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 |
|---|---|---|---|---|---|---|---|
WebApp1K-React | mistral-large-2 | Insights from Benchmarking Frontier Language Models on Web App Code Generation | 2024-09-08T00:00:00 | https://arxiv.org/abs/2409.05177v1 | [
"https://github.com/onekq/webapp1k"
] | In the paper 'Insights from Benchmarking Frontier Language Models on Web App Code Generation', what pass@1 score did the mistral-large-2 model get on the WebApp1K-React dataset
| 0.7804 |
DEplain-APA-sent | mBART (trained on DEplain-APA-sent & DEplain-web-sent) | DEPLAIN: A German Parallel Corpus with Intralingual Translations into Plain Language for Sentence and Document Simplification | 2023-05-30T00:00:00 | https://arxiv.org/abs/2305.18939v1 | [
"https://github.com/rstodden/deplain"
] | In the paper 'DEPLAIN: A German Parallel Corpus with Intralingual Translations into Plain Language for Sentence and Document Simplification', what SARI (EASSE>=0.2.1) score did the mBART (trained on DEplain-APA-sent & DEplain-web-sent) model get on the DEplain-APA-sent dataset
| 34.904 |
SVAMP | 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 Execution Accuracy score did the MMOS-DeepSeekMath-7B(0-shot) model get on the SVAMP dataset
| 79.3 |
MM-Vet | LLaVA-1.5-13B (+ MMFuser) | MMFuser: Multimodal Multi-Layer Feature Fuser for Fine-Grained Vision-Language Understanding | 2024-10-15T00:00:00 | https://arxiv.org/abs/2410.11829v1 | [
"https://github.com/yuecao0119/MMFuser"
] | In the paper 'MMFuser: Multimodal Multi-Layer Feature Fuser for Fine-Grained Vision-Language Understanding', what GPT-4 score score did the LLaVA-1.5-13B (+ MMFuser) model get on the MM-Vet dataset
| 36.6 |
GTSRB | 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 GTSRB dataset
| 48.4 |
Cornell | H2GCN + UniGAP | UniGAP: A Universal and Adaptive Graph Upsampling Approach to Mitigate Over-Smoothing in Node Classification Tasks | 2024-07-28T00:00:00 | https://arxiv.org/abs/2407.19420v1 | [
"https://github.com/wangxiaotang0906/unigap"
] | In the paper 'UniGAP: A Universal and Adaptive Graph Upsampling Approach to Mitigate Over-Smoothing in Node Classification Tasks', what Accuracy score did the H2GCN + UniGAP model get on the Cornell dataset
| 84.96 ± 5.0 |
SMAC 27m_vs_30m | QPLEX | 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 QPLEX model get on the SMAC 27m_vs_30m dataset
| 78.12 |
ETTh1 (336) Multivariate | CPNet | Boosting MLPs with a Coarsening Strategy for Long-Term Time Series Forecasting | 2024-05-06T00:00:00 | https://arxiv.org/abs/2405.03199v2 | [
"https://github.com/nannanbian/cpnet"
] | In the paper 'Boosting MLPs with a Coarsening Strategy for Long-Term Time Series Forecasting', what MSE score did the CPNet model get on the ETTh1 (336) Multivariate dataset
| 0.479 |
allrecipes.com | 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 BLEU score did the LLaVA-Chef model get on the allrecipes.com dataset
| 6.0 |
BKAI-IGH NeoPolyp-Small | RaBiT | RaBiT: An Efficient Transformer using Bidirectional Feature Pyramid Network with Reverse Attention for Colon Polyp Segmentation | 2023-07-12T00:00:00 | https://arxiv.org/abs/2307.06420v1 | [
"https://github.com/nguyenhoangthuan99/RaBiT"
] | In the paper 'RaBiT: An Efficient Transformer using Bidirectional Feature Pyramid Network with Reverse Attention for Colon Polyp Segmentation', what Average Dice score did the RaBiT model get on the BKAI-IGH NeoPolyp-Small dataset
| 0.94 |
MPI-INF-3DHP | Regular Splitting Graph Network | Regular Splitting Graph Network for 3D Human Pose Estimation | 2023-05-09T00:00:00 | https://arxiv.org/abs/2305.05785v1 | [
"https://github.com/nies14/rs-net"
] | In the paper 'Regular Splitting Graph Network for 3D Human Pose Estimation', what AUC score did the Regular Splitting Graph Network model get on the MPI-INF-3DHP dataset
| 53.2 |
WildDESED | CRNN | WildDESED: An LLM-Powered Dataset for Wild Domestic Environment Sound Event Detection System | 2024-07-04T00:00:00 | https://arxiv.org/abs/2407.03656v3 | [
"https://github.com/swagshaw/wilddesed"
] | In the paper 'WildDESED: An LLM-Powered Dataset for Wild Domestic Environment Sound Event Detection System', what PSDS1 (-5dB) score did the CRNN model get on the WildDESED dataset
| 0.017 |
AudioSet | EAT | EAT: Self-Supervised Pre-Training with Efficient Audio Transformer | 2024-01-07T00:00:00 | https://arxiv.org/abs/2401.03497v1 | [
"https://github.com/cwx-worst-one/eat"
] | In the paper 'EAT: Self-Supervised Pre-Training with Efficient Audio Transformer', what Test mAP score did the EAT model get on the AudioSet dataset
| 0.486 |
ETTm1 (336) 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 (336) Multivariate dataset
| 0.365 |
QVHighlights | NumPro | Number it: Temporal Grounding Videos like Flipping Manga | 2024-11-15T00:00:00 | https://arxiv.org/abs/2411.10332v2 | [
"https://github.com/yongliang-wu/numpro"
] | In the paper 'Number it: Temporal Grounding Videos like Flipping Manga', what mAP score did the NumPro model get on the QVHighlights dataset
| 40.54 |
ScanObjectNN | Point-FEMAE | Towards Compact 3D Representations via Point Feature Enhancement Masked Autoencoders | 2023-12-17T00:00:00 | https://arxiv.org/abs/2312.10726v1 | [
"https://github.com/zyh16143998882/aaai24-pointfemae"
] | In the paper 'Towards Compact 3D Representations via Point Feature Enhancement Masked Autoencoders', what Overall Accuracy score did the Point-FEMAE model get on the ScanObjectNN dataset
| 90.22 |
Atari 2600 Fishing Derby | 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 Fishing Derby dataset
| 35.1 |
DocVQA test | 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 DocVQA test dataset
| 0.876 |
STS12 | PromptEOL+CSE+OPT-13B | Scaling Sentence Embeddings with Large Language Models | 2023-07-31T00:00:00 | https://arxiv.org/abs/2307.16645v1 | [
"https://github.com/kongds/scaling_sentemb"
] | In the paper 'Scaling Sentence Embeddings with Large Language Models', what Spearman Correlation score did the PromptEOL+CSE+OPT-13B model get on the STS12 dataset
| 0.8020 |
EuroSAT-SAR | FG-MAE (ViT-S/16) | Feature Guided Masked Autoencoder for Self-supervised Learning in Remote Sensing | 2023-10-28T00:00:00 | https://arxiv.org/abs/2310.18653v1 | [
"https://github.com/zhu-xlab/fgmae"
] | In the paper 'Feature Guided Masked Autoencoder for Self-supervised Learning in Remote Sensing', what Overall Accuracy score did the FG-MAE (ViT-S/16) model get on the EuroSAT-SAR dataset
| 85.9 |
BTAD | URD | Unlocking the Potential of Reverse Distillation for Anomaly Detection | 2024-12-10T00:00:00 | https://arxiv.org/abs/2412.07579v1 | [
"https://github.com/hito2448/urd"
] | In the paper 'Unlocking the Potential of Reverse Distillation for Anomaly Detection', what Segmentation AUROC score did the URD model get on the BTAD dataset
| 98.1 |
horse2zebra | CycleGANAS | CycleGANAS: Differentiable Neural Architecture Search for CycleGAN | 2023-11-13T00:00:00 | https://arxiv.org/abs/2311.07162v1 | [
"https://github.com/antaegun20/CycleGANAS"
] | In the paper 'CycleGANAS: Differentiable Neural Architecture Search for CycleGAN', what Frechet Inception Distance score did the CycleGANAS model get on the horse2zebra dataset
| 38.06 |
S3DIS | PPT + SparseUNet | Towards Large-scale 3D Representation Learning with Multi-dataset Point Prompt Training | 2023-08-18T00:00:00 | https://arxiv.org/abs/2308.09718v2 | [
"https://github.com/Pointcept/Pointcept"
] | In the paper 'Towards Large-scale 3D Representation Learning with Multi-dataset Point Prompt Training', what Mean IoU score did the PPT + SparseUNet model get on the S3DIS dataset
| 78.1 |
HumanML3D | MMM (predict 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 (predict length) model get on the HumanML3D dataset
| 0.080 |
LingOly | Gemini 1.5 Pro | LINGOLY: A Benchmark of Olympiad-Level Linguistic Reasoning Puzzles in Low-Resource and Extinct Languages | 2024-06-10T00:00:00 | https://arxiv.org/abs/2406.06196v3 | [
"https://github.com/am-bean/lingOly"
] | In the paper 'LINGOLY: A Benchmark of Olympiad-Level Linguistic Reasoning Puzzles in Low-Resource and Extinct Languages', what Exact Match Accuracy score did the Gemini 1.5 Pro model get on the LingOly dataset
| 32.1% |
DomainNet | 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 DomainNet dataset
| 68.3 |
ETTh1 (336) Multivariate | Pathformer | Pathformer: Multi-scale Transformers with Adaptive Pathways for Time Series Forecasting | 2024-02-04T00:00:00 | https://arxiv.org/abs/2402.05956v5 | [
"https://github.com/decisionintelligence/pathformer"
] | In the paper 'Pathformer: Multi-scale Transformers with Adaptive Pathways for Time Series Forecasting', what MSE score did the Pathformer model get on the ETTh1 (336) Multivariate dataset
| 0.454 |
LibriSpeech 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 test-clean dataset
| 2.11 |
Texas (60%/20%/20% random splits) | HH-GAT | 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 1:1 Accuracy score did the HH-GAT model get on the Texas (60%/20%/20% random splits) dataset
| 80.54 ± 4.80 |
MM-Vet | VisionZip (Retain 64 Tokens, fine-tuning) | VisionZip: Longer is Better but Not Necessary in Vision Language Models | 2024-12-05T00:00:00 | https://arxiv.org/abs/2412.04467v1 | [
"https://github.com/dvlab-research/visionzip"
] | In the paper 'VisionZip: Longer is Better but Not Necessary in Vision Language Models', what GPT-4 score score did the VisionZip (Retain 64 Tokens, fine-tuning) model get on the MM-Vet dataset
| 30.2 |
CBVS | UniCLP | CBVS: A Large-Scale Chinese Image-Text Benchmark for Real-World Short Video Search Scenarios | 2024-01-19T00:00:00 | https://arxiv.org/abs/2401.10475v2 | [
"https://github.com/QQBrowserVideoSearch/CBVS-UniCLIP"
] | In the paper 'CBVS: A Large-Scale Chinese Image-Text Benchmark for Real-World Short Video Search Scenarios', what PNR score did the UniCLP model get on the CBVS dataset
| 3.069 |
COCO minival | GLEE-Pro | General Object Foundation Model for Images and Videos at Scale | 2023-12-14T00:00:00 | https://arxiv.org/abs/2312.09158v1 | [
"https://github.com/FoundationVision/GLEE"
] | In the paper 'General Object Foundation Model for Images and Videos at Scale', what mask AP score did the GLEE-Pro model get on the COCO minival dataset
| 54.2 |
Amazon-Beauty | HetroFair | Heterophily-Aware Fair Recommendation using Graph Convolutional Networks | 2024-01-31T00:00:00 | https://arxiv.org/abs/2402.03365v2 | [
"https://github.com/nematgh/hetrofair"
] | In the paper 'Heterophily-Aware Fair Recommendation using Graph Convolutional Networks', what NDCG@20 score did the HetroFair model get on the Amazon-Beauty dataset
| 0.2308 |
Data3D−R2N2 | LRGT | Long-Range Grouping Transformer for Multi-View 3D Reconstruction | 2023-08-17T00:00:00 | https://arxiv.org/abs/2308.08724v1 | [
"https://github.com/liyingcv/long-range-grouping-transformer"
] | In the paper 'Long-Range Grouping Transformer for Multi-View 3D Reconstruction', what 3DIoU score did the LRGT model get on the Data3D−R2N2 dataset
| 0.696 |
RLBench | RVT-2 | RVT-2: Learning Precise Manipulation from Few Demonstrations | 2024-06-12T00:00:00 | https://arxiv.org/abs/2406.08545v1 | [
"https://github.com/NVlabs/RVT"
] | In the paper 'RVT-2: Learning Precise Manipulation from Few Demonstrations', what Succ. Rate (18 tasks, 100 demo/task) score did the RVT-2 model get on the RLBench dataset
| 81.4 |
VideoInstruct | CAT-7B | CAT: Enhancing Multimodal Large Language Model to Answer Questions in Dynamic Audio-Visual Scenarios | 2024-03-07T00:00:00 | https://arxiv.org/abs/2403.04640v1 | [
"https://github.com/rikeilong/bay-cat"
] | In the paper 'CAT: Enhancing Multimodal Large Language Model to Answer Questions in Dynamic Audio-Visual Scenarios', what Correctness of Information score did the CAT-7B model get on the VideoInstruct dataset
| 3.08 |
MATH | OpenMath-Llama2-70B (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-Llama2-70B (w/ code) model get on the MATH dataset
| 46.3 |
Arxiv HEP-TH citation graph | SRformer-BART | Segmented Recurrent Transformer: An Efficient Sequence-to-Sequence Model | 2023-05-24T00:00:00 | https://arxiv.org/abs/2305.16340v3 | [
"https://github.com/yinghanlong/SRtransformer"
] | In the paper 'Segmented Recurrent Transformer: An Efficient Sequence-to-Sequence Model', what ROUGE-1 score did the SRformer-BART model get on the Arxiv HEP-TH citation graph dataset
| 42.99 |
SMAC 6h_vs_9z | DPLEX | 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 DPLEX model get on the SMAC 6h_vs_9z dataset
| 14.84 |
PubMed with Public Split: fixed 20 nodes per class | GEM | Graph Entropy Minimization for Semi-supervised Node Classification | 2023-05-31T00:00:00 | https://arxiv.org/abs/2305.19502v1 | [
"https://github.com/cf020031308/gem"
] | In the paper 'Graph Entropy Minimization for Semi-supervised Node Classification', what Accuracy score did the GEM model get on the PubMed with Public Split: fixed 20 nodes per class dataset
| 78.48 |
Sleep-EDFx (single-channel) | NeuroNet (Fpz-Cz only) | NeuroNet: A Novel Hybrid Self-Supervised Learning Framework for Sleep Stage Classification Using Single-Channel EEG | 2024-04-10T00:00:00 | https://arxiv.org/abs/2404.17585v2 | [
"https://github.com/dlcjfgmlnasa/NeuroNet"
] | In the paper 'NeuroNet: A Novel Hybrid Self-Supervised Learning Framework for Sleep Stage Classification Using Single-Channel EEG', what Accuracy score did the NeuroNet (Fpz-Cz only) model get on the Sleep-EDFx (single-channel) dataset
| 85.24% |
LRS2 | RTFS-Net-12 | 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-12 model get on the LRS2 dataset
| 14.9 |
Atari 2600 Solaris | 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 Solaris dataset
| 3506.8 |
Sphere Simple | 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 Sphere Simple dataset
| 30.41±1.71 |
CHILI-100K | PMLP | 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 PMLP model get on the CHILI-100K dataset
| 0.486 +/- 0.014 |
IMDb-M | G-Tuning | Fine-tuning Graph Neural Networks by Preserving Graph Generative Patterns | 2023-12-21T00:00:00 | https://arxiv.org/abs/2312.13583v1 | [
"https://github.com/zjunet/G-Tuning"
] | In the paper 'Fine-tuning Graph Neural Networks by Preserving Graph Generative Patterns', what Accuracy (10-fold) score did the G-Tuning model get on the IMDb-M dataset
| 51.80 |
MSD (Mirror Segmentation Dataset) | 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 MAE score did the SAM2-UNet model get on the MSD (Mirror Segmentation Dataset) dataset
| 0.022 |
LIDC-IDRI | GVAE | Variational Autoencoders for Feature Exploration and Malignancy Prediction of Lung Lesions | 2023-11-27T00:00:00 | https://arxiv.org/abs/2311.15719v1 | [
"https://github.com/benkeel/vae_lung_lesion_bmvc"
] | In the paper 'Variational Autoencoders for Feature Exploration and Malignancy Prediction of Lung Lesions', what Accuracy score did the GVAE model get on the LIDC-IDRI dataset
| 93.1 |
DTD | SaSPA + CAL | Advancing Fine-Grained Classification by Structure and Subject Preserving Augmentation | 2024-06-20T00:00:00 | https://arxiv.org/abs/2406.14551v2 | [
"https://github.com/eyalmichaeli/saspa-aug"
] | In the paper 'Advancing Fine-Grained Classification by Structure and Subject Preserving Augmentation', what 8-shot Accuracy score did the SaSPA + CAL model get on the DTD dataset
| 54.8 |
PECC | Llama-3-8B-Instruct | PECC: Problem Extraction and Coding Challenges | 2024-04-29T00:00:00 | https://arxiv.org/abs/2404.18766v1 | [
"https://github.com/hallerpatrick/pecc"
] | In the paper 'PECC: Problem Extraction and Coding Challenges', what Pass@3 score did the Llama-3-8B-Instruct model get on the PECC dataset
| 3.1 |
LeukemiaAttri | AttriDet | A Large-scale Multi Domain Leukemia Dataset for the White Blood Cells Detection with Morphological Attributes for Explainability | 2024-05-17T00:00:00 | https://arxiv.org/abs/2405.10803v1 | [
"https://github.com/intelligentMachines-ITU/Blood-Cancer-Dataset-Lukemia-Attri-MICCAI-2024"
] | In the paper 'A Large-scale Multi Domain Leukemia Dataset for the White Blood Cells Detection with Morphological Attributes for Explainability', what mAP 50-95 score did the AttriDet model get on the LeukemiaAttri dataset
| 28.2 |
CVC-ClinicDB | Yolo-SAM 2 | Self-Prompting Polyp Segmentation in Colonoscopy using Hybrid Yolo-SAM 2 Model | 2024-09-14T00:00:00 | https://arxiv.org/abs/2409.09484v1 | [
"https://github.com/sajjad-sh33/yolo_sam2"
] | In the paper 'Self-Prompting Polyp Segmentation in Colonoscopy using Hybrid Yolo-SAM 2 Model', what mean Dice score did the Yolo-SAM 2 model get on the CVC-ClinicDB dataset
| 0.951 |
RICH | WHAM (ViT) | WHAM: Reconstructing World-grounded Humans with Accurate 3D Motion | 2023-12-12T00:00:00 | https://arxiv.org/abs/2312.07531v2 | [
"https://github.com/yohanshin/WHAM"
] | In the paper 'WHAM: Reconstructing World-grounded Humans with Accurate 3D Motion', what MPJPE score did the WHAM (ViT) model get on the RICH dataset
| 80 |
DND | DRANet | Dual Residual Attention Network for Image Denoising | 2023-05-07T00:00:00 | https://arxiv.org/abs/2305.04269v1 | [
"https://github.com/WenCongWu/DRANet"
] | In the paper 'Dual Residual Attention Network for Image Denoising', what Average PSNR score did the DRANet model get on the DND dataset
| 39.64 |
MS COCO | 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 mIoU score did the HyperSeg model get on the MS COCO dataset
| 77.2 |
RealBlur-J | ALGNet | Learning Enriched Features via Selective State Spaces Model for Efficient Image Deblurring | 2024-03-29T00:00:00 | https://arxiv.org/abs/2403.20106v2 | [
"https://github.com/Tombs98/ALGNet"
] | In the paper 'Learning Enriched Features via Selective State Spaces Model for Efficient Image Deblurring', what SSIM (sRGB) score did the ALGNet model get on the RealBlur-J dataset
| 0.946 |
GSM8K | OpenMath-CodeLlama-13B (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-CodeLlama-13B (w/ code, SC, k=50) model get on the GSM8K dataset
| 86.8 |
Atari 2600 Alien | 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 Alien dataset
| 6955.2 |
ImageNet | GTP-ViT-B-Patch8/P20 | GTP-ViT: Efficient Vision Transformers via Graph-based Token Propagation | 2023-11-06T00:00:00 | https://arxiv.org/abs/2311.03035v2 | [
"https://github.com/ackesnal/gtp-vit"
] | In the paper 'GTP-ViT: Efficient Vision Transformers via Graph-based Token Propagation', what Top 1 Accuracy score did the GTP-ViT-B-Patch8/P20 model get on the ImageNet dataset
| 85.8% |
Tedlium | Whispering-LLaMa-7b | HyPoradise: An Open Baseline for Generative Speech Recognition with Large Language Models | 2023-09-27T00:00:00 | https://arxiv.org/abs/2309.15701v2 | [
"https://github.com/hypotheses-paradise/hypo2trans"
] | In the paper 'HyPoradise: An Open Baseline for Generative Speech Recognition with Large Language Models', what Word Error Rate (WER) score did the Whispering-LLaMa-7b model get on the Tedlium dataset
| 4.6 |
DTD | Real-Guidance + CAL | Is Synthetic Data From Diffusion Models Ready for Knowledge Distillation? | 2023-05-22T00:00:00 | https://arxiv.org/abs/2305.12954v1 | [
"https://github.com/zhengli97/dm-kd"
] | In the paper 'Is Synthetic Data From Diffusion Models Ready for Knowledge Distillation?', what 8-shot Accuracy score did the Real-Guidance + CAL model get on the DTD dataset
| 50.6 |
Yelp2018 | NESCL | Neighborhood-Enhanced Supervised Contrastive Learning for Collaborative Filtering | 2024-02-18T00:00:00 | https://arxiv.org/abs/2402.11523v1 | [
"https://github.com/PeiJieSun/NESCL"
] | In the paper 'Neighborhood-Enhanced Supervised Contrastive Learning for Collaborative Filtering', what Recall@20 score did the NESCL model get on the Yelp2018 dataset
| 0.0743 |
CUHK-Shadow | SDDNet (MM 2023) (512x512) | 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) (512x512) model get on the CUHK-Shadow dataset
| 7.65 |
MAESTRO | YourMT3+ (YPTF.MoE+M) noPS | YourMT3+: Multi-instrument Music Transcription with Enhanced Transformer Architectures and Cross-dataset Stem Augmentation | 2024-07-05T00:00:00 | https://arxiv.org/abs/2407.04822v3 | [
"https://github.com/mimbres/yourmt3"
] | In the paper 'YourMT3+: Multi-instrument Music Transcription with Enhanced Transformer Architectures and Cross-dataset Stem Augmentation', what Onset F1 score did the YourMT3+ (YPTF.MoE+M) noPS model get on the MAESTRO dataset
| 96.98 |
Image Denoising on SID x300 | ExposureDiffusion (UNet+paired data) | ExposureDiffusion: Learning to Expose for Low-light Image Enhancement | 2023-07-15T00:00:00 | https://arxiv.org/abs/2307.07710v2 | [
"https://github.com/wyf0912/ExposureDiffusion"
] | In the paper 'ExposureDiffusion: Learning to Expose for Low-light Image Enhancement', what PSNR (Raw) score did the ExposureDiffusion (UNet+paired data) model get on the Image Denoising on SID x300 dataset
| 36.82 |
SMAP | CARLA | CARLA: Self-supervised Contrastive Representation Learning for Time Series Anomaly Detection | 2023-08-18T00:00:00 | https://arxiv.org/abs/2308.09296v4 | [
"https://github.com/zamanzadeh/CARLA"
] | In the paper 'CARLA: Self-supervised Contrastive Representation Learning for Time Series Anomaly Detection', what precision score did the CARLA model get on the SMAP dataset
| 0.3944 |
Weather2K79 (720) | MoLE-DLinear | Mixture-of-Linear-Experts for Long-term Time Series Forecasting | 2023-12-11T00:00:00 | https://arxiv.org/abs/2312.06786v3 | [
"https://github.com/rogerni/mole"
] | In the paper 'Mixture-of-Linear-Experts for Long-term Time Series Forecasting', what MSE score did the MoLE-DLinear model get on the Weather2K79 (720) dataset
| 0.535 |
CIFAR-10 | PFGM++ + CS | Compensation Sampling for Improved Convergence in Diffusion Models | 2023-12-11T00:00:00 | https://arxiv.org/abs/2312.06285v1 | [
"https://github.com/hotfinda/Compensation-sampling"
] | In the paper 'Compensation Sampling for Improved Convergence in Diffusion Models', what FID score did the PFGM++ + CS model get on the CIFAR-10 dataset
| 1.5 |
ShapeNet Car | DiT-3D | DiT-3D: Exploring Plain Diffusion Transformers for 3D Shape Generation | 2023-07-04T00:00:00 | https://arxiv.org/abs/2307.01831v1 | [
"https://github.com/DiT-3D/DiT-3D"
] | In the paper 'DiT-3D: Exploring Plain Diffusion Transformers for 3D Shape Generation', what 1-NNA-CD score did the DiT-3D model get on the ShapeNet Car dataset
| 51.04 |
ETTh2 (192) 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 (192) Multivariate dataset
| 0.336 |
Atari 2600 Q*Bert | 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 Q*Bert dataset
| 24548.8 |
CCTSDB2021 | YOLO-CCSPNet | CCSPNet-Joint: Efficient Joint Training Method for Traffic Sign Detection Under Extreme Conditions | 2023-09-13T00:00:00 | https://arxiv.org/abs/2309.06902v4 | [
"https://github.com/haoqinhong/ccspnet-joint"
] | In the paper 'CCSPNet-Joint: Efficient Joint Training Method for Traffic Sign Detection Under Extreme Conditions', what mAP@0.5 score did the YOLO-CCSPNet model get on the CCTSDB2021 dataset
| 95.8 |
MedConceptsQA | epfl-llm/meditron-70b | MEDITRON-70B: Scaling Medical Pretraining for Large Language Models | 2023-11-27T00:00:00 | https://arxiv.org/abs/2311.16079v1 | [
"https://github.com/epfllm/meditron"
] | In the paper 'MEDITRON-70B: Scaling Medical Pretraining for Large Language Models', what Accuracy score did the epfl-llm/meditron-70b model get on the MedConceptsQA dataset
| 25.262 |
CHILI-100K | PMLP | 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 PMLP model get on the CHILI-100K dataset
| 0.191 +/- 0.000 |
CompCars | Resnet50 + PMAL | Progressive Multi-task Anti-Noise Learning and Distilling Frameworks for Fine-grained Vehicle Recognition | 2024-01-25T00:00:00 | https://arxiv.org/abs/2401.14336v1 | [
"https://github.com/dichao-liu/anti-noise_fgvr"
] | In the paper 'Progressive Multi-task Anti-Noise Learning and Distilling Frameworks for Fine-grained Vehicle Recognition', what Accuracy score did the Resnet50 + PMAL model get on the CompCars dataset
| 99.1% |
S3DIS | Point-GCC+TR3D | Point-GCC: Universal Self-supervised 3D Scene Pre-training via Geometry-Color Contrast | 2023-05-31T00:00:00 | https://arxiv.org/abs/2305.19623v2 | [
"https://github.com/asterisci/point-gcc"
] | In the paper 'Point-GCC: Universal Self-supervised 3D Scene Pre-training via Geometry-Color Contrast', what mAP@0.5 score did the Point-GCC+TR3D model get on the S3DIS dataset
| 56.7 |
ADE20K training-free zero-shot segmentation | GEM (CLIP) | Grounding Everything: Emerging Localization Properties in Vision-Language Transformers | 2023-12-01T00:00:00 | https://arxiv.org/abs/2312.00878v3 | [
"https://github.com/walbouss/gem"
] | In the paper 'Grounding Everything: Emerging Localization Properties in Vision-Language Transformers', what mIoU score did the GEM (CLIP) model get on the ADE20K training-free zero-shot segmentation dataset
| 15.7 |
SRD | ShadowMaskFormer (arXiv 2024) (256x256) | ShadowMaskFormer: Mask Augmented Patch Embeddings for Shadow Removal | 2024-04-29T00:00:00 | https://arxiv.org/abs/2404.18433v2 | [
"https://github.com/lizhh268/shadowmaskformer"
] | In the paper 'ShadowMaskFormer: Mask Augmented Patch Embeddings for Shadow Removal', what RMSE score did the ShadowMaskFormer (arXiv 2024) (256x256) model get on the SRD dataset
| 4.69 |
ASDiv-A | MMOS-CODE-34B(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 Execution Accuracy score did the MMOS-CODE-34B(0-shot) model get on the ASDiv-A dataset
| 85.1 |
MS-COCO (1-shot) | UniFS | UniFS: Universal Few-shot Instance Perception with Point Representations | 2024-04-30T00:00:00 | https://arxiv.org/abs/2404.19401v3 | [
"https://github.com/jin-s13/unifs"
] | In the paper 'UniFS: Universal Few-shot Instance Perception with Point Representations', what AP score did the UniFS model get on the MS-COCO (1-shot) dataset
| 12.7 |
NAS-Bench-201, CIFAR-100 | IS-DARTS | IS-DARTS: Stabilizing DARTS through Precise Measurement on Candidate Importance | 2023-12-19T00:00:00 | https://arxiv.org/abs/2312.12648v1 | [
"https://github.com/hy-he/is-darts"
] | In the paper 'IS-DARTS: Stabilizing DARTS through Precise Measurement on Candidate Importance', what Accuracy (Test) score did the IS-DARTS model get on the NAS-Bench-201, CIFAR-100 dataset
| 73.51 |
MVTEC AD textures | Mixed-Teacher | MixedTeacher : Knowledge Distillation for fast inference textural anomaly detection | 2023-06-16T00:00:00 | https://arxiv.org/abs/2306.09859v1 | [
"https://github.com/SimonThomine/MixedTeacher"
] | In the paper 'MixedTeacher : Knowledge Distillation for fast inference textural anomaly detection', what Detection AUROC score did the Mixed-Teacher model get on the MVTEC AD textures dataset
| 99.8 |
COCO-Stuff-27 | PriMaPs+HP (DINO ViT-S/8) | Boosting Unsupervised Semantic Segmentation with Principal Mask Proposals | 2024-04-25T00:00:00 | https://arxiv.org/abs/2404.16818v2 | [
"https://github.com/visinf/primaps"
] | In the paper 'Boosting Unsupervised Semantic Segmentation with Principal Mask Proposals', what Accuracy score did the PriMaPs+HP (DINO ViT-S/8) model get on the COCO-Stuff-27 dataset
| 57.8 |
MMConv | PaCE | PaCE: Unified Multi-modal Dialogue Pre-training with Progressive and Compositional Experts | 2023-05-24T00:00:00 | https://arxiv.org/abs/2305.14839v2 | [
"https://github.com/AlibabaResearch/DAMO-ConvAI/tree/main/pace"
] | In the paper 'PaCE: Unified Multi-modal Dialogue Pre-training with Progressive and Compositional Experts', what Inform score did the PaCE model get on the MMConv dataset
| 34.5 |
Set14 - 4x upscaling | DAT+ | Dual Aggregation Transformer for Image Super-Resolution | 2023-08-07T00:00:00 | https://arxiv.org/abs/2308.03364v2 | [
"https://github.com/zhengchen1999/dat"
] | In the paper 'Dual Aggregation Transformer for Image Super-Resolution', what PSNR score did the DAT+ model get on the Set14 - 4x upscaling dataset
| 29.29 |
Vinoground | InternLM-XC-2.5 | InternLM-XComposer-2.5: A Versatile Large Vision Language Model Supporting Long-Contextual Input and Output | 2024-07-03T00:00:00 | https://arxiv.org/abs/2407.03320v1 | [
"https://github.com/internlm/internlm-xcomposer"
] | In the paper 'InternLM-XComposer-2.5: A Versatile Large Vision Language Model Supporting Long-Contextual Input and Output', what Text Score score did the InternLM-XC-2.5 model get on the Vinoground dataset
| 28.8 |
WDC Products-80%cc-seen-medium | Llama3.1_8B_error-based_example_selection | 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_error-based_example_selection model get on the WDC Products-80%cc-seen-medium dataset
| 74.37 |
ImageNet 512x512 | EDM2-XXL w/ guidance interval | Applying Guidance in a Limited Interval Improves Sample and Distribution Quality in Diffusion Models | 2024-04-11T00:00:00 | https://arxiv.org/abs/2404.07724v2 | [
"https://github.com/kynkaat/guidance-interval"
] | In the paper 'Applying Guidance in a Limited Interval Improves Sample and Distribution Quality in Diffusion Models', what FID score did the EDM2-XXL w/ guidance interval model get on the ImageNet 512x512 dataset
| 1.40 |
SAFIM | starcoderbase | 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 starcoderbase model get on the SAFIM dataset
| 44.11 |
MSMT17 | CLIP-ReID 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 Rank-1 score did the CLIP-ReID Baseline + UFFM +AMC model get on the MSMT17 dataset
| 83.8 |
Peptides-struct | CIN++-500k | CIN++: Enhancing Topological Message Passing | 2023-06-06T00:00:00 | https://arxiv.org/abs/2306.03561v1 | [
"https://github.com/twitter-research/cwn"
] | In the paper 'CIN++: Enhancing Topological Message Passing', what MAE score did the CIN++-500k model get on the Peptides-struct dataset
| 0.2523 |
Casia V1+ | 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 Casia V1+ dataset
| .929 |
ETTh1 (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 ETTh1 (720) Multivariate dataset
| 0.442 |
DTD | 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 DTD dataset
| 57.3 |
LRS2 | TDFNet (MHSA + Shared) | TDFNet: An Efficient Audio-Visual Speech Separation Model with Top-down Fusion | 2024-01-25T00:00:00 | https://arxiv.org/abs/2401.14185v1 | [
"https://github.com/spkgyk/TDFNet"
] | In the paper 'TDFNet: An Efficient Audio-Visual Speech Separation Model with Top-down Fusion', what SI-SNRi score did the TDFNet (MHSA + Shared) model get on the LRS2 dataset
| 15.0 |
mini WebVision 1.0 | LRA-diffusion (CLIP ViT) | Label-Retrieval-Augmented Diffusion Models for Learning from Noisy Labels | 2023-05-31T00:00:00 | https://arxiv.org/abs/2305.19518v2 | [
"https://github.com/puar-playground/lra-diffusion"
] | In the paper 'Label-Retrieval-Augmented Diffusion Models for Learning from Noisy Labels', what Top-1 Accuracy score did the LRA-diffusion (CLIP ViT) model get on the mini WebVision 1.0 dataset
| 84.16 |
Charades-STA | UnLoc-B | UnLoc: A Unified Framework for Video Localization Tasks | 2023-08-21T00:00:00 | https://arxiv.org/abs/2308.11062v1 | [
"https://github.com/google-research/scenic"
] | In the paper 'UnLoc: A Unified Framework for Video Localization Tasks', what R@1 IoU=0.5 score did the UnLoc-B model get on the Charades-STA dataset
| 58.1 |
Lost and Found | Mask2Anomaly | Unmasking Anomalies in Road-Scene Segmentation | 2023-07-25T00:00:00 | https://arxiv.org/abs/2307.13316v1 | [
"https://github.com/shyam671/mask2anomaly-unmasking-anomalies-in-road-scene-segmentation"
] | In the paper 'Unmasking Anomalies in Road-Scene Segmentation', what AP score did the Mask2Anomaly model get on the Lost and Found dataset
| 86.59 |
LSUN Bedroom | BOSS | Bellman Optimal Stepsize Straightening of Flow-Matching Models | 2023-12-27T00:00:00 | https://arxiv.org/abs/2312.16414v3 | [
"https://github.com/nguyenngocbaocmt02/boss"
] | In the paper 'Bellman Optimal Stepsize Straightening of Flow-Matching Models', what clean-FID score did the BOSS model get on the LSUN Bedroom dataset
| 12.13 |
GQA test-dev | CuMo-7B | CuMo: Scaling Multimodal LLM with Co-Upcycled Mixture-of-Experts | 2024-05-09T00:00:00 | https://arxiv.org/abs/2405.05949v1 | [
"https://github.com/shi-labs/cumo"
] | In the paper 'CuMo: Scaling Multimodal LLM with Co-Upcycled Mixture-of-Experts', what Accuracy score did the CuMo-7B model get on the GQA test-dev dataset
| 64.9 |
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