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 |
|---|---|---|---|---|---|---|---|
RefCOCOg-val | EVF-SAM | EVF-SAM: Early Vision-Language Fusion for Text-Prompted Segment Anything Model | 2024-06-28T00:00:00 | https://arxiv.org/abs/2406.20076v4 | [
"https://github.com/hustvl/evf-sam"
] | In the paper 'EVF-SAM: Early Vision-Language Fusion for Text-Prompted Segment Anything Model', what Overall IoU score did the EVF-SAM model get on the RefCOCOg-val dataset
| 76.8 |
CULane | FENetV1 | FENet: Focusing Enhanced Network for Lane Detection | 2023-12-28T00:00:00 | https://arxiv.org/abs/2312.17163v6 | [
"https://github.com/hanyangzhong/fenet"
] | In the paper 'FENet: Focusing Enhanced Network for Lane Detection', what F1 score score did the FENetV1 model get on the CULane dataset
| 80.15 |
RSITMD | RemoteCLIP | RemoteCLIP: A Vision Language Foundation Model for Remote Sensing | 2023-06-19T00:00:00 | https://arxiv.org/abs/2306.11029v4 | [
"https://github.com/chendelong1999/remoteclip"
] | In the paper 'RemoteCLIP: A Vision Language Foundation Model for Remote Sensing', what Mean Recall score did the RemoteCLIP model get on the RSITMD dataset
| 50.52% |
Inverse-Text | DeepSolo (ViTAEv2-S, TextOCR) | DeepSolo++: Let Transformer Decoder with Explicit Points Solo for Multilingual Text Spotting | 2023-05-31T00:00:00 | https://arxiv.org/abs/2305.19957v2 | [
"https://github.com/vitae-transformer/deepsolo"
] | In the paper 'DeepSolo++: Let Transformer Decoder with Explicit Points Solo for Multilingual Text Spotting', what F-measure (%) - No Lexicon score did the DeepSolo (ViTAEv2-S, TextOCR) model get on the Inverse-Text dataset
| 68.8 |
AmsterTime | 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 AmsterTime dataset
| 63.0 |
MSCOCO | 3SHNet | 3SHNet: Boosting Image-Sentence Retrieval via Visual Semantic-Spatial Self-Highlighting | 2024-04-26T00:00:00 | https://arxiv.org/abs/2404.17273v1 | [
"https://github.com/xurige1995/3shnet"
] | In the paper '3SHNet: Boosting Image-Sentence Retrieval via Visual Semantic-Spatial Self-Highlighting', what Image-to-text R@1 score did the 3SHNet model get on the MSCOCO dataset
| 85.8 |
Something-Something V2 | TDS-CLIP-ViT-L/14(8frames) | TDS-CLIP: Temporal Difference Side Network for Image-to-Video Transfer Learning | 2024-08-20T00:00:00 | https://arxiv.org/abs/2408.10688v1 | [
"https://github.com/BBYL9413/TDS-CLIP"
] | In the paper 'TDS-CLIP: Temporal Difference Side Network for Image-to-Video Transfer Learning', what Top-1 Accuracy score did the TDS-CLIP-ViT-L/14(8frames) model get on the Something-Something V2 dataset
| 73.4 |
DomainNet | VL2V-SD (CLIP, ViT-B/16) | Leveraging Vision-Language Models for Improving Domain Generalization in Image Classification | 2023-10-12T00:00:00 | https://arxiv.org/abs/2310.08255v2 | [
"https://github.com/val-iisc/VL2V-ADiP"
] | In the paper 'Leveraging Vision-Language Models for Improving Domain Generalization in Image Classification', what Average Accuracy score did the VL2V-SD (CLIP, ViT-B/16) model get on the DomainNet dataset
| 62.79 |
Amazon Photo | GAT | 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 GAT model get on the Amazon Photo dataset
| 96.60 ± 0.33 |
MassSpecGym | SELFIES Transformer | 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 Top-1 MCES score did the SELFIES Transformer model get on the MassSpecGym dataset
| 33.28 |
SMAC MMM2_7m2M1M_vs_9m3M1M | DDN | 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 DDN model get on the SMAC MMM2_7m2M1M_vs_9m3M1M dataset
| 90.34 |
ASQP | ChatGPT (gpt-3.5-turbo, zero-shot) | MvP: Multi-view Prompting Improves Aspect Sentiment Tuple Prediction | 2023-05-22T00:00:00 | https://arxiv.org/abs/2305.12627v1 | [
"https://github.com/ZubinGou/multi-view-prompting"
] | In the paper 'MvP: Multi-view Prompting Improves Aspect Sentiment Tuple Prediction', what F1 (R15) score did the ChatGPT (gpt-3.5-turbo, zero-shot) model get on the ASQP dataset
| 22.87 |
Human3.6M | SMPLer-L | SMPLer: Taming Transformers for Monocular 3D Human Shape and Pose Estimation | 2024-04-23T00:00:00 | https://arxiv.org/abs/2404.15276v1 | [
"https://github.com/xuxy09/smpler"
] | In the paper 'SMPLer: Taming Transformers for Monocular 3D Human Shape and Pose Estimation', what Average MPJPE (mm) score did the SMPLer-L model get on the Human3.6M dataset
| 45.2 |
CAT2000 | SUM | SUM: Saliency Unification through Mamba for Visual Attention Modeling | 2024-06-25T00:00:00 | https://arxiv.org/abs/2406.17815v2 | [
"https://github.com/Arhosseini77/SUM"
] | In the paper 'SUM: Saliency Unification through Mamba for Visual Attention Modeling', what AUC score did the SUM model get on the CAT2000 dataset
| 0.888 |
ACMPS | 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 ACMPS dataset
| 0.9982 |
CIFAR-10 | EDM-AOT | Improving Diffusion-Based Generative Models via Approximated Optimal Transport | 2024-03-08T00:00:00 | https://arxiv.org/abs/2403.05069v1 | [
"https://github.com/large-scale-kim/EDM-AOT"
] | In the paper 'Improving Diffusion-Based Generative Models via Approximated Optimal Transport', what FID score did the EDM-AOT model get on the CIFAR-10 dataset
| 1.73 |
CC3M-TagMask | TTD (TCL) | TTD: Text-Tag Self-Distillation Enhancing Image-Text Alignment in CLIP to Alleviate Single Tag Bias | 2024-03-30T00:00:00 | https://arxiv.org/abs/2404.00384v2 | [
"https://github.com/shjo-april/TTD"
] | In the paper 'TTD: Text-Tag Self-Distillation Enhancing Image-Text Alignment in CLIP to Alleviate Single Tag Bias', what mIoU score did the TTD (TCL) model get on the CC3M-TagMask dataset
| 65.5 |
MM-Vet | Dynamic-LLaVA-13B | Dynamic-LLaVA: Efficient Multimodal Large Language Models via Dynamic Vision-language Context Sparsification | 2024-12-01T00:00:00 | https://arxiv.org/abs/2412.00876v2 | [
"https://github.com/osilly/dynamic_llava"
] | In the paper 'Dynamic-LLaVA: Efficient Multimodal Large Language Models via Dynamic Vision-language Context Sparsification', what GPT-4 score score did the Dynamic-LLaVA-13B model get on the MM-Vet dataset
| 37.3 |
OTB-2015 | SAMURAI-L | SAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking with Motion-Aware Memory | 2024-11-18T00:00:00 | https://arxiv.org/abs/2411.11922v2 | [
"https://github.com/yangchris11/samurai"
] | In the paper 'SAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking with Motion-Aware Memory', what AUC score did the SAMURAI-L model get on the OTB-2015 dataset
| 0.715 |
RefCoCo 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 Overall IoU score did the HyperSeg model get on the RefCoCo val dataset
| 84.8 |
AISHELL-2 | Paraformer | FunASR: A Fundamental End-to-End Speech Recognition Toolkit | 2023-05-18T00:00:00 | https://arxiv.org/abs/2305.11013v1 | [
"https://github.com/alibaba-damo-academy/FunASR"
] | In the paper 'FunASR: A Fundamental End-to-End Speech Recognition Toolkit', what Word Error Rate (WER) score did the Paraformer model get on the AISHELL-2 dataset
| 5.73 |
GTSRB | SAG-ViT | SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision Transformers | 2024-11-14T00:00:00 | https://arxiv.org/abs/2411.09420v2 | [
"https://github.com/shravan-18/SAG-ViT"
] | In the paper 'SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision Transformers', what F1 score did the SAG-ViT model get on the GTSRB dataset
| 99.58 |
CIFAR-10 | 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 CIFAR-10 dataset
| 0.995 |
MAWPS | DeBERTa (PM + VM) | Math Word Problem Solving by Generating Linguistic Variants of Problem Statements | 2023-06-24T00:00:00 | https://arxiv.org/abs/2306.13899v1 | [
"https://github.com/starscream-11813/variational-mathematical-reasoning"
] | In the paper 'Math Word Problem Solving by Generating Linguistic Variants of Problem Statements', what Accuracy (%) score did the DeBERTa (PM + VM) model get on the MAWPS dataset
| 91.0 |
MATH | MetaMath 13B | MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models | 2023-09-21T00:00:00 | https://arxiv.org/abs/2309.12284v4 | [
"https://github.com/meta-math/MetaMath"
] | In the paper 'MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models', what Accuracy score did the MetaMath 13B model get on the MATH dataset
| 22.5 |
Weather (96) | 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 (96) dataset
| 0.142 |
AISHELL-1 | Paraformer | FunASR: A Fundamental End-to-End Speech Recognition Toolkit | 2023-05-18T00:00:00 | https://arxiv.org/abs/2305.11013v1 | [
"https://github.com/alibaba-damo-academy/FunASR"
] | In the paper 'FunASR: A Fundamental End-to-End Speech Recognition Toolkit', what Word Error Rate (WER) score did the Paraformer model get on the AISHELL-1 dataset
| 4.95 |
LAVIB | FLAVR | LAVIB: A Large-scale Video Interpolation Benchmark | 2024-06-14T00:00:00 | https://arxiv.org/abs/2406.09754v2 | [
"https://github.com/alexandrosstergiou/lavib"
] | In the paper 'LAVIB: A Large-scale Video Interpolation Benchmark', what PSNR score did the FLAVR model get on the LAVIB dataset
| 33.44 |
PubMed (48%/32%/20% fixed splits) | GESN | Addressing Heterophily in Node Classification with Graph Echo State Networks | 2023-05-14T00:00:00 | https://arxiv.org/abs/2305.08233v2 | [
"https://github.com/dtortorella/addressing-heterophily-gesn"
] | In the paper 'Addressing Heterophily in Node Classification with Graph Echo State Networks', what 1:1 Accuracy score did the GESN model get on the PubMed (48%/32%/20% fixed splits) dataset
| 89.20 ± 0.34 |
ESOL | ChemBFN | A Bayesian Flow Network Framework for Chemistry Tasks | 2024-07-28T00:00:00 | https://arxiv.org/abs/2407.20294v1 | [
"https://github.com/Augus1999/bayesian-flow-network-for-chemistry"
] | In the paper 'A Bayesian Flow Network Framework for Chemistry Tasks', what RMSE score did the ChemBFN model get on the ESOL dataset
| 0.884 |
EgoExoLearn | Action anticipation baseline (co-training, with gaze) | EgoExoLearn: A Dataset for Bridging Asynchronous Ego- and Exo-centric View of Procedural Activities in Real World | 2024-03-24T00:00:00 | https://arxiv.org/abs/2403.16182v2 | [
"https://github.com/opengvlab/egoexolearn"
] | In the paper 'EgoExoLearn: A Dataset for Bridging Asynchronous Ego- and Exo-centric View of Procedural Activities in Real World', what Accuracy score did the Action anticipation baseline (co-training, with gaze) model get on the EgoExoLearn dataset
| 45.45 |
Vid4 - 4x upscaling | EvTexture+ | EvTexture: Event-driven Texture Enhancement for Video Super-Resolution | 2024-06-19T00:00:00 | https://arxiv.org/abs/2406.13457v1 | [
"https://github.com/dachunkai/evtexture"
] | In the paper 'EvTexture: Event-driven Texture Enhancement for Video Super-Resolution', what PSNR score did the EvTexture+ model get on the Vid4 - 4x upscaling dataset
| 29.78 |
3DPW | ZeDO (Cross Dataset) | 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 PA-MPJPE score did the ZeDO (Cross Dataset) model get on the 3DPW dataset
| 42.6 |
RSTPReid | APTM | Towards Unified Text-based Person Retrieval: A Large-scale Multi-Attribute and Language Search Benchmark | 2023-06-05T00:00:00 | https://arxiv.org/abs/2306.02898v4 | [
"https://github.com/Shuyu-XJTU/APTM"
] | In the paper 'Towards Unified Text-based Person Retrieval: A Large-scale Multi-Attribute and Language Search Benchmark', what R@1 score did the APTM model get on the RSTPReid dataset
| 67.50 |
SFCHD | RetinaNet | Large, Complex, and Realistic Safety Clothing and Helmet Detection: Dataset and Method | 2023-06-03T00:00:00 | https://arxiv.org/abs/2306.02098v2 | [
"https://github.com/lijfrank-open/SFCHD-SCALE"
] | In the paper 'Large, Complex, and Realistic Safety Clothing and Helmet Detection: Dataset and Method', what mAP@0.50 score did the RetinaNet model get on the SFCHD dataset
| 75.9 |
GSM8K | Shepherd + Mistral-7B (SFT on MetaMATH + PRM RL) | Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations | 2023-12-14T00:00:00 | https://arxiv.org/abs/2312.08935v3 | [
"https://huggingface.co/datasets/peiyi9979/Math-Shepherd"
] | In the paper 'Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations', what Accuracy score did the Shepherd + Mistral-7B (SFT on MetaMATH + PRM RL) model get on the GSM8K dataset
| 84.1 |
ETTm1 (96) 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 ETTm1 (96) Multivariate dataset
| 0.278 |
SVT | CLIP4STR-H (DFN-5B) | 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-H (DFN-5B) model get on the SVT dataset
| 99.1 |
Something-Something V1 | TAdaFormer-L/14 | Temporally-Adaptive Models for Efficient Video Understanding | 2023-08-10T00:00:00 | https://arxiv.org/abs/2308.05787v1 | [
"https://github.com/alibaba-mmai-research/TAdaConv"
] | In the paper 'Temporally-Adaptive Models for Efficient Video Understanding', what Top 1 Accuracy score did the TAdaFormer-L/14 model get on the Something-Something V1 dataset
| 63.7 |
Wisconsin | CoED | Improving Graph Neural Networks by Learning Continuous Edge Directions | 2024-10-18T00:00:00 | https://arxiv.org/abs/2410.14109v1 | [
"https://github.com/hormoz-lab/coed-gnn"
] | In the paper 'Improving Graph Neural Networks by Learning Continuous Edge Directions', what Accuracy score did the CoED model get on the Wisconsin dataset
| 87.84±3.70 |
REDS4- 4x upscaling | EvTexture+ | EvTexture: Event-driven Texture Enhancement for Video Super-Resolution | 2024-06-19T00:00:00 | https://arxiv.org/abs/2406.13457v1 | [
"https://github.com/dachunkai/evtexture"
] | In the paper 'EvTexture: Event-driven Texture Enhancement for Video Super-Resolution', what PSNR score did the EvTexture+ model get on the REDS4- 4x upscaling dataset
| 32.93 |
PreCo | Maverick_incr | Maverick: Efficient and Accurate Coreference Resolution Defying Recent Trends | 2024-07-31T00:00:00 | https://arxiv.org/abs/2407.21489v1 | [
"https://github.com/sapienzanlp/maverick-coref"
] | In the paper 'Maverick: Efficient and Accurate Coreference Resolution Defying Recent Trends', what F1 score did the Maverick_incr model get on the PreCo dataset
| 88.0 |
YouTube-VIS validation | UniVS(Swin-L) | UniVS: Unified and Universal Video Segmentation with Prompts as Queries | 2024-02-28T00:00:00 | https://arxiv.org/abs/2402.18115v2 | [
"https://github.com/minghanli/univs"
] | In the paper 'UniVS: Unified and Universal Video Segmentation with Prompts as Queries', what mask AP score did the UniVS(Swin-L) model get on the YouTube-VIS validation dataset
| 60.0 |
Kvasir-SEG | PVT-GCASCADE | G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation | 2023-10-24T00:00:00 | https://arxiv.org/abs/2310.16175v1 | [
"https://github.com/SLDGroup/G-CASCADE"
] | In the paper 'G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation', what mean Dice score did the PVT-GCASCADE model get on the Kvasir-SEG dataset
| 0.9274 |
TAO | GLEE-Plus | 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 TETA score did the GLEE-Plus model get on the TAO dataset
| 41.5 |
LSUN Churches 256 x 256 | BOSS | Bellman Optimal Stepsize Straightening of Flow-Matching Models | 2023-12-27T00:00:00 | https://arxiv.org/abs/2312.16414v3 | [
"https://github.com/nguyenngocbaocmt02/boss"
] | In the paper 'Bellman Optimal Stepsize Straightening of Flow-Matching Models', what clean-FID score did the BOSS model get on the LSUN Churches 256 x 256 dataset
| 13.21 |
ACE 2005 | GoLLIE | GoLLIE: Annotation Guidelines improve Zero-Shot Information-Extraction | 2023-10-05T00:00:00 | https://arxiv.org/abs/2310.03668v5 | [
"https://github.com/hitz-zentroa/gollie"
] | In the paper 'GoLLIE: Annotation Guidelines improve Zero-Shot Information-Extraction', what RE Micro F1 score did the GoLLIE model get on the ACE 2005 dataset
| 70.1 |
VisDA2017 | RCL | Empowering Source-Free Domain Adaptation with MLLM-driven Curriculum Learning | 2024-05-28T00:00:00 | https://arxiv.org/abs/2405.18376v1 | [
"https://github.com/Dong-Jie-Chen/RCL"
] | In the paper 'Empowering Source-Free Domain Adaptation with MLLM-driven Curriculum Learning', what Accuracy score did the RCL model get on the VisDA2017 dataset
| 93.2 |
CIFAR-100-LT (ρ=100) | LIFT (ViT-B/16, ImageNet-21K pre-training) | 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, ImageNet-21K pre-training) model get on the CIFAR-100-LT (ρ=100) dataset
| 10.9 |
Electricity (192) | CycleNet | CycleNet: Enhancing Time Series Forecasting through Modeling Periodic Patterns | 2024-09-27T00:00:00 | https://arxiv.org/abs/2409.18479v2 | [
"https://github.com/ACAT-SCUT/CycleNet"
] | In the paper 'CycleNet: Enhancing Time Series Forecasting through Modeling Periodic Patterns', what MSE score did the CycleNet model get on the Electricity (192) dataset
| 0.144 |
ENZYMES | R-GCN + 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 R-GCN + PANDA model get on the ENZYMES dataset
| 43.9 |
WDC Products-80%cc-seen-medium | Llama3.1_70B_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_70B_structured_explanations model get on the WDC Products-80%cc-seen-medium dataset
| 76.70 |
EARS-WHAM | Schrödinger Bridge (PESQ loss) | Investigating Training Objectives for Generative Speech Enhancement | 2024-09-16T00:00:00 | https://arxiv.org/abs/2409.10753v1 | [
"https://github.com/sp-uhh/sgmse"
] | In the paper 'Investigating Training Objectives for Generative Speech Enhancement', what PESQ-WB score did the Schrödinger Bridge (PESQ loss) model get on the EARS-WHAM dataset
| 3.09 |
MVSEC-SEG | EventSAM | Segment Any Events via Weighted Adaptation of Pivotal Tokens | 2023-12-24T00:00:00 | https://arxiv.org/abs/2312.16222v1 | [
"https://github.com/happychenpipi/eventsam"
] | In the paper 'Segment Any Events via Weighted Adaptation of Pivotal Tokens', what mIoU score did the EventSAM model get on the MVSEC-SEG dataset
| 0.40 |
Fashion IQ | SPRC | Sentence-level Prompts Benefit Composed Image Retrieval | 2023-10-09T00:00:00 | https://arxiv.org/abs/2310.05473v1 | [
"https://github.com/chunmeifeng/sprc"
] | In the paper 'Sentence-level Prompts Benefit Composed Image Retrieval', what (Recall@10+Recall@50)/2 score did the SPRC model get on the Fashion IQ dataset
| 64.85 |
DELIVER | GeminiFusion | GeminiFusion: Efficient Pixel-wise Multimodal Fusion for Vision Transformer | 2024-06-03T00:00:00 | https://arxiv.org/abs/2406.01210v2 | [
"https://github.com/jiadingcn/geminifusion"
] | In the paper 'GeminiFusion: Efficient Pixel-wise Multimodal Fusion for Vision Transformer', what mIoU score did the GeminiFusion model get on the DELIVER dataset
| 66.9 |
ImageNet | KD++(T:resnet50 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:resnet50 S:resnet18) model get on the ImageNet dataset
| 72.53 |
ICFG-PEDES | PLIP-RN50 | PLIP: Language-Image Pre-training for Person Representation Learning | 2023-05-15T00:00:00 | https://arxiv.org/abs/2305.08386v2 | [
"https://github.com/zplusdragon/plip"
] | In the paper 'PLIP: Language-Image Pre-training for Person Representation Learning', what R@1 score did the PLIP-RN50 model get on the ICFG-PEDES dataset
| 64.25 |
Set14 - 4x upscaling | Extracter-rec | EXTRACTER: Efficient Texture Matching with Attention and Gradient Enhancing for Large Scale Image Super Resolution | 2023-10-02T00:00:00 | https://arxiv.org/abs/2310.01379v1 | [
"https://github.com/esteban-rs/extracter"
] | In the paper 'EXTRACTER: Efficient Texture Matching with Attention and Gradient Enhancing for Large Scale Image Super Resolution', what PSNR score did the Extracter-rec model get on the Set14 - 4x upscaling dataset
| 28.09 |
ADE20K | CLIPSelf | CLIPSelf: Vision Transformer Distills Itself for Open-Vocabulary Dense Prediction | 2023-10-02T00:00:00 | https://arxiv.org/abs/2310.01403v2 | [
"https://github.com/wusize/clipself"
] | In the paper 'CLIPSelf: Vision Transformer Distills Itself for Open-Vocabulary Dense Prediction', what PQ score did the CLIPSelf model get on the ADE20K dataset
| 23.7 |
SIQA | phi-1.5-web 1.3B (zero-shot) | Textbooks Are All You Need II: phi-1.5 technical report | 2023-09-11T00:00:00 | https://arxiv.org/abs/2309.05463v1 | [
"https://github.com/knowlab/bi-weekly-paper-presentation"
] | In the paper 'Textbooks Are All You Need II: phi-1.5 technical report', what Accuracy score did the phi-1.5-web 1.3B (zero-shot) model get on the SIQA dataset
| 53.0 |
CelebA 64x64 | PDM+CS | Compensation Sampling for Improved Convergence in Diffusion Models | 2023-12-11T00:00:00 | https://arxiv.org/abs/2312.06285v1 | [
"https://github.com/hotfinda/Compensation-sampling"
] | In the paper 'Compensation Sampling for Improved Convergence in Diffusion Models', what FID score did the PDM+CS model get on the CelebA 64x64 dataset
| 1.38 |
Quora Question Pairs | SplitEE-S | SplitEE: Early Exit in Deep Neural Networks with Split Computing | 2023-09-17T00:00:00 | https://arxiv.org/abs/2309.09195v1 | [
"https://github.com/Div290/SplitEE/blob/main/README.md"
] | In the paper 'SplitEE: Early Exit in Deep Neural Networks with Split Computing', what Accuarcy score did the SplitEE-S model get on the Quora Question Pairs dataset
| 71.1 |
DeLiVER | StitchFusion (RGB-Event) | 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-Event) model get on the DeLiVER dataset
| 57.44 |
AMZ Comp | GCN | Half-Hop: A graph upsampling approach for slowing down message passing | 2023-08-17T00:00:00 | https://arxiv.org/abs/2308.09198v1 | [
"https://github.com/nerdslab/halfhop"
] | In the paper 'Half-Hop: A graph upsampling approach for slowing down message passing', what Accuracy score did the GCN model get on the AMZ Comp dataset
| 90.22% |
MVBench | PLLaVA | PLLaVA : Parameter-free LLaVA Extension from Images to Videos for Video Dense Captioning | 2024-04-25T00:00:00 | https://arxiv.org/abs/2404.16994v2 | [
"https://github.com/magic-research/PLLaVA"
] | In the paper 'PLLaVA : Parameter-free LLaVA Extension from Images to Videos for Video Dense Captioning', what Avg. score did the PLLaVA model get on the MVBench dataset
| 58.1 |
ImageNet | ReviewKD++(T:resnet50, S:mobilenet-v1) | 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 ReviewKD++(T:resnet50, S:mobilenet-v1) model get on the ImageNet dataset
| 72.96 |
ChEBI-20 | TGM-DLM w/o corr | Text-Guided Molecule Generation with Diffusion Language Model | 2024-02-20T00:00:00 | https://arxiv.org/abs/2402.13040v1 | [
"https://github.com/deno-v/tgm-dlm"
] | In the paper 'Text-Guided Molecule Generation with Diffusion Language Model', what Text2Mol score did the TGM-DLM w/o corr model get on the ChEBI-20 dataset
| 58.9 |
DeLiVER | StitchFusion (RGB-D-LiDAR) | 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-D-LiDAR) model get on the DeLiVER dataset
| 66.65 |
Event-Camera Dataset | HyperE2VID | HyperE2VID: Improving Event-Based Video Reconstruction via Hypernetworks | 2023-05-10T00:00:00 | https://arxiv.org/abs/2305.06382v2 | [
"https://github.com/ercanburak/HyperE2VID"
] | In the paper 'HyperE2VID: Improving Event-Based Video Reconstruction via Hypernetworks', what Mean Squared Error score did the HyperE2VID model get on the Event-Camera Dataset dataset
| 0.033 |
PACS | VL2V-SD (CLIP, ViT-B/16) | Leveraging Vision-Language Models for Improving Domain Generalization in Image Classification | 2023-10-12T00:00:00 | https://arxiv.org/abs/2310.08255v2 | [
"https://github.com/val-iisc/VL2V-ADiP"
] | In the paper 'Leveraging Vision-Language Models for Improving Domain Generalization in Image Classification', what Average Accuracy score did the VL2V-SD (CLIP, ViT-B/16) model get on the PACS dataset
| 96.68 |
MAWPS | GPT-3.5 turbo (175B) | Math Word Problem Solving by Generating Linguistic Variants of Problem Statements | 2023-06-24T00:00:00 | https://arxiv.org/abs/2306.13899v1 | [
"https://github.com/starscream-11813/variational-mathematical-reasoning"
] | In the paper 'Math Word Problem Solving by Generating Linguistic Variants of Problem Statements', what Accuracy (%) score did the GPT-3.5 turbo (175B) model get on the MAWPS dataset
| 80.3 |
Abt-Buy | gpt-4o-2024-08-06 | 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 gpt-4o-2024-08-06 model get on the Abt-Buy dataset
| 92.20 |
RealBlur-R | MLWNet | Efficient Multi-scale Network with Learnable Discrete Wavelet Transform for Blind Motion Deblurring | 2023-12-29T00:00:00 | https://arxiv.org/abs/2401.00027v2 | [
"https://github.com/thqiu0419/mlwnet"
] | In the paper 'Efficient Multi-scale Network with Learnable Discrete Wavelet Transform for Blind Motion Deblurring', what PSNR (sRGB) score did the MLWNet model get on the RealBlur-R dataset
| 40.69 |
DEplain-web-sent | mBART (trained on DEplain-APA-sent) | DEPLAIN: A German Parallel Corpus with Intralingual Translations into Plain Language for Sentence and Document Simplification | 2023-05-30T00:00:00 | https://arxiv.org/abs/2305.18939v1 | [
"https://github.com/rstodden/deplain"
] | In the paper 'DEPLAIN: A German Parallel Corpus with Intralingual Translations into Plain Language for Sentence and Document Simplification', what SARI (EASSE>=0.2.1) score did the mBART (trained on DEplain-APA-sent) model get on the DEplain-web-sent dataset
| 30.867 |
WHU Building Dataset | SGSLN/128 | 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/128 model get on the WHU Building Dataset dataset
| 0.9168 |
CAMELYON16 | CAMIL | CAMIL: Context-Aware Multiple Instance Learning for Cancer Detection and Subtyping in Whole Slide Images | 2023-05-09T00:00:00 | https://arxiv.org/abs/2305.05314v3 | [
"https://github.com/olgarithmics/ICLR_CAMIL"
] | In the paper 'CAMIL: Context-Aware Multiple Instance Learning for Cancer Detection and Subtyping in Whole Slide Images', what AUC score did the CAMIL model get on the CAMELYON16 dataset
| 0.959 |
BC4CHEMD | UniNER-7B | UniversalNER: Targeted Distillation from Large Language Models for Open Named Entity Recognition | 2023-08-07T00:00:00 | https://arxiv.org/abs/2308.03279v2 | [
"https://github.com/emma1066/retrieval-augmented-it-openner"
] | In the paper 'UniversalNER: Targeted Distillation from Large Language Models for Open Named Entity Recognition', what F1 score did the UniNER-7B model get on the BC4CHEMD dataset
| 89.21 |
CUB | MSENet | Enhancing Few-Shot Image Classification through Learnable Multi-Scale Embedding and Attention Mechanisms | 2024-09-12T00:00:00 | https://arxiv.org/abs/2409.07989v1 | [
"https://github.com/FatemehAskari/MSENet"
] | In the paper 'Enhancing Few-Shot Image Classification through Learnable Multi-Scale Embedding and Attention Mechanisms', what 5 shot score did the MSENet model get on the CUB dataset
| 71.59 |
ChEBI-20 | MolCA, Galac125M | MolCA: Molecular Graph-Language Modeling with Cross-Modal Projector and Uni-Modal Adapter | 2023-10-19T00:00:00 | https://arxiv.org/abs/2310.12798v4 | [
"https://github.com/acharkq/molca"
] | In the paper 'MolCA: Molecular Graph-Language Modeling with Cross-Modal Projector and Uni-Modal Adapter', what BLEU-2 score did the MolCA, Galac125M model get on the ChEBI-20 dataset
| 61.6 |
TXL-PBC: a freely accessible labeled peripheral blood cell dataset | yolov5s | TXL-PBC: a freely accessible labeled peripheral blood cell dataset | 2024-07-18T00:00:00 | https://arxiv.org/abs/2407.13214v1 | [
"https://github.com/lugan113/TXL-PBC_Dataset"
] | In the paper 'TXL-PBC: a freely accessible labeled peripheral blood cell dataset', what mAP50 score did the yolov5s model get on the TXL-PBC: a freely accessible labeled peripheral blood cell dataset dataset
| 0.97 |
ogbl-ddi | GCN (node embedding) | Can GNNs Learn Link Heuristics? A Concise Review and Evaluation of Link Prediction Methods | 2024-11-22T00:00:00 | https://arxiv.org/abs/2411.14711v1 | [
"https://github.com/astroming/GNNHE"
] | In the paper 'Can GNNs Learn Link Heuristics? A Concise Review and Evaluation of Link Prediction Methods', what Test Hits@20 score did the GCN (node embedding) model get on the ogbl-ddi dataset
| 0.9549 ± 0.0073 |
LIDC-IDRI | MST | Medical Slice Transformer: Improved Diagnosis and Explainability on 3D Medical Images with DINOv2 | 2024-11-24T00:00:00 | https://arxiv.org/abs/2411.15802v1 | [
"https://github.com/mueller-franzes/mst"
] | In the paper 'Medical Slice Transformer: Improved Diagnosis and Explainability on 3D Medical Images with DINOv2', what AUC score did the MST model get on the LIDC-IDRI dataset
| 95 |
ETTh1 (336) Multivariate | AutoTimes | AutoTimes: Autoregressive Time Series Forecasters via Large Language Models | 2024-02-04T00:00:00 | https://arxiv.org/abs/2402.02370v4 | [
"https://github.com/thuml/AutoTimes"
] | In the paper 'AutoTimes: Autoregressive Time Series Forecasters via Large Language Models', what MSE score did the AutoTimes model get on the ETTh1 (336) Multivariate dataset
| 0.401 |
CodeContests | MapCoder (GPT-4) | MapCoder: Multi-Agent Code Generation for Competitive Problem Solving | 2024-05-18T00:00:00 | https://arxiv.org/abs/2405.11403v1 | [
"https://github.com/md-ashraful-pramanik/mapcoder"
] | In the paper 'MapCoder: Multi-Agent Code Generation for Competitive Problem Solving', what Test Set pass@1 score did the MapCoder (GPT-4) model get on the CodeContests dataset
| 28.5 |
LibriSpeech test-other | Zipformer+pruned transducer w/ CR-CTC
(no external language model) | CR-CTC: Consistency regularization on CTC for improved speech recognition | 2024-10-07T00:00:00 | https://arxiv.org/abs/2410.05101v3 | [
"https://github.com/k2-fsa/icefall"
] | In the paper 'CR-CTC: Consistency regularization on CTC for improved speech recognition', what Word Error Rate (WER) score did the Zipformer+pruned transducer w/ CR-CTC
(no external language model) model get on the LibriSpeech test-other dataset
| 3.95 |
RES-Q | QurrentOS-coder + GPT-4 Turbo | RES-Q: Evaluating Code-Editing Large Language Model Systems at the Repository Scale | 2024-06-24T00:00:00 | https://arxiv.org/abs/2406.16801v2 | [
"https://github.com/qurrent-ai/res-q"
] | In the paper 'RES-Q: Evaluating Code-Editing Large Language Model Systems at the Repository Scale', what pass@1 score did the QurrentOS-coder + GPT-4 Turbo model get on the RES-Q dataset
| 37.0 |
MATH | MetaMath 7B | MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models | 2023-09-21T00:00:00 | https://arxiv.org/abs/2309.12284v4 | [
"https://github.com/meta-math/MetaMath"
] | In the paper 'MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models', what Accuracy score did the MetaMath 7B model get on the MATH dataset
| 19.4 |
Split CIFAR-10 | Model with negotiation paradigm | Negotiated Representations to Prevent Forgetting in Machine Learning Applications | 2023-11-30T00:00:00 | https://arxiv.org/abs/2312.00237v1 | [
"https://github.com/nurikorhan/negotiated-representations-for-continual-learning"
] | In the paper 'Negotiated Representations to Prevent Forgetting in Machine Learning Applications', what Percentage Average accuracy - 5 tasks score did the Model with negotiation paradigm model get on the Split CIFAR-10 dataset
| 46.5 |
miniF2F-valid | LEGO-Prover ChatGPT | LEGO-Prover: Neural Theorem Proving with Growing Libraries | 2023-10-01T00:00:00 | https://arxiv.org/abs/2310.00656v3 | [
"https://github.com/wiio12/LEGO-Prover"
] | In the paper 'LEGO-Prover: Neural Theorem Proving with Growing Libraries', what Pass@100 score did the LEGO-Prover ChatGPT model get on the miniF2F-valid dataset
| 57.0 |
Foggy Cityscapes | MILA | MILA: Memory-Based Instance-Level Adaptation for Cross-Domain Object Detection | 2023-11-20T00:00:00 | https://arxiv.org/abs/2309.01086v1 | [
"https://github.com/hitachi-rd-cv/MILA"
] | In the paper 'MILA: Memory-Based Instance-Level Adaptation for Cross-Domain Object Detection', what mAP score did the MILA model get on the Foggy Cityscapes dataset
| 50.6 |
SWDE | InstrucTE (zero-shot) | Schema-Driven Information Extraction from Heterogeneous Tables | 2023-05-23T00:00:00 | https://arxiv.org/abs/2305.14336v5 | [
"https://github.com/bflashcp3f/schema-to-json"
] | In the paper 'Schema-Driven Information Extraction from Heterogeneous Tables', what Avg F1 score did the InstrucTE (zero-shot) model get on the SWDE dataset
| 95.7 |
ScanObjectNN | ULIP-2 + PointNeXt (no voting) | ULIP-2: Towards Scalable Multimodal Pre-training for 3D Understanding | 2023-05-14T00:00:00 | https://arxiv.org/abs/2305.08275v4 | [
"https://github.com/salesforce/ulip"
] | In the paper 'ULIP-2: Towards Scalable Multimodal Pre-training for 3D Understanding', what Overall Accuracy score did the ULIP-2 + PointNeXt (no voting) model get on the ScanObjectNN dataset
| 90.8 |
MATH | ToRA-Code 34B (w/ code) | ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving | 2023-09-29T00:00:00 | https://arxiv.org/abs/2309.17452v4 | [
"https://github.com/microsoft/tora"
] | In the paper 'ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving', what Accuracy score did the ToRA-Code 34B (w/ code) model get on the MATH dataset
| 50.8 |
SPKL | CarNet | 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 CarNet model get on the SPKL dataset
| 0.7131 |
MBPP | DeepSeek-Coder-Base 6.7B (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 6.7B (few-shot) model get on the MBPP dataset
| 60.6 |
Winoground | KeyComp* (GPT-4) | Prompting Large Vision-Language Models for Compositional Reasoning | 2024-01-20T00:00:00 | https://arxiv.org/abs/2401.11337v1 | [
"https://github.com/tossowski/keycomp"
] | In the paper 'Prompting Large Vision-Language Models for Compositional Reasoning', what Text Score score did the KeyComp* (GPT-4) model get on the Winoground dataset
| 43.5 |
Office-Home | VL2V-SD (CLIP, ViT-B/16) | Leveraging Vision-Language Models for Improving Domain Generalization in Image Classification | 2023-10-12T00:00:00 | https://arxiv.org/abs/2310.08255v2 | [
"https://github.com/val-iisc/VL2V-ADiP"
] | In the paper 'Leveraging Vision-Language Models for Improving Domain Generalization in Image Classification', what Average Accuracy score did the VL2V-SD (CLIP, ViT-B/16) model get on the Office-Home dataset
| 87.38 |
CropHarvest - Kenya | Input Fusion with TAE | In the Search for Optimal Multi-view Learning Models for Crop Classification with Global Remote Sensing Data | 2024-03-25T00:00:00 | https://arxiv.org/abs/2403.16582v2 | [
"https://github.com/fmenat/optimal-multiview-crop-classifier"
] | In the paper 'In the Search for Optimal Multi-view Learning Models for Crop Classification with Global Remote Sensing Data', what Average Accuracy score did the Input Fusion with TAE model get on the CropHarvest - Kenya dataset
| 0.673 |
PRCC | CAL+GEFF+DLCR | DLCR: A Generative Data Expansion Framework via Diffusion for Clothes-Changing Person Re-ID | 2024-11-11T00:00:00 | https://arxiv.org/abs/2411.07205v2 | [
"https://github.com/croitorualin/dlcr"
] | In the paper 'DLCR: A Generative Data Expansion Framework via Diffusion for Clothes-Changing Person Re-ID', what Rank-1 score did the CAL+GEFF+DLCR model get on the PRCC dataset
| 84.6 |
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