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 |
|---|---|---|---|---|---|---|---|
CUB 200 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 CUB 200 5-way 5-shot dataset
| 97.12 |
ImageNet 128x128 | TarFlow | Normalizing Flows are Capable Generative Models | 2024-12-09T00:00:00 | https://arxiv.org/abs/2412.06329v2 | [
"https://github.com/apple/ml-tarflow"
] | In the paper 'Normalizing Flows are Capable Generative Models', what FID score did the TarFlow model get on the ImageNet 128x128 dataset
| 5.03 |
Stanford Cars | 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 Stanford Cars dataset
| 74.69 |
PACS | GMDG (e RegNetY-16GF) | Rethinking Multi-domain Generalization with A General Learning Objective | 2024-02-29T00:00:00 | https://arxiv.org/abs/2402.18853v1 | [
"https://github.com/zhaorui-tan/GMDG_cvpr2024"
] | In the paper 'Rethinking Multi-domain Generalization with A General Learning Objective', what Average Accuracy score did the GMDG (e RegNetY-16GF) model get on the PACS dataset
| 97.3 |
CROHME 2019 | ICAL | ICAL: Implicit Character-Aided Learning for Enhanced Handwritten Mathematical Expression Recognition | 2024-05-15T00:00:00 | https://arxiv.org/abs/2405.09032v4 | [
"https://github.com/qingzhenduyu/ical"
] | In the paper 'ICAL: Implicit Character-Aided Learning for Enhanced Handwritten Mathematical Expression Recognition', what ExpRate score did the ICAL model get on the CROHME 2019 dataset
| 60.51 |
ETTm1 (720) Multivariate | 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 ETTm1 (720) Multivariate dataset
| 0.416 |
EconLogicQA | Mistral-7B-Instruct-v0.2 | EconLogicQA: A Question-Answering Benchmark for Evaluating Large Language Models in Economic Sequential Reasoning | 2024-05-13T00:00:00 | https://arxiv.org/abs/2405.07938v2 | [
"https://github.com/yinzhu-quan/lm-evaluation-harness"
] | In the paper 'EconLogicQA: A Question-Answering Benchmark for Evaluating Large Language Models in Economic Sequential Reasoning', what Accuracy score did the Mistral-7B-Instruct-v0.2 model get on the EconLogicQA dataset
| 0.3154 |
ImageNet | 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 ViT-S model get on the ImageNet dataset
| 82.54% |
Uber-Text | CLIP4STR-L (DataComp-1B) | CLIP4STR: A Simple Baseline for Scene Text Recognition with Pre-trained Vision-Language Model | 2023-05-23T00:00:00 | https://arxiv.org/abs/2305.14014v3 | [
"https://github.com/VamosC/CLIP4STR"
] | In the paper 'CLIP4STR: A Simple Baseline for Scene Text Recognition with Pre-trained Vision-Language Model', what Accuracy (%) score did the CLIP4STR-L (DataComp-1B) model get on the Uber-Text dataset
| 92.2 |
Moving MNIST | PredFormer | PredFormer: Transformers Are Effective Spatial-Temporal Predictive Learners | 2024-10-07T00:00:00 | https://arxiv.org/abs/2410.04733v2 | [
"https://github.com/yyyujintang/predformer"
] | In the paper 'PredFormer: Transformers Are Effective Spatial-Temporal Predictive Learners', what MSE score did the PredFormer model get on the Moving MNIST dataset
| 11.62 |
RWTH-PHOENIX-Weather 2014 T | SlowFastSign | SlowFast Network for Continuous Sign Language Recognition | 2023-09-21T00:00:00 | https://arxiv.org/abs/2309.12304v1 | [
"https://github.com/kaistmm/SlowFastSign"
] | In the paper 'SlowFast Network for Continuous Sign Language Recognition', what Word Error Rate (WER) score did the SlowFastSign model get on the RWTH-PHOENIX-Weather 2014 T dataset
| 18.7 |
Mini-Imagenet 5-way (1-shot) | PT+MAP+SF+BPA (transductive) | The Balanced-Pairwise-Affinities Feature Transform | 2024-06-25T00:00:00 | https://arxiv.org/abs/2407.01467v1 | [
"https://github.com/danielshalam/bpa"
] | In the paper 'The Balanced-Pairwise-Affinities Feature Transform', what Accuracy score did the PT+MAP+SF+BPA (transductive) model get on the Mini-Imagenet 5-way (1-shot) dataset
| 85.59 |
AMZ Photo | GraphSAGE | 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 GraphSAGE model get on the AMZ Photo dataset
| 95.03% |
InfographicVQA | PaLI-3 | PaLI-3 Vision Language Models: Smaller, Faster, Stronger | 2023-10-13T00:00:00 | https://arxiv.org/abs/2310.09199v2 | [
"https://github.com/kyegomez/PALI3"
] | In the paper 'PaLI-3 Vision Language Models: Smaller, Faster, Stronger', what ANLS score did the PaLI-3 model get on the InfographicVQA dataset
| 57.8 |
GoPro | 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 score did the ID-Blau (Stripformer) model get on the GoPro dataset
| 33.66 |
ImageNet | ReViT-B | ReViT: Enhancing Vision Transformers Feature Diversity with Attention Residual Connections | 2024-02-17T00:00:00 | https://arxiv.org/abs/2402.11301v2 | [
"https://github.com/adiko1997/revit"
] | In the paper 'ReViT: Enhancing Vision Transformers Feature Diversity with Attention Residual Connections', what Top 1 Accuracy score did the ReViT-B model get on the ImageNet dataset
| 82.4 |
Hockey | MSQNet | Actor-agnostic Multi-label Action Recognition with Multi-modal Query | 2023-07-20T00:00:00 | https://arxiv.org/abs/2307.10763v3 | [
"https://github.com/mondalanindya/msqnet"
] | In the paper 'Actor-agnostic Multi-label Action Recognition with Multi-modal Query', what Accuracy score did the MSQNet model get on the Hockey dataset
| 3.05 |
The Pile | Test-Time Fine-Tuning with SIFT + Llama-3.2 (3B) | 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 Test-Time Fine-Tuning with SIFT + Llama-3.2 (3B) model get on the The Pile dataset
| 0.557 |
ChEBI-20 | InstructMol-GS | InstructMol: Multi-Modal Integration for Building a Versatile and Reliable Molecular Assistant in Drug Discovery | 2023-11-27T00:00:00 | https://arxiv.org/abs/2311.16208v1 | [
"https://github.com/idea-xl/instructmol"
] | In the paper 'InstructMol: Multi-Modal Integration for Building a Versatile and Reliable Molecular Assistant in Drug Discovery', what BLEU-2 score did the InstructMol-GS model get on the ChEBI-20 dataset
| 47.5 |
MATH | OpenMath-CodeLlama-13B (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-13B (w/ code) model get on the MATH dataset
| 45.5 |
MM-Vet | SPHINX-Plus | SPHINX-X: Scaling Data and Parameters for a Family of Multi-modal Large Language Models | 2024-02-08T00:00:00 | https://arxiv.org/abs/2402.05935v2 | [
"https://github.com/alpha-vllm/llama2-accessory"
] | In the paper 'SPHINX-X: Scaling Data and Parameters for a Family of Multi-modal Large Language Models', what GPT-4 score score did the SPHINX-Plus model get on the MM-Vet dataset
| 47.9 |
Texas | 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 Texas dataset
| 84.59±4.53 |
ICDAR2013 | 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 Accuracy score did the CLIP4STR-B model get on the ICDAR2013 dataset
| 98.3 |
LSMDC | vid-TLDR (UMT-L) | vid-TLDR: Training Free Token merging for Light-weight Video Transformer | 2024-03-20T00:00:00 | https://arxiv.org/abs/2403.13347v2 | [
"https://github.com/mlvlab/vid-tldr"
] | In the paper 'vid-TLDR: Training Free Token merging for Light-weight Video Transformer', what text-to-video R@1 score did the vid-TLDR (UMT-L) model get on the LSMDC dataset
| 43.1 |
PeMSD7(L) | STD-MAE | Spatial-Temporal-Decoupled Masked Pre-training for Spatiotemporal Forecasting | 2023-12-01T00:00:00 | https://arxiv.org/abs/2312.00516v3 | [
"https://github.com/jimmy-7664/std-mae"
] | In the paper 'Spatial-Temporal-Decoupled Masked Pre-training for Spatiotemporal Forecasting', what 12 steps MAE score did the STD-MAE model get on the PeMSD7(L) dataset
| 2.64 |
Something-Something V2 | CAST-B/16 | CAST: Cross-Attention in Space and Time for Video Action Recognition | 2023-11-30T00:00:00 | https://arxiv.org/abs/2311.18825v2 | [
"https://github.com/khu-vll/cast"
] | In the paper 'CAST: Cross-Attention in Space and Time for Video Action Recognition', what Top-1 Accuracy score did the CAST-B/16 model get on the Something-Something V2 dataset
| 71.6 |
Action-Camera Parking | 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 Action-Camera Parking dataset
| 0.9125 |
ImageNet - 1% labeled data | SimMatch + EPASS (ResNet-50) | Debiasing, calibrating, and improving Semi-supervised Learning performance via simple Ensemble Projector | 2023-10-24T00:00:00 | https://arxiv.org/abs/2310.15764v1 | [
"https://github.com/beandkay/epass"
] | In the paper 'Debiasing, calibrating, and improving Semi-supervised Learning performance via simple Ensemble Projector', what Top 5 Accuracy score did the SimMatch + EPASS (ResNet-50) model get on the ImageNet - 1% labeled data dataset
| 87.6 |
Now You're Cooking! | LLaVA-Chef | LLaVA-Chef: A Multi-modal Generative Model for Food Recipes | 2024-08-29T00:00:00 | https://arxiv.org/abs/2408.16889v1 | [
"https://github.com/mohbattharani/LLaVA-Chef"
] | In the paper 'LLaVA-Chef: A Multi-modal Generative Model for Food Recipes', what Perplexity score did the LLaVA-Chef model get on the Now You're Cooking! dataset
| 2.6 |
INRIA Aerial Image Labeling | UANet(PVT-V2-B2) | Building Extraction from Remote Sensing Images via an Uncertainty-Aware Network | 2023-07-23T00:00:00 | https://arxiv.org/abs/2307.12309v1 | [
"https://github.com/henryjiepanli/uncertainty-aware-network"
] | In the paper 'Building Extraction from Remote Sensing Images via an Uncertainty-Aware Network', what IoU score did the UANet(PVT-V2-B2) model get on the INRIA Aerial Image Labeling dataset
| 83.34 |
Words in Context | PaLM 2-M (one-shot) | PaLM 2 Technical Report | 2023-05-17T00:00:00 | https://arxiv.org/abs/2305.10403v3 | [
"https://github.com/eternityyw/tram-benchmark"
] | In the paper 'PaLM 2 Technical Report', what Accuracy score did the PaLM 2-M (one-shot) model get on the Words in Context dataset
| 52.0 |
ETTh2 (720) Univariate | AutoCon | Self-Supervised Contrastive Learning for Long-term Forecasting | 2024-02-03T00:00:00 | https://arxiv.org/abs/2402.02023v2 | [
"https://github.com/junwoopark92/self-supervised-contrastive-forecsating"
] | In the paper 'Self-Supervised Contrastive Learning for Long-term Forecasting', what MSE score did the AutoCon model get on the ETTh2 (720) Univariate dataset
| 0.177 |
VP-Air | CLIP | AnyLoc: Towards Universal Visual Place Recognition | 2023-08-01T00:00:00 | https://arxiv.org/abs/2308.00688v2 | [
"https://github.com/AnyLoc/AnyLoc"
] | In the paper 'AnyLoc: Towards Universal Visual Place Recognition', what Recall@1 score did the CLIP model get on the VP-Air dataset
| 36.59 |
LaSOT | 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 LaSOT dataset
| 74.2 |
ETTm1 (192) Multivariate | 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 ETTm1 (192) Multivariate dataset
| 0.333 |
TAO | AED (RegionCLIP) | Associate Everything Detected: Facilitating Tracking-by-Detection to the Unknown | 2024-09-14T00:00:00 | https://arxiv.org/abs/2409.09293v1 | [
"https://github.com/balabooooo/aed"
] | In the paper 'Associate Everything Detected: Facilitating Tracking-by-Detection to the Unknown', what TETA score did the AED (RegionCLIP) model get on the TAO dataset
| 37.0 |
YouCook2 | Norton | Multi-granularity Correspondence Learning from Long-term Noisy Videos | 2024-01-30T00:00:00 | https://arxiv.org/abs/2401.16702v1 | [
"https://github.com/XLearning-SCU/2024-ICLR-Norton"
] | In the paper 'Multi-granularity Correspondence Learning from Long-term Noisy Videos', what Cap. Avg. R@1 score did the Norton model get on the YouCook2 dataset
| 75.5 |
Haze4k | MixDehazeNet | MixDehazeNet : Mix Structure Block For Image Dehazing Network | 2023-05-28T00:00:00 | https://arxiv.org/abs/2305.17654v1 | [
"https://github.com/ameryxiong/mixdehazenet"
] | In the paper 'MixDehazeNet : Mix Structure Block For Image Dehazing Network', what PSNR score did the MixDehazeNet model get on the Haze4k dataset
| 35.64 |
MRR-Benchmark | Idefics-80B | OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents | 2023-06-21T00:00:00 | https://arxiv.org/abs/2306.16527v2 | [
"https://github.com/huggingface/obelics"
] | In the paper 'OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents', what Total Column Score score did the Idefics-80B model get on the MRR-Benchmark dataset
| 139 |
ANLI test | 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 A1 score did the PaLM 2-S (one-shot) model get on the ANLI test dataset
| 53.1 |
QNLI | GOLD (T5-base) | GOLD: Generalized Knowledge Distillation via Out-of-Distribution-Guided Language Data Generation | 2024-03-28T00:00:00 | https://arxiv.org/abs/2403.19754v1 | [
"https://github.com/mgholamikn/GOLD"
] | In the paper 'GOLD: Generalized Knowledge Distillation via Out-of-Distribution-Guided Language Data Generation', what Accuracy score did the GOLD (T5-base) model get on the QNLI dataset
| 91.7 |
NIR2RGB VCIP Challange Dataset | ColorMamba | ColorMamba: Towards High-quality NIR-to-RGB Spectral Translation with Mamba | 2024-08-15T00:00:00 | https://arxiv.org/abs/2408.08087v1 | [
"https://github.com/alexyangxx/colormamba"
] | In the paper 'ColorMamba: Towards High-quality NIR-to-RGB Spectral Translation with Mamba', what PSNR score did the ColorMamba model get on the NIR2RGB VCIP Challange Dataset dataset
| 24.56 |
GOT-10k | MITS | Integrating Boxes and Masks: A Multi-Object Framework for Unified Visual Tracking and Segmentation | 2023-08-25T00:00:00 | https://arxiv.org/abs/2308.13266v3 | [
"https://github.com/yoxu515/mits"
] | In the paper 'Integrating Boxes and Masks: A Multi-Object Framework for Unified Visual Tracking and Segmentation', what Average Overlap score did the MITS model get on the GOT-10k dataset
| 80.4 |
DiDeMo | vid-TLDR (UMT-L) | vid-TLDR: Training Free Token merging for Light-weight Video Transformer | 2024-03-20T00:00:00 | https://arxiv.org/abs/2403.13347v2 | [
"https://github.com/mlvlab/vid-tldr"
] | In the paper 'vid-TLDR: Training Free Token merging for Light-weight Video Transformer', what text-to-video R@1 score did the vid-TLDR (UMT-L) model get on the DiDeMo dataset
| 72.3 |
MassSpecGym | Precursor m/z | MassSpecGym: A benchmark for the discovery and identification of molecules | 2024-10-30T00:00:00 | https://arxiv.org/abs/2410.23326v1 | [
"https://github.com/pluskal-lab/massspecgym"
] | In the paper 'MassSpecGym: A benchmark for the discovery and identification of molecules', what Cosine Similarity score did the Precursor m/z model get on the MassSpecGym dataset
| 0.15 |
SVTP | CLIP4STR-L (DataComp-1B) | CLIP4STR: A Simple Baseline for Scene Text Recognition with Pre-trained Vision-Language Model | 2023-05-23T00:00:00 | https://arxiv.org/abs/2305.14014v3 | [
"https://github.com/VamosC/CLIP4STR"
] | In the paper 'CLIP4STR: A Simple Baseline for Scene Text Recognition with Pre-trained Vision-Language Model', what Accuracy score did the CLIP4STR-L (DataComp-1B) model get on the SVTP dataset
| 98.1 |
3DPW | SMPLer-X | SMPLer-X: Scaling Up Expressive Human Pose and Shape Estimation | 2023-09-29T00:00:00 | https://arxiv.org/abs/2309.17448v3 | [
"https://github.com/caizhongang/SMPLer-X"
] | In the paper 'SMPLer-X: Scaling Up Expressive Human Pose and Shape Estimation', what MPJPE score did the SMPLer-X model get on the 3DPW dataset
| 75.2 |
SPKL | CFEN | 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 CFEN model get on the SPKL dataset
| 0.5367 |
Wisconsin | TE-GCNN | Transfer Entropy in Graph Convolutional Neural Networks | 2024-06-08T00:00:00 | https://arxiv.org/abs/2406.06632v1 | [
"https://github.com/avmoldovan/Heterophily_and_oversmoothing-forked"
] | In the paper 'Transfer Entropy in Graph Convolutional Neural Networks', what Accuracy score did the TE-GCNN model get on the Wisconsin dataset
| 87.45 ± 3.70 |
WikiSQL | CABINET | CABINET: Content Relevance based Noise Reduction for Table Question Answering | 2024-02-02T00:00:00 | https://arxiv.org/abs/2402.01155v3 | [
"https://github.com/sohanpatnaik106/cabinet_qa"
] | In the paper 'CABINET: Content Relevance based Noise Reduction for Table Question Answering', what Denotation accuracy (test) score did the CABINET model get on the WikiSQL dataset
| 89.5 |
FRMT (Chinese - Taiwan) | PaLM | PaLM 2 Technical Report | 2023-05-17T00:00:00 | https://arxiv.org/abs/2305.10403v3 | [
"https://github.com/eternityyw/tram-benchmark"
] | In the paper 'PaLM 2 Technical Report', what BLEURT score did the PaLM model get on the FRMT (Chinese - Taiwan) dataset
| 68.6 |
SSC | SNN with Dilated Convolution with Learnable Spacings | Learning Delays in Spiking Neural Networks using Dilated Convolutions with Learnable Spacings | 2023-06-30T00:00:00 | https://arxiv.org/abs/2306.17670v3 | [
"https://github.com/thvnvtos/snn-delays"
] | In the paper 'Learning Delays in Spiking Neural Networks using Dilated Convolutions with Learnable Spacings', what Accuracy score did the SNN with Dilated Convolution with Learnable Spacings model get on the SSC dataset
| 80.69 |
VideoInstruct | TS-LLaVA-34B | TS-LLaVA: Constructing Visual Tokens through Thumbnail-and-Sampling for Training-Free Video Large Language Models | 2024-11-17T00:00:00 | https://arxiv.org/abs/2411.11066v1 | [
"https://github.com/tingyu215/ts-llava"
] | In the paper 'TS-LLaVA: Constructing Visual Tokens through Thumbnail-and-Sampling for Training-Free Video Large Language Models', what mean score did the TS-LLaVA-34B model get on the VideoInstruct dataset
| 3.38 |
CHASE_DB1 | 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 F1 score score did the PVT-GCASCADE model get on the CHASE_DB1 dataset
| 0.8251 |
ImageNet | 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 NMI score did the TURTLE (CLIP + DINOv2) model get on the ImageNet dataset
| 88.2 |
DAVIS 2017 (val) | 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 J&F 1st frame score did the UniVS(Swin-L) model get on the DAVIS 2017 (val) dataset
| 59.4? |
COCO-20i (1-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 (1-shot) dataset
| 45.69 |
PCQM4M-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 PCQM4M-LSC dataset
| 0.1193 |
PeMS07 | PM-DMNet(P) | Pattern-Matching Dynamic Memory Network for Dual-Mode Traffic Prediction | 2024-08-12T00:00:00 | https://arxiv.org/abs/2408.07100v1 | [
"https://github.com/wengwenchao123/PM-DMNet"
] | In the paper 'Pattern-Matching Dynamic Memory Network for Dual-Mode Traffic Prediction', what MAE@1h score did the PM-DMNet(P) model get on the PeMS07 dataset
| 19.35 |
Cityscapes to Foggy Cityscapes | MIC (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 MIC (ResNet50-FPN) model get on the Cityscapes to Foggy Cityscapes dataset
| 61.7 |
3DPW | CycleAdapt (w/ 2D GT) | Cyclic Test-Time Adaptation on Monocular Video for 3D Human Mesh Reconstruction | 2023-08-12T00:00:00 | https://arxiv.org/abs/2308.06554v1 | [
"https://github.com/hygenie1228/cycleadapt_release"
] | In the paper 'Cyclic Test-Time Adaptation on Monocular Video for 3D Human Mesh Reconstruction', what PA-MPJPE score did the CycleAdapt (w/ 2D GT) model get on the 3DPW dataset
| 39.9 |
Synapse multi-organ CT | 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 Avg DSC score did the EMCAD model get on the Synapse multi-organ CT dataset
| 83.63 |
GRAZPEDWRI-DX | YOLOv8+GE | Pediatric Wrist Fracture Detection Using Feature Context Excitation Modules in X-ray Images | 2024-10-01T00:00:00 | https://arxiv.org/abs/2410.01031v2 | [
"https://github.com/ruiyangju/fce-yolov8"
] | In the paper 'Pediatric Wrist Fracture Detection Using Feature Context Excitation Modules in X-ray Images', what mAP score did the YOLOv8+GE model get on the GRAZPEDWRI-DX dataset
| 34.01 |
CV-Cities | CV-Cities | CV-Cities: Advancing Cross-View Geo-Localization in Global Cities | 2024-11-19T00:00:00 | https://arxiv.org/abs/2411.12431v1 | [
"https://github.com/gaoshuang98/cvcities"
] | In the paper 'CV-Cities: Advancing Cross-View Geo-Localization in Global Cities', what Recall@1 score did the CV-Cities model get on the CV-Cities dataset
| 82.91 |
GSM8K | MathCoder-CL-34B | MathCoder: Seamless Code Integration in LLMs for Enhanced Mathematical Reasoning | 2023-10-05T00:00:00 | https://arxiv.org/abs/2310.03731v1 | [
"https://github.com/mathllm/mathcoder"
] | In the paper 'MathCoder: Seamless Code Integration in LLMs for Enhanced Mathematical Reasoning', what Accuracy score did the MathCoder-CL-34B model get on the GSM8K dataset
| 81.7 |
CelebA-HQ 256x256 | RDM | Relay Diffusion: Unifying diffusion process across resolutions for image synthesis | 2023-09-04T00:00:00 | https://arxiv.org/abs/2309.03350v1 | [
"https://github.com/THUDM/RelayDiffusion"
] | In the paper 'Relay Diffusion: Unifying diffusion process across resolutions for image synthesis', what FID score did the RDM model get on the CelebA-HQ 256x256 dataset
| 3.15 |
COCO Captions | BLIP-FuseCap | FuseCap: Leveraging Large Language Models for Enriched Fused Image Captions | 2023-05-28T00:00:00 | https://arxiv.org/abs/2305.17718v2 | [
"https://github.com/RotsteinNoam/FuseCap"
] | In the paper 'FuseCap: Leveraging Large Language Models for Enriched Fused Image Captions', what CLIPScore score did the BLIP-FuseCap model get on the COCO Captions dataset
| 78.5 |
OVIS validation | GRAtt-VIS (Swin-L) | GRAtt-VIS: Gated Residual Attention for Auto Rectifying Video Instance Segmentation | 2023-05-26T00:00:00 | https://arxiv.org/abs/2305.17096v1 | [
"https://github.com/tanveer81/grattvis"
] | In the paper 'GRAtt-VIS: Gated Residual Attention for Auto Rectifying Video Instance Segmentation', what mask AP score did the GRAtt-VIS (Swin-L) model get on the OVIS validation dataset
| 45.7 |
MathToF | GPT-4 (Teaching-Inspired) | Teaching-Inspired Integrated Prompting Framework: A Novel Approach for Enhancing Reasoning in Large Language Models | 2024-10-10T00:00:00 | https://arxiv.org/abs/2410.08068v1 | [
"https://github.com/sallytan13/teaching-inspired-prompting"
] | In the paper 'Teaching-Inspired Integrated Prompting Framework: A Novel Approach for Enhancing Reasoning in Large Language Models', what Accuracy score did the GPT-4 (Teaching-Inspired) model get on the MathToF dataset
| 89.2 |
ASDiv-A | 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 Execution Accuracy score did the OpenMath-CodeLlama-70B (w/ code) model get on the ASDiv-A dataset
| 84.7 |
BSDS300 | PaddingFlow | PaddingFlow: Improving Normalizing Flows with Padding-Dimensional Noise | 2024-03-13T00:00:00 | https://arxiv.org/abs/2403.08216v2 | [
"https://github.com/adamqlmeng/paddingflow"
] | In the paper 'PaddingFlow: Improving Normalizing Flows with Padding-Dimensional Noise', what CD score did the PaddingFlow model get on the BSDS300 dataset
| 0.495 |
VLCS | 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 VLCS dataset
| 84.5 |
Set14 - 4x upscaling | AESOP | Auto-Encoded Supervision for Perceptual Image Super-Resolution | 2024-11-28T00:00:00 | https://arxiv.org/abs/2412.00124v1 | [
"https://github.com/2minkyulee/aesop-auto-encoded-supervision-for-perceptual-image-super-resolution"
] | In the paper 'Auto-Encoded Supervision for Perceptual Image Super-Resolution', what PSNR score did the AESOP model get on the Set14 - 4x upscaling dataset
| 27.421 |
Breakfast | LTContext | How Much Temporal Long-Term Context is Needed for Action Segmentation? | 2023-08-22T00:00:00 | https://arxiv.org/abs/2308.11358v2 | [
"https://github.com/ltcontext/ltcontext"
] | In the paper 'How Much Temporal Long-Term Context is Needed for Action Segmentation?', what F1@10% score did the LTContext model get on the Breakfast dataset
| 77.6 |
WHU-CD | SRC-Net | SRC-Net: Bi-Temporal Spatial Relationship Concerned Network for Change Detection | 2024-06-09T00:00:00 | https://arxiv.org/abs/2406.05668v2 | [
"https://github.com/Chnja/SRCNet"
] | In the paper 'SRC-Net: Bi-Temporal Spatial Relationship Concerned Network for Change Detection', what F1 score did the SRC-Net model get on the WHU-CD dataset
| 92.06 |
KIT Motion-Language | ST-MLP | Guided Attention for Interpretable Motion Captioning | 2023-10-11T00:00:00 | https://arxiv.org/abs/2310.07324v2 | [
"https://github.com/rd20karim/m2t-interpretable"
] | In the paper 'Guided Attention for Interpretable Motion Captioning', what BLEU-4 score did the ST-MLP model get on the KIT Motion-Language dataset
| 24.4 |
Something-Something V1 | TAdaConvNeXtV2-B | 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 TAdaConvNeXtV2-B model get on the Something-Something V1 dataset
| 60.7 |
CIFAR-10 | TRADES-ANCRA/ResNet18 | Enhancing Robust Representation in Adversarial Training: Alignment and Exclusion Criteria | 2023-10-05T00:00:00 | https://arxiv.org/abs/2310.03358v2 | [
"https://github.com/changzhang777/ancra"
] | In the paper 'Enhancing Robust Representation in Adversarial Training: Alignment and Exclusion Criteria', what Attack: AutoAttack score did the TRADES-ANCRA/ResNet18 model get on the CIFAR-10 dataset
| 59.70 |
Fashion-MNIST | ResNet-18 | Vision Eagle Attention: a new lens for advancing image classification | 2024-11-15T00:00:00 | https://arxiv.org/abs/2411.10564v2 | [
"https://github.com/MahmudulHasan11085/Vision-Eagle-Attention"
] | In the paper 'Vision Eagle Attention: a new lens for advancing image classification', what Percentage error score did the ResNet-18 model get on the Fashion-MNIST dataset
| 7.72 |
WDC Products-80%cc-seen-medium | Llama3.1_8B | Fine-tuning Large Language Models for Entity Matching | 2024-09-12T00:00:00 | https://arxiv.org/abs/2409.08185v1 | [
"https://github.com/wbsg-uni-mannheim/tailormatch"
] | In the paper 'Fine-tuning Large Language Models for Entity Matching', what F1 (%) score did the Llama3.1_8B model get on the WDC Products-80%cc-seen-medium dataset
| 53.36 |
VTAB-1k(Structured<8>) | GateVPT(ViT-B/16_MAE_pretrained_ImageNet-1K) | Improving Visual Prompt Tuning for Self-supervised Vision Transformers | 2023-06-08T00:00:00 | https://arxiv.org/abs/2306.05067v1 | [
"https://github.com/ryongithub/gatedprompttuning"
] | In the paper 'Improving Visual Prompt Tuning for Self-supervised Vision Transformers', what Mean Accuracy score did the GateVPT(ViT-B/16_MAE_pretrained_ImageNet-1K) model get on the VTAB-1k(Structured<8>) dataset
| 36.80 |
GAP | 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 Overall F1 score did the Maverick_incr model get on the GAP dataset
| 91.2 |
S2Looking | C2FNet | C2F-SemiCD: A Coarse-to-Fine Semi-Supervised Change Detection Method Based on Consistency Regularization in High-Resolution Remote Sensing Images | 2024-04-22T00:00:00 | https://arxiv.org/abs/2404.13838v1 | [
"https://github.com/chengxihan/c2f-semicd-and-c2f-cdnet"
] | In the paper 'C2F-SemiCD: A Coarse-to-Fine Semi-Supervised Change Detection Method Based on Consistency Regularization in High-Resolution Remote Sensing Images', what F1-Score score did the C2FNet model get on the S2Looking dataset
| 62.83 |
LLVIP | MiPa | MiPa: Mixed Patch Infrared-Visible Modality Agnostic Object Detection | 2024-04-29T00:00:00 | https://arxiv.org/abs/2404.18849v2 | [
"https://github.com/heitorrapela/mipa"
] | In the paper 'MiPa: Mixed Patch Infrared-Visible Modality Agnostic Object Detection', what AP score did the MiPa model get on the LLVIP dataset
| 0.665 |
Aria Synthetic Environments | EVL | 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 EVL model get on the Aria Synthetic Environments dataset
| 75 |
LSUN Bedroom 256 x 256 | LFM | Flow Matching in Latent Space | 2023-07-17T00:00:00 | https://arxiv.org/abs/2307.08698v1 | [
"https://github.com/vinairesearch/lfm"
] | In the paper 'Flow Matching in Latent Space', what FID score did the LFM model get on the LSUN Bedroom 256 x 256 dataset
| 4.92 |
ETTh1 (336) Multivariate | ATFNet | ATFNet: Adaptive Time-Frequency Ensembled Network for Long-term Time Series Forecasting | 2024-04-08T00:00:00 | https://arxiv.org/abs/2404.05192v1 | [
"https://github.com/yhyhyhyhyhy/atfnet"
] | In the paper 'ATFNet: Adaptive Time-Frequency Ensembled Network for Long-term Time Series Forecasting', what MSE score did the ATFNet model get on the ETTh1 (336) Multivariate dataset
| 0.514 |
EuroSAT | 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 EuroSAT dataset
| 96.6 |
Office-Home | MoA (OpenCLIP, ViT-B/16) | Domain Generalization Using Large Pretrained Models with Mixture-of-Adapters | 2023-10-17T00:00:00 | https://arxiv.org/abs/2310.11031v2 | [
"https://github.com/KU-CVLAB/MoA"
] | In the paper 'Domain Generalization Using Large Pretrained Models with Mixture-of-Adapters', what Average Accuracy score did the MoA (OpenCLIP, ViT-B/16) model get on the Office-Home dataset
| 90.6 |
MM-Vet | InternLM-XComposer2 | InternLM-XComposer2: Mastering Free-form Text-Image Composition and Comprehension in Vision-Language Large Model | 2024-01-29T00:00:00 | https://arxiv.org/abs/2401.16420v1 | [
"https://github.com/internlm/internlm-xcomposer"
] | In the paper 'InternLM-XComposer2: Mastering Free-form Text-Image Composition and Comprehension in Vision-Language Large Model', what GPT-4 score score did the InternLM-XComposer2 model get on the MM-Vet dataset
| 51.2 |
Comic2k | CDDMSL | Semi-Supervised Domain Generalization for Object Detection via Language-Guided Feature Alignment | 2023-09-24T00:00:00 | https://arxiv.org/abs/2309.13525v1 | [
"https://github.com/sinamalakouti/CDDMSL"
] | In the paper 'Semi-Supervised Domain Generalization for Object Detection via Language-Guided Feature Alignment', what mAP score did the CDDMSL model get on the Comic2k dataset
| 45.9 |
ACOS | MvP | 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 model get on the ACOS dataset
| 43.92 |
CNRPark+EXT | VGG-19 | 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 VGG-19 model get on the CNRPark+EXT dataset
| 0.9629 |
ETTh2 (192) Multivariate | 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 ETTh2 (192) Multivariate dataset
| 0.33 |
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 |
SHD - Adding | ELM Neuron | 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 ELM Neuron model get on the SHD - Adding dataset
| 82 |
VLCS | GMDG (ResNet-50, SWAD) | Rethinking Multi-domain Generalization with A General Learning Objective | 2024-02-29T00:00:00 | https://arxiv.org/abs/2402.18853v1 | [
"https://github.com/zhaorui-tan/GMDG_cvpr2024"
] | In the paper 'Rethinking Multi-domain Generalization with A General Learning Objective', what Average Accuracy score did the GMDG (ResNet-50, SWAD) model get on the VLCS dataset
| 79.6 |
MS COCO | BUCTD (PETR, with generative sampling) | Rethinking pose estimation in crowds: overcoming the detection information-bottleneck and ambiguity | 2023-06-13T00:00:00 | https://arxiv.org/abs/2306.07879v2 | [
"https://github.com/amathislab/BUCTD"
] | In the paper 'Rethinking pose estimation in crowds: overcoming the detection information-bottleneck and ambiguity', what AP score did the BUCTD (PETR, with generative sampling) model get on the MS COCO dataset
| 77.8 |
VibraVox (headset microphone) | ECAPA2 | Vibravox: A Dataset of French Speech Captured with Body-conduction Audio Sensors | 2024-07-16T00:00:00 | https://arxiv.org/abs/2407.11828v2 | [
"https://github.com/jhauret/vibravox"
] | In the paper 'Vibravox: A Dataset of French Speech Captured with Body-conduction Audio Sensors', what Test EER score did the ECAPA2 model get on the VibraVox (headset microphone) dataset
| 0.0026 |
GSM8K | MuggleMATH 7B | MuggleMath: Assessing the Impact of Query and Response Augmentation on Math Reasoning | 2023-10-09T00:00:00 | https://arxiv.org/abs/2310.05506v3 | [
"https://github.com/ofa-sys/gsm8k-screl"
] | In the paper 'MuggleMath: Assessing the Impact of Query and Response Augmentation on Math Reasoning', what Accuracy score did the MuggleMATH 7B model get on the GSM8K dataset
| 69.8 |
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