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 |
|---|---|---|---|---|---|---|---|
MOSE | Cutie | Putting the Object Back into Video Object Segmentation | 2023-10-19T00:00:00 | https://arxiv.org/abs/2310.12982v2 | [
"https://github.com/hkchengrex/Cutie"
] | In the paper 'Putting the Object Back into Video Object Segmentation', what J&F score did the Cutie model get on the MOSE dataset
| 68.3 |
FLoRes-200 | ALMA-13B | A Paradigm Shift in Machine Translation: Boosting Translation Performance of Large Language Models | 2023-09-20T00:00:00 | https://arxiv.org/abs/2309.11674v2 | [
"https://github.com/fe1ixxu/alma"
] | In the paper 'A Paradigm Shift in Machine Translation: Boosting Translation Performance of Large Language Models', what BLEU score did the ALMA-13B model get on the FLoRes-200 dataset
| 18.0 |
PeMSD4 | 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 12 steps MAE score did the PM-DMNet(P) model get on the PeMSD4 dataset
| 18.34 |
IC19-Art | CLIP4STR-L | 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 model get on the IC19-Art dataset
| 85.9 |
DreamBooth | Emu2 SDXL v1.0 | Generative Multimodal Models are In-Context Learners | 2023-12-20T00:00:00 | https://arxiv.org/abs/2312.13286v2 | [
"https://github.com/baaivision/emu"
] | In the paper 'Generative Multimodal Models are In-Context Learners', what Concept Preservation (CP) score did the Emu2 SDXL v1.0 model get on the DreamBooth dataset
| 0.528 |
BIRD (BIg Bench for LaRge-scale Database Grounded Text-to-SQL Evaluation) | DAIL-SQL + GPT-4 | Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation | 2023-08-29T00:00:00 | https://arxiv.org/abs/2308.15363v4 | [
"https://github.com/beachwang/dail-sql"
] | In the paper 'Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation', what Execution Accuracy % (Test) score did the DAIL-SQL + GPT-4 model get on the BIRD (BIg Bench for LaRge-scale Database Grounded Text-to-SQL Evaluation) dataset
| 57.41 |
DomainNet | SWG | Combining inherent knowledge of vision-language models with unsupervised domain adaptation through strong-weak guidance | 2023-12-07T00:00:00 | https://arxiv.org/abs/2312.04066v4 | [
"https://github.com/ThomasWestfechtel/SWG"
] | In the paper 'Combining inherent knowledge of vision-language models with unsupervised domain adaptation through strong-weak guidance', what Accuracy score did the SWG model get on the DomainNet dataset
| 66.1 |
AFAD | ResNet-50-Cross-Entropy | A Call to Reflect on Evaluation Practices for Age Estimation: Comparative Analysis of the State-of-the-Art and a Unified Benchmark | 2023-07-10T00:00:00 | https://arxiv.org/abs/2307.04570v3 | [
"https://github.com/paplhjak/facial-age-estimation-benchmark"
] | In the paper 'A Call to Reflect on Evaluation Practices for Age Estimation: Comparative Analysis of the State-of-the-Art and a Unified Benchmark', what MAE score did the ResNet-50-Cross-Entropy model get on the AFAD dataset
| 3.14 |
spider | MSc-SQL | MSc-SQL: Multi-Sample Critiquing Small Language Models For Text-To-SQL Translation | 2024-10-16T00:00:00 | https://arxiv.org/abs/2410.12916v1 | [
"https://github.com/layer6ai-labs/msc-sql"
] | In the paper 'MSc-SQL: Multi-Sample Critiquing Small Language Models For Text-To-SQL Translation', what Execution Accuracy (Test) score did the MSc-SQL model get on the spider dataset
| 84.7 |
HKU-IS | M3Net-R | M$^3$Net: Multilevel, Mixed and Multistage Attention Network for Salient Object Detection | 2023-09-15T00:00:00 | https://arxiv.org/abs/2309.08365v1 | [
"https://github.com/I2-Multimedia-Lab/M3Net"
] | In the paper 'M$^3$Net: Multilevel, Mixed and Multistage Attention Network for Salient Object Detection', what MAE score did the M3Net-R model get on the HKU-IS dataset
| 0.026 |
MM-Vet | VisionZip (Retain 128 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 128 Tokens, fine-tuning) model get on the MM-Vet dataset
| 32.9 |
PASCAL VOC 2012 val | CIM + Mask R-CNN | Complete Instances Mining for Weakly Supervised Instance Segmentation | 2024-02-12T00:00:00 | https://arxiv.org/abs/2402.07633v1 | [
"https://github.com/ZechengLi19/CIM"
] | In the paper 'Complete Instances Mining for Weakly Supervised Instance Segmentation', what mAP@0.25 score did the CIM + Mask R-CNN model get on the PASCAL VOC 2012 val dataset
| 68.7 |
NYU Depth v2 | PolyMaX(ConvNeXt-L) | PolyMaX: General Dense Prediction with Mask Transformer | 2023-11-09T00:00:00 | https://arxiv.org/abs/2311.05770v1 | [
"https://github.com/google-research/deeplab2"
] | In the paper 'PolyMaX: General Dense Prediction with Mask Transformer', what Mean IoU score did the PolyMaX(ConvNeXt-L) model get on the NYU Depth v2 dataset
| 58.08% |
GoogleGZ-CD | CGNet | Change Guiding Network: Incorporating Change Prior to Guide Change Detection in Remote Sensing Imagery | 2024-04-14T00:00:00 | https://arxiv.org/abs/2404.09179v1 | [
"https://github.com/chengxihan/cgnet-cd"
] | In the paper 'Change Guiding Network: Incorporating Change Prior to Guide Change Detection in Remote Sensing Imagery', what F1 score did the CGNet model get on the GoogleGZ-CD dataset
| 85.89 |
Mapillary val | 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 Mapillary val dataset
| 91.2 |
WHU-CD | DDLNet | DDLNet: Boosting Remote Sensing Change Detection with Dual-Domain Learning | 2024-06-19T00:00:00 | https://arxiv.org/abs/2406.13606v1 | [
"https://github.com/xwmaxwma/rschange"
] | In the paper 'DDLNet: Boosting Remote Sensing Change Detection with Dual-Domain Learning', what F1 score did the DDLNet model get on the WHU-CD dataset
| 90.56 |
MSU SR-QA Dataset | TOPIQ | TOPIQ: A Top-down Approach from Semantics to Distortions for Image Quality Assessment | 2023-08-06T00:00:00 | https://arxiv.org/abs/2308.03060v1 | [
"https://github.com/chaofengc/iqa-pytorch"
] | In the paper 'TOPIQ: A Top-down Approach from Semantics to Distortions for Image Quality Assessment', what SROCC score did the TOPIQ model get on the MSU SR-QA Dataset dataset
| 0.57341 |
PASCAL VOC 2007 | GKGNet | GKGNet: Group K-Nearest Neighbor based Graph Convolutional Network for Multi-Label Image Recognition | 2023-08-28T00:00:00 | https://arxiv.org/abs/2308.14378v3 | [
"https://github.com/jin-s13/gkgnet"
] | In the paper 'GKGNet: Group K-Nearest Neighbor based Graph Convolutional Network for Multi-Label Image Recognition', what mAP score did the GKGNet model get on the PASCAL VOC 2007 dataset
| 96.8 |
URMP | MT3 | 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 MT3 model get on the URMP dataset
| 77 |
PASCAL-5i (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 PASCAL-5i (5-Shot) dataset
| 71.99 |
LibriSpeech test-clean | Zipformer+pruned transducer w/ CR-CTC (no external language model) | CR-CTC: Consistency regularization on CTC for improved speech recognition | 2024-10-07T00:00:00 | https://arxiv.org/abs/2410.05101v3 | [
"https://github.com/k2-fsa/icefall"
] | In the paper 'CR-CTC: Consistency regularization on CTC for improved speech recognition', what Word Error Rate (WER) score did the Zipformer+pruned transducer w/ CR-CTC (no external language model) model get on the LibriSpeech test-clean dataset
| 1.88 |
SUN-RGBD | DFormer-B | DFormer: Rethinking RGBD Representation Learning for Semantic Segmentation | 2023-09-18T00:00:00 | https://arxiv.org/abs/2309.09668v2 | [
"https://github.com/VCIP-RGBD/DFormer"
] | In the paper 'DFormer: Rethinking RGBD Representation Learning for Semantic Segmentation', what Mean IoU score did the DFormer-B model get on the SUN-RGBD dataset
| 51.2% |
Cityscapes test | EAGLE (DINO, ViT-S/8) | EAGLE: Eigen Aggregation Learning for Object-Centric Unsupervised Semantic Segmentation | 2024-03-03T00:00:00 | https://arxiv.org/abs/2403.01482v4 | [
"https://github.com/MICV-yonsei/EAGLE"
] | In the paper 'EAGLE: Eigen Aggregation Learning for Object-Centric Unsupervised Semantic Segmentation', what mIoU score did the EAGLE (DINO, ViT-S/8) model get on the Cityscapes test dataset
| 19.7 |
CROHME 2016 | TAMER | TAMER: Tree-Aware Transformer for Handwritten Mathematical Expression Recognition | 2024-08-16T00:00:00 | https://arxiv.org/abs/2408.08578v2 | [
"https://github.com/qingzhenduyu/tamer"
] | In the paper 'TAMER: Tree-Aware Transformer for Handwritten Mathematical Expression Recognition', what ExpRate score did the TAMER model get on the CROHME 2016 dataset
| 60.26 |
CUB-200-2011 | LDM Correspondences | Unsupervised Semantic Correspondence Using Stable Diffusion | 2023-05-24T00:00:00 | https://arxiv.org/abs/2305.15581v2 | [
"https://github.com/ubc-vision/LDM_correspondences"
] | In the paper 'Unsupervised Semantic Correspondence Using Stable Diffusion', what Mean PCK@0.05 score did the LDM Correspondences model get on the CUB-200-2011 dataset
| 61.6 |
CelebA 64x64 | DDIM+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 DDIM+CS model get on the CelebA 64x64 dataset
| 2.11 |
COCO-Stuff-27 | PriMaPs+STEGO (DINO ViT-B/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+STEGO (DINO ViT-B/8) model get on the COCO-Stuff-27 dataset
| 57.9 |
Natural Questions | PaLM 2-L (one-shot) | PaLM 2 Technical Report | 2023-05-17T00:00:00 | https://arxiv.org/abs/2305.10403v3 | [
"https://github.com/eternityyw/tram-benchmark"
] | In the paper 'PaLM 2 Technical Report', what EM score did the PaLM 2-L (one-shot) model get on the Natural Questions dataset
| 37.5 |
VOT2020 | ODTrack-L | ODTrack: Online Dense Temporal Token Learning for Visual Tracking | 2024-01-03T00:00:00 | https://arxiv.org/abs/2401.01686v1 | [
"https://github.com/gxnu-zhonglab/odtrack"
] | In the paper 'ODTrack: Online Dense Temporal Token Learning for Visual Tracking', what EAO score did the ODTrack-L model get on the VOT2020 dataset
| 0.605 |
CIFAR-100 | ABNet-2G-R0 | ANDHRA Bandersnatch: Training Neural Networks to Predict Parallel Realities | 2024-11-28T00:00:00 | https://arxiv.org/abs/2411.19213v1 | [
"https://github.com/dvssajay/New_World"
] | In the paper 'ANDHRA Bandersnatch: Training Neural Networks to Predict Parallel Realities', what Percentage correct score did the ABNet-2G-R0 model get on the CIFAR-100 dataset
| 73.930 |
EQ-Bench | OpenAI gpt-3.5-turbo-0301 | EQ-Bench: An Emotional Intelligence Benchmark for Large Language Models | 2023-12-11T00:00:00 | https://arxiv.org/abs/2312.06281v2 | [
"https://github.com/eq-bench/eq-bench"
] | In the paper 'EQ-Bench: An Emotional Intelligence Benchmark for Large Language Models', what EQ-Bench Score score did the OpenAI gpt-3.5-turbo-0301 model get on the EQ-Bench dataset
| 47.61 |
YouTube-VOS 2019 | DEVA | Tracking Anything in High Quality | 2023-07-26T00:00:00 | https://arxiv.org/abs/2307.13974v1 | [
"https://github.com/jiawen-zhu/hqtrack"
] | In the paper 'Tracking Anything in High Quality', what Overall score did the DEVA model get on the YouTube-VOS 2019 dataset
| 86.2 |
GQA test-dev | Video-LaVIT | Video-LaVIT: Unified Video-Language Pre-training with Decoupled Visual-Motional Tokenization | 2024-02-05T00:00:00 | https://arxiv.org/abs/2402.03161v3 | [
"https://github.com/jy0205/lavit"
] | In the paper 'Video-LaVIT: Unified Video-Language Pre-training with Decoupled Visual-Motional Tokenization', what Accuracy score did the Video-LaVIT model get on the GQA test-dev dataset
| 64.4 |
ogbl-ppa | GCN (node embedding) | Can GNNs Learn Link Heuristics? A Concise Review and Evaluation of Link Prediction Methods | 2024-11-22T00:00:00 | https://arxiv.org/abs/2411.14711v1 | [
"https://github.com/astroming/GNNHE"
] | In the paper 'Can GNNs Learn Link Heuristics? A Concise Review and Evaluation of Link Prediction Methods', what Test Hits@100 score did the GCN (node embedding) model get on the ogbl-ppa dataset
| 0.6354 ± 0.0121 |
OVAD benchmark | BLIP | Open-ended VQA benchmarking of Vision-Language models by exploiting Classification datasets and their semantic hierarchy | 2024-02-11T00:00:00 | https://arxiv.org/abs/2402.07270v2 | [
"https://github.com/lmb-freiburg/ovqa"
] | In the paper 'Open-ended VQA benchmarking of Vision-Language models by exploiting Classification datasets and their semantic hierarchy', what Contains w. Synonyms score did the BLIP model get on the OVAD benchmark dataset
| 45.70 |
Materials Project | PotNet | Efficient Approximations of Complete Interatomic Potentials for Crystal Property Prediction | 2023-06-12T00:00:00 | https://arxiv.org/abs/2306.10045v9 | [
"https://github.com/divelab/AIRS/tree/main/OpenMat/PotNet"
] | In the paper 'Efficient Approximations of Complete Interatomic Potentials for Crystal Property Prediction', what MAE score did the PotNet model get on the Materials Project dataset
| 18.8 |
UZLF | VascX | VascX Models: Model Ensembles for Retinal Vascular Analysis from Color Fundus Images | 2024-09-24T00:00:00 | https://arxiv.org/abs/2409.16016v2 | [
"https://github.com/eyened/rtnls_vascx_models"
] | In the paper 'VascX Models: Model Ensembles for Retinal Vascular Analysis from Color Fundus Images', what Average Dice (0.5*Dice_a + 0.5*Dice_v) score did the VascX model get on the UZLF dataset
| 80.6 |
Avazu | CETN | CETN: Contrast-enhanced Through Network for CTR Prediction | 2023-12-15T00:00:00 | https://arxiv.org/abs/2312.09715v2 | [
"https://github.com/salmon1802/cetn"
] | In the paper 'CETN: Contrast-enhanced Through Network for CTR Prediction', what AUC score did the CETN model get on the Avazu dataset
| 0.7962 |
WinoGrande | PaLM 2-M (1-shot) | PaLM 2 Technical Report | 2023-05-17T00:00:00 | https://arxiv.org/abs/2305.10403v3 | [
"https://github.com/eternityyw/tram-benchmark"
] | In the paper 'PaLM 2 Technical Report', what Accuracy score did the PaLM 2-M (1-shot) model get on the WinoGrande dataset
| 79.2 |
MSVD-CTN | GIT | GiT: Towards Generalist Vision Transformer through Universal Language Interface | 2024-03-14T00:00:00 | https://arxiv.org/abs/2403.09394v1 | [
"https://github.com/haiyang-w/git"
] | In the paper 'GiT: Towards Generalist Vision Transformer through Universal Language Interface', what CIDEr score did the GIT model get on the MSVD-CTN dataset
| 45.63 |
WHU-CD | CDMaskFormer | Rethinking Remote Sensing Change Detection With A Mask View | 2024-06-21T00:00:00 | https://arxiv.org/abs/2406.15320v1 | [
"https://github.com/xwmaxwma/rschange"
] | In the paper 'Rethinking Remote Sensing Change Detection With A Mask View', what F1 score did the CDMaskFormer model get on the WHU-CD dataset
| 91.56 |
Amazon-Google | Meta-Llama-3.1-8B-Instruct | 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 Meta-Llama-3.1-8B-Instruct model get on the Amazon-Google dataset
| 49.16 |
CHILI-3K | Mean | 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 Mean model get on the CHILI-3K dataset
| 0.265 |
WebApp1K-React | deepseek-v2.5 | A Case Study of Web App Coding with OpenAI Reasoning Models | 2024-09-19T00:00:00 | https://arxiv.org/abs/2409.13773v1 | [
"https://github.com/onekq/webapp1k"
] | In the paper 'A Case Study of Web App Coding with OpenAI Reasoning Models', what pass@1 score did the deepseek-v2.5 model get on the WebApp1K-React dataset
| 0.834 |
VITON-HD | IDM-VTON | Improving Diffusion Models for Authentic Virtual Try-on in the Wild | 2024-03-08T00:00:00 | https://arxiv.org/abs/2403.05139v3 | [
"https://github.com/yisol/IDM-VTON"
] | In the paper 'Improving Diffusion Models for Authentic Virtual Try-on in the Wild', what FID score did the IDM-VTON model get on the VITON-HD dataset
| 6.290 |
FreeSolv | ChemBFN | A Bayesian Flow Network Framework for Chemistry Tasks | 2024-07-28T00:00:00 | https://arxiv.org/abs/2407.20294v1 | [
"https://github.com/Augus1999/bayesian-flow-network-for-chemistry"
] | In the paper 'A Bayesian Flow Network Framework for Chemistry Tasks', what RMSE score did the ChemBFN model get on the FreeSolv dataset
| 1.418 |
CHILI-100K | EdgeCNN | 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 EdgeCNN model get on the CHILI-100K dataset
| 0.030 +/- 0.001 |
SMAC 27m_vs_30m | 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 Median Win Rate score did the DPLEX model get on the SMAC 27m_vs_30m dataset
| 90.62 |
IEMOCAP | CORECT (4-class) | Conversation Understanding using Relational Temporal Graph Neural Networks with Auxiliary Cross-Modality Interaction | 2023-11-08T00:00:00 | https://arxiv.org/abs/2311.04507v3 | [
"https://github.com/leson502/CORECT_EMNLP2023"
] | In the paper 'Conversation Understanding using Relational Temporal Graph Neural Networks with Auxiliary Cross-Modality Interaction', what F1 score did the CORECT (4-class) model get on the IEMOCAP dataset
| 0.846 |
MBPP | DeepSeek-Coder-Instruct 6.7B (few-shot) | DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence | 2024-01-25T00:00:00 | https://arxiv.org/abs/2401.14196v2 | [
"https://github.com/deepseek-ai/DeepSeek-Coder"
] | In the paper 'DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence', what Accuracy score did the DeepSeek-Coder-Instruct 6.7B (few-shot) model get on the MBPP dataset
| 65.4 |
DUTS-TE | BiRefNet (DUTS) | 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) model get on the DUTS-TE dataset
| 0.019 |
ARMBench | RISE (VIT-B) | Robot Instance Segmentation with Few Annotations for Grasping | 2024-07-01T00:00:00 | https://arxiv.org/abs/2407.01302v1 | [
"https://github.com/mkimhi/RISE"
] | In the paper 'Robot Instance Segmentation with Few Annotations for Grasping', what AP50 score did the RISE (VIT-B) model get on the ARMBench dataset
| 86.37 |
ModelNet40 | Point-RAE | Regress Before Construct: Regress Autoencoder for Point Cloud Self-supervised Learning | 2023-09-25T00:00:00 | https://arxiv.org/abs/2310.03670v1 | [
"https://github.com/liuyyy111/point-rae"
] | In the paper 'Regress Before Construct: Regress Autoencoder for Point Cloud Self-supervised Learning', what Overall Accuracy score did the Point-RAE model get on the ModelNet40 dataset
| 94.1 |
THUMOS' 14 | MAT (Ours) Trans | Memory-and-Anticipation Transformer for Online Action Understanding | 2023-08-15T00:00:00 | https://arxiv.org/abs/2308.07893v1 | [
"https://github.com/echo0125/memory-and-anticipation-transformer"
] | In the paper 'Memory-and-Anticipation Transformer for Online Action Understanding', what mAP score did the MAT (Ours) Trans model get on the THUMOS' 14 dataset
| 71.6 |
DUTS-TE | BiRefNet (HRSOD, 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 (HRSOD, UHRSD) model get on the DUTS-TE dataset
| 0.020 |
StrategyQA | PaLM 2 (few-shot, CoT, SC) | 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 (few-shot, CoT, SC) model get on the StrategyQA dataset
| 90.4 |
S3DIS | SuperCluster | Scalable 3D Panoptic Segmentation As Superpoint Graph Clustering | 2024-01-12T00:00:00 | https://arxiv.org/abs/2401.06704v2 | [
"https://github.com/drprojects/superpoint_transformer"
] | In the paper 'Scalable 3D Panoptic Segmentation As Superpoint Graph Clustering', what Mean IoU score did the SuperCluster model get on the S3DIS dataset
| 75.3 |
COCO minival | GLEE-Plus | General Object Foundation Model for Images and Videos at Scale | 2023-12-14T00:00:00 | https://arxiv.org/abs/2312.09158v1 | [
"https://github.com/FoundationVision/GLEE"
] | In the paper 'General Object Foundation Model for Images and Videos at Scale', what box AP score did the GLEE-Plus model get on the COCO minival dataset
| 60.4 |
PACS | GMDG (RegNetY-16GF, 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 (RegNetY-16GF, SWAD) model get on the PACS dataset
| 97.9 |
NExT-QA | PAXION | Paxion: Patching Action Knowledge in Video-Language Foundation Models | 2023-05-18T00:00:00 | https://arxiv.org/abs/2305.10683v4 | [
"https://github.com/mikewangwzhl/paxion"
] | In the paper 'Paxion: Patching Action Knowledge in Video-Language Foundation Models', what Accuracy score did the PAXION model get on the NExT-QA dataset
| 56.9 |
TabFact | Chain-of-Table | Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding | 2024-01-09T00:00:00 | https://arxiv.org/abs/2401.04398v2 | [
"https://github.com/google-research/chain-of-table"
] | In the paper 'Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding', what Test score did the Chain-of-Table model get on the TabFact dataset
| 86.61 |
MSMT17 | CA-Jaccard | CA-Jaccard: Camera-aware Jaccard Distance for Person Re-identification | 2023-11-17T00:00:00 | https://arxiv.org/abs/2311.10605v2 | [
"https://github.com/chen960/ca-jaccard"
] | In the paper 'CA-Jaccard: Camera-aware Jaccard Distance for Person Re-identification', what Rank-1 score did the CA-Jaccard model get on the MSMT17 dataset
| 86.2 |
Office-31 | EUDA | EUDA: An Efficient Unsupervised Domain Adaptation via Self-Supervised Vision Transformer | 2024-07-31T00:00:00 | https://arxiv.org/abs/2407.21311v1 | [
"https://github.com/a-abedi/euda"
] | In the paper 'EUDA: An Efficient Unsupervised Domain Adaptation via Self-Supervised Vision Transformer', what Accuracy score did the EUDA model get on the Office-31 dataset
| 92 |
AIOZ-GDANCE | GCD | Controllable Group Choreography using Contrastive Diffusion | 2023-10-29T00:00:00 | https://arxiv.org/abs/2310.18986v2 | [
"https://github.com/aioz-ai/GCD"
] | In the paper 'Controllable Group Choreography using Contrastive Diffusion', what FID score did the GCD model get on the AIOZ-GDANCE dataset
| 31.16 |
SVT-P | ABINet-LV+TPS++ | TPS++: Attention-Enhanced Thin-Plate Spline for Scene Text Recognition | 2023-05-09T00:00:00 | https://arxiv.org/abs/2305.05322v1 | [
"https://github.com/simplify23/tps_pp"
] | In the paper 'TPS++: Attention-Enhanced Thin-Plate Spline for Scene Text Recognition', what Accuracy score did the ABINet-LV+TPS++ model get on the SVT-P dataset
| 89.6 |
ImageNet 256x256 | TiTok-B-64 | An Image is Worth 32 Tokens for Reconstruction and Generation | 2024-06-11T00:00:00 | https://arxiv.org/abs/2406.07550v1 | [
"https://github.com/bytedance/1d-tokenizer"
] | In the paper 'An Image is Worth 32 Tokens for Reconstruction and Generation', what FID score did the TiTok-B-64 model get on the ImageNet 256x256 dataset
| 2.48 |
UMVM-dbp-zh-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 UMVM-dbp-zh-en dataset
| 0.800 |
MNIST-full | VMM | The VampPrior Mixture Model | 2024-02-06T00:00:00 | https://arxiv.org/abs/2402.04412v2 | [
"https://github.com/astirn/vampprior-mixture-model"
] | In the paper 'The VampPrior Mixture Model', what NMI score did the VMM model get on the MNIST-full dataset
| 0.920 |
nuScenes | GPT-Driver | GPT-Driver: Learning to Drive with GPT | 2023-10-02T00:00:00 | https://arxiv.org/abs/2310.01415v3 | [
"https://github.com/pointscoder/gpt-driver"
] | In the paper 'GPT-Driver: Learning to Drive with GPT', what L2 score did the GPT-Driver model get on the nuScenes dataset
| 0.48 |
DSO-1 | 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 DSO-1 dataset
| .958 |
GRAZPEDWRI-DX | YOLOv8x | Enhancing Wrist Fracture Detection with YOLO | 2024-07-17T00:00:00 | https://arxiv.org/abs/2407.12597v2 | [
"https://github.com/ammarlodhi255/pediatric_wrist_abnormality_detection-end-to-end-implementation"
] | In the paper 'Enhancing Wrist Fracture Detection with YOLO', what mAP score did the YOLOv8x model get on the GRAZPEDWRI-DX dataset
| 77.00 |
Weather (96) | 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 Weather (96) dataset
| 0.147 |
MassSpecGym | FFN Fingerprint | 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 FFN Fingerprint model get on the MassSpecGym dataset
| 0.25 |
MM-Vet | LLaVA-65B (Data Mixing) | An Empirical Study of Scaling Instruct-Tuned Large Multimodal Models | 2023-09-18T00:00:00 | https://arxiv.org/abs/2309.09958v1 | [
"https://github.com/haotian-liu/LLaVA"
] | In the paper 'An Empirical Study of Scaling Instruct-Tuned Large Multimodal Models', what GPT-4 score score did the LLaVA-65B (Data Mixing) model get on the MM-Vet dataset
| 36.4 |
MLO-Cn2 | Linear Forecast | 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 Linear Forecast model get on the MLO-Cn2 dataset
| 0.930 |
BDD100K val | 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 BDD100K val dataset
| 31.44 |
ImageNet | WTTM (T: ResNet-34 S:ResNet-18) | Knowledge Distillation Based on Transformed Teacher Matching | 2024-02-17T00:00:00 | https://arxiv.org/abs/2402.11148v2 | [
"https://github.com/zkxufo/TTM"
] | In the paper 'Knowledge Distillation Based on Transformed Teacher Matching', what Top-1 accuracy % score did the WTTM (T: ResNet-34 S:ResNet-18) model get on the ImageNet dataset
| 72.19 |
ModelNet40 | 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 ModelNet40 dataset
| 94.5 |
3DPW | ZeDO (S=1,J=17) | Back to Optimization: Diffusion-based Zero-Shot 3D Human Pose Estimation | 2023-07-07T00:00:00 | https://arxiv.org/abs/2307.03833v3 | [
"https://github.com/ipl-uw/ZeDO-Release"
] | In the paper 'Back to Optimization: Diffusion-based Zero-Shot 3D Human Pose Estimation', what PA-MPJPE score did the ZeDO (S=1,J=17) model get on the 3DPW dataset
| 40.3 |
DramaQA | LLaMA-VQA | Large Language Models are Temporal and Causal Reasoners for Video Question Answering | 2023-10-24T00:00:00 | https://arxiv.org/abs/2310.15747v2 | [
"https://github.com/mlvlab/Flipped-VQA"
] | In the paper 'Large Language Models are Temporal and Causal Reasoners for Video Question Answering', what Accuracy score did the LLaMA-VQA model get on the DramaQA dataset
| 84.1 |
MM-Vet | VisionZip (Retain 128 Tokens) | 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 128 Tokens) model get on the MM-Vet dataset
| 32.6 |
MM-Vet v2 | InternVL-Chat-V1-5 | How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites | 2024-04-25T00:00:00 | https://arxiv.org/abs/2404.16821v2 | [
"https://github.com/opengvlab/internvl"
] | In the paper 'How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites', what GPT-4 score score did the InternVL-Chat-V1-5 model get on the MM-Vet v2 dataset
| 51.5±0.2 |
MVTec AD | ReConPatch Ensemble | 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 Segmentation AUROC score did the ReConPatch Ensemble model get on the MVTec AD dataset
| 98.67 |
ETTm2 (192) 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 ETTm2 (192) Multivariate dataset
| 0.219 |
MNIST | fKAN | fKAN: Fractional Kolmogorov-Arnold Networks with trainable Jacobi basis functions | 2024-06-11T00:00:00 | https://arxiv.org/abs/2406.07456v1 | [
"https://github.com/alirezaafzalaghaei/fKAN"
] | In the paper 'fKAN: Fractional Kolmogorov-Arnold Networks with trainable Jacobi basis functions', what Accuracy score did the fKAN model get on the MNIST dataset
| 99.228 |
ISIC 2018 | ProMISe | ProMISe: Promptable Medical Image Segmentation using SAM | 2024-03-07T00:00:00 | https://arxiv.org/abs/2403.04164v3 | [
"https://github.com/xinkunwang111/promise"
] | In the paper 'ProMISe: Promptable Medical Image Segmentation using SAM', what DSC score did the ProMISe model get on the ISIC 2018 dataset
| 92.10 |
FB15k-237 | KERMIT | KERMIT: Knowledge Graph Completion of Enhanced Relation Modeling with Inverse Transformation | 2023-09-26T00:00:00 | https://arxiv.org/abs/2309.14770v2 | [
"https://github.com/lirt1231/kermit"
] | In the paper 'KERMIT: Knowledge Graph Completion of Enhanced Relation Modeling with Inverse Transformation', what MRR score did the KERMIT model get on the FB15k-237 dataset
| 0.359 |
EuroSAT | 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 EuroSAT dataset
| 62.7 |
Nordland | BoQ | BoQ: A Place is Worth a Bag of Learnable Queries | 2024-05-12T00:00:00 | https://arxiv.org/abs/2405.07364v3 | [
"https://github.com/amaralibey/bag-of-queries"
] | In the paper 'BoQ: A Place is Worth a Bag of Learnable Queries', what Recall@1 score did the BoQ model get on the Nordland dataset
| 90.6 |
BIRD (BIg Bench for LaRge-scale Database Grounded Text-to-SQL Evaluation) | MSc-SQL | MSc-SQL: Multi-Sample Critiquing Small Language Models For Text-To-SQL Translation | 2024-10-16T00:00:00 | https://arxiv.org/abs/2410.12916v1 | [
"https://github.com/layer6ai-labs/msc-sql"
] | In the paper 'MSc-SQL: Multi-Sample Critiquing Small Language Models For Text-To-SQL Translation', what Execution Accuracy % (Dev) score did the MSc-SQL model get on the BIRD (BIg Bench for LaRge-scale Database Grounded Text-to-SQL Evaluation) dataset
| 65.6 |
VoxCeleb1 | ReDimNet-B3-LM-ASNorm (3.0M) | 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-B3-LM-ASNorm (3.0M) model get on the VoxCeleb1 dataset
| 0.47 |
The Pile | Llama-3.2-Instruct 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 Llama-3.2-Instruct 3B model get on the The Pile dataset
| 0.737 |
Inverse-Text | DeepSolo (ResNet-50) | DeepSolo++: Let Transformer Decoder with Explicit Points Solo for Multilingual Text Spotting | 2023-05-31T00:00:00 | https://arxiv.org/abs/2305.19957v2 | [
"https://github.com/vitae-transformer/deepsolo"
] | In the paper 'DeepSolo++: Let Transformer Decoder with Explicit Points Solo for Multilingual Text Spotting', what F-measure (%) - No Lexicon score did the DeepSolo (ResNet-50) model get on the Inverse-Text dataset
| 48.5 |
Replica | Open3DIS | Open3DIS: Open-Vocabulary 3D Instance Segmentation with 2D Mask Guidance | 2023-12-17T00:00:00 | https://arxiv.org/abs/2312.10671v3 | [
"https://github.com/VinAIResearch/Open3DIS"
] | In the paper 'Open3DIS: Open-Vocabulary 3D Instance Segmentation with 2D Mask Guidance', what mAP score did the Open3DIS model get on the Replica dataset
| 18.1 |
Mol-Instruction | BioT5+ | BioT5+: Towards Generalized Biological Understanding with IUPAC Integration and Multi-task Tuning | 2024-02-27T00:00:00 | https://arxiv.org/abs/2402.17810v2 | [
"https://github.com/QizhiPei/BioT5"
] | In the paper 'BioT5+: Towards Generalized Biological Understanding with IUPAC Integration and Multi-task Tuning', what Exact score did the BioT5+ model get on the Mol-Instruction dataset
| 0.257 |
LVIS v1.0 | DE-ViT | Detect Everything with Few Examples | 2023-09-22T00:00:00 | https://arxiv.org/abs/2309.12969v4 | [
"https://github.com/mlzxy/devit"
] | In the paper 'Detect Everything with Few Examples', what AP novel-LVIS base training score did the DE-ViT model get on the LVIS v1.0 dataset
| 34.3 |
ogbn-proteins | LD+GAT | Label Deconvolution for Node Representation Learning on Large-scale Attributed Graphs against Learning Bias | 2023-09-26T00:00:00 | https://arxiv.org/abs/2309.14907v1 | [
"https://github.com/MIRALab-USTC/LD"
] | In the paper 'Label Deconvolution for Node Representation Learning on Large-scale Attributed Graphs against Learning Bias', what Test ROC-AUC score did the LD+GAT model get on the ogbn-proteins dataset
| 0.8942 ± 0.0007 |
CATT | Sakhr | CATT: Character-based Arabic Tashkeel Transformer | 2024-07-03T00:00:00 | https://arxiv.org/abs/2407.03236v3 | [
"https://github.com/abjadai/catt"
] | In the paper 'CATT: Character-based Arabic Tashkeel Transformer', what DER(%) score did the Sakhr model get on the CATT dataset
| 13.841 |
Nature | Zhu et al. | Reversible Decoupling Network for Single Image Reflection Removal | 2024-10-10T00:00:00 | https://arxiv.org/abs/2410.08063v1 | [
"https://github.com/lime-j/RDNet"
] | In the paper 'Reversible Decoupling Network for Single Image Reflection Removal', what PSNR score did the Zhu et al. model get on the Nature dataset
| 26.04 |
GSM8K | WizardMath-7B-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-7B-V1.0 model get on the GSM8K dataset
| 54.9 |
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