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 |
|---|---|---|---|---|---|---|---|
Cityscapes test | PriMaPs-EM + 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 mIoU score did the PriMaPs-EM + STEGO (DINO ViT-B/8) model get on the Cityscapes test dataset
| 21.6 |
CUHK-PEDES | RDE | Noisy-Correspondence Learning for Text-to-Image Person Re-identification | 2023-08-19T00:00:00 | https://arxiv.org/abs/2308.09911v3 | [
"https://github.com/QinYang79/RDE"
] | In the paper 'Noisy-Correspondence Learning for Text-to-Image Person Re-identification', what Rank-1 score did the RDE model get on the CUHK-PEDES dataset
| 74.46 |
Set5 - 2x upscaling | DRCT | DRCT: Saving Image Super-resolution away from Information Bottleneck | 2024-03-31T00:00:00 | https://arxiv.org/abs/2404.00722v5 | [
"https://github.com/ming053l/drct"
] | In the paper 'DRCT: Saving Image Super-resolution away from Information Bottleneck', what PSNR score did the DRCT model get on the Set5 - 2x upscaling dataset
| 38.72 |
ETTm1 (96) Multivariate | DiPE-Linear | Disentangled Interpretable Representation for Efficient Long-term Time Series Forecasting | 2024-11-26T00:00:00 | https://arxiv.org/abs/2411.17257v1 | [
"https://github.com/wintertee/dipe-linear"
] | In the paper 'Disentangled Interpretable Representation for Efficient Long-term Time Series Forecasting', what MSE score did the DiPE-Linear model get on the ETTm1 (96) Multivariate dataset
| 0.309 |
Atari 2600 Time Pilot | 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 Time Pilot dataset
| 12071 |
CVC-ClinicDB | ADSNet | Adaptation of Distinct Semantics for Uncertain Areas in Polyp Segmentation | 2024-05-13T00:00:00 | https://arxiv.org/abs/2405.07523v1 | [
"https://github.com/vinhhust2806/ADSNet"
] | In the paper 'Adaptation of Distinct Semantics for Uncertain Areas in Polyp Segmentation', what mean Dice score did the ADSNet model get on the CVC-ClinicDB dataset
| 0.938 |
COCO-Stuff-27 | DynaSeg - FSF (ResNet-18 FPN) | DynaSeg: A Deep Dynamic Fusion Method for Unsupervised Image Segmentation Incorporating Feature Similarity and Spatial Continuity | 2024-05-09T00:00:00 | https://arxiv.org/abs/2405.05477v4 | [
"https://github.com/ryersonmultimedialab/dynaseg"
] | In the paper 'DynaSeg: A Deep Dynamic Fusion Method for Unsupervised Image Segmentation Incorporating Feature Similarity and Spatial Continuity', what Accuracy score did the DynaSeg - FSF (ResNet-18 FPN) model get on the COCO-Stuff-27 dataset
| 81.1 |
Oxford RobotCar Dataset | AnyLoc-VLAD-DINOv2 | 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 AnyLoc-VLAD-DINOv2 model get on the Oxford RobotCar Dataset dataset
| 98.95 |
Oxford 102 Flower | DePT | DePT: Decoupled Prompt Tuning | 2023-09-14T00:00:00 | https://arxiv.org/abs/2309.07439v2 | [
"https://github.com/koorye/dept"
] | In the paper 'DePT: Decoupled Prompt Tuning', what Harmonic mean score did the DePT model get on the Oxford 102 Flower dataset
| 86.46 |
HJDB | Beat This! | Beat this! Accurate beat tracking without DBN postprocessing | 2024-07-31T00:00:00 | https://arxiv.org/abs/2407.21658v1 | [
"https://github.com/CPJKU/beat_this"
] | In the paper 'Beat this! Accurate beat tracking without DBN postprocessing', what F1 score did the Beat This! model get on the HJDB dataset
| 98.2 |
CodeContests | CodeChain + WizardCoder-15B | CodeChain: Towards Modular Code Generation Through Chain of Self-revisions with Representative Sub-modules | 2023-10-13T00:00:00 | https://arxiv.org/abs/2310.08992v3 | [
"https://github.com/SalesforceAIResearch/CodeChain"
] | In the paper 'CodeChain: Towards Modular Code Generation Through Chain of Self-revisions with Representative Sub-modules', what Test Set pass@1 score did the CodeChain + WizardCoder-15B model get on the CodeContests dataset
| 2.35 |
DocVQA test | PaLI-3 (w/ OCR) | 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 (w/ OCR) model get on the DocVQA test dataset
| 0.886 |
ModelNet40 | ExpPoint-MAE | ExpPoint-MAE: Better interpretability and performance for self-supervised point cloud transformers | 2023-06-19T00:00:00 | https://arxiv.org/abs/2306.10798v3 | [
"https://github.com/vvrpanda/exppoint-mae"
] | In the paper 'ExpPoint-MAE: Better interpretability and performance for self-supervised point cloud transformers', what Overall Accuracy score did the ExpPoint-MAE model get on the ModelNet40 dataset
| 94.2 |
UCF101 | OST | OST: Refining Text Knowledge with Optimal Spatio-Temporal Descriptor for General Video Recognition | 2023-11-30T00:00:00 | https://arxiv.org/abs/2312.00096v2 | [
"https://github.com/tomchen-ctj/OST"
] | In the paper 'OST: Refining Text Knowledge with Optimal Spatio-Temporal Descriptor for General Video Recognition', what Top-1 Accuracy score did the OST model get on the UCF101 dataset
| 79.7 |
ChEBI-20 | GIT-Mol-caption | GIT-Mol: A Multi-modal Large Language Model for Molecular Science with Graph, Image, and Text | 2023-08-14T00:00:00 | https://arxiv.org/abs/2308.06911v3 | [
"https://github.com/ai-hpc-research-team/git-mol"
] | In the paper 'GIT-Mol: A Multi-modal Large Language Model for Molecular Science with Graph, Image, and Text', what BLEU score did the GIT-Mol-caption model get on the ChEBI-20 dataset
| 75.6 |
dacl10k v1 testdev | DeepLabv3+ EfficientNet-B4 | dacl10k: Benchmark for Semantic Bridge Damage Segmentation | 2023-09-01T00:00:00 | https://arxiv.org/abs/2309.00460v1 | [
"https://github.com/phiyodr/dacl10k-toolkit"
] | In the paper 'dacl10k: Benchmark for Semantic Bridge Damage Segmentation', what mIoU score did the DeepLabv3+ EfficientNet-B4 model get on the dacl10k v1 testdev dataset
| 0.411 |
Color FERET | VGG based | IdentiFace : A VGG Based Multimodal Facial Biometric System | 2024-01-02T00:00:00 | https://arxiv.org/abs/2401.01227v2 | [
"https://github.com/MahmoudRabea13/IdentiFace"
] | In the paper 'IdentiFace : A VGG Based Multimodal Facial Biometric System', what 5-class test accuracy score did the VGG based model get on the Color FERET dataset
| 99.2% |
COCO test-dev | LeYOLO-Medium | LeYOLO, New Scalable and Efficient CNN Architecture for Object Detection | 2024-06-20T00:00:00 | https://arxiv.org/abs/2406.14239v1 | [
"https://github.com/LilianHollard/LeYOLO"
] | In the paper 'LeYOLO, New Scalable and Efficient CNN Architecture for Object Detection', what box mAP score did the LeYOLO-Medium model get on the COCO test-dev dataset
| 39.3 |
BanglaBook | XGBoost (char 2-gram + char 3-gram) | BanglaBook: A Large-scale Bangla Dataset for Sentiment Analysis from Book Reviews | 2023-05-11T00:00:00 | https://arxiv.org/abs/2305.06595v3 | [
"https://github.com/mohsinulkabir14/banglabook"
] | In the paper 'BanglaBook: A Large-scale Bangla Dataset for Sentiment Analysis from Book Reviews', what Weighted Average F1-score score did the XGBoost (char 2-gram + char 3-gram) model get on the BanglaBook dataset
| 0.8723 |
ENZYMES | GIN + PANDA | PANDA: Expanded Width-Aware Message Passing Beyond Rewiring | 2024-06-06T00:00:00 | https://arxiv.org/abs/2406.03671v2 | [
"https://github.com/jeongwhanchoi/panda"
] | In the paper 'PANDA: Expanded Width-Aware Message Passing Beyond Rewiring', what Accuracy score did the GIN + PANDA model get on the ENZYMES dataset
| 46.2 |
VisA | AnomalyDINO-S (1-shot) | AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2 | 2024-05-23T00:00:00 | https://arxiv.org/abs/2405.14529v2 | [
"https://github.com/dammsi/AnomalyDINO"
] | In the paper 'AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2', what Detection AUROC score did the AnomalyDINO-S (1-shot) model get on the VisA dataset
| 87.4 |
SportsMOT | MeMOTR | MeMOTR: Long-Term Memory-Augmented Transformer for Multi-Object Tracking | 2023-07-28T00:00:00 | https://arxiv.org/abs/2307.15700v3 | [
"https://github.com/mcg-nju/memotr"
] | In the paper 'MeMOTR: Long-Term Memory-Augmented Transformer for Multi-Object Tracking', what HOTA score did the MeMOTR model get on the SportsMOT dataset
| 70.0 |
ImageNet | ViT-B/16 | Kolmogorov-Arnold Transformer | 2024-09-16T00:00:00 | https://arxiv.org/abs/2409.10594v1 | [
"https://github.com/Adamdad/kat"
] | In the paper 'Kolmogorov-Arnold Transformer', what Top 1 Accuracy score did the ViT-B/16 model get on the ImageNet dataset
| 79.1 |
ETTm2 (96) 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 ETTm2 (96) Multivariate dataset
| 0.164 |
Mip-NeRF 360 | Compact3D | CompGS: Smaller and Faster Gaussian Splatting with Vector Quantization | 2023-11-30T00:00:00 | https://arxiv.org/abs/2311.18159v3 | [
"https://github.com/ucdvision/compact3d"
] | In the paper 'CompGS: Smaller and Faster Gaussian Splatting with Vector Quantization', what PSNR score did the Compact3D model get on the Mip-NeRF 360 dataset
| 27.16 |
ETH (trained on 3DMatch) | GeoTransformer | GeoTransformer: Fast and Robust Point Cloud Registration with Geometric Transformer | 2023-07-25T00:00:00 | https://arxiv.org/abs/2308.03768v1 | [
"https://github.com/qinzheng93/geotransformer"
] | In the paper 'GeoTransformer: Fast and Robust Point Cloud Registration with Geometric Transformer', what Recall (30cm, 5 degrees) score did the GeoTransformer model get on the ETH (trained on 3DMatch) dataset
| 4.91 |
MSR-VTT | TF-T2V | A Recipe for Scaling up Text-to-Video Generation with Text-free Videos | 2023-12-25T00:00:00 | https://arxiv.org/abs/2312.15770v1 | [
"https://github.com/ali-vilab/i2vgen-xl"
] | In the paper 'A Recipe for Scaling up Text-to-Video Generation with Text-free Videos', what FID score did the TF-T2V model get on the MSR-VTT dataset
| 8.19 |
MixATIS | MISCA | MISCA: A Joint Model for Multiple Intent Detection and Slot Filling with Intent-Slot Co-Attention | 2023-12-10T00:00:00 | https://arxiv.org/abs/2312.05741v1 | [
"https://github.com/vinairesearch/misca"
] | In the paper 'MISCA: A Joint Model for Multiple Intent Detection and Slot Filling with Intent-Slot Co-Attention', what Micro F1 score did the MISCA model get on the MixATIS dataset
| 90.5 |
TID2013 | UNIQA | You Only Train Once: A Unified Framework for Both Full-Reference and No-Reference Image Quality Assessment | 2023-10-14T00:00:00 | https://arxiv.org/abs/2310.09560v2 | [
"https://github.com/barcodereader/yoto"
] | In the paper 'You Only Train Once: A Unified Framework for Both Full-Reference and No-Reference Image Quality Assessment', what SRCC score did the UNIQA model get on the TID2013 dataset
| 0.953 |
nuScenes Camera Only | SeaBird | SeaBird: Segmentation in Bird's View with Dice Loss Improves Monocular 3D Detection of Large Objects | 2024-03-29T00:00:00 | https://arxiv.org/abs/2403.20318v1 | [
"https://github.com/abhi1kumar/seabird"
] | In the paper 'SeaBird: Segmentation in Bird's View with Dice Loss Improves Monocular 3D Detection of Large Objects', what NDS score did the SeaBird model get on the nuScenes Camera Only dataset
| 59.7 |
CIFAR-10 | CAF | Constant Acceleration Flow | 2024-11-01T00:00:00 | https://arxiv.org/abs/2411.00322v1 | [
"https://github.com/mlvlab/CAF"
] | In the paper 'Constant Acceleration Flow', what FID score did the CAF model get on the CIFAR-10 dataset
| 1.39 |
BIG-bench (Sports Understanding) | PaLM 2 (few-shot, k=3, Direct) | 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, k=3, Direct) model get on the BIG-bench (Sports Understanding) dataset
| 90.8 |
California Housing Prices | Binary Diffusion | Tabular Data Generation using Binary Diffusion | 2024-09-20T00:00:00 | https://arxiv.org/abs/2409.13882v2 | [
"https://github.com/vkinakh/binary-diffusion-tabular"
] | In the paper 'Tabular Data Generation using Binary Diffusion', what Parameters(M) score did the Binary Diffusion model get on the California Housing Prices dataset
| 1.5 |
RLBench | ARP+ | Autoregressive Action Sequence Learning for Robotic Manipulation | 2024-10-04T00:00:00 | https://arxiv.org/abs/2410.03132v3 | [
"https://github.com/mlzxy/arp"
] | In the paper 'Autoregressive Action Sequence Learning for Robotic Manipulation', what Succ. Rate (18 tasks, 100 demo/task) score did the ARP+ model get on the RLBench dataset
| 86.0 |
LRS2 | RTFS-Net-4 | RTFS-Net: Recurrent Time-Frequency Modelling for Efficient Audio-Visual Speech Separation | 2023-09-29T00:00:00 | https://arxiv.org/abs/2309.17189v4 | [
"https://github.com/spkgyk/RTFS-Net"
] | In the paper 'RTFS-Net: Recurrent Time-Frequency Modelling for Efficient Audio-Visual Speech Separation', what SI-SNRi score did the RTFS-Net-4 model get on the LRS2 dataset
| 14.1 |
EconLogicQA | GPT-4-Turbo | 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 GPT-4-Turbo model get on the EconLogicQA dataset
| 0.5692 |
DEplain-APA-sent | mBART (trained on DEplain-APA-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) model get on the DEplain-APA-sent dataset
| 34.818 |
MedConceptsQA | epfl-llm/meditron-7b | 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-7b model get on the MedConceptsQA dataset
| 23.787 |
ToolLens | COLT | Towards Completeness-Oriented Tool Retrieval for Large Language Models | 2024-05-25T00:00:00 | https://arxiv.org/abs/2405.16089v2 | [
"https://github.com/quchangle1/colt"
] | In the paper 'Towards Completeness-Oriented Tool Retrieval for Large Language Models', what COMP@ score did the COLT model get on the ToolLens dataset
| 84.55 |
SFCHD | FCOS+SCALE | Large, Complex, and Realistic Safety Clothing and Helmet Detection: Dataset and Method | 2023-06-03T00:00:00 | https://arxiv.org/abs/2306.02098v2 | [
"https://github.com/lijfrank-open/SFCHD-SCALE"
] | In the paper 'Large, Complex, and Realistic Safety Clothing and Helmet Detection: Dataset and Method', what mAP@0.50 score did the FCOS+SCALE model get on the SFCHD dataset
| 76.3 |
ETTm2 (192) Multivariate | SCNN | Disentangling Structured Components: Towards Adaptive, Interpretable and Scalable Time Series Forecasting | 2023-05-22T00:00:00 | https://arxiv.org/abs/2305.13036v3 | [
"https://github.com/JLDeng/SCNN"
] | In the paper 'Disentangling Structured Components: Towards Adaptive, Interpretable and Scalable Time Series Forecasting', what MSE score did the SCNN model get on the ETTm2 (192) Multivariate dataset
| 0.221 |
GOT-10k | 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 Average Overlap score did the SAMURAI-L model get on the GOT-10k dataset
| 81.7 |
ZINC-500k | N2-GNN | Extending the Design Space of Graph Neural Networks by Rethinking Folklore Weisfeiler-Lehman | 2023-06-05T00:00:00 | https://arxiv.org/abs/2306.03266v3 | [
"https://github.com/jiaruifeng/n2gnn"
] | In the paper 'Extending the Design Space of Graph Neural Networks by Rethinking Folklore Weisfeiler-Lehman', what MAE score did the N2-GNN model get on the ZINC-500k dataset
| 0.059 |
MM-Vet | DeepSeek-VL | DeepSeek-VL: Towards Real-World Vision-Language Understanding | 2024-03-08T00:00:00 | https://arxiv.org/abs/2403.05525v2 | [
"https://github.com/deepseek-ai/deepseek-vl"
] | In the paper 'DeepSeek-VL: Towards Real-World Vision-Language Understanding', what GPT-4 score score did the DeepSeek-VL model get on the MM-Vet dataset
| 41.5 |
Criteo | TF4CTR | TF4CTR: Twin Focus Framework for CTR Prediction via Adaptive Sample Differentiation | 2024-05-06T00:00:00 | https://arxiv.org/abs/2405.03167v2 | [
"https://github.com/salmon1802/tf4ctr"
] | In the paper 'TF4CTR: Twin Focus Framework for CTR Prediction via Adaptive Sample Differentiation', what AUC score did the TF4CTR model get on the Criteo dataset
| 0.8150 |
ImageNet | M2-Encoder | M2-Encoder: Advancing Bilingual Image-Text Understanding by Large-scale Efficient Pretraining | 2024-01-29T00:00:00 | https://arxiv.org/abs/2401.15896v2 | [
"https://github.com/alipay/Ant-Multi-Modal-Framework/tree/main/prj/M2_Encoder"
] | In the paper 'M2-Encoder: Advancing Bilingual Image-Text Understanding by Large-scale Efficient Pretraining', what Accuracy (Private) score did the M2-Encoder model get on the ImageNet dataset
| 88.5 |
MM-Vet | JanusFlow | JanusFlow: Harmonizing Autoregression and Rectified Flow for Unified Multimodal Understanding and Generation | 2024-11-12T00:00:00 | https://arxiv.org/abs/2411.07975v1 | [
"https://github.com/deepseek-ai/janus"
] | In the paper 'JanusFlow: Harmonizing Autoregression and Rectified Flow for Unified Multimodal Understanding and Generation', what GPT-4 score score did the JanusFlow model get on the MM-Vet dataset
| 30.9 |
Elephant | Snuffy | Snuffy: Efficient Whole Slide Image Classifier | 2024-08-15T00:00:00 | https://arxiv.org/abs/2408.08258v2 | [
"https://github.com/jafarinia/snuffy"
] | In the paper 'Snuffy: Efficient Whole Slide Image Classifier', what ACC score did the Snuffy model get on the Elephant dataset
| 0.923 |
CHILI-100K | GIN | 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 GIN model get on the CHILI-100K dataset
| 0.336 +/- 0.005 |
Squirrel | M2M-GNN | Sign is Not a Remedy: Multiset-to-Multiset Message Passing for Learning on Heterophilic Graphs | 2024-05-31T00:00:00 | https://arxiv.org/abs/2405.20652v1 | [
"https://github.com/Jinx-byebye/m2mgnn"
] | In the paper 'Sign is Not a Remedy: Multiset-to-Multiset Message Passing for Learning on Heterophilic Graphs', what Accuracy score did the M2M-GNN model get on the Squirrel dataset
| 63.60 ± 1.7 |
SODA-D | CFINet | Small Object Detection via Coarse-to-fine Proposal Generation and Imitation Learning | 2023-08-18T00:00:00 | https://arxiv.org/abs/2308.09534v1 | [
"https://github.com/shaunyuan22/cfinet"
] | In the paper 'Small Object Detection via Coarse-to-fine Proposal Generation and Imitation Learning', what mAP@0.5:0.95 score did the CFINet model get on the SODA-D dataset
| 30.7 |
WeiboPolls | UniPoll | UniPoll: A Unified Social Media Poll Generation Framework via Multi-Objective Optimization | 2023-06-12T00:00:00 | https://arxiv.org/abs/2306.06851v2 | [
"https://github.com/X1AOX1A/UniPoll"
] | In the paper 'UniPoll: A Unified Social Media Poll Generation Framework via Multi-Objective Optimization', what ROUGE-1 score did the UniPoll model get on the WeiboPolls dataset
| 49.6 |
MSR-VTT | CoCap (ViT/L14) | Accurate and Fast Compressed Video Captioning | 2023-09-22T00:00:00 | https://arxiv.org/abs/2309.12867v2 | [
"https://github.com/acherstyx/CoCap"
] | In the paper 'Accurate and Fast Compressed Video Captioning', what CIDEr score did the CoCap (ViT/L14) model get on the MSR-VTT dataset
| 57.2 |
LIVE-FB LSVQ | OneAlign | Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels | 2023-12-28T00:00:00 | https://arxiv.org/abs/2312.17090v1 | [
"https://github.com/q-future/q-align"
] | In the paper 'Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels', what PLCC score did the OneAlign model get on the LIVE-FB LSVQ dataset
| 0.886 |
CULane | CLRKDNet (ResNet-18) | CLRKDNet: Speeding up Lane Detection with Knowledge Distillation | 2024-05-21T00:00:00 | https://arxiv.org/abs/2405.12503v1 | [
"https://github.com/weiqingq/CLRKDNet"
] | In the paper 'CLRKDNet: Speeding up Lane Detection with Knowledge Distillation', what F1 score score did the CLRKDNet (ResNet-18) model get on the CULane dataset
| 79.66 |
BIG-bench (Disambiguation QA) | PaLM 2 (few-shot, k=3, CoT) | 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, k=3, CoT) model get on the BIG-bench (Disambiguation QA) dataset
| 77.6 |
Mapillary val | DINOv2 SALAD | Optimal Transport Aggregation for Visual Place Recognition | 2023-11-27T00:00:00 | https://arxiv.org/abs/2311.15937v2 | [
"https://github.com/serizba/salad"
] | In the paper 'Optimal Transport Aggregation for Visual Place Recognition', what Recall@1 score did the DINOv2 SALAD model get on the Mapillary val dataset
| 92.2 |
GigaSpeech DEV | Zipformer+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+CR-CTC
(no external language model) model get on the GigaSpeech DEV dataset
| 10.15 |
Market-1501 | BoT+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 BoT+UFFM+AMC model get on the Market-1501 dataset
| 96.2 |
CIFAR-100 | ABNet-2G-R2 | 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-R2 model get on the CIFAR-100 dataset
| 80.354 |
CIFAR-100-LT (ρ=100) | LIFT (ViT-B/16, CLIP) | Long-Tail Learning with Foundation Model: Heavy Fine-Tuning Hurts | 2023-09-18T00:00:00 | https://arxiv.org/abs/2309.10019v3 | [
"https://github.com/shijxcs/lift"
] | In the paper 'Long-Tail Learning with Foundation Model: Heavy Fine-Tuning Hurts', what Error Rate score did the LIFT (ViT-B/16, CLIP) model get on the CIFAR-100-LT (ρ=100) dataset
| 18.3 |
Fishyscapes L&F | 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 Fishyscapes L&F dataset
| 46.04 |
RealCQA | vlt5 - 11th ep FineTune | RealCQA: Scientific Chart Question Answering as a Test-bed for First-Order Logic | 2023-08-03T00:00:00 | https://arxiv.org/abs/2308.01979v1 | [
"https://github.com/cse-ai-lab/RealCQA"
] | In the paper 'RealCQA: Scientific Chart Question Answering as a Test-bed for First-Order Logic', what 1:1 Accuracy score did the vlt5 - 11th ep FineTune model get on the RealCQA dataset
| 0.310618012706403 |
ETTm1 (192) Multivariate | 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 ETTm1 (192) Multivariate dataset
| 0.328 |
GRAZPEDWRI-DX | YOLOv9-E | YOLOv9 for Fracture Detection in Pediatric Wrist Trauma X-ray Images | 2024-03-17T00:00:00 | https://arxiv.org/abs/2403.11249v2 | [
"https://github.com/ruiyangju/yolov9-fracture-detection"
] | In the paper 'YOLOv9 for Fracture Detection in Pediatric Wrist Trauma X-ray Images', what mAP score did the YOLOv9-E model get on the GRAZPEDWRI-DX dataset
| 65.62 |
CIFAR-100 | 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 CIFAR-100 dataset
| 74.2 |
ASDiv-A | MMOS-CODE-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-CODE-7B(0-shot) model get on the ASDiv-A dataset
| 78.6 |
CHILI-100K | GCN | 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 GCN model get on the CHILI-100K dataset
| 0.275 +/- 0.002 |
MATH | 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 MATH dataset
| 45.2 |
ETTh2 (720) Multivariate | 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 ETTh2 (720) Multivariate dataset
| 0.605 |
DUTS-TE | BiRefNet (DUTS, HRSOD) | 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, HRSOD) model get on the DUTS-TE dataset
| 0.018 |
SA-1B | SAM | Segment Anything without Supervision | 2024-06-28T00:00:00 | https://arxiv.org/abs/2406.20081v1 | [
"https://github.com/frank-xwang/unsam"
] | In the paper 'Segment Anything without Supervision', what Average Precision score did the SAM model get on the SA-1B dataset
| 38.9 |
ETTm1 (192) Multivariate | DiPE-Linear | Disentangled Interpretable Representation for Efficient Long-term Time Series Forecasting | 2024-11-26T00:00:00 | https://arxiv.org/abs/2411.17257v1 | [
"https://github.com/wintertee/dipe-linear"
] | In the paper 'Disentangled Interpretable Representation for Efficient Long-term Time Series Forecasting', what MSE score did the DiPE-Linear model get on the ETTm1 (192) Multivariate dataset
| 0.339 |
BIG-bench (Date Understanding) | PaLM 2 (few-shot, k=3, CoT) | 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, k=3, CoT) model get on the BIG-bench (Date Understanding) dataset
| 91.2 |
AIFB | BoP | From Primes to Paths: Enabling Fast Multi-Relational Graph Analysis | 2024-11-17T00:00:00 | https://arxiv.org/abs/2411.11149v1 | [
"https://github.com/kbogas/PAM_BoP"
] | In the paper 'From Primes to Paths: Enabling Fast Multi-Relational Graph Analysis', what Accuracy score did the BoP model get on the AIFB dataset
| 92.22 |
Atari 2600 Bowling | 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 Bowling dataset
| 62.4 |
IEMOCAP | CORECT (6-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 (6-class) model get on the IEMOCAP dataset
| 0.702 |
SemanticKITTI | 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 val mIoU score did the PPT+SparseUNet model get on the SemanticKITTI dataset
| 71.4% |
LoveDA | AerialFormer-B | AerialFormer: Multi-resolution Transformer for Aerial Image Segmentation | 2023-06-12T00:00:00 | https://arxiv.org/abs/2306.06842v2 | [
"https://github.com/UARK-AICV/AerialFormer"
] | In the paper 'AerialFormer: Multi-resolution Transformer for Aerial Image Segmentation', what Category mIoU score did the AerialFormer-B model get on the LoveDA dataset
| 54.1 |
SIMMC2.0 | 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 BLEU score did the PaCE model get on the SIMMC2.0 dataset
| 34.1 |
UCSD Ped2 | MULDE-object-centric-micro | MULDE: Multiscale Log-Density Estimation via Denoising Score Matching for Video Anomaly Detection | 2024-03-21T00:00:00 | https://arxiv.org/abs/2403.14497v1 | [
"https://github.com/jakubmicorek/MULDE-Multiscale-Log-Density-Estimation-via-Denoising-Score-Matching-for-Video-Anomaly-Detection"
] | In the paper 'MULDE: Multiscale Log-Density Estimation via Denoising Score Matching for Video Anomaly Detection', what AUC score did the MULDE-object-centric-micro model get on the UCSD Ped2 dataset
| 99.7% |
CUB-200-2011 | EfficientDML-VPTSP-G/512 | Learning Semantic Proxies from Visual Prompts for Parameter-Efficient Fine-Tuning in Deep Metric Learning | 2024-02-04T00:00:00 | https://arxiv.org/abs/2402.02340v2 | [
"https://github.com/noahsark/parameterefficient-dml"
] | In the paper 'Learning Semantic Proxies from Visual Prompts for Parameter-Efficient Fine-Tuning in Deep Metric Learning', what R@1 score did the EfficientDML-VPTSP-G/512 model get on the CUB-200-2011 dataset
| 88.5 |
ActivityNet Captions | VTimeLLM | VTimeLLM: Empower LLM to Grasp Video Moments | 2023-11-30T00:00:00 | https://arxiv.org/abs/2311.18445v1 | [
"https://github.com/huangb23/vtimellm"
] | In the paper 'VTimeLLM: Empower LLM to Grasp Video Moments', what CIDEr score did the VTimeLLM model get on the ActivityNet Captions dataset
| 27.6 |
SVT | NRTR+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 NRTR+TPS++ model get on the SVT dataset
| 94.6 |
PASCAL VOC | TFOC | Training-free Object Counting with Prompts | 2023-06-30T00:00:00 | https://arxiv.org/abs/2307.00038v2 | [
"https://github.com/shizenglin/training-free-object-counter"
] | In the paper 'Training-free Object Counting with Prompts', what mRMSE score did the TFOC model get on the PASCAL VOC dataset
| 0.0084 |
PeMSD7 | 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 dataset
| 18.31 |
Cooperative Vision-and-Dialogue Navigation | VLN-PETL | VLN-PETL: Parameter-Efficient Transfer Learning for Vision-and-Language Navigation | 2023-08-20T00:00:00 | https://arxiv.org/abs/2308.10172v1 | [
"https://github.com/yanyuanqiao/vln-petl"
] | In the paper 'VLN-PETL: Parameter-Efficient Transfer Learning for Vision-and-Language Navigation', what dist_to_end_reduction score did the VLN-PETL model get on the Cooperative Vision-and-Dialogue Navigation dataset
| 6.13 |
OpenMIC-2018 | EAsT-KD + PaSST | Audio Embeddings as Teachers for Music Classification | 2023-06-30T00:00:00 | https://arxiv.org/abs/2306.17424v1 | [
"https://github.com/suncerock/EAsT-music-classification"
] | In the paper 'Audio Embeddings as Teachers for Music Classification', what mean average precision score did the EAsT-KD + PaSST model get on the OpenMIC-2018 dataset
| .852 |
IMDB-Clean | MiVOLO-V2 | Beyond Specialization: Assessing the Capabilities of MLLMs in Age and Gender Estimation | 2024-03-04T00:00:00 | https://arxiv.org/abs/2403.02302v3 | [
"https://github.com/wildchlamydia/mivolo"
] | In the paper 'Beyond Specialization: Assessing the Capabilities of MLLMs in Age and Gender Estimation', what Average mean absolute error score did the MiVOLO-V2 model get on the IMDB-Clean dataset
| 3.97 |
FSS-1000 (5-shot) | Annotation-free FSS (With Annotation,ResNet-50) | Self-supervised Few-shot Learning for Semantic Segmentation: An Annotation-free Approach | 2023-07-26T00:00:00 | https://arxiv.org/abs/2307.14446v1 | [
"https://github.com/mindflow-institue/annotation_free_fewshot"
] | In the paper 'Self-supervised Few-shot Learning for Semantic Segmentation: An Annotation-free Approach', what Mean IoU score did the Annotation-free FSS (With Annotation,ResNet-50) model get on the FSS-1000 (5-shot) dataset
| 87.9 |
MeerKAT: Meerkat Kalahari Audio Transcripts | animal2vec | animal2vec and MeerKAT: A self-supervised transformer for rare-event raw audio input and a large-scale reference dataset for bioacoustics | 2024-06-03T00:00:00 | https://arxiv.org/abs/2406.01253v2 | [
"https://github.com/livingingroups/animal2vec"
] | In the paper 'animal2vec and MeerKAT: A self-supervised transformer for rare-event raw audio input and a large-scale reference dataset for bioacoustics', what AP score did the animal2vec model get on the MeerKAT: Meerkat Kalahari Audio Transcripts dataset
| 0.91 |
VoiceBank + DEMAND | Schrödinger bridge (PESQ loss) | Investigating Training Objectives for Generative Speech Enhancement | 2024-09-16T00:00:00 | https://arxiv.org/abs/2409.10753v1 | [
"https://github.com/sp-uhh/sgmse"
] | In the paper 'Investigating Training Objectives for Generative Speech Enhancement', what PESQ score did the Schrödinger bridge (PESQ loss) model get on the VoiceBank + DEMAND dataset
| 3.70 |
CATT | Shakkala | 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 Shakkala model get on the CATT dataset
| 13.494 |
GTAV-to-Cityscapes Labels | CMFormer | Learning Content-enhanced Mask Transformer for Domain Generalized Urban-Scene Segmentation | 2023-07-01T00:00:00 | https://arxiv.org/abs/2307.00371v5 | [
"https://github.com/BiQiWHU/CMFormer"
] | In the paper 'Learning Content-enhanced Mask Transformer for Domain Generalized Urban-Scene Segmentation', what mIoU score did the CMFormer model get on the GTAV-to-Cityscapes Labels dataset
| 55.3 |
STAR Benchmark | SeViLA | Self-Chained Image-Language Model for Video Localization and Question Answering | 2023-05-11T00:00:00 | https://arxiv.org/abs/2305.06988v2 | [
"https://github.com/yui010206/sevila"
] | In the paper 'Self-Chained Image-Language Model for Video Localization and Question Answering', what Average Accuracy score did the SeViLA model get on the STAR Benchmark dataset
| 64.9 |
ReCoRD | 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 F1 score did the PaLM 2-S (one-shot) model get on the ReCoRD dataset
| 92.1 |
LIVE-VQC | ReLaX-VQA | ReLaX-VQA: Residual Fragment and Layer Stack Extraction for Enhancing Video Quality Assessment | 2024-07-16T00:00:00 | https://arxiv.org/abs/2407.11496v1 | [
"https://github.com/xinyiw915/relax-vqa"
] | In the paper 'ReLaX-VQA: Residual Fragment and Layer Stack Extraction for Enhancing Video Quality Assessment', what PLCC score did the ReLaX-VQA model get on the LIVE-VQC dataset
| 0.8079 |
ANLI test | ChatGPT | A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets | 2023-05-29T00:00:00 | https://arxiv.org/abs/2305.18486v4 | [
"https://github.com/ntunlp/chatgpt_eval"
] | In the paper 'A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets', what A1 score did the ChatGPT model get on the ANLI test dataset
| 62.3 |
SFCHD | FCOS | Large, Complex, and Realistic Safety Clothing and Helmet Detection: Dataset and Method | 2023-06-03T00:00:00 | https://arxiv.org/abs/2306.02098v2 | [
"https://github.com/lijfrank-open/SFCHD-SCALE"
] | In the paper 'Large, Complex, and Realistic Safety Clothing and Helmet Detection: Dataset and Method', what mAP@0.50 score did the FCOS model get on the SFCHD dataset
| 76.4 |
MBPP | DeepSeek-Coder-Base 33B (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-Base 33B (few-shot) model get on the MBPP dataset
| 66 |
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