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2D Classification

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전체 기법전체 데이터셋

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/AlexGidiotis/PMC-StructuredAbstracts-Dataset

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Structured Summarization of Academic Publications

https://github.com/Alexei95/FasTrCaps

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FasTrCaps: An Integrated Framework for Fast yet Accurate Training of Capsule Networks

https://github.com/AlexeyAB/darknet

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CSPNet: A New Backbone that can Enhance Learning Capability of CNN

https://github.com/Alibaba-MIIL/ASL

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Asymmetric Loss For Multi-Label Classification

https://github.com/AlibabaResearch/efficientteacher

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Efficient Teacher: Semi-Supervised Object Detection for YOLOv5

https://github.com/AlifAshrafee/ViT-pytorch-for-Cooking-State-Recognition

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Rethinking Cooking State Recognition with Vision Transformers

https://github.com/AlmoonYsl/OPEN

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OPEN: Object-wise Position Embedding for Multi-view 3D Object Detection

https://github.com/AlonZolfi/YolOOD

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YolOOD: Utilizing Object Detection Concepts for Multi-Label Out-of-Distribution Detection

https://github.com/Amirhosein2c/SegFreeOP

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Segmentation-Free Outcome Prediction from Head and Neck Cancer PET/CT Images: Deep Learning-Based Feature Extraction from Multi-Angle Maximum Intensity Projections (MA-MIPs)

https://github.com/AmirhoseinCh/DynamicPatch

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Dynamic Adversarial Attacks on Autonomous Driving Systems

https://github.com/Amirhossein-Nayebi/Nordic-Vehicle-Dataset

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Nordic Vehicle Dataset (NVD): Performance of vehicle detectors using newly captured NVD from UAV in different snowy weather conditions

https://github.com/Amshaker/GroupMamba

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GroupMamba: Efficient Group-Based Visual State Space Model

https://github.com/AnasIbrahim/image_agnostic_segmentation

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DoUnseen: Tuning-Free Class-Adaptive Object Detection of Unseen Objects for Robotic Grasping

https://github.com/Andrew-Qibin/CoordAttention

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Coordinate Attention for Efficient Mobile Network Design

https://github.com/Andrew-Qibin/ssdlite-pytorch

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Rethinking Bottleneck Structure for Efficient Mobile Network Design

https://github.com/Andrews2017/KINNEWS-and-KIRNEWS-Corpus

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KINNEWS and KIRNEWS: Benchmarking Cross-Lingual Text Classification for Kinyarwanda and Kirundi

https://github.com/Andy-jqa/bioelmo

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Probing Biomedical Embeddings from Language Models

https://github.com/Anna146/HiddenAttributeModels

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Listening between the Lines: Learning Personal Attributes from Conversations

https://github.com/Annbless/ViTAE

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ViTAE: Vision Transformer Advanced by Exploring Intrinsic Inductive Bias

https://github.com/Anon6627/Pruning-PGP

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Progressive Gradient Pruning for Classification, Detection and DomainAdaptation

https://github.com/Anup-Deshmukh/TREC_background_linking

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IR-BERT: Leveraging BERT for Semantic Search in Background Linking for News Articles

https://github.com/Anymake/DRN_CVPR2020

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Dynamic Refinement Network for Oriented and Densely Packed Object Detection

https://github.com/Apogentus/style-transfer-with-strength-control

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Real-Time Style Transfer With Strength Control

https://github.com/ApolloScapeAuto/dataset-api

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CVPR 2019 WAD Challenge on Trajectory Prediction and 3D Perception

https://github.com/Ardibid/ArtisticStyleRoboticPainting

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Artistic Style in Robotic Painting; a Machine Learning Approach to Learning Brushstroke from Human Artists

https://github.com/ArminLee/OWOD_Review

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Open World Object Detection: A Survey

https://github.com/ArrowHuang/NumHG

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NumHG: A Dataset for Number-Focused Headline Generation

https://github.com/ArrowLuo/GRACE

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GRACE: Gradient Harmonized and Cascaded Labeling for Aspect-based Sentiment Analysis

https://github.com/ArturoDeza/NeuroFovea

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Towards Metamerism via Foveated Style Transfer

https://github.com/Ascend-Research/AIO-P

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AIO-P: Expanding Neural Performance Predictors Beyond Image Classification

https://github.com/Ascend-Research/CascadedGaze

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CascadedGaze: Efficiency in Global Context Extraction for Image Restoration

https://github.com/Ascend-Research/Turtle

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Learning Truncated Causal History Model for Video Restoration

https://github.com/AtlantixJJ/frame-difference-loss

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Frame Difference-Based Temporal Loss for Video Stylization

https://github.com/AtticusXu/B-Net2

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Butterfly-Net2: Simplified Butterfly-Net and Fourier Transform Initialization

https://github.com/Au3C2/Generator-Versus-Segmentor

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Generator Versus Segmentor: Pseudo-healthy Synthesis

https://github.com/AutoAILab/FusionDepth

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Advancing Self-supervised Monocular Depth Learning with Sparse LiDAR

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.