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 |
|---|---|---|---|---|---|---|---|
MATH | OpenMath-Mistral-7B (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-Mistral-7B (w/ code) model get on the MATH dataset
| 44.5 |
MathMC | GPT-4 (Teaching-Inspired) | Teaching-Inspired Integrated Prompting Framework: A Novel Approach for Enhancing Reasoning in Large Language Models | 2024-10-10T00:00:00 | https://arxiv.org/abs/2410.08068v1 | [
"https://github.com/sallytan13/teaching-inspired-prompting"
] | In the paper 'Teaching-Inspired Integrated Prompting Framework: A Novel Approach for Enhancing Reasoning in Large Language Models', what Accuracy score did the GPT-4 (Teaching-Inspired) model get on the MathMC dataset
| 92.2 |
MBPP | Branch-Train-Merge 4x7B (top-2) | Branch-Train-MiX: Mixing Expert LLMs into a Mixture-of-Experts LLM | 2024-03-12T00:00:00 | https://arxiv.org/abs/2403.07816v1 | [
"https://github.com/Leeroo-AI/mergoo"
] | In the paper 'Branch-Train-MiX: Mixing Expert LLMs into a Mixture-of-Experts LLM', what Accuracy score did the Branch-Train-Merge 4x7B (top-2) model get on the MBPP dataset
| 42.6 |
NeedForSpeed | PiVOT-L | Improving Visual Object Tracking through Visual Prompting | 2024-09-27T00:00:00 | https://arxiv.org/abs/2409.18901v1 | [
"https://github.com/chenshihfang/GOT"
] | In the paper 'Improving Visual Object Tracking through Visual Prompting', what AUC score did the PiVOT-L model get on the NeedForSpeed dataset
| 0.682 |
MM-Vet | LLaVA-1.5+CoS | Chain-of-Spot: Interactive Reasoning Improves Large Vision-Language Models | 2024-03-19T00:00:00 | https://arxiv.org/abs/2403.12966v2 | [
"https://github.com/dongyh20/chain-of-spot"
] | In the paper 'Chain-of-Spot: Interactive Reasoning Improves Large Vision-Language Models', what GPT-4 score score did the LLaVA-1.5+CoS model get on the MM-Vet dataset
| 37.6 |
H2O (2 Hands and Objects) | SHARP | SHARP: Segmentation of Hands and Arms by Range using Pseudo-Depth for Enhanced Egocentric 3D Hand Pose Estimation and Action Recognition | 2024-08-19T00:00:00 | https://arxiv.org/abs/2408.10037v1 | [
"https://github.com/wiktormucha/SHARP"
] | In the paper 'SHARP: Segmentation of Hands and Arms by Range using Pseudo-Depth for Enhanced Egocentric 3D Hand Pose Estimation and Action Recognition', what Actions Top-1 score did the SHARP model get on the H2O (2 Hands and Objects) dataset
| 91.73 |
MM-Vet | LLaVA-1.5 + DenseFusion-1M (Vicuna-7B) | DenseFusion-1M: Merging Vision Experts for Comprehensive Multimodal Perception | 2024-07-11T00:00:00 | https://arxiv.org/abs/2407.08303v2 | [
"https://github.com/baaivision/densefusion"
] | In the paper 'DenseFusion-1M: Merging Vision Experts for Comprehensive Multimodal Perception', what GPT-4 score score did the LLaVA-1.5 + DenseFusion-1M (Vicuna-7B) model get on the MM-Vet dataset
| 37.8 |
MVTec AD Textures Domain Generalization | FABLE | FABLE : Fabric Anomaly Detection Automation Process | 2023-06-16T00:00:00 | https://arxiv.org/abs/2306.10089v1 | [
"https://github.com/SimonThomine/FABLE"
] | In the paper 'FABLE : Fabric Anomaly Detection Automation Process', what Detection AUROC score did the FABLE model get on the MVTec AD Textures Domain Generalization dataset
| 97.5 |
Inside Out | SegVLAD-FineT (M) | Revisit Anything: Visual Place Recognition via Image Segment Retrieval | 2024-09-26T00:00:00 | https://arxiv.org/abs/2409.18049v1 | [
"https://github.com/anyloc/revisit-anything"
] | In the paper 'Revisit Anything: Visual Place Recognition via Image Segment Retrieval', what Recall@1 score did the SegVLAD-FineT (M) model get on the Inside Out dataset
| 7.2 |
HMDB51 | MSQNet | Actor-agnostic Multi-label Action Recognition with Multi-modal Query | 2023-07-20T00:00:00 | https://arxiv.org/abs/2307.10763v3 | [
"https://github.com/mondalanindya/msqnet"
] | In the paper 'Actor-agnostic Multi-label Action Recognition with Multi-modal Query', what Accuracy score did the MSQNet model get on the HMDB51 dataset
| 93.25 |
CLUSTER | TIGT | Topology-Informed Graph Transformer | 2024-02-03T00:00:00 | https://arxiv.org/abs/2402.02005v1 | [
"https://github.com/leemingo/tigt"
] | In the paper 'Topology-Informed Graph Transformer', what Accuracy score did the TIGT model get on the CLUSTER dataset
| 78.033 |
MCubeS (P) | ShareCMP (B2 RGB-D) | ShareCMP: Polarization-Aware RGB-P Semantic Segmentation | 2023-12-06T00:00:00 | https://arxiv.org/abs/2312.03430v2 | [
"https://github.com/lefteyex/sharecmp"
] | In the paper 'ShareCMP: Polarization-Aware RGB-P Semantic Segmentation', what mIoU score did the ShareCMP (B2 RGB-D) model get on the MCubeS (P) dataset
| 50.55 |
MATH | PaLM 2 (few-shot, k=4, 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=4, CoT) model get on the MATH dataset
| 34.3 |
CFC-DAOD | UMT (ResNet50-FPN) | Align and Distill: Unifying and Improving Domain Adaptive Object Detection | 2024-03-18T00:00:00 | https://arxiv.org/abs/2403.12029v2 | [
"https://github.com/justinkay/aldi"
] | In the paper 'Align and Distill: Unifying and Improving Domain Adaptive Object Detection', what AP@0.5 score did the UMT (ResNet50-FPN) model get on the CFC-DAOD dataset
| 61.2 |
TNL2K | LoRAT-L-378 | Tracking Meets LoRA: Faster Training, Larger Model, Stronger Performance | 2024-03-08T00:00:00 | https://arxiv.org/abs/2403.05231v2 | [
"https://github.com/litinglin/lorat"
] | In the paper 'Tracking Meets LoRA: Faster Training, Larger Model, Stronger Performance', what precision score did the LoRAT-L-378 model get on the TNL2K dataset
| 67.0 |
VNHSGE-Physics | ChatGPT | VNHSGE: VietNamese High School Graduation Examination Dataset for Large Language Models | 2023-05-20T00:00:00 | https://arxiv.org/abs/2305.12199v1 | [
"https://github.com/xdao85/vnhsge"
] | In the paper 'VNHSGE: VietNamese High School Graduation Examination Dataset for Large Language Models', what Accuracy score did the ChatGPT model get on the VNHSGE-Physics dataset
| 61 |
SVAMP (1:N) | ATHENA (roberta-large) | ATHENA: Mathematical Reasoning with Thought Expansion | 2023-11-02T00:00:00 | https://arxiv.org/abs/2311.01036v1 | [
"https://github.com/the-jb/athena-math"
] | In the paper 'ATHENA: Mathematical Reasoning with Thought Expansion', what Execution Accuracy score did the ATHENA (roberta-large) model get on the SVAMP (1:N) dataset
| 67.8 |
COCO-Stuff-27 | CAUSE (DINOv2, ViT-B/14) | Causal Unsupervised Semantic Segmentation | 2023-10-11T00:00:00 | https://arxiv.org/abs/2310.07379v1 | [
"https://github.com/ByungKwanLee/Causal-Unsupervised-Segmentation"
] | In the paper 'Causal Unsupervised Semantic Segmentation', what Accuracy score did the CAUSE (DINOv2, ViT-B/14) model get on the COCO-Stuff-27 dataset
| 78.0 |
RotKITTI Registration Benchmark | 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 RR@(1.5,0.3) score did the GeoTransformer model get on the RotKITTI Registration Benchmark dataset
| 78.5 |
CIFAR-100-LT (ρ=10) | GML (ResNet-32) | Long-Tailed Recognition by Mutual Information Maximization between Latent Features and Ground-Truth Labels | 2023-05-02T00:00:00 | https://arxiv.org/abs/2305.01160v3 | [
"https://github.com/bluecdm/Long-tailed-recognition"
] | In the paper 'Long-Tailed Recognition by Mutual Information Maximization between Latent Features and Ground-Truth Labels', what Error Rate score did the GML (ResNet-32) model get on the CIFAR-100-LT (ρ=10) dataset
| 33.0 |
WebQuestions | 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 EM score did the PaLM 2-S (one-shot) model get on the WebQuestions dataset
| 21.8 |
WDC Products-80%cc-seen-medium | gpt4-0613_zeroshot | Entity Matching using Large Language Models | 2023-10-17T00:00:00 | https://arxiv.org/abs/2310.11244v4 | [
"https://github.com/wbsg-uni-mannheim/matchgpt"
] | In the paper 'Entity Matching using Large Language Models', what F1 (%) score did the gpt4-0613_zeroshot model get on the WDC Products-80%cc-seen-medium dataset
| 89.61 |
nuScenes | HyDRa | Unleashing HyDRa: Hybrid Fusion, Depth Consistency and Radar for Unified 3D Perception | 2024-03-12T00:00:00 | https://arxiv.org/abs/2403.07746v2 | [
"https://github.com/phi-wol/hydra"
] | In the paper 'Unleashing HyDRa: Hybrid Fusion, Depth Consistency and Radar for Unified 3D Perception', what NDS score did the HyDRa model get on the nuScenes dataset
| 0.64 |
MM-Vet | SoM-LLaVA-1.5 | List Items One by One: A New Data Source and Learning Paradigm for Multimodal LLMs | 2024-04-25T00:00:00 | https://arxiv.org/abs/2404.16375v1 | [
"https://github.com/zzxslp/som-llava"
] | In the paper 'List Items One by One: A New Data Source and Learning Paradigm for Multimodal LLMs', what GPT-4 score score did the SoM-LLaVA-1.5 model get on the MM-Vet dataset
| 35.9 |
MCubeS | MMSFormer (RGB-A-D-N) | MMSFormer: Multimodal Transformer for Material and Semantic Segmentation | 2023-09-07T00:00:00 | https://arxiv.org/abs/2309.04001v4 | [
"https://github.com/csiplab/mmsformer"
] | In the paper 'MMSFormer: Multimodal Transformer for Material and Semantic Segmentation', what mIoU score did the MMSFormer (RGB-A-D-N) model get on the MCubeS dataset
| 53.11% |
STL-10, 40 Labels | ShrinkMatch | Shrinking Class Space for Enhanced Certainty in Semi-Supervised Learning | 2023-08-13T00:00:00 | https://arxiv.org/abs/2308.06777v1 | [
"https://github.com/LiheYoung/ShrinkMatch"
] | In the paper 'Shrinking Class Space for Enhanced Certainty in Semi-Supervised Learning', what Accuracy score did the ShrinkMatch model get on the STL-10, 40 Labels dataset
| 85.98 |
LVIS v1.0 | OVMR | OVMR: Open-Vocabulary Recognition with Multi-Modal References | 2024-06-07T00:00:00 | https://arxiv.org/abs/2406.04675v1 | [
"https://github.com/zehong-ma/ovmr"
] | In the paper 'OVMR: Open-Vocabulary Recognition with Multi-Modal References', what AP novel-LVIS base training score did the OVMR model get on the LVIS v1.0 dataset
| 34.4 |
GuitarSet | 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 GuitarSet dataset
| 92.0 |
BIOSCAN_1M_Insect Dataset | BIOSCAN_1M_order_classifier | A Step Towards Worldwide Biodiversity Assessment: The BIOSCAN-1M Insect Dataset | 2023-07-19T00:00:00 | https://arxiv.org/abs/2307.10455v3 | [
"https://github.com/zahrag/BIOSCAN-1M"
] | In the paper 'A Step Towards Worldwide Biodiversity Assessment: The BIOSCAN-1M Insect Dataset', what Macro F1 score did the BIOSCAN_1M_order_classifier model get on the BIOSCAN_1M_Insect Dataset dataset
| 92.65 |
MRR-Benchmark | GPT-4V | The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision) | 2023-09-29T00:00:00 | https://arxiv.org/abs/2309.17421v2 | [
"https://github.com/qi-zhangyang/gemini-vs-gpt4v"
] | In the paper 'The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision)', what Total Column Score score did the GPT-4V model get on the MRR-Benchmark dataset
| 415 |
Amazon Fashion | ProxyRCA | Proxy-based Item Representation for Attribute and Context-aware Recommendation | 2023-12-11T00:00:00 | https://arxiv.org/abs/2312.06145v1 | [
"https://github.com/theeluwin/ProxyRCA"
] | In the paper 'Proxy-based Item Representation for Attribute and Context-aware Recommendation', what nDCG@10 (100 Neg. Samples) score did the ProxyRCA model get on the Amazon Fashion dataset
| 0.446 |
SMD | 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 SMD dataset
| 0.4276 |
CocoGlide | Early Fusion | MMFusion: Combining Image Forensic Filters for Visual Manipulation Detection and Localization | 2023-12-04T00:00:00 | https://arxiv.org/abs/2312.01790v2 | [
"https://github.com/idt-iti/mmfusion-iml"
] | In the paper 'MMFusion: Combining Image Forensic Filters for Visual Manipulation Detection and Localization', what Average Pixel F1(Fixed threshold) score did the Early Fusion model get on the CocoGlide dataset
| .553 |
EMOTIC | CAGE | CAGE: Circumplex Affect Guided Expression Inference | 2024-04-23T00:00:00 | https://arxiv.org/abs/2404.14975v1 | [
"https://github.com/wagner-niklas/cage_expression_inference"
] | In the paper 'CAGE: Circumplex Affect Guided Expression Inference', what Top-3 Accuracy (%) score did the CAGE model get on the EMOTIC dataset
| 14.73 |
ISTD+ | 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 ISTD+ dataset
| 3.39 |
LingOly | GPT-4 | 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 GPT-4 model get on the LingOly dataset
| 33.4% |
iSAID | AerialFormer-T | 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 mIoU score did the AerialFormer-T model get on the iSAID dataset
| 67.5 |
St Lucia | SelaVPR | Towards Seamless Adaptation of Pre-trained Models for Visual Place Recognition | 2024-02-22T00:00:00 | https://arxiv.org/abs/2402.14505v3 | [
"https://github.com/Lu-Feng/SelaVPR"
] | In the paper 'Towards Seamless Adaptation of Pre-trained Models for Visual Place Recognition', what Recall@1 score did the SelaVPR model get on the St Lucia dataset
| 99.8 |
SPEC-MTP | W-HMR | W-HMR: Monocular Human Mesh Recovery in World Space with Weak-Supervised Calibration | 2023-11-29T00:00:00 | https://arxiv.org/abs/2311.17460v6 | [
"https://github.com/yw0208/W-HMR"
] | In the paper 'W-HMR: Monocular Human Mesh Recovery in World Space with Weak-Supervised Calibration', what W-MPJPE score did the W-HMR model get on the SPEC-MTP dataset
| 118.7 |
Action-Camera Parking | mAlexNet | Revising deep learning methods in parking lot occupancy detection | 2023-06-07T00:00:00 | https://arxiv.org/abs/2306.04288v3 | [
"https://github.com/eighonet/parking-research"
] | In the paper 'Revising deep learning methods in parking lot occupancy detection', what F1-score score did the mAlexNet model get on the Action-Camera Parking dataset
| 0.8577 |
MIMIC-II | HP-CDE | Hawkes Process Based on Controlled Differential Equations | 2023-05-09T00:00:00 | https://arxiv.org/abs/2305.07031v2 | [
"https://github.com/kookseungji/Hawkes-Process-Based-on-Controlled-Differential-Equations"
] | In the paper 'Hawkes Process Based on Controlled Differential Equations', what RMSE score did the HP-CDE model get on the MIMIC-II dataset
| 0.726±0.042 |
ETH/UCY | PPT | Progressive Pretext Task Learning for Human Trajectory Prediction | 2024-07-16T00:00:00 | https://arxiv.org/abs/2407.11588v1 | [
"https://github.com/isee-laboratory/ppt"
] | In the paper 'Progressive Pretext Task Learning for Human Trajectory Prediction', what ADE-8/12 score did the PPT model get on the ETH/UCY dataset
| 0.20 |
SIM10K to Cityscapes | MILA | MILA: Memory-Based Instance-Level Adaptation for Cross-Domain Object Detection | 2023-09-03T00:00:00 | https://arxiv.org/abs/2309.01086v1 | [
"https://github.com/hitachi-rd-cv/MILA"
] | In the paper 'MILA: Memory-Based Instance-Level Adaptation for Cross-Domain Object Detection', what mAP@0.5 score did the MILA model get on the SIM10K to Cityscapes dataset
| 57.4 |
VehicleID Small | MBR-4B (without RK) | Strength in Diversity: Multi-Branch Representation Learning for Vehicle Re-Identification | 2023-10-02T00:00:00 | https://arxiv.org/abs/2310.01129v1 | [
"https://github.com/videturfortuna/vehicle_reid_itsc2023"
] | In the paper 'Strength in Diversity: Multi-Branch Representation Learning for Vehicle Re-Identification', what mAP score did the MBR-4B (without RK) model get on the VehicleID Small dataset
| 92.5 |
VoxCeleb | ReDimNet-B3-LM (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 (3.0M) model get on the VoxCeleb dataset
| 0.5 |
Flowers (Tensorflow) | CNN+ Wilson-Cowan model RNN | Learning in Wilson-Cowan model for metapopulation | 2024-06-24T00:00:00 | https://arxiv.org/abs/2406.16453v2 | [
"https://github.com/raffaelemarino/learning_in_wilsoncowan"
] | In the paper 'Learning in Wilson-Cowan model for metapopulation', what Accuracy score did the CNN+ Wilson-Cowan model RNN model get on the Flowers (Tensorflow) dataset
| 84.85 |
RSTPReid | RaSa | RaSa: Relation and Sensitivity Aware Representation Learning for Text-based Person Search | 2023-05-23T00:00:00 | https://arxiv.org/abs/2305.13653v1 | [
"https://github.com/flame-chasers/rasa"
] | In the paper 'RaSa: Relation and Sensitivity Aware Representation Learning for Text-based Person Search', what R@1 score did the RaSa model get on the RSTPReid dataset
| 66.90 |
MVBench | HawkEye | HawkEye: Training Video-Text LLMs for Grounding Text in Videos | 2024-03-15T00:00:00 | https://arxiv.org/abs/2403.10228v1 | [
"https://github.com/yellow-binary-tree/hawkeye"
] | In the paper 'HawkEye: Training Video-Text LLMs for Grounding Text in Videos', what Avg. score did the HawkEye model get on the MVBench dataset
| 47.55 |
ACDC Scribbles | ScribbleVC | ScribbleVC: Scribble-supervised Medical Image Segmentation with Vision-Class Embedding | 2023-07-30T00:00:00 | https://arxiv.org/abs/2307.16226v1 | [
"https://github.com/huanglizi/scribblevc"
] | In the paper 'ScribbleVC: Scribble-supervised Medical Image Segmentation with Vision-Class Embedding', what Dice (Average) score did the ScribbleVC model get on the ACDC Scribbles dataset
| 88.4% |
cb | OPT-1.3B | Achieving Dimension-Free Communication in Federated Learning via Zeroth-Order Optimization | 2024-05-24T00:00:00 | https://arxiv.org/abs/2405.15861v3 | [
"https://github.com/ZidongLiu/DeComFL"
] | In the paper 'Achieving Dimension-Free Communication in Federated Learning via Zeroth-Order Optimization', what Test Accuracy score did the OPT-1.3B model get on the cb dataset
| 75.71% |
GoPro | ID-Blau (Restormer) | ID-Blau: Image Deblurring by Implicit Diffusion-based reBLurring AUgmentation | 2023-12-18T00:00:00 | https://arxiv.org/abs/2312.10998v2 | [
"https://github.com/plusgood-steven/id-blau"
] | In the paper 'ID-Blau: Image Deblurring by Implicit Diffusion-based reBLurring AUgmentation', what PSNR score did the ID-Blau (Restormer) model get on the GoPro dataset
| 33.51 |
One-class CIFAR-100 | GeneralAD | GeneralAD: Anomaly Detection Across Domains by Attending to Distorted Features | 2024-07-17T00:00:00 | https://arxiv.org/abs/2407.12427v1 | [
"https://github.com/LucStrater/GeneralAD"
] | In the paper 'GeneralAD: Anomaly Detection Across Domains by Attending to Distorted Features', what AUROC score did the GeneralAD model get on the One-class CIFAR-100 dataset
| 98.4 |
TAP-Vid-Kinetics-First | LocoTrack-B | Local All-Pair Correspondence for Point Tracking | 2024-07-22T00:00:00 | https://arxiv.org/abs/2407.15420v1 | [
"https://github.com/ku-cvlab/locotrack"
] | In the paper 'Local All-Pair Correspondence for Point Tracking', what Average Jaccard score did the LocoTrack-B model get on the TAP-Vid-Kinetics-First dataset
| 52.3 |
S3DIS | Superpoint Transformer | Efficient 3D Semantic Segmentation with Superpoint Transformer | 2023-06-13T00:00:00 | https://arxiv.org/abs/2306.08045v2 | [
"https://github.com/drprojects/superpoint_transformer"
] | In the paper 'Efficient 3D Semantic Segmentation with Superpoint Transformer', what mIoU score did the Superpoint Transformer model get on the S3DIS dataset
| 76.0 |
FDMSE-ISL | HWGAT | Hierarchical Windowed Graph Attention Network and a Large Scale Dataset for Isolated Indian Sign Language Recognition | 2024-07-19T00:00:00 | https://arxiv.org/abs/2407.14224v2 | [
"https://github.com/suvajit-patra/sl-hwgat"
] | In the paper 'Hierarchical Windowed Graph Attention Network and a Large Scale Dataset for Isolated Indian Sign Language Recognition', what Top-1 Accuracy score did the HWGAT model get on the FDMSE-ISL dataset
| 93.86 |
Vinoground | Gemini-1.5-Pro | Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context | 2024-03-08T00:00:00 | https://arxiv.org/abs/2403.05530v4 | [
"https://github.com/dlvuldet/primevul"
] | In the paper 'Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context', what Text Score score did the Gemini-1.5-Pro model get on the Vinoground dataset
| 35.8 |
USNA-Cn2 (long-term) | Offshore Macro Meteorological | 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 Offshore Macro Meteorological model get on the USNA-Cn2 (long-term) dataset
| 0.675 |
CLUSTER | GPTrans-Nano | Graph Propagation Transformer for Graph Representation Learning | 2023-05-19T00:00:00 | https://arxiv.org/abs/2305.11424v3 | [
"https://github.com/czczup/gptrans"
] | In the paper 'Graph Propagation Transformer for Graph Representation Learning', what Accuracy score did the GPTrans-Nano model get on the CLUSTER dataset
| 78.07 |
ChEBI-20 | Song et al. | Towards Cross-Modal Text-Molecule Retrieval with Better Modality Alignment | 2024-10-31T00:00:00 | https://arxiv.org/abs/2410.23715v1 | [
"https://github.com/DeepLearnXMU/CMTMR"
] | In the paper 'Towards Cross-Modal Text-Molecule Retrieval with Better Modality Alignment', what Mean Rank score did the Song et al. model get on the ChEBI-20 dataset
| 12.66 |
Cityscapes val | DSNet(single-scale) | DSNet: A Novel Way to Use Atrous Convolutions in Semantic Segmentation | 2024-06-06T00:00:00 | https://arxiv.org/abs/2406.03702v1 | [
"https://github.com/takaniwa/dsnet"
] | In the paper 'DSNet: A Novel Way to Use Atrous Convolutions in Semantic Segmentation', what mIoU score did the DSNet(single-scale) model get on the Cityscapes val dataset
| 80.4 |
CNN / Daily Mail | Fourier Transformer | Fourier Transformer: Fast Long Range Modeling by Removing Sequence Redundancy with FFT Operator | 2023-05-24T00:00:00 | https://arxiv.org/abs/2305.15099v1 | [
"https://github.com/lumia-group/fouriertransformer"
] | In the paper 'Fourier Transformer: Fast Long Range Modeling by Removing Sequence Redundancy with FFT Operator', what ROUGE-1 score did the Fourier Transformer model get on the CNN / Daily Mail dataset
| 44.76 |
SMAC MMM2_7m2M1M_vs_8m4M1M | VDN | 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 VDN model get on the SMAC MMM2_7m2M1M_vs_8m4M1M dataset
| 13.35 |
CHILI-3K | GraphUNet | 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 GraphUNet model get on the CHILI-3K dataset
| 0.055 +/- 0.001 |
VisA | D3AD | Dynamic Addition of Noise in a Diffusion Model for Anomaly Detection | 2024-01-09T00:00:00 | https://arxiv.org/abs/2401.04463v2 | [
"https://github.com/JustinTebbe/D3AD"
] | In the paper 'Dynamic Addition of Noise in a Diffusion Model for Anomaly Detection', what Detection AUROC score did the D3AD model get on the VisA dataset
| 96.0 |
Amazon Beauty | ProxyRCA | Proxy-based Item Representation for Attribute and Context-aware Recommendation | 2023-12-11T00:00:00 | https://arxiv.org/abs/2312.06145v1 | [
"https://github.com/theeluwin/ProxyRCA"
] | In the paper 'Proxy-based Item Representation for Attribute and Context-aware Recommendation', what Hit@10 score did the ProxyRCA model get on the Amazon Beauty dataset
| 0.626 |
Domain-independent anomalies datasets | Spatial Embedding MLP (Wide-ResNet50-2) | Domain-independent detection of known anomalies | 2024-07-03T00:00:00 | https://arxiv.org/abs/2407.02910v1 | [
"https://github.com/Jonas1302/anomalib"
] | In the paper 'Domain-independent detection of known anomalies', what Detection AUROC score did the Spatial Embedding MLP (Wide-ResNet50-2) model get on the Domain-independent anomalies datasets dataset
| 87.2 |
MM-Vet | Vary-base | Vary: Scaling up the Vision Vocabulary for Large Vision-Language Models | 2023-12-11T00:00:00 | https://arxiv.org/abs/2312.06109v1 | [
"https://github.com/Ucas-HaoranWei/Vary"
] | In the paper 'Vary: Scaling up the Vision Vocabulary for Large Vision-Language Models', what GPT-4 score score did the Vary-base model get on the MM-Vet dataset
| 36.2 |
LibriSpeech 100h test-other | 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 100h test-other dataset
| 22.25 |
GoPro | CAPTNet | Prompt-based Ingredient-Oriented All-in-One Image Restoration | 2023-09-06T00:00:00 | https://arxiv.org/abs/2309.03063v2 | [
"https://github.com/Tombs98/CAPTNet"
] | In the paper 'Prompt-based Ingredient-Oriented All-in-One Image Restoration', what PSNR score did the CAPTNet model get on the GoPro dataset
| 33.74 |
KIT Motion-Language | MLP+GRU | Motion2Language, unsupervised learning of synchronized semantic motion segmentation | 2023-10-16T00:00:00 | https://arxiv.org/abs/2310.10594v2 | [
"https://github.com/rd20karim/M2T-Segmentation"
] | In the paper 'Motion2Language, unsupervised learning of synchronized semantic motion segmentation', what BLEU-4 score did the MLP+GRU model get on the KIT Motion-Language dataset
| 25.4 |
NYUv2 | SwinMTL | SwinMTL: A Shared Architecture for Simultaneous Depth Estimation and Semantic Segmentation from Monocular Camera Images | 2024-03-15T00:00:00 | https://arxiv.org/abs/2403.10662v1 | [
"https://github.com/pardistaghavi/swinmtl"
] | In the paper 'SwinMTL: A Shared Architecture for Simultaneous Depth Estimation and Semantic Segmentation from Monocular Camera Images', what Mean IoU score did the SwinMTL model get on the NYUv2 dataset
| 58.14 |
Unpaired-abdomen-CT | CLIP+ViT | Spatially Covariant Image Registration with Text Prompts | 2023-11-27T00:00:00 | https://arxiv.org/abs/2311.15607v2 | [
"https://github.com/tinymilky/NeRD"
] | In the paper 'Spatially Covariant Image Registration with Text Prompts', what DSC score did the CLIP+ViT model get on the Unpaired-abdomen-CT dataset
| 0.5933 |
Citeseer: fixed 20 node per class | ScaleNet | Scale Invariance of Graph Neural Networks | 2024-11-28T00:00:00 | https://arxiv.org/abs/2411.19392v2 | [
"https://github.com/qin87/scalenet"
] | In the paper 'Scale Invariance of Graph Neural Networks', what Accuracy score did the ScaleNet model get on the Citeseer: fixed 20 node per class dataset
| 68.3±1.5 |
WHAMR! | TD-Conformer (L) + DM | On Time Domain Conformer Models for Monaural Speech Separation in Noisy Reverberant Acoustic Environments | 2023-10-09T00:00:00 | https://arxiv.org/abs/2310.06125v1 | [
"https://github.com/jwr1995/pubsep"
] | In the paper 'On Time Domain Conformer Models for Monaural Speech Separation in Noisy Reverberant Acoustic Environments', what SI-SDRi score did the TD-Conformer (L) + DM model get on the WHAMR! dataset
| 13.4 |
Traffic (720) | 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 Traffic (720) dataset
| 0.424 |
PACS | QT-DoG (ResNet-50) | QT-DoG: Quantization-aware Training for Domain Generalization | 2024-10-08T00:00:00 | https://arxiv.org/abs/2410.06020v1 | [
"https://github.com/saqibjaved1/QT-DoG"
] | In the paper 'QT-DoG: Quantization-aware Training for Domain Generalization', what Average Accuracy score did the QT-DoG (ResNet-50) model get on the PACS dataset
| 87.89 |
Winoground | PaLI (ft SNLI-VE + Synthetic Data) | What You See is What You Read? Improving Text-Image Alignment Evaluation | 2023-05-17T00:00:00 | https://arxiv.org/abs/2305.10400v4 | [
"https://github.com/yonatanbitton/wysiwyr"
] | In the paper 'What You See is What You Read? Improving Text-Image Alignment Evaluation', what Text Score score did the PaLI (ft SNLI-VE + Synthetic Data) model get on the Winoground dataset
| 46.5 |
RoadTextVQA | GIT | Reading Between the Lanes: Text VideoQA on the Road | 2023-07-08T00:00:00 | https://arxiv.org/abs/2307.03948v1 | [
"https://github.com/georg3tom/RoadTextVQA"
] | In the paper 'Reading Between the Lanes: Text VideoQA on the Road', what ACCURACY score did the GIT model get on the RoadTextVQA dataset
| 29.58 |
LingOly | Llama 2 70B | 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 Llama 2 70B model get on the LingOly dataset
| 6.4% |
SOD4SB Public Test | E2 method (Normalized Gaussian Wasserstein Distance + Switch Hard Augmentation + Multi scale train + Weight Moving Average + CenterNet + VarifocalNet) | MVA2023 Small Object Detection Challenge for Spotting Birds: Dataset, Methods, and Results | 2023-07-18T00:00:00 | https://arxiv.org/abs/2307.09143v1 | [
"https://github.com/iim-ttij/mva2023smallobjectdetection4spottingbirds"
] | In the paper 'MVA2023 Small Object Detection Challenge for Spotting Birds: Dataset, Methods, and Results', what AP50 score did the E2 method (Normalized Gaussian Wasserstein Distance + Switch Hard Augmentation + Multi scale train + Weight Moving Average + CenterNet + VarifocalNet) model get on the SOD4SB Public Test dataset
| 69.6 |
PEMS-BAY | 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 MAE @ 12 step score did the STD-MAE model get on the PEMS-BAY dataset
| 1.77 |
iNaturalist | AIMv2-3B | Multimodal Autoregressive Pre-training of Large Vision Encoders | 2024-11-21T00:00:00 | https://arxiv.org/abs/2411.14402v1 | [
"https://github.com/apple/ml-aim"
] | In the paper 'Multimodal Autoregressive Pre-training of Large Vision Encoders', what Top 1 Accuracy score did the AIMv2-3B model get on the iNaturalist dataset
| 81.5 |
TriviaQA | DPA-RAG | Understand What LLM Needs: Dual Preference Alignment for Retrieval-Augmented Generation | 2024-06-26T00:00:00 | https://arxiv.org/abs/2406.18676v2 | [
"https://github.com/dongguanting/dpa-rag"
] | In the paper 'Understand What LLM Needs: Dual Preference Alignment for Retrieval-Augmented Generation', what F1 score did the DPA-RAG model get on the TriviaQA dataset
| 80.08 |
CIFAR-10 | Transformer+SSA | The Information Pathways Hypothesis: Transformers are Dynamic Self-Ensembles | 2023-06-02T00:00:00 | https://arxiv.org/abs/2306.01705v1 | [
"https://github.com/shamim-hussain/ssa"
] | In the paper 'The Information Pathways Hypothesis: Transformers are Dynamic Self-Ensembles', what bits/dimension score did the Transformer+SSA model get on the CIFAR-10 dataset
| 2.774 |
STAR Benchmark | GF(sup) | Glance and Focus: Memory Prompting for Multi-Event Video Question Answering | 2024-01-03T00:00:00 | https://arxiv.org/abs/2401.01529v1 | [
"https://github.com/byz0e/glance-focus"
] | In the paper 'Glance and Focus: Memory Prompting for Multi-Event Video Question Answering', what Average Accuracy score did the GF(sup) model get on the STAR Benchmark dataset
| 53.94 |
CTCUG | D-DFFNet | Depth and DOF Cues Make A Better Defocus Blur Detector | 2023-06-20T00:00:00 | https://arxiv.org/abs/2306.11334v1 | [
"https://github.com/yuxinjin-whu/d-dffnet"
] | In the paper 'Depth and DOF Cues Make A Better Defocus Blur Detector', what MAE score did the D-DFFNet model get on the CTCUG dataset
| 0.074 |
VideoInstruct | PPLLaVA-7B | PPLLaVA: Varied Video Sequence Understanding With Prompt Guidance | 2024-11-04T00:00:00 | https://arxiv.org/abs/2411.02327v2 | [
"https://github.com/farewellthree/ppllava"
] | In the paper 'PPLLaVA: Varied Video Sequence Understanding With Prompt Guidance', what gpt-score score did the PPLLaVA-7B model get on the VideoInstruct dataset
| 3.85 |
Charades-STA | LLMEPET | Prior Knowledge Integration via LLM Encoding and Pseudo Event Regulation for Video Moment Retrieval | 2024-07-21T00:00:00 | https://arxiv.org/abs/2407.15051v3 | [
"https://github.com/fletcherjiang/llmepet"
] | In the paper 'Prior Knowledge Integration via LLM Encoding and Pseudo Event Regulation for Video Moment Retrieval', what R@1 IoU=0.5 score did the LLMEPET model get on the Charades-STA dataset
| 58.31 |
AfriSenti | AfriBERTa | UCAS-IIE-NLP at SemEval-2023 Task 12: Enhancing Generalization of Multilingual BERT for Low-resource Sentiment Analysis | 2023-06-01T00:00:00 | https://arxiv.org/abs/2306.01093v1 | [
"https://github.com/zerohd4869/sacl"
] | In the paper 'UCAS-IIE-NLP at SemEval-2023 Task 12: Enhancing Generalization of Multilingual BERT for Low-resource Sentiment Analysis', what weighted-F1 score score did the AfriBERTa model get on the AfriSenti dataset
| 0.439 |
KITTI-360 | 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 PQ score did the SuperCluster model get on the KITTI-360 dataset
| 48.3 |
Synthetic Dynamic Networks | Static Features | Learning the mechanisms of network growth | 2024-03-31T00:00:00 | https://arxiv.org/abs/2404.00793v3 | [
"https://github.com/LourensT/DynamicNetworkSimulation"
] | In the paper 'Learning the mechanisms of network growth', what Accuracy score did the Static Features model get on the Synthetic Dynamic Networks dataset
| 92.81% |
INRIA Aerial Image Labeling | UANet(VGG-16) | Building Extraction from Remote Sensing Images via an Uncertainty-Aware Network | 2023-07-23T00:00:00 | https://arxiv.org/abs/2307.12309v1 | [
"https://github.com/henryjiepanli/uncertainty-aware-network"
] | In the paper 'Building Extraction from Remote Sensing Images via an Uncertainty-Aware Network', what IoU score did the UANet(VGG-16) model get on the INRIA Aerial Image Labeling dataset
| 83.08 |
Peptides-struct | ViT-PS | Learning Probabilistic Symmetrization for Architecture Agnostic Equivariance | 2023-06-05T00:00:00 | https://arxiv.org/abs/2306.02866v3 | [
"https://github.com/jw9730/lps"
] | In the paper 'Learning Probabilistic Symmetrization for Architecture Agnostic Equivariance', what MAE score did the ViT-PS model get on the Peptides-struct dataset
| 0.2559 |
GSM8K | OpenMath-CodeLlama-13B (w/ code) | OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset | 2024-02-15T00:00:00 | https://arxiv.org/abs/2402.10176v2 | [
"https://github.com/kipok/nemo-skills"
] | In the paper 'OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset', what Accuracy score did the OpenMath-CodeLlama-13B (w/ code) model get on the GSM8K dataset
| 78.8 |
SPED | 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 SPED dataset
| 92.5 |
ColonINST-v1 (Seen) | Bunny-v1.0-3B (w/ LoRA, w/ extra data) | Efficient Multimodal Learning from Data-centric Perspective | 2024-02-18T00:00:00 | https://arxiv.org/abs/2402.11530v3 | [
"https://github.com/baai-dcai/bunny"
] | In the paper 'Efficient Multimodal Learning from Data-centric Perspective', what Accuray score did the Bunny-v1.0-3B (w/ LoRA, w/ extra data) model get on the ColonINST-v1 (Seen) dataset
| 92.47 |
FSC147 | DAVE | DAVE -- A Detect-and-Verify Paradigm for Low-Shot Counting | 2024-04-25T00:00:00 | https://arxiv.org/abs/2404.16622v1 | [
"https://github.com/jerpelhan/dave"
] | In the paper 'DAVE -- A Detect-and-Verify Paradigm for Low-Shot Counting', what MAE(val) score did the DAVE model get on the FSC147 dataset
| 8.91 |
StreetTryOn | Street TryOn | Street TryOn: Learning In-the-Wild Virtual Try-On from Unpaired Person Images | 2023-11-27T00:00:00 | https://arxiv.org/abs/2311.16094v3 | [
"https://github.com/cuiaiyu/street-tryon-benchmark"
] | In the paper 'Street TryOn: Learning In-the-Wild Virtual Try-On from Unpaired Person Images', what FID score did the Street TryOn model get on the StreetTryOn dataset
| 33.039 |
PeMS07 | PM-DMnet(R) | Pattern-Matching Dynamic Memory Network for Dual-Mode Traffic Prediction | 2024-08-12T00:00:00 | https://arxiv.org/abs/2408.07100v1 | [
"https://github.com/wengwenchao123/PM-DMNet"
] | In the paper 'Pattern-Matching Dynamic Memory Network for Dual-Mode Traffic Prediction', what MAE@1h score did the PM-DMnet(R) model get on the PeMS07 dataset
| 19.18 |
MSVD-QA | vid-TLDR (UMT-L) | vid-TLDR: Training Free Token merging for Light-weight Video Transformer | 2024-03-20T00:00:00 | https://arxiv.org/abs/2403.13347v2 | [
"https://github.com/mlvlab/vid-tldr"
] | In the paper 'vid-TLDR: Training Free Token merging for Light-weight Video Transformer', what Accuracy score did the vid-TLDR (UMT-L) model get on the MSVD-QA dataset
| 0.549 |
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