dataset stringlengths 1 82 | model_name stringlengths 0 150 | paper_title stringlengths 22 175 | paper_date timestamp[ns]date 2023-05-02 00:00:00 2024-12-12 00:00:00 | paper_url stringlengths 32 35 | code_links listlengths 1 1 | prompts stringlengths 115 330 | answer stringlengths 1 22 | paper_text stringlengths 83 737k | year_bin stringclasses 2
values | benchmark_split stringclasses 1
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|---|---|---|---|---|---|---|---|---|---|---|
Cornell | CATv3-sup | CAT: A Causally Graph Attention Network for Trimming Heterophilic Graph | 2023-12-14T00:00:00 | https://arxiv.org/abs/2312.08672v3 | [
"https://github.com/geox-lab/cat"
] | In the paper 'CAT: A Causally Graph Attention Network for Trimming Heterophilic Graph', what Accuracy score did the CATv3-sup model get on the Cornell dataset
| 88.8±2.1 | Title: CAT: A Causally Graph Attention Network for Trimming Heterophilic Graph
Abstract: AbstractLocal Attention-guided Message Passing Mechanism (LAMP) adopted in Graph Attention Networks (GATs) is designed to adaptively learn the importance of neighboring nodes for better local aggregation on the graph, which is abl... | 2023 | public |
AIST++ | Lodge (DDPM) | Lodge: A Coarse to Fine Diffusion Network for Long Dance Generation Guided by the Characteristic Dance Primitives | 2024-03-15T00:00:00 | https://arxiv.org/abs/2403.10518v3 | [
"https://github.com/li-ronghui/LODGE"
] | In the paper 'Lodge: A Coarse to Fine Diffusion Network for Long Dance Generation Guided by the Characteristic Dance Primitives', what Beat alignment score score did the Lodge (DDPM) model get on the AIST++ dataset
| 0.24 | Title: Lodge: A Coarse to Fine Diffusion Network forLong Dance Generation Guided by the Characteristic Dance Primitives
Abstract: AbstractWe propose Lodge, a network capable of generating extremely long dance sequences conditioned on given music. We design Lodge as a two-stage coarse to fine diffusion architecture, an... | 2024-2025 | public |
CIFAR-100, 400 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 Percentage error score did the ShrinkMatch model get on the CIFAR-100, 400 Labels dataset
| 35.36 | Title: Shrinking Class Space for Enhanced Certainty in Semi-Supervised Learning
Abstract: AbstractSemi-supervised learning is attracting blooming attention, due to its success in combining unlabeled data. To mitigate potentially incorrect pseudo labels, recent frameworks mostly set a fixed confidence threshold to disc... | 2023 | public |
EC-FUNSD | RORE (LayoutLMv3-large) | Modeling Layout Reading Order as Ordering Relations for Visually-rich Document Understanding | 2024-09-29T00:00:00 | https://arxiv.org/abs/2409.19672v1 | [
"https://github.com/chongzhangFDU/ROOR"
] | In the paper 'Modeling Layout Reading Order as Ordering Relations for Visually-rich Document Understanding', what F1 score did the RORE (LayoutLMv3-large) model get on the EC-FUNSD dataset
| 79.33 | Title: Modeling Layout Reading Order as Ordering Relations for Visually-rich Document Understanding
Abstract: AbstractModeling and leveraging layout reading order in visually-rich documents (VrDs) is critical in document intelligence as it captures the rich structure semantics within documents. Previous works typicall... | 2024-2025 | public |
IllusionVQA | Gemini-Pro 4-shot | IllusionVQA: A Challenging Optical Illusion Dataset for Vision Language Models | 2024-03-23T00:00:00 | https://arxiv.org/abs/2403.15952v3 | [
"https://github.com/csebuetnlp/illusionvqa"
] | In the paper 'IllusionVQA: A Challenging Optical Illusion Dataset for Vision Language Models', what Accuracy score did the Gemini-Pro 4-shot model get on the IllusionVQA dataset
| 52.87 | Title: IllusionVQA: A Challenging Optical Illusion Datasetfor Vision Language Models
Abstract: AbstractThe advent of Vision Language Models (VLM) has allowed researchers to investigate the visual understanding of a neural network using natural language. Beyond object classification and detection, VLMs are capable of v... | 2024-2025 | public |
GRAZPEDWRI-DX | YOLOv8+GCT | Pediatric Wrist Fracture Detection Using Feature Context Excitation Modules in X-ray Images | 2024-10-01T00:00:00 | https://arxiv.org/abs/2410.01031v2 | [
"https://github.com/ruiyangju/fce-yolov8"
] | In the paper 'Pediatric Wrist Fracture Detection Using Feature Context Excitation Modules in X-ray Images', what mAP score did the YOLOv8+GCT model get on the GRAZPEDWRI-DX dataset
| 65.67 | Title: FCE-YOLOv8: YOLOv8 with Feature Context Excitation Modules for Fracture Detection in Pediatric Wrist X-ray Images
Abstract: AbstractChildren often suffer wrist trauma in daily life, while they usually need radiologists to analyze and interpret X-ray images before surgical treatment by surgeons. The development ... | 2024-2025 | public |
S3DIS | PonderV2 + SparseUNet | PonderV2: Pave the Way for 3D Foundation Model with A Universal Pre-training Paradigm | 2023-10-12T00:00:00 | https://arxiv.org/abs/2310.08586v3 | [
"https://github.com/OpenGVLab/PonderV2"
] | In the paper 'PonderV2: Pave the Way for 3D Foundation Model with A Universal Pre-training Paradigm', what Mean IoU score did the PonderV2 + SparseUNet model get on the S3DIS dataset
| 79.9 | Title: PonderV2: Pave the Way for 3D Foundation Model with A Universal Pre-training Paradigm
Abstract: AbstractIn contrast to numerous NLP and 2D computer vision foundational models, the learning of a robust and highly generalized 3D foundational model poses considerably greater challenges. This is primarily due to th... | 2023 | public |
Ego4D | RGNet | RGNet: A Unified Clip Retrieval and Grounding Network for Long Videos | 2023-12-11T00:00:00 | https://arxiv.org/abs/2312.06729v3 | [
"https://github.com/tanveer81/rgnet"
] | In the paper 'RGNet: A Unified Clip Retrieval and Grounding Network for Long Videos', what R@1 IoU=0.3 score did the RGNet model get on the Ego4D dataset
| 20.63 | Title: RGNet: A Unified Retrieval and Grounding Network for Long Videos
Abstract: AbstractWe present a novel end-to-end method for long-form video temporal grounding to locate specific moments described by natural language queries. Prior long-video methods for this task typically contain two stages: proposal selection... | 2023 | public |
Fashion IQ | CoVR-BLIP | CoVR-2: Automatic Data Construction for Composed Video Retrieval | 2023-08-28T00:00:00 | https://arxiv.org/abs/2308.14746v4 | [
"https://github.com/lucas-ventura/CoVR"
] | In the paper 'CoVR-2: Automatic Data Construction for Composed Video Retrieval', what (Recall@10+Recall@50)/2 score did the CoVR-BLIP model get on the Fashion IQ dataset
| 59.39 | Title: CoVR: Learning Composed Video Retrievalfrom Web Video Captions
Abstract: AbstractComposed Image Retrieval (CoIR) has recently gained popularity
as a task that considersbothtext and image queries together, to search for relevant images in a database. Most CoIR approaches require manually annotated datasets, comp... | 2023 | public |
COCO test-dev | LeYOLO-Medium@640 | 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 Params (M) score did the LeYOLO-Medium@640 model get on the COCO test-dev dataset
| 2.4 | Title: LeYOLO, New Scalable and Efficient CNN Architecture for Object Detection
Abstract: AbstractComputational efficiency in deep neural networks is critical for object detection, especially as newer models prioritize speed over efficient computation (FLOP). This evolution has somewhat left behind embedded and mobile... | 2024-2025 | public |
MedConceptsQA | BioMistral/BioMistral-7B-DARE | BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical Domains | 2024-02-15T00:00:00 | https://arxiv.org/abs/2402.10373v3 | [
"https://github.com/biomistral/biomistral"
] | In the paper 'BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical Domains', what Accuracy score did the BioMistral/BioMistral-7B-DARE model get on the MedConceptsQA dataset
| 25.058 | Title: BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical Domains
Abstract: AbstractLarge Language Models (LLMs) have demonstrated remarkable versatility in recent years, offering potential applications across specialized domains such as healthcare and medicine. Despite the availabili... | 2024-2025 | public |
MVTec AD | 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 MVTec AD dataset
| 96.6 | Title: AnomalyDINO:
Boosting Patch-based Few-shot Anomaly Detection with DINOv2
Abstract: AbstractRecent advances in multimodal foundation models have set new standards in few-shot anomaly detection. This paper explores whether high-quality visual features alone are sufficient to rival existing state-of-the-art vision... | 2024-2025 | public |
PASCAL Context-59 | CLIPSelf | CLIPSelf: Vision Transformer Distills Itself for Open-Vocabulary Dense Prediction | 2023-10-02T00:00:00 | https://arxiv.org/abs/2310.01403v2 | [
"https://github.com/wusize/clipself"
] | In the paper 'CLIPSelf: Vision Transformer Distills Itself for Open-Vocabulary Dense Prediction', what mIoU score did the CLIPSelf model get on the PASCAL Context-59 dataset
| 62.3 | Title: CLIPSelf: Vision Transformer Distills Itself for Open-Vocabulary Dense Prediction
Abstract: AbstractOpen-vocabulary dense prediction tasks including object detection and image segmentation have been advanced by the success of Contrastive Language-Image Pre-training (CLIP). CLIP models, particularly those incorp... | 2023 | public |
Cityscapes to Foggy Cityscapes | 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 mAP@0.5 score did the UMT (ResNet50-FPN) model get on the Cityscapes to Foggy Cityscapes dataset
| 61.4 | Title: Align and Distill: Unifying and Improving
Domain Adaptive Object Detection
Abstract: AbstractObject detectors often perform poorly on data that differs from their training set. Domain adaptive object detection (DAOD) methods have recently demonstrated strong results on addressing this challenge. Unfortunately, ... | 2024-2025 | public |
AIR | ReGaDa | Video-adverb retrieval with compositional adverb-action embeddings | 2023-09-26T00:00:00 | https://arxiv.org/abs/2309.15086v1 | [
"https://github.com/ExplainableML/ReGaDa"
] | In the paper 'Video-adverb retrieval with compositional adverb-action embeddings', what mAP W score did the ReGaDa model get on the AIR dataset
| 0.704 | Title: Video-adverb retrieval with compositional adverb-action embeddings
Abstract: AbstractRetrieving adverbs that describe an action in a video poses a crucial step towards fine-grained video understanding. We propose a framework for video-to-adverb retrieval (and vice versa) that aligns video embeddings with their ... | 2023 | public |
TXL-PBC: a freely accessible labeled peripheral blood cell dataset | yolov8s | TXL-PBC: a freely accessible labeled peripheral blood cell dataset | 2024-07-18T00:00:00 | https://arxiv.org/abs/2407.13214v1 | [
"https://github.com/lugan113/TXL-PBC_Dataset"
] | In the paper 'TXL-PBC: a freely accessible labeled peripheral blood cell dataset', what mAP50 score did the yolov8s model get on the TXL-PBC: a freely accessible labeled peripheral blood cell dataset dataset
| 0.977 | Title: TXL-PBC: a freely accessible labeled peripheral blood cell dataset
Abstract: AbstractIn a recent study, we found that publicly BCCD and BCD datasets have significant issues such as labeling errors, insufficient sample size, and poor data quality. To address these problems, we performed sample deletion, re-label... | 2024-2025 | public |
Weather2K850 (720) | MoLE-DLinear | Mixture-of-Linear-Experts for Long-term Time Series Forecasting | 2023-12-11T00:00:00 | https://arxiv.org/abs/2312.06786v3 | [
"https://github.com/rogerni/mole"
] | In the paper 'Mixture-of-Linear-Experts for Long-term Time Series Forecasting', what MSE score did the MoLE-DLinear model get on the Weather2K850 (720) dataset
| 0.461 | Title: Mixture-of-Linear-Experts for Long-term Time Series Forecasting
Abstract: AbstractLong-term time series forecasting (LTSF) aims to predict future values of a time series given the past values. The current state-of-the-art (SOTA) on this problem is attained in some cases bylinear-centricmodels, which primarily f... | 2023 | public |
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