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 |
|---|---|---|---|---|---|---|---|
MPI-INF-3DHP | MotionAGFormer-B (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-B (T=81) model get on the MPI-INF-3DHP dataset
| 84.2 |
GSM8K | 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 GSM8K dataset
| 66.4 |
ETTh2 (720) Multivariate | RLinear | Revisiting Long-term Time Series Forecasting: An Investigation on Linear Mapping | 2023-05-18T00:00:00 | https://arxiv.org/abs/2305.10721v1 | [
"https://github.com/plumprc/rtsf"
] | In the paper 'Revisiting Long-term Time Series Forecasting: An Investigation on Linear Mapping', what MSE score did the RLinear model get on the ETTh2 (720) Multivariate dataset
| 0.372 |
CHILI-3K | GraphSAGE | 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 MSE score did the GraphSAGE model get on the CHILI-3K dataset
| 0.055 +/- 0.002 |
St Lucia | BoQ
(DINOv2) | 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
(DINOv2) model get on the St Lucia dataset
| 100.0 |
RealBlur-R | ID-Blau (Stripformer) | ID-Blau: Image Deblurring by Implicit Diffusion-based reBLurring AUgmentation | 2023-12-18T00:00:00 | https://arxiv.org/abs/2312.10998v2 | [
"https://github.com/plusgood-steven/id-blau"
] | In the paper 'ID-Blau: Image Deblurring by Implicit Diffusion-based reBLurring AUgmentation', what PSNR (sRGB) score did the ID-Blau (Stripformer) model get on the RealBlur-R dataset
| 41.06 |
USNA-Cn2 (short-duration) | GBRT | 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 GBRT model get on the USNA-Cn2 (short-duration) dataset
| 0.160 |
WikiOFGraph | T5-large | Ontology-Free General-Domain Knowledge Graph-to-Text Generation Dataset Synthesis using Large Language Model | 2024-09-11T00:00:00 | https://arxiv.org/abs/2409.07088v1 | [
"https://github.com/daehuikim/WikiOFGraph"
] | In the paper 'Ontology-Free General-Domain Knowledge Graph-to-Text Generation Dataset Synthesis using Large Language Model', what BLEU score did the T5-large model get on the WikiOFGraph dataset
| 69.27 |
SF-XL test v2 | ProGEO | ProGEO: Generating Prompts through Image-Text Contrastive Learning for Visual Geo-localization | 2024-06-04T00:00:00 | https://arxiv.org/abs/2406.01906v1 | [
"https://github.com/chain-mao/progeo"
] | In the paper 'ProGEO: Generating Prompts through Image-Text Contrastive Learning for Visual Geo-localization', what Recall@1 score did the ProGEO model get on the SF-XL test v2 dataset
| 93.0 |
DRIVE | MERIT-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 F1 score score did the MERIT-GCASCADE model get on the DRIVE dataset
| 0.8290 |
COCO-20i (5-shot) | MIANet (VGG-16) | 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 (VGG-16) model get on the COCO-20i (5-shot) dataset
| 51.03 |
Occ3D-nuScenes | HyDRa R50 | 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 mIoU score did the HyDRa R50 model get on the Occ3D-nuScenes dataset
| 44.4 |
EM | EMCAD | EMCAD: Efficient Multi-scale Convolutional Attention Decoding for Medical Image Segmentation | 2024-05-11T00:00:00 | https://arxiv.org/abs/2405.06880v1 | [
"https://github.com/sldgroup/emcad"
] | In the paper 'EMCAD: Efficient Multi-scale Convolutional Attention Decoding for Medical Image Segmentation', what DSC score did the EMCAD model get on the EM dataset
| 95.53 |
CocoGlide | Late Fusion | MMFusion: Combining Image Forensic Filters for Visual Manipulation Detection and Localization | 2023-12-04T00:00:00 | https://arxiv.org/abs/2312.01790v2 | [
"https://github.com/idt-iti/mmfusion-iml"
] | In the paper 'MMFusion: Combining Image Forensic Filters for Visual Manipulation Detection and Localization', what AUC score did the Late Fusion model get on the CocoGlide dataset
| .760 |
Pittsburgh-30k-test | SegVLAD-FineT (M) | Revisit Anything: Visual Place Recognition via Image Segment Retrieval | 2024-09-26T00:00:00 | https://arxiv.org/abs/2409.18049v1 | [
"https://github.com/anyloc/revisit-anything"
] | In the paper 'Revisit Anything: Visual Place Recognition via Image Segment Retrieval', what Recall@1 score did the SegVLAD-FineT (M) model get on the Pittsburgh-30k-test dataset
| 93.1 |
HumanEval | AFlow(GPT-4o-mini) | AFlow: Automating Agentic Workflow Generation | 2024-10-14T00:00:00 | https://arxiv.org/abs/2410.10762v1 | [
"https://github.com/geekan/metagpt"
] | In the paper 'AFlow: Automating Agentic Workflow Generation', what Pass@1 score did the AFlow(GPT-4o-mini) model get on the HumanEval dataset
| 94.7 |
TpuGraphs Layout mean | TpuGraphs | TpuGraphs: A Performance Prediction Dataset on Large Tensor Computational Graphs | 2023-08-25T00:00:00 | https://arxiv.org/abs/2308.13490v3 | [
"https://github.com/google-research-datasets/tpu_graphs"
] | In the paper 'TpuGraphs: A Performance Prediction Dataset on Large Tensor Computational Graphs', what Kendall's Tau score did the TpuGraphs model get on the TpuGraphs Layout mean dataset
| 0.298 |
E-commerce | DialMAE | Dial-MAE: ConTextual Masked Auto-Encoder for Retrieval-based Dialogue Systems | 2023-06-07T00:00:00 | https://arxiv.org/abs/2306.04357v5 | [
"https://github.com/suu990901/Dial-MAE"
] | In the paper 'Dial-MAE: ConTextual Masked Auto-Encoder for Retrieval-based Dialogue Systems', what R10@1 score did the DialMAE model get on the E-commerce dataset
| 0.930 |
ETTh1 (336) Multivariate | TimesFM | A decoder-only foundation model for time-series forecasting | 2023-10-14T00:00:00 | https://arxiv.org/abs/2310.10688v4 | [
"https://github.com/google-research/timesfm"
] | In the paper 'A decoder-only foundation model for time-series forecasting', what MAE score did the TimesFM model get on the ETTh1 (336) Multivariate dataset
| 0.436 |
ACOS | MvP (muilti-task) | 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 (Laptop) score did the MvP (muilti-task) model get on the ACOS dataset
| 43.84 |
rt-inod-bias | Gemma | Benchmarking Llama2, Mistral, Gemma and GPT for Factuality, Toxicity, Bias and Propensity for Hallucinations | 2024-04-15T00:00:00 | https://arxiv.org/abs/2404.09785v1 | [
"https://github.com/innodatalabs/innodata-llm-safety"
] | In the paper 'Benchmarking Llama2, Mistral, Gemma and GPT for Factuality, Toxicity, Bias and Propensity for Hallucinations', what Best-of score did the Gemma model get on the rt-inod-bias dataset
| 0.41 |
SYNTHIA | Resnet50 | MRFP: Learning Generalizable Semantic Segmentation from Sim-2-Real with Multi-Resolution Feature Perturbation | 2023-11-30T00:00:00 | https://arxiv.org/abs/2311.18331v2 | [
"https://github.com/airl-iisc/MRFP"
] | In the paper 'MRFP: Learning Generalizable Semantic Segmentation from Sim-2-Real with Multi-Resolution Feature Perturbation', what mIoU score did the Resnet50 model get on the SYNTHIA dataset
| 25.84 |
BACE | elEmBERT-V1 | Structure to Property: Chemical Element Embeddings and a Deep Learning Approach for Accurate Prediction of Chemical Properties | 2023-09-17T00:00:00 | https://arxiv.org/abs/2309.09355v3 | [
"https://github.com/dmamur/elembert"
] | In the paper 'Structure to Property: Chemical Element Embeddings and a Deep Learning Approach for Accurate Prediction of Chemical Properties', what AUC score did the elEmBERT-V1 model get on the BACE dataset
| 0.856 |
OVIS validation | DVIS++(R50, Offline) | DVIS++: Improved Decoupled Framework for Universal Video Segmentation | 2023-12-20T00:00:00 | https://arxiv.org/abs/2312.13305v1 | [
"https://github.com/zhang-tao-whu/DVIS_Plus"
] | In the paper 'DVIS++: Improved Decoupled Framework for Universal Video Segmentation', what mask AP score did the DVIS++(R50, Offline) model get on the OVIS validation dataset
| 41.2 |
MOSE | DEVA (with OVIS) | Tracking Anything with Decoupled Video Segmentation | 2023-09-07T00:00:00 | https://arxiv.org/abs/2309.03903v1 | [
"https://github.com/hkchengrex/Tracking-Anything-with-DEVA"
] | In the paper 'Tracking Anything with Decoupled Video Segmentation', what J&F score did the DEVA (with OVIS) model get on the MOSE dataset
| 66.5 |
COCO test-dev | LeYOLO-nano@480 | LeYOLO, New Scalable and Efficient CNN Architecture for Object Detection | 2024-06-20T00:00:00 | https://arxiv.org/abs/2406.14239v1 | [
"https://github.com/LilianHollard/LeYOLO"
] | In the paper 'LeYOLO, New Scalable and Efficient CNN Architecture for Object Detection', what GFLOPs score did the LeYOLO-nano@480 model get on the COCO test-dev dataset
| 1.47 |
CULane | CLRKDNet (DLA-34) | CLRKDNet: Speeding up Lane Detection with Knowledge Distillation | 2024-05-21T00:00:00 | https://arxiv.org/abs/2405.12503v1 | [
"https://github.com/weiqingq/CLRKDNet"
] | In the paper 'CLRKDNet: Speeding up Lane Detection with Knowledge Distillation', what F1 score score did the CLRKDNet (DLA-34) model get on the CULane dataset
| 80.68 |
IC19-Art | MixNet | MixNet: Toward Accurate Detection of Challenging Scene Text in the Wild | 2023-08-23T00:00:00 | https://arxiv.org/abs/2308.12817v2 | [
"https://github.com/D641593/MixNet"
] | In the paper 'MixNet: Toward Accurate Detection of Challenging Scene Text in the Wild', what H-Mean score did the MixNet model get on the IC19-Art dataset
| 79.7 |
GOT-10k | ARTrackV2-L | ARTrackV2: Prompting Autoregressive Tracker Where to Look and How to Describe | 2023-12-28T00:00:00 | https://arxiv.org/abs/2312.17133v3 | [
"https://github.com/miv-xjtu/artrack"
] | In the paper 'ARTrackV2: Prompting Autoregressive Tracker Where to Look and How to Describe', what Average Overlap score did the ARTrackV2-L model get on the GOT-10k dataset
| 79.5 |
Food-101 | 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 Food-101 dataset
| 87.9 |
SHD - Adding | LSTM | 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 LSTM model get on the SHD - Adding dataset
| 10 |
COVERAGE | Early Fusion | MMFusion: Combining Image Forensic Filters for Visual Manipulation Detection and Localization | 2023-12-04T00:00:00 | https://arxiv.org/abs/2312.01790v2 | [
"https://github.com/idt-iti/mmfusion-iml"
] | In the paper 'MMFusion: Combining Image Forensic Filters for Visual Manipulation Detection and Localization', what Average Pixel F1(Fixed threshold) score did the Early Fusion model get on the COVERAGE dataset
| .663 |
Weather2K850 (96) | MoLE-RLinear | 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-RLinear model get on the Weather2K850 (96) dataset
| 0.471 |
RealBlur-J | ID-Blau (Stripformer) | ID-Blau: Image Deblurring by Implicit Diffusion-based reBLurring AUgmentation | 2023-12-18T00:00:00 | https://arxiv.org/abs/2312.10998v2 | [
"https://github.com/plusgood-steven/id-blau"
] | In the paper 'ID-Blau: Image Deblurring by Implicit Diffusion-based reBLurring AUgmentation', what SSIM (sRGB) score did the ID-Blau (Stripformer) model get on the RealBlur-J dataset
| 0.940 |
CommonsenseQA | Phi 3 3.8B | Hierarchical Prompting Taxonomy: A Universal Evaluation Framework for Large Language Models Aligned with Human Cognitive Principles | 2024-06-18T00:00:00 | https://arxiv.org/abs/2406.12644v4 | [
"https://github.com/devichand579/HPT"
] | In the paper 'Hierarchical Prompting Taxonomy: A Universal Evaluation Framework for Large Language Models Aligned with Human Cognitive Principles', what Accuracy score did the Phi 3 3.8B model get on the CommonsenseQA dataset
| 88.452 |
Set14 - 4x upscaling | HMA† | HMANet: Hybrid Multi-Axis Aggregation Network for Image Super-Resolution | 2024-05-08T00:00:00 | https://arxiv.org/abs/2405.05001v1 | [
"https://github.com/korouuuuu/hma"
] | In the paper 'HMANet: Hybrid Multi-Axis Aggregation Network for Image Super-Resolution', what PSNR score did the HMA† model get on the Set14 - 4x upscaling dataset
| 29.51 |
SUIM | DatUS^2 | DatUS^2: Data-driven Unsupervised Semantic Segmentation with Pre-trained Self-supervised Vision Transformer | 2024-01-23T00:00:00 | https://arxiv.org/abs/2401.12820v1 | [
"https://github.com/SonalKumar95/DatUS"
] | In the paper 'DatUS^2: Data-driven Unsupervised Semantic Segmentation with Pre-trained Self-supervised Vision Transformer', what Clustering [mIoU] score did the DatUS^2 model get on the SUIM dataset
| 34.02 |
KITTI Test (Online Methods) | IMM-JHSE | One Homography is All You Need: IMM-based Joint Homography and Multiple Object State Estimation | 2024-09-04T00:00:00 | https://arxiv.org/abs/2409.02562v2 | [
"https://github.com/Paulkie99/imm-jhse"
] | In the paper 'One Homography is All You Need: IMM-based Joint Homography and Multiple Object State Estimation', what HOTA score did the IMM-JHSE model get on the KITTI Test (Online Methods) dataset
| 79.21 |
HumanML3D | MMM (gt length) | MMM: Generative Masked Motion Model | 2023-12-06T00:00:00 | https://arxiv.org/abs/2312.03596v2 | [
"https://github.com/exitudio/MMM"
] | In the paper 'MMM: Generative Masked Motion Model', what FID score did the MMM (gt length) model get on the HumanML3D dataset
| 0.089 |
AG News | vONTSS | vONTSS: vMF based semi-supervised neural topic modeling with optimal transport | 2023-07-03T00:00:00 | https://arxiv.org/abs/2307.01226v2 | [
"https://github.com/xuweijieshuai/vONTSS"
] | In the paper 'vONTSS: vMF based semi-supervised neural topic modeling with optimal transport', what C_v score did the vONTSS model get on the AG News dataset
| 0.49 |
Nature | 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 Nature dataset
| 26.21 |
MAS3K | SAM2-UNet | SAM2-UNet: Segment Anything 2 Makes Strong Encoder for Natural and Medical Image Segmentation | 2024-08-16T00:00:00 | https://arxiv.org/abs/2408.08870v1 | [
"https://github.com/wzh0120/sam2-unet"
] | In the paper 'SAM2-UNet: Segment Anything 2 Makes Strong Encoder for Natural and Medical Image Segmentation', what S-measure score did the SAM2-UNet model get on the MAS3K dataset
| 0.903 |
CHILI-3K | Most Frequent Class | 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 Most Frequent Class model get on the CHILI-3K dataset
| 0.461 |
RefCOCO+ testA | 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 RefCOCO+ testA dataset
| 78.3 |
WPC | COPP-Net | No-Reference Point Cloud Quality Assessment via Weighted Patch Quality Prediction | 2023-05-13T00:00:00 | https://arxiv.org/abs/2305.07829v2 | [
"https://github.com/philox12358/COPP-Net"
] | In the paper 'No-Reference Point Cloud Quality Assessment via Weighted Patch Quality Prediction', what PLCC score did the COPP-Net model get on the WPC dataset
| 0.9324 |
IllusionVQA | Gemini-Pro 4-shot+CoT | IllusionVQA: A Challenging Optical Illusion Dataset for Vision Language Models | 2024-03-23T00:00:00 | https://arxiv.org/abs/2403.15952v3 | [
"https://github.com/csebuetnlp/illusionvqa"
] | In the paper 'IllusionVQA: A Challenging Optical Illusion Dataset for Vision Language Models', what Accuracy score did the Gemini-Pro 4-shot+CoT model get on the IllusionVQA dataset
| 33.9 |
ETTh1 (336) Multivariate | SAMformer | SAMformer: Unlocking the Potential of Transformers in Time Series Forecasting with Sharpness-Aware Minimization and Channel-Wise Attention | 2024-02-15T00:00:00 | https://arxiv.org/abs/2402.10198v3 | [
"https://github.com/romilbert/samformer"
] | In the paper 'SAMformer: Unlocking the Potential of Transformers in Time Series Forecasting with Sharpness-Aware Minimization and Channel-Wise Attention', what MSE score did the SAMformer model get on the ETTh1 (336) Multivariate dataset
| 0.423 |
MATH | OpenMath-CodeLlama-70B (w/ code) | 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-CodeLlama-70B (w/ code) model get on the MATH dataset
| 50.7 |
DUTS-TE | BiRefNet (DUTS, 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 (DUTS, UHRSD) model get on the DUTS-TE dataset
| 0.018 |
LaSOT-ext | ARTrackV2-L | ARTrackV2: Prompting Autoregressive Tracker Where to Look and How to Describe | 2023-12-28T00:00:00 | https://arxiv.org/abs/2312.17133v3 | [
"https://github.com/miv-xjtu/artrack"
] | In the paper 'ARTrackV2: Prompting Autoregressive Tracker Where to Look and How to Describe', what AUC score did the ARTrackV2-L model get on the LaSOT-ext dataset
| 53.4 |
ETTh2 (336) Multivariate | MoLE-RLinear | 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-RLinear model get on the ETTh2 (336) Multivariate dataset
| 0.371 |
fastMRI Knee Val 8x | PromptMR | Fill the K-Space and Refine the Image: Prompting for Dynamic and Multi-Contrast MRI Reconstruction | 2023-09-25T00:00:00 | https://arxiv.org/abs/2309.13839v1 | [
"https://github.com/hellopipu/promptmr"
] | In the paper 'Fill the K-Space and Refine the Image: Prompting for Dynamic and Multi-Contrast MRI Reconstruction', what SSIM score did the PromptMR model get on the fastMRI Knee Val 8x dataset
| 0.8983 |
DSO-1 | Early Fusion | MMFusion: Combining Image Forensic Filters for Visual Manipulation Detection and Localization | 2023-12-04T00:00:00 | https://arxiv.org/abs/2312.01790v2 | [
"https://github.com/idt-iti/mmfusion-iml"
] | In the paper 'MMFusion: Combining Image Forensic Filters for Visual Manipulation Detection and Localization', what AUC score did the Early Fusion model get on the DSO-1 dataset
| .966 |
Aria Everyday Objects | Cube R-CNN | EFM3D: A Benchmark for Measuring Progress Towards 3D Egocentric Foundation Models | 2024-06-14T00:00:00 | https://arxiv.org/abs/2406.10224v1 | [
"https://github.com/facebookresearch/efm3d"
] | In the paper 'EFM3D: A Benchmark for Measuring Progress Towards 3D Egocentric Foundation Models', what mAP score did the Cube R-CNN model get on the Aria Everyday Objects dataset
| 8 |
LRS3 | RTFS-Net-4 | RTFS-Net: Recurrent Time-Frequency Modelling for Efficient Audio-Visual Speech Separation | 2023-09-29T00:00:00 | https://arxiv.org/abs/2309.17189v4 | [
"https://github.com/spkgyk/RTFS-Net"
] | In the paper 'RTFS-Net: Recurrent Time-Frequency Modelling for Efficient Audio-Visual Speech Separation', what SI-SNRi score did the RTFS-Net-4 model get on the LRS3 dataset
| 15.5 |
Fashion-MNIST | CNN+ Wilson-Cowan model RNN | Learning in Wilson-Cowan model for metapopulation | 2024-06-24T00:00:00 | https://arxiv.org/abs/2406.16453v2 | [
"https://github.com/raffaelemarino/learning_in_wilsoncowan"
] | In the paper 'Learning in Wilson-Cowan model for metapopulation', what Accuracy score did the CNN+ Wilson-Cowan model RNN model get on the Fashion-MNIST dataset
| 91.35 |
Amazon-Google | 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 Amazon-Google dataset
| 63.45 |
Electricity (96) | 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 (96) dataset
| 0.126 |
GSM8K | MMOS-DeepSeekMath-7B(0-shot) | An Empirical Study of Data Ability Boundary in LLMs' Math Reasoning | 2024-02-23T00:00:00 | https://arxiv.org/abs/2403.00799v1 | [
"https://github.com/cyzhh/MMOS"
] | In the paper 'An Empirical Study of Data Ability Boundary in LLMs' Math Reasoning', what Accuracy score did the MMOS-DeepSeekMath-7B(0-shot) model get on the GSM8K dataset
| 80.5 |
MSD Heart | OneNete,4 | 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 mIoU score did the OneNete,4 model get on the MSD Heart dataset
| 6.6 |
CIFAR100-B0(50 tasks)-no-exemplars | SEED | Divide and not forget: Ensemble of selectively trained experts in Continual Learning | 2024-01-18T00:00:00 | https://arxiv.org/abs/2401.10191v3 | [
"https://github.com/grypesc/seed"
] | In the paper 'Divide and not forget: Ensemble of selectively trained experts in Continual Learning', what Average Incremental Accuracy score did the SEED model get on the CIFAR100-B0(50 tasks)-no-exemplars dataset
| 42.6 |
Aff-Wild2 | ARBEx | ARBEx: Attentive Feature Extraction with Reliability Balancing for Robust Facial Expression Learning | 2023-05-02T00:00:00 | https://arxiv.org/abs/2305.01486v5 | [
"https://github.com/takihasan/arbex"
] | In the paper 'ARBEx: Attentive Feature Extraction with Reliability Balancing for Robust Facial Expression Learning', what Accuracy score did the ARBEx model get on the Aff-Wild2 dataset
| 72.48 |
VNHSGE-Literature | Bing Chat | VNHSGE: VietNamese High School Graduation Examination Dataset for Large Language Models | 2023-05-20T00:00:00 | https://arxiv.org/abs/2305.12199v1 | [
"https://github.com/xdao85/vnhsge"
] | In the paper 'VNHSGE: VietNamese High School Graduation Examination Dataset for Large Language Models', what Accuracy score did the Bing Chat model get on the VNHSGE-Literature dataset
| 56.8 |
Occluded-DukeMTMC | CLIPReID-Baseline+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 CLIPReID-Baseline+UFFM+AMC model get on the Occluded-DukeMTMC dataset
| 61.9 |
Office-Home | PromptStyler (CLIP, ViT-L/14) | 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-L/14) model get on the Office-Home dataset
| 89.1 |
SID SonyA7S2 x300 | LED | Make Explicit Calibration Implicit: Calibrate Denoiser Instead of the Noise Model | 2023-08-07T00:00:00 | https://arxiv.org/abs/2308.03448v2 | [
"https://github.com/srameo/led"
] | In the paper 'Make Explicit Calibration Implicit: Calibrate Denoiser Instead of the Noise Model', what PSNR (Raw) score did the LED model get on the SID SonyA7S2 x300 dataset
| 36.67 |
Winoground | InstructBLIP | Compositional Chain-of-Thought Prompting for Large Multimodal Models | 2023-11-27T00:00:00 | https://arxiv.org/abs/2311.17076v3 | [
"https://github.com/chancharikmitra/ccot"
] | In the paper 'Compositional Chain-of-Thought Prompting for Large Multimodal Models', what Text Score score did the InstructBLIP model get on the Winoground dataset
| 7.0 |
MATH | WizardMath-13B-V1.0 | WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct | 2023-08-18T00:00:00 | https://arxiv.org/abs/2308.09583v1 | [
"https://github.com/nlpxucan/wizardlm"
] | In the paper 'WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct', what Accuracy score did the WizardMath-13B-V1.0 model get on the MATH dataset
| 14.0 |
ImageNet 64x64 | SCT | Stable Consistency Tuning: Understanding and Improving Consistency Models | 2024-10-24T00:00:00 | https://arxiv.org/abs/2410.18958v3 | [
"https://github.com/G-U-N/Stable-Consistency-Tuning"
] | In the paper 'Stable Consistency Tuning: Understanding and Improving Consistency Models', what FID score did the SCT model get on the ImageNet 64x64 dataset
| 1.47 |
ChEBI-20 | BioT5 | BioT5: Enriching Cross-modal Integration in Biology with Chemical Knowledge and Natural Language Associations | 2023-10-11T00:00:00 | https://arxiv.org/abs/2310.07276v3 | [
"https://github.com/QizhiPei/BioT5"
] | In the paper 'BioT5: Enriching Cross-modal Integration in Biology with Chemical Knowledge and Natural Language Associations', what Text2Mol score did the BioT5 model get on the ChEBI-20 dataset
| 57.6 |
VNHSGE-Literature | ChatGPT | VNHSGE: VietNamese High School Graduation Examination Dataset for Large Language Models | 2023-05-20T00:00:00 | https://arxiv.org/abs/2305.12199v1 | [
"https://github.com/xdao85/vnhsge"
] | In the paper 'VNHSGE: VietNamese High School Graduation Examination Dataset for Large Language Models', what Accuracy score did the ChatGPT model get on the VNHSGE-Literature dataset
| 68 |
STAC | Structured | Structured Dialogue Discourse Parsing | 2023-06-26T00:00:00 | https://arxiv.org/abs/2306.15103v1 | [
"https://github.com/chijames/structured_dialogue_discourse_parsing"
] | In the paper 'Structured Dialogue Discourse Parsing', what Link F1 score did the Structured model get on the STAC dataset
| 74.4 |
ImageNet-LT | LIFT (ViT-B/16) | 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 Top-1 Accuracy score did the LIFT (ViT-B/16) model get on the ImageNet-LT dataset
| 78.3 |
Mini-Imagenet 5-way (5-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 Mini-Imagenet 5-way (5-shot) dataset
| 91.34 |
dbp15k ja-en | UMAEA (w/o surf & iter ) | Rethinking Uncertainly Missing and Ambiguous Visual Modality in Multi-Modal Entity Alignment | 2023-07-30T00:00:00 | https://arxiv.org/abs/2307.16210v2 | [
"https://github.com/zjukg/umaea"
] | In the paper 'Rethinking Uncertainly Missing and Ambiguous Visual Modality in Multi-Modal Entity Alignment', what Hits@1 score did the UMAEA (w/o surf & iter ) model get on the dbp15k ja-en dataset
| 0.801 |
VoxCeleb1 | ReDimNet-B5-SF2-LM (9.2M) | Reshape Dimensions Network for Speaker Recognition | 2024-07-25T00:00:00 | https://arxiv.org/abs/2407.18223v2 | [
"https://github.com/IDRnD/ReDimNet"
] | In the paper 'Reshape Dimensions Network for Speaker Recognition', what EER score did the ReDimNet-B5-SF2-LM (9.2M) model get on the VoxCeleb1 dataset
| 0.43 |
NExT-QA | LLaVA-OV(72B) | LLaVA-OneVision: Easy Visual Task Transfer | 2024-08-06T00:00:00 | https://arxiv.org/abs/2408.03326v3 | [
"https://github.com/evolvinglmms-lab/lmms-eval"
] | In the paper 'LLaVA-OneVision: Easy Visual Task Transfer', what Accuracy score did the LLaVA-OV(72B) model get on the NExT-QA dataset
| 80.2 |
SID SonyA7S2 x100 | LED | Make Explicit Calibration Implicit: Calibrate Denoiser Instead of the Noise Model | 2023-08-07T00:00:00 | https://arxiv.org/abs/2308.03448v2 | [
"https://github.com/srameo/led"
] | In the paper 'Make Explicit Calibration Implicit: Calibrate Denoiser Instead of the Noise Model', what PSNR (Raw) score did the LED model get on the SID SonyA7S2 x100 dataset
| 41.98 |
Electricity (720) | MoLE-RMLP | 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-RMLP model get on the Electricity (720) dataset
| 0.178 |
PASCAL Context-59 | TaAlign(trained with image-text pairs) | TagAlign: Improving Vision-Language Alignment with Multi-Tag Classification | 2023-12-21T00:00:00 | https://arxiv.org/abs/2312.14149v4 | [
"https://github.com/Qinying-Liu/TagAlign"
] | In the paper 'TagAlign: Improving Vision-Language Alignment with Multi-Tag Classification', what mIoU score did the TaAlign(trained with image-text pairs) model get on the PASCAL Context-59 dataset
| 37.6 |
MCubeS (P) | MMSFormer (RGB-A-D) | MMSFormer: Multimodal Transformer for Material and Semantic Segmentation | 2023-09-07T00:00:00 | https://arxiv.org/abs/2309.04001v4 | [
"https://github.com/csiplab/mmsformer"
] | In the paper 'MMSFormer: Multimodal Transformer for Material and Semantic Segmentation', what mIoU score did the MMSFormer (RGB-A-D) model get on the MCubeS (P) dataset
| 52.03 |
Id Pattern Dataset | Claude 3 Opus | Identification of Stone Deterioration Patterns with Large Multimodal Models | 2024-06-05T00:00:00 | https://arxiv.org/abs/2406.03207v1 | [
"https://github.com/dcorradetti/redai_id_pattern"
] | In the paper 'Identification of Stone Deterioration Patterns with Large Multimodal Models', what Percentage correct score did the Claude 3 Opus model get on the Id Pattern Dataset dataset
| 24.3% |
Deforming Plate | HCMT | Learning Flexible Body Collision Dynamics with Hierarchical Contact Mesh Transformer | 2023-12-19T00:00:00 | https://arxiv.org/abs/2312.12467v3 | [
"https://github.com/yuyudeep/hcmt"
] | In the paper 'Learning Flexible Body Collision Dynamics with Hierarchical Contact Mesh Transformer', what Rollout RMSE-all [1e3] Position score did the HCMT model get on the Deforming Plate dataset
| 7.49±0.07 |
Fishyscapes L&F | FlowEneDet | Concurrent Misclassification and Out-of-Distribution Detection for Semantic Segmentation via Energy-Based Normalizing Flow | 2023-05-16T00:00:00 | https://arxiv.org/abs/2305.09610v1 | [
"https://github.com/gudovskiy/flowenedet"
] | In the paper 'Concurrent Misclassification and Out-of-Distribution Detection for Semantic Segmentation via Energy-Based Normalizing Flow', what AP score did the FlowEneDet model get on the Fishyscapes L&F dataset
| 50.15 |
PASCAL VOC | GMTR | GMTR: Graph Matching Transformers | 2023-11-14T00:00:00 | https://arxiv.org/abs/2311.08141v2 | [
"https://github.com/jp-guo/gm-transformer"
] | In the paper 'GMTR: Graph Matching Transformers', what matching accuracy score did the GMTR model get on the PASCAL VOC dataset
| 0.836 |
Near-OOD | SCALE (ResNet50) | Scaling for Training Time and Post-hoc Out-of-distribution Detection Enhancement | 2023-09-30T00:00:00 | https://arxiv.org/abs/2310.00227v1 | [
"https://github.com/kai422/scale"
] | In the paper 'Scaling for Training Time and Post-hoc Out-of-distribution Detection Enhancement', what ID ACC score did the SCALE (ResNet50) model get on the Near-OOD dataset
| 76.18 |
Tanks and Temples | MVSFormer++ | MVSFormer++: Revealing the Devil in Transformer's Details for Multi-View Stereo | 2024-01-22T00:00:00 | https://arxiv.org/abs/2401.11673v1 | [
"https://github.com/maybelx/mvsformerplusplus"
] | In the paper 'MVSFormer++: Revealing the Devil in Transformer's Details for Multi-View Stereo', what Mean F1 (Intermediate) score did the MVSFormer++ model get on the Tanks and Temples dataset
| 67.03 |
DESED | MDFD-CRNN | Pushing the Limit of Sound Event Detection with Multi-Dilated Frequency Dynamic Convolution | 2024-06-19T00:00:00 | https://arxiv.org/abs/2406.13312v3 | [
"https://github.com/frednam93/MDFD-SED"
] | In the paper 'Pushing the Limit of Sound Event Detection with Multi-Dilated Frequency Dynamic Convolution', what PSDS1 score did the MDFD-CRNN model get on the DESED dataset
| 0.485 |
TriviaQA | GaC(Qwen2-72B-Instruct + Llama-3-70B-Instruct) | Breaking the Ceiling of the LLM Community by Treating Token Generation as a Classification for Ensembling | 2024-06-18T00:00:00 | https://arxiv.org/abs/2406.12585v2 | [
"https://github.com/yaoching0/gac"
] | In the paper 'Breaking the Ceiling of the LLM Community by Treating Token Generation as a Classification for Ensembling', what EM score did the GaC(Qwen2-72B-Instruct + Llama-3-70B-Instruct) model get on the TriviaQA dataset
| 79.29 |
ImageNet | Wave-ViT-S | Which Transformer to Favor: A Comparative Analysis of Efficiency in Vision Transformers | 2023-08-18T00:00:00 | https://arxiv.org/abs/2308.09372v3 | [
"https://github.com/tobna/whattransformertofavor"
] | In the paper 'Which Transformer to Favor: A Comparative Analysis of Efficiency in Vision Transformers', what Top 1 Accuracy score did the Wave-ViT-S model get on the ImageNet dataset
| 83.61% |
ScanNet200 | OpenMask3D | OpenMask3D: Open-Vocabulary 3D Instance Segmentation | 2023-06-23T00:00:00 | https://arxiv.org/abs/2306.13631v2 | [
"https://github.com/OpenMask3D/openmask3d"
] | In the paper 'OpenMask3D: Open-Vocabulary 3D Instance Segmentation', what mAP score did the OpenMask3D model get on the ScanNet200 dataset
| 15.4 |
GSO | Unique3D | Unique3D: High-Quality and Efficient 3D Mesh Generation from a Single Image | 2024-05-30T00:00:00 | https://arxiv.org/abs/2405.20343v3 | [
"https://github.com/AiuniAI/Unique3D"
] | In the paper 'Unique3D: High-Quality and Efficient 3D Mesh Generation from a Single Image', what Chamfer Distance score did the Unique3D model get on the GSO dataset
| 0.0145 |
BoolQ | LLaMA2-7b | GIFT-SW: Gaussian noise Injected Fine-Tuning of Salient Weights for LLMs | 2024-08-27T00:00:00 | https://arxiv.org/abs/2408.15300v1 | [
"https://github.com/On-Point-RND/GIFT_SW"
] | In the paper 'GIFT-SW: Gaussian noise Injected Fine-Tuning of Salient Weights for LLMs', what Accuracy (% ) score did the LLaMA2-7b model get on the BoolQ dataset
| 82.63 |
MS COCO | Kandinsky | Kandinsky: an Improved Text-to-Image Synthesis with Image Prior and Latent Diffusion | 2023-10-05T00:00:00 | https://arxiv.org/abs/2310.03502v1 | [
"https://github.com/ai-forever/Kandinsky-2"
] | In the paper 'Kandinsky: an Improved Text-to-Image Synthesis with Image Prior and Latent Diffusion', what FID score did the Kandinsky model get on the MS COCO dataset
| 8.03 |
SVOX-Snow | BoQ (ResNet-50) | 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 (ResNet-50) model get on the SVOX-Snow dataset
| 98.7 |
Atari 2600 Pong | 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 Pong dataset
| 21 |
TrackingNet | LoRAT-g-378 | Tracking Meets LoRA: Faster Training, Larger Model, Stronger Performance | 2024-03-08T00:00:00 | https://arxiv.org/abs/2403.05231v2 | [
"https://github.com/litinglin/lorat"
] | In the paper 'Tracking Meets LoRA: Faster Training, Larger Model, Stronger Performance', what Precision score did the LoRAT-g-378 model get on the TrackingNet dataset
| 86.1 |
WDC Products-80%cc-seen-medium | gpt-4o-mini-2024-07-18 | 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 model get on the WDC Products-80%cc-seen-medium dataset
| 81.61 |
WiC | OPT-1.3B | Achieving Dimension-Free Communication in Federated Learning via Zeroth-Order Optimization | 2024-05-24T00:00:00 | https://arxiv.org/abs/2405.15861v3 | [
"https://github.com/ZidongLiu/DeComFL"
] | In the paper 'Achieving Dimension-Free Communication in Federated Learning via Zeroth-Order Optimization', what Test Accuracy score did the OPT-1.3B model get on the WiC dataset
| 56.14% |
MVTec AD | ReConPatch Ensemble (+RefineNet) | ReConPatch : Contrastive Patch Representation Learning for Industrial Anomaly Detection | 2023-05-26T00:00:00 | https://arxiv.org/abs/2305.16713v3 | [
"https://github.com/travishsu/ReConPatch-TF"
] | In the paper 'ReConPatch : Contrastive Patch Representation Learning for Industrial Anomaly Detection', what Detection AUROC score did the ReConPatch Ensemble (+RefineNet) model get on the MVTec AD dataset
| 99.72 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.