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 |
|---|---|---|---|---|---|---|---|
CUHK-Shadow | SDDNet (MM 2023) (256x256) | SDDNet: Style-guided Dual-layer Disentanglement Network for Shadow Detection | 2023-08-17T00:00:00 | https://arxiv.org/abs/2308.08935v2 | [
"https://github.com/rmcong/sddnet_acmmm23"
] | In the paper 'SDDNet: Style-guided Dual-layer Disentanglement Network for Shadow Detection', what BER score did the SDDNet (MM 2023) (256x256) model get on the CUHK-Shadow dataset
| 8.66 |
SemTabNet | T5 | Statements: Universal Information Extraction from Tables with Large Language Models for ESG KPIs | 2024-06-27T00:00:00 | https://arxiv.org/abs/2406.19102v1 | [
"https://github.com/ds4sd/semtabnet"
] | In the paper 'Statements: Universal Information Extraction from Tables with Large Language Models for ESG KPIs', what average Tree Similarity Score score did the T5 model get on the SemTabNet dataset
| 81.76 |
CIFAR-10-LT (ρ=10) | VS + ADRW + TLA | A Unified Generalization Analysis of Re-Weighting and Logit-Adjustment for Imbalanced Learning. paper with code | 2023-10-07T00:00:00 | https://arxiv.org/abs/2310.04752 | [
"https://github.com/wang22ti/DDC"
] | In the paper 'A Unified Generalization Analysis of Re-Weighting and Logit-Adjustment for Imbalanced Learning. paper with code', what Error Rate score did the VS + ADRW + TLA model get on the CIFAR-10-LT (ρ=10) dataset
| 8.18 |
View-of-Delft (val) | HyDRa | Unleashing HyDRa: Hybrid Fusion, Depth Consistency and Radar for Unified 3D Perception | 2024-03-12T00:00:00 | https://arxiv.org/abs/2403.07746v2 | [
"https://github.com/phi-wol/hydra"
] | In the paper 'Unleashing HyDRa: Hybrid Fusion, Depth Consistency and Radar for Unified 3D Perception', what mAP score did the HyDRa model get on the View-of-Delft (val) dataset
| 60.9 |
PerSeg | P^2SAM | Part-aware Personalized Segment Anything Model for Patient-Specific Segmentation | 2024-03-08T00:00:00 | https://arxiv.org/abs/2403.05433v1 | [
"https://github.com/Zch0414/P2SAM"
] | In the paper 'Part-aware Personalized Segment Anything Model for Patient-Specific Segmentation', what mIoU score did the P^2SAM model get on the PerSeg dataset
| 95.66 |
KITTI Odometry Benchmark | SCIPaD | SCIPaD: Incorporating Spatial Clues into Unsupervised Pose-Depth Joint Learning | 2024-07-07T00:00:00 | https://arxiv.org/abs/2407.05283v1 | [
"https://github.com/fengyi233/SCIPaD"
] | In the paper 'SCIPaD: Incorporating Spatial Clues into Unsupervised Pose-Depth Joint Learning', what Absolute Trajectory Error [m] score did the SCIPaD model get on the KITTI Odometry Benchmark dataset
| 20.83 |
GRAZPEDWRI-DX | YOLOv5x | Enhancing Wrist Fracture Detection with YOLO | 2024-07-17T00:00:00 | https://arxiv.org/abs/2407.12597v2 | [
"https://github.com/ammarlodhi255/pediatric_wrist_abnormality_detection-end-to-end-implementation"
] | In the paper 'Enhancing Wrist Fracture Detection with YOLO', what mAP score did the YOLOv5x model get on the GRAZPEDWRI-DX dataset
| 69.00 |
USNA-Cn2 (short-duration) | Persistence | 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 Persistence model get on the USNA-Cn2 (short-duration) dataset
| 0.821 |
WOST | CLIP4STR-B | 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 1:1 Accuracy score did the CLIP4STR-B model get on the WOST dataset
| 87.0 |
COCO-20i (1-shot) | Matcher(DINOv2) | Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching | 2023-05-22T00:00:00 | https://arxiv.org/abs/2305.13310v2 | [
"https://github.com/aim-uofa/matcher"
] | In the paper 'Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching', what Mean IoU score did the Matcher(DINOv2) model get on the COCO-20i (1-shot) dataset
| 52.7 |
MM-Vet | Dynamic-LLaVA-7B | 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-7B model get on the MM-Vet dataset
| 32.2 |
MATH | ToRA-Code 13B (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 13B (w/ code) model get on the MATH dataset
| 48.1 |
COCO minival | GLEE-Lite | 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-Lite model get on the COCO minival dataset
| 55.0 |
UK Biobank Brain MRI | NeuroPath | NeuroPath: A Neural Pathway Transformer for Joining the Dots of Human Connectomes | 2024-09-26T00:00:00 | https://arxiv.org/abs/2409.17510v3 | [
"https://github.com/Chrisa142857/neuro_detour"
] | In the paper 'NeuroPath: A Neural Pathway Transformer for Joining the Dots of Human Connectomes', what Accuracy score did the NeuroPath model get on the UK Biobank Brain MRI dataset
| 99.59 |
NExT-QA (Open-ended VideoQA) | Flash-VStream | Flash-VStream: Memory-Based Real-Time Understanding for Long Video Streams | 2024-06-12T00:00:00 | https://arxiv.org/abs/2406.08085v2 | [
"https://github.com/IVGSZ/Flash-VStream"
] | In the paper 'Flash-VStream: Memory-Based Real-Time Understanding for Long Video Streams', what Accuracy score did the Flash-VStream model get on the NExT-QA (Open-ended VideoQA) dataset
| 61.6 |
Caltech-101 | RPO | Read-only Prompt Optimization for Vision-Language Few-shot Learning | 2023-08-29T00:00:00 | https://arxiv.org/abs/2308.14960v2 | [
"https://github.com/mlvlab/rpo"
] | In the paper 'Read-only Prompt Optimization for Vision-Language Few-shot Learning', what Harmonic mean score did the RPO model get on the Caltech-101 dataset
| 96.03 |
CropDisease | RFS+MLP | Improving Cross-domain Few-shot Classification with Multilayer Perceptron | 2023-12-15T00:00:00 | https://arxiv.org/abs/2312.09589v1 | [
"https://github.com/BaiShuanghao/CDFSC-MLP"
] | In the paper 'Improving Cross-domain Few-shot Classification with Multilayer Perceptron', what 5 shot score did the RFS+MLP model get on the CropDisease dataset
| 89.68 |
MSCOCO | LP-OVOD | 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 model get on the MSCOCO dataset
| 40.5 |
Weather (192) | 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 (192) dataset
| 0.191 |
ARC (Challenge) | PaLM 2-S (1-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 Accuracy score did the PaLM 2-S (1-shot) model get on the ARC (Challenge) dataset
| 59.6 |
PASCAL VOC | OneNete,4-S | OneNet: A Channel-Wise 1D Convolutional U-Net | 2024-11-14T00:00:00 | https://arxiv.org/abs/2411.09838v1 | [
"https://github.com/shbyun080/onenet"
] | In the paper 'OneNet: A Channel-Wise 1D Convolutional U-Net', what mAP0.5 score did the OneNete,4-S model get on the PASCAL VOC dataset
| 52.75 |
FGVC-Aircraft | 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 FGVC-Aircraft dataset
| 29.1 |
S2Looking | CGNet | Change Guiding Network: Incorporating Change Prior to Guide Change Detection in Remote Sensing Imagery | 2024-04-14T00:00:00 | https://arxiv.org/abs/2404.09179v1 | [
"https://github.com/chengxihan/cgnet-cd"
] | In the paper 'Change Guiding Network: Incorporating Change Prior to Guide Change Detection in Remote Sensing Imagery', what F1-Score score did the CGNet model get on the S2Looking dataset
| 64.33 |
ADE20K training-free zero-shot segmentation | COSMOS ViT-B/16 | COSMOS: Cross-Modality Self-Distillation for Vision Language Pre-training | 2024-12-02T00:00:00 | https://arxiv.org/abs/2412.01814v1 | [
"https://github.com/ExplainableML/cosmos"
] | In the paper 'COSMOS: Cross-Modality Self-Distillation for Vision Language Pre-training', what mIoU score did the COSMOS ViT-B/16 model get on the ADE20K training-free zero-shot segmentation dataset
| 17.7 |
Kvasir-SEG | ADSNet | Adaptation of Distinct Semantics for Uncertain Areas in Polyp Segmentation | 2024-05-13T00:00:00 | https://arxiv.org/abs/2405.07523v1 | [
"https://github.com/vinhhust2806/ADSNet"
] | In the paper 'Adaptation of Distinct Semantics for Uncertain Areas in Polyp Segmentation', what mean Dice score did the ADSNet model get on the Kvasir-SEG dataset
| 0.92 |
PPI | GCN + SAF | The Split Matters: Flat Minima Methods for Improving the Performance of GNNs | 2023-06-15T00:00:00 | https://arxiv.org/abs/2306.09121v1 | [
"https://github.com/foisunt/fmms-in-gnns"
] | In the paper 'The Split Matters: Flat Minima Methods for Improving the Performance of GNNs', what F1 score did the GCN + SAF model get on the PPI dataset
| 99.38 ± 0.01% |
ActivityNet Captions | CM² | Do You Remember? Dense Video Captioning with Cross-Modal Memory Retrieval | 2024-04-11T00:00:00 | https://arxiv.org/abs/2404.07610v1 | [
"https://github.com/ailab-kyunghee/cm2_dvc"
] | In the paper 'Do You Remember? Dense Video Captioning with Cross-Modal Memory Retrieval', what METEOR score did the CM² model get on the ActivityNet Captions dataset
| 8.55 |
FreiHAND | WiLoR | WiLoR: End-to-end 3D Hand Localization and Reconstruction in-the-wild | 2024-09-18T00:00:00 | https://arxiv.org/abs/2409.12259v1 | [
"https://github.com/rolpotamias/WiLoR"
] | In the paper 'WiLoR: End-to-end 3D Hand Localization and Reconstruction in-the-wild', what PA-MPVPE score did the WiLoR model get on the FreiHAND dataset
| 5.1 |
CrowdPose | RTMO-l | RTMO: Towards High-Performance One-Stage Real-Time Multi-Person Pose Estimation | 2023-12-12T00:00:00 | https://arxiv.org/abs/2312.07526v2 | [
"https://github.com/open-mmlab/mmpose"
] | In the paper 'RTMO: Towards High-Performance One-Stage Real-Time Multi-Person Pose Estimation', what mAP @0.5:0.95 score did the RTMO-l model get on the CrowdPose dataset
| 83.8 |
BSD100 - 4x upscaling | WaveMixSR | WaveMixSR: A Resource-efficient Neural Network for Image Super-resolution | 2023-07-01T00:00:00 | https://arxiv.org/abs/2307.00430v1 | [
"https://github.com/pranavphoenix/WaveMixSR"
] | In the paper 'WaveMixSR: A Resource-efficient Neural Network for Image Super-resolution', what SSIM score did the WaveMixSR model get on the BSD100 - 4x upscaling dataset
| 0.7605 |
COCO-20i (5-shot) | MSDNet (ResNet-101) | MSDNet: Multi-Scale Decoder for Few-Shot Semantic Segmentation via Transformer-Guided Prototyping | 2024-09-17T00:00:00 | https://arxiv.org/abs/2409.11316v1 | [
"https://github.com/amirrezafateh/msdnet"
] | In the paper 'MSDNet: Multi-Scale Decoder for Few-Shot Semantic Segmentation via Transformer-Guided Prototyping', what Mean IoU score did the MSDNet (ResNet-101) model get on the COCO-20i (5-shot) dataset
| 55.3 |
BanglaBook | Multinomial NB (BoW) | BanglaBook: A Large-scale Bangla Dataset for Sentiment Analysis from Book Reviews | 2023-05-11T00:00:00 | https://arxiv.org/abs/2305.06595v3 | [
"https://github.com/mohsinulkabir14/banglabook"
] | In the paper 'BanglaBook: A Large-scale Bangla Dataset for Sentiment Analysis from Book Reviews', what Weighted Average F1-score score did the Multinomial NB (BoW) model get on the BanglaBook dataset
| 0.8564 |
ICFG-PEDES | MARS | MARS: Paying more attention to visual attributes for text-based person search | 2024-07-05T00:00:00 | https://arxiv.org/abs/2407.04287v1 | [
"https://github.com/ergastialex/mars"
] | In the paper 'MARS: Paying more attention to visual attributes for text-based person search', what mAP score did the MARS model get on the ICFG-PEDES dataset
| 44.93 |
MVTec AD | AnomalyDINO-S (4-shot) | AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2 | 2024-05-23T00:00:00 | https://arxiv.org/abs/2405.14529v2 | [
"https://github.com/dammsi/AnomalyDINO"
] | In the paper 'AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2', what Detection AUROC score did the AnomalyDINO-S (4-shot) model get on the MVTec AD dataset
| 97.7 |
RST-DT | DMRST | Bilingual Rhetorical Structure Parsing with Large Parallel Annotations | 2024-09-23T00:00:00 | https://arxiv.org/abs/2409.14969v1 | [
"https://github.com/tchewik/bilingualrsp"
] | In the paper 'Bilingual Rhetorical Structure Parsing with Large Parallel Annotations', what Standard Parseval (Span) score did the DMRST model get on the RST-DT dataset
| 78.7 ± 0.4 |
TVBench | PLLaVA-13B | 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 Average Accuracy score did the PLLaVA-13B model get on the TVBench dataset
| 36.4 |
RefCOCO+ testA | Florence-2-large-ft | Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks | 2023-11-10T00:00:00 | https://arxiv.org/abs/2311.06242v1 | [
"https://github.com/retkowsky/florence-2"
] | In the paper 'Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks', what Accuracy (%) score did the Florence-2-large-ft model get on the RefCOCO+ testA dataset
| 95.3 |
MATH | DART-Math-Llama3-70B-Uniform (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-Llama3-70B-Uniform (0-shot CoT, w/o code) model get on the MATH dataset
| 54.9 |
MAWPS | ATHENA (roberta-base) | ATHENA: Mathematical Reasoning with Thought Expansion | 2023-11-02T00:00:00 | https://arxiv.org/abs/2311.01036v1 | [
"https://github.com/the-jb/athena-math"
] | In the paper 'ATHENA: Mathematical Reasoning with Thought Expansion', what Accuracy (%) score did the ATHENA (roberta-base) model get on the MAWPS dataset
| 92.2 |
ChEBI-20 | TGM-DLM | 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 model get on the ChEBI-20 dataset
| 58.1 |
WSJ0-2mix | TD-Conformer (XL) + DM | On Time Domain Conformer Models for Monaural Speech Separation in Noisy Reverberant Acoustic Environments | 2023-10-09T00:00:00 | https://arxiv.org/abs/2310.06125v1 | [
"https://github.com/jwr1995/pubsep"
] | In the paper 'On Time Domain Conformer Models for Monaural Speech Separation in Noisy Reverberant Acoustic Environments', what SI-SDRi score did the TD-Conformer (XL) + DM model get on the WSJ0-2mix dataset
| 21.2 |
Cora | CGT | Mitigating Degree Biases in Message Passing Mechanism by Utilizing Community Structures | 2023-12-28T00:00:00 | https://arxiv.org/abs/2312.16788v1 | [
"https://github.com/nslab-cuk/community-aware-graph-transformer"
] | In the paper 'Mitigating Degree Biases in Message Passing Mechanism by Utilizing Community Structures', what Accuracy score did the CGT model get on the Cora dataset
| 87.10±1.53 |
AI2D | Gemini Ultra | Gemini: A Family of Highly Capable Multimodal Models | 2023-12-19T00:00:00 | https://arxiv.org/abs/2312.11805v4 | [
"https://github.com/valdecy/pybibx"
] | In the paper 'Gemini: A Family of Highly Capable Multimodal Models', what EM score did the Gemini Ultra model get on the AI2D dataset
| 79.5 |
WinoGrande | LLaMA3 8B+MoSLoRA | Mixture-of-Subspaces in Low-Rank Adaptation | 2024-06-16T00:00:00 | https://arxiv.org/abs/2406.11909v3 | [
"https://github.com/wutaiqiang/moslora"
] | In the paper 'Mixture-of-Subspaces in Low-Rank Adaptation', what Accuracy score did the LLaMA3 8B+MoSLoRA model get on the WinoGrande dataset
| 85.8 |
Synapse multi-organ CT | SegFormer3D | SegFormer3D: an Efficient Transformer for 3D Medical Image Segmentation | 2024-04-15T00:00:00 | https://arxiv.org/abs/2404.10156v2 | [
"https://github.com/osupcvlab/segformer3d"
] | In the paper 'SegFormer3D: an Efficient Transformer for 3D Medical Image Segmentation', what Avg DSC score did the SegFormer3D model get on the Synapse multi-organ CT dataset
| 82.15 |
MixSNIPS | 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 Micro F1 score did the BiSLU model get on the MixSNIPS dataset
| 97.2 |
The Pile | Phi-3 3.8B | Efficiently Learning at Test-Time: Active Fine-Tuning of LLMs | 2024-10-10T00:00:00 | https://arxiv.org/abs/2410.08020v2 | [
"https://github.com/jonhue/activeft"
] | In the paper 'Efficiently Learning at Test-Time: Active Fine-Tuning of LLMs', what Bits per byte score did the Phi-3 3.8B model get on the The Pile dataset
| 0.679 |
MVTec LOCO AD | PUAD-S | PUAD: Frustratingly Simple Method for Robust Anomaly Detection | 2024-02-23T00:00:00 | https://arxiv.org/abs/2402.15143v1 | [
"https://github.com/LeapMind/PUAD"
] | In the paper 'PUAD: Frustratingly Simple Method for Robust Anomaly Detection', what Avg. Detection AUROC score did the PUAD-S model get on the MVTec LOCO AD dataset
| 93.1 |
VulScribeR | Reveal Model - Tested on Reveal (Training on Devign + VulScribeR 20K + Extra Cleans) | Exploring RAG-based Vulnerability Augmentation with LLMs | 2024-08-07T00:00:00 | https://arxiv.org/abs/2408.04125v2 | [
"https://github.com/VulScribeR/VulScribeR"
] | In the paper 'Exploring RAG-based Vulnerability Augmentation with LLMs', what F1 Score score did the Reveal Model - Tested on Reveal (Training on Devign + VulScribeR 20K + Extra Cleans) model get on the VulScribeR dataset
| 26.18 |
CSL-Daily | AdaBrowse | AdaBrowse: Adaptive Video Browser for Efficient Continuous Sign Language Recognition | 2023-08-16T00:00:00 | https://arxiv.org/abs/2308.08327v1 | [
"https://github.com/hulianyuyy/adabrowse"
] | In the paper 'AdaBrowse: Adaptive Video Browser for Efficient Continuous Sign Language Recognition', what Word Error Rate (WER) score did the AdaBrowse model get on the CSL-Daily dataset
| 30.6 |
QuerYD | 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 text-to-video R@1 score did the TESTA (ViT-B/16) model get on the QuerYD dataset
| 83.4 |
CUHK-PEDES | RaSa | RaSa: Relation and Sensitivity Aware Representation Learning for Text-based Person Search | 2023-05-23T00:00:00 | https://arxiv.org/abs/2305.13653v1 | [
"https://github.com/flame-chasers/rasa"
] | In the paper 'RaSa: Relation and Sensitivity Aware Representation Learning for Text-based Person Search', what R@1 score did the RaSa model get on the CUHK-PEDES dataset
| 76.51 |
kickstarter | LightGBM + RoBERTa 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 LightGBM + RoBERTa embedding model get on the kickstarter dataset
| 0.767 |
HIV dataset | SMA | Self-Guided Masked Autoencoders for Domain-Agnostic Self-Supervised Learning | 2024-02-22T00:00:00 | https://arxiv.org/abs/2402.14789v1 | [
"https://github.com/johnathan-xie/sma"
] | In the paper 'Self-Guided Masked Autoencoders for Domain-Agnostic Self-Supervised Learning', what AUC score did the SMA model get on the HIV dataset dataset
| 0.789 |
Amazon Games | ProxyRCA | Proxy-based Item Representation for Attribute and Context-aware Recommendation | 2023-12-11T00:00:00 | https://arxiv.org/abs/2312.06145v1 | [
"https://github.com/theeluwin/ProxyRCA"
] | In the paper 'Proxy-based Item Representation for Attribute and Context-aware Recommendation', what Hit@10 score did the ProxyRCA model get on the Amazon Games dataset
| 0.809 |
TSS | SD+DINO (Zero-shot) | A Tale of Two Features: Stable Diffusion Complements DINO for Zero-Shot Semantic Correspondence | 2023-05-24T00:00:00 | https://arxiv.org/abs/2305.15347v2 | [
"https://github.com/Junyi42/sd-dino"
] | In the paper 'A Tale of Two Features: Stable Diffusion Complements DINO for Zero-Shot Semantic Correspondence', what Average PCK@0.05 score did the SD+DINO (Zero-shot) model get on the TSS dataset
| 79.7 |
SMAC MMM2_7m2M1M_vs_9m3M1M | VDN | 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 VDN model get on the SMAC MMM2_7m2M1M_vs_9m3M1M dataset
| 75.00 |
MM-Vet | SeVa-13B | Self-Supervised Visual Preference Alignment | 2024-04-16T00:00:00 | https://arxiv.org/abs/2404.10501v2 | [
"https://github.com/Kevinz-code/SeVa"
] | In the paper 'Self-Supervised Visual Preference Alignment', what GPT-4 score score did the SeVa-13B model get on the MM-Vet dataset
| 41.0 |
Microsoft COCO dataset | VTON-IT | VTON-IT: Virtual Try-On using Image Translation | 2023-10-06T00:00:00 | https://arxiv.org/abs/2310.04558v2 | [
"https://github.com/shuntos/viton-it"
] | In the paper 'VTON-IT: Virtual Try-On using Image Translation', what SSIM score did the VTON-IT model get on the Microsoft COCO dataset dataset
| 0.93 |
UCF101 | 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 UCF101 dataset
| 76.3 |
SIM10K to Cityscapes | PT (ResNet50-FPN) | Align and Distill: Unifying and Improving Domain Adaptive Object Detection | 2024-03-18T00:00:00 | https://arxiv.org/abs/2403.12029v2 | [
"https://github.com/justinkay/aldi"
] | In the paper 'Align and Distill: Unifying and Improving Domain Adaptive Object Detection', what mAP@0.5 score did the PT (ResNet50-FPN) model get on the SIM10K to Cityscapes dataset
| 70.6 |
MSR-VTT | HiGen | Hierarchical Spatio-temporal Decoupling for Text-to-Video Generation | 2023-12-07T00:00:00 | https://arxiv.org/abs/2312.04483v1 | [
"https://github.com/ali-vilab/VGen"
] | In the paper 'Hierarchical Spatio-temporal Decoupling for Text-to-Video Generation', what FID score did the HiGen model get on the MSR-VTT dataset
| 8.60 |
SAFIM | codegen-350M-multi | 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 codegen-350M-multi model get on the SAFIM dataset
| 16.30 |
LibriSpeech test-clean | FAdam | FAdam: Adam is a natural gradient optimizer using diagonal empirical Fisher information | 2024-05-21T00:00:00 | https://arxiv.org/abs/2405.12807v10 | [
"https://github.com/lessw2020/fadam_pytorch"
] | In the paper 'FAdam: Adam is a natural gradient optimizer using diagonal empirical Fisher information', what Word Error Rate (WER) score did the FAdam model get on the LibriSpeech test-clean dataset
| 1.34 |
MATH | DART-Math-Mistral-7B-Uniform (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-Mistral-7B-Uniform (0-shot CoT, w/o code) model get on the MATH dataset
| 43.5 |
SIR^2(Wild) | RDNet | Reversible Decoupling Network for Single Image Reflection Removal | 2024-10-10T00:00:00 | https://arxiv.org/abs/2410.08063v1 | [
"https://github.com/lime-j/RDNet"
] | In the paper 'Reversible Decoupling Network for Single Image Reflection Removal', what PSNR score did the RDNet model get on the SIR^2(Wild) dataset
| 27.7 |
MAESTRO | hFT-Transformer | Automatic Piano Transcription with Hierarchical Frequency-Time Transformer | 2023-07-10T00:00:00 | https://arxiv.org/abs/2307.04305v1 | [
"https://github.com/sony/hft-transformer"
] | In the paper 'Automatic Piano Transcription with Hierarchical Frequency-Time Transformer', what Onset F1 score did the hFT-Transformer model get on the MAESTRO dataset
| 97.44 |
MPI-INF-3DHP | MotionAGFormer-L (T=81) | MotionAGFormer: Enhancing 3D Human Pose Estimation with a Transformer-GCNFormer Network | 2023-10-25T00:00:00 | https://arxiv.org/abs/2310.16288v1 | [
"https://github.com/taatiteam/motionagformer"
] | In the paper 'MotionAGFormer: Enhancing 3D Human Pose Estimation with a Transformer-GCNFormer Network', what AUC score did the MotionAGFormer-L (T=81) model get on the MPI-INF-3DHP dataset
| 85.3 |
ScanNet200 | Open-YOLO 3D | Open-YOLO 3D: Towards Fast and Accurate Open-Vocabulary 3D Instance Segmentation | 2024-06-04T00:00:00 | https://arxiv.org/abs/2406.02548v2 | [
"https://github.com/aminebdj/openyolo3d"
] | In the paper 'Open-YOLO 3D: Towards Fast and Accurate Open-Vocabulary 3D Instance Segmentation', what mAP score did the Open-YOLO 3D model get on the ScanNet200 dataset
| 24.7 |
HumanML3D | 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 HumanML3D dataset
| 0.109 |
LaSOT | ODTrack-L | 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 AUC score did the ODTrack-L model get on the LaSOT dataset
| 74.0 |
DomainNet | UniDG + CORAL + ConvNeXt-B | Towards Unified and Effective Domain Generalization | 2023-10-16T00:00:00 | https://arxiv.org/abs/2310.10008v1 | [
"https://github.com/invictus717/UniDG"
] | In the paper 'Towards Unified and Effective Domain Generalization', what Average Accuracy score did the UniDG + CORAL + ConvNeXt-B model get on the DomainNet dataset
| 59.5 |
TriviaQA | PaLM 2-S (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 EM score did the PaLM 2-S (one-shot) model get on the TriviaQA dataset
| 75.2 |
MATH | MMIQC-72B | Augmenting Math Word Problems via Iterative Question Composing | 2024-01-17T00:00:00 | https://arxiv.org/abs/2401.09003v4 | [
"https://github.com/iiis-ai/iterativequestioncomposing"
] | In the paper 'Augmenting Math Word Problems via Iterative Question Composing', what Accuracy score did the MMIQC-72B model get on the MATH dataset
| 45.0 |
PF-PASCAL | SD+DINO (Supervised) | A Tale of Two Features: Stable Diffusion Complements DINO for Zero-Shot Semantic Correspondence | 2023-05-24T00:00:00 | https://arxiv.org/abs/2305.15347v2 | [
"https://github.com/Junyi42/sd-dino"
] | In the paper 'A Tale of Two Features: Stable Diffusion Complements DINO for Zero-Shot Semantic Correspondence', what PCK score did the SD+DINO (Supervised) model get on the PF-PASCAL dataset
| 93.6 |
ICBHI Respiratory Sound Database | AFT on Mixed-500 | Adversarial Fine-tuning using Generated Respiratory Sound to Address Class Imbalance | 2023-11-11T00:00:00 | https://arxiv.org/abs/2311.06480v1 | [
"https://github.com/kaen2891/adversarial_fine-tuning_using_generated_respiratory_sound"
] | In the paper 'Adversarial Fine-tuning using Generated Respiratory Sound to Address Class Imbalance', what ICBHI Score score did the AFT on Mixed-500 model get on the ICBHI Respiratory Sound Database dataset
| 61.79 |
WebApp1k-Duo-React | o1-mini | A Case Study of Web App Coding with OpenAI Reasoning Models | 2024-09-19T00:00:00 | https://arxiv.org/abs/2409.13773v1 | [
"https://github.com/onekq/webapp1k"
] | In the paper 'A Case Study of Web App Coding with OpenAI Reasoning Models', what pass@1 score did the o1-mini model get on the WebApp1k-Duo-React dataset
| 0.667 |
DotPrompts | SantaCoder | Guiding Language Models of Code with Global Context using Monitors | 2023-06-19T00:00:00 | https://arxiv.org/abs/2306.10763v2 | [
"https://github.com/microsoft/monitors4codegen"
] | In the paper 'Guiding Language Models of Code with Global Context using Monitors', what Compilation Rate score did the SantaCoder model get on the DotPrompts dataset
| 59.79 |
ZINC | BoP | From Primes to Paths: Enabling Fast Multi-Relational Graph Analysis | 2024-11-17T00:00:00 | https://arxiv.org/abs/2411.11149v1 | [
"https://github.com/kbogas/PAM_BoP"
] | In the paper 'From Primes to Paths: Enabling Fast Multi-Relational Graph Analysis', what MAE score did the BoP model get on the ZINC dataset
| 0.297 |
ADE-OoD | DOoD | Diffusion for Out-of-Distribution Detection on Road Scenes and Beyond | 2024-07-22T00:00:00 | https://arxiv.org/abs/2407.15739v1 | [
"https://github.com/lmb-freiburg/diffusion-for-ood"
] | In the paper 'Diffusion for Out-of-Distribution Detection on Road Scenes and Beyond', what AP score did the DOoD model get on the ADE-OoD dataset
| 63.03 |
ImageNet-LT | APA (SE-ResNet-50) | Adaptive Parametric Activation | 2024-07-11T00:00:00 | https://arxiv.org/abs/2407.08567v2 | [
"https://github.com/kostas1515/aglu"
] | In the paper 'Adaptive Parametric Activation', what Top-1 Accuracy score did the APA (SE-ResNet-50) model get on the ImageNet-LT dataset
| 57.9 |
WDC Products-80%cc-seen-medium | gpt-4o-mini-2024-07-18_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 gpt-4o-mini-2024-07-18_structured_explanations model get on the WDC Products-80%cc-seen-medium dataset
| 84.38 |
REDDIT-BINARY | 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 REDDIT-BINARY dataset
| 80.2 |
Stanford Cars | 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 Stanford Cars dataset
| 76.72 |
MATH | OpenMath-Llama2-70B (w/ code, SC, k=50) | OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset | 2024-02-15T00:00:00 | https://arxiv.org/abs/2402.10176v2 | [
"https://github.com/kipok/nemo-skills"
] | In the paper 'OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset', what Accuracy score did the OpenMath-Llama2-70B (w/ code, SC, k=50) model get on the MATH dataset
| 58.3 |
LAGENDA age | MiVOLO-D1 | MiVOLO: Multi-input Transformer for Age and Gender Estimation | 2023-07-10T00:00:00 | https://arxiv.org/abs/2307.04616v2 | [
"https://github.com/wildchlamydia/mivolo"
] | In the paper 'MiVOLO: Multi-input Transformer for Age and Gender Estimation', what MAE score did the MiVOLO-D1 model get on the LAGENDA age dataset
| 3.99 |
RTE | PaLM 2-M (1-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 Accuracy score did the PaLM 2-M (1-shot) model get on the RTE dataset
| 81.9% |
FGVC-Aircraft | 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 FGVC-Aircraft dataset
| 29 |
DTD | 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 DTD dataset
| 51.2 |
SMAC 3s5z_vs_4s6z | QMIX | 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 Average Score score did the QMIX model get on the SMAC 3s5z_vs_4s6z dataset
| 13.09 |
SHD - Adding | LIF-SNN | The Expressive Leaky Memory Neuron: an Efficient and Expressive Phenomenological Neuron Model Can Solve Long-Horizon Tasks | 2023-06-14T00:00:00 | https://arxiv.org/abs/2306.16922v3 | [
"https://github.com/AaronSpieler/elmneuron"
] | In the paper 'The Expressive Leaky Memory Neuron: an Efficient and Expressive Phenomenological Neuron Model Can Solve Long-Horizon Tasks', what Accuracy (%) score did the LIF-SNN model get on the SHD - Adding dataset
| FAIL |
RefCOCOg-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 RefCOCOg-val dataset
| 79.4 |
SID SonyA7S2 x300 | LRD | Towards General Low-Light Raw Noise Synthesis and Modeling | 2023-07-31T00:00:00 | https://arxiv.org/abs/2307.16508v2 | [
"https://github.com/fengzhang427/LRD"
] | In the paper 'Towards General Low-Light Raw Noise Synthesis and Modeling', what PSNR (Raw) score did the LRD model get on the SID SonyA7S2 x300 dataset
| 36.03 |
LibriSpeech 100h test-clean | Branchformer + GFSA | Graph Convolutions Enrich the Self-Attention in Transformers! | 2023-12-07T00:00:00 | https://arxiv.org/abs/2312.04234v5 | [
"https://github.com/jeongwhanchoi/gfsa"
] | In the paper 'Graph Convolutions Enrich the Self-Attention in Transformers!', what Word Error Rate (WER) score did the Branchformer + GFSA model get on the LibriSpeech 100h test-clean dataset
| 9.6 |
VisDA2017 | SFDA2++ | SF(DA)$^2$: Source-free Domain Adaptation Through the Lens of Data Augmentation | 2024-03-16T00:00:00 | https://arxiv.org/abs/2403.10834v1 | [
"https://github.com/shinyflight/sfda2"
] | In the paper 'SF(DA)$^2$: Source-free Domain Adaptation Through the Lens of Data Augmentation', what Accuracy score did the SFDA2++ model get on the VisDA2017 dataset
| 89.6 |
AudioCaps | Consistency TTA (Single-step generation) | ConsistencyTTA: Accelerating Diffusion-Based Text-to-Audio Generation with Consistency Distillation | 2023-09-19T00:00:00 | https://arxiv.org/abs/2309.10740v3 | [
"https://github.com/Bai-YT/ConsistencyTTA"
] | In the paper 'ConsistencyTTA: Accelerating Diffusion-Based Text-to-Audio Generation with Consistency Distillation', what FAD score did the Consistency TTA (Single-step generation) model get on the AudioCaps dataset
| 2.18 |
PCQM4Mv2-LSC | Graphormer + GFSA | Graph Convolutions Enrich the Self-Attention in Transformers! | 2023-12-07T00:00:00 | https://arxiv.org/abs/2312.04234v5 | [
"https://github.com/jeongwhanchoi/gfsa"
] | In the paper 'Graph Convolutions Enrich the Self-Attention in Transformers!', what Validation MAE score did the Graphormer + GFSA model get on the PCQM4Mv2-LSC dataset
| 0.0860 |
SIR^2(Objects) | Zhu et al. | Reversible Decoupling Network for Single Image Reflection Removal | 2024-10-10T00:00:00 | https://arxiv.org/abs/2410.08063v1 | [
"https://github.com/lime-j/RDNet"
] | In the paper 'Reversible Decoupling Network for Single Image Reflection Removal', what SSIM score did the Zhu et al. model get on the SIR^2(Objects) dataset
| 0.931 |
LAM(line-level) | HTR-VT | HTR-VT: Handwritten Text Recognition with Vision Transformer | 2024-09-13T00:00:00 | https://arxiv.org/abs/2409.08573v1 | [
"https://github.com/yutingli0606/htr-vt"
] | In the paper 'HTR-VT: Handwritten Text Recognition with Vision Transformer', what Test CER score did the HTR-VT model get on the LAM(line-level) dataset
| 2.8 |
HumanML3D | AttT2M | AttT2M: Text-Driven Human Motion Generation with Multi-Perspective Attention Mechanism | 2023-09-02T00:00:00 | https://arxiv.org/abs/2309.00796v1 | [
"https://github.com/zcymonkey/attt2m"
] | In the paper 'AttT2M: Text-Driven Human Motion Generation with Multi-Perspective Attention Mechanism', what FID score did the AttT2M model get on the HumanML3D dataset
| 0.112 |
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