
] | In the paper 'Revising deep learning methods in parking lot occupancy detection', what F1-score score did the MobileNetV2 model get on the ACMPS dataset
| 0.9971 |
ScanNetV2 | V-DETR | V-DETR: DETR with Vertex Relative Position Encoding for 3D Object Detection | 2023-08-08T00:00:00 | https://arxiv.org/abs/2308.04409v1 | [
"https://github.com/yichaoshen-ms/v-detr"
] | In the paper 'V-DETR: DETR with Vertex Relative Position Encoding for 3D Object Detection', what mAP@0.25 score did the V-DETR model get on the ScanNetV2 dataset
| 77.8 |
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 Overall (Top5 mAP) score did the EgoVideo model get on the Ego4D dataset
| 7.21 |
Turbulence | GPT-3.5-Turbo | Turbulence: Systematically and Automatically Testing Instruction-Tuned Large Language Models for Code | 2023-12-22T00:00:00 | https://arxiv.org/abs/2312.14856v2 | [
"https://github.com/shahinhonarvar/turbulence-benchmark"
] | In the paper 'Turbulence: Systematically and Automatically Testing Instruction-Tuned Large Language Models for Code', what CorrSc score did the GPT-3.5-Turbo model get on the Turbulence dataset
| 0.617 |
Weather (192) | SCNN | Disentangling Structured Components: Towards Adaptive, Interpretable and Scalable Time Series Forecasting | 2023-05-22T00:00:00 | https://arxiv.org/abs/2305.13036v3 | [
"https://github.com/JLDeng/SCNN"
] | In the paper 'Disentangling Structured Components: Towards Adaptive, Interpretable and Scalable Time Series Forecasting', what MSE score did the SCNN model get on the Weather (192) dataset
| 0.188 |
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