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2D Classification

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

https://github.com/lihaoxin2020/summarization-based-document-ids-for-generative-retrieval

공식 코드

Summarization-Based Document IDs for Generative Retrieval with Language Models

https://github.com/lihenryhfl/diffusion_optimal_control

공식 코드

Solving Inverse Problems via Diffusion Optimal Control

https://github.com/lijfrank-open/SFCHD-SCALE

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Large, Complex, and Realistic Safety Clothing and Helmet Detection: Dataset and Method

https://github.com/lijuncen/Sentiment-and-Style-Transfer

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Delete, Retrieve, Generate: A Simple Approach to Sentiment and Style Transfer

https://github.com/liketheflower/simcrosstrans

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simCrossTrans: A Simple Cross-Modality Transfer Learning for Object Detection with ConvNets or Vision Transformers

https://github.com/liketheflower/uoddm

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Unified Object Detector for Different Modalities based on Vision Transformers

https://github.com/likojack/bnv_fusion

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BNV-Fusion: Dense 3D Reconstruction using Bi-level Neural Volume Fusion

https://github.com/likojack/odam

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ODAM: Object Detection, Association, and Mapping using Posed RGB Video

https://github.com/lil-lab/newsroom

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Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategies

https://github.com/lilyli2004/satire

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A Multi-Modal Method for Satire Detection using Textual and Visual Cues

https://github.com/limbo0000/InstanceLoc

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Instance Localization for Self-supervised Detection Pretraining

https://github.com/lime-j/lime-eval

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LIME-Eval: Rethinking Low-light Image Enhancement Evaluation via Object Detection

https://github.com/liming-ai/AlignDet

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AlignDet: Aligning Pre-training and Fine-tuning in Object Detection

https://github.com/limingcv/Photorealistic-Style-Transfer

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High-Resolution Network for Photorealistic Style Transfer

https://github.com/linear95/rlm

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Replacing Language Model for Style Transfer

https://github.com/linfeng-tang/psfusion

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Rethinking the Evaluation of Visible and Infrared Image Fusion

https://github.com/linfeng93/large-unidet

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Universal Object Detection with Large Vision Model

https://github.com/linfengWen98/CAP-VSTNet

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CAP-VSTNet: Content Affinity Preserved Versatile Style Transfer

https://github.com/lingxiaoli94/pol

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Learning Proximal Operators to Discover Multiple Optima

https://github.com/lingyanruan/DRBNet

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Learning to Deblur using Light Field Generated and Real Defocus Images

https://github.com/lingyanruan/lakdnet

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Revisiting Image Deblurring with an Efficient ConvNet

https://github.com/linhanwang/DC-Gaussian

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DC-Gaussian: Improving 3D Gaussian Splatting for Reflective Dash Cam Videos

https://github.com/linjing7/VR-Baseline

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Flow-Guided Sparse Transformer for Video Deblurring

https://github.com/linkon87/exploring-diversity-based-active-learning-for-3d-object-detection-in-autonomous-driving

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Exploring Diversity-based Active Learning for 3D Object Detection in Autonomous Driving

https://github.com/linllll/maskst

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Less is More: Masking Elements in Image Condition Features Avoids Content Leakages in Style Transfer Diffusion Models

https://github.com/linnanwang/AlphaX-NASBench101

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AlphaX: eXploring Neural Architectures with Deep Neural Networks and Monte Carlo Tree Search

https://github.com/linshan-bin/occnerf

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OccNeRF: Advancing 3D Occupancy Prediction in LiDAR-Free Environments

https://github.com/lintotao/out-of-bbox-attack

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Out-of-Bounding-Box Triggers: A Stealthy Approach to Cheat Object Detectors

https://github.com/linusericsson/ssl-transfer

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How Well Do Self-Supervised Models Transfer?

https://github.com/linuxsino/senet

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A Simple yet Effective Network based on Vision Transformer for Camouflaged Object and Salient Object Detection

https://github.com/linwei-chen/fadc

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Frequency-Adaptive Dilated Convolution for Semantic Segmentation

https://github.com/linwei-chen/fdconv

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Frequency Dynamic Convolution for Dense Image Prediction

https://github.com/linwei-chen/lis

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Instance Segmentation in the Dark

https://github.com/linxuewu/sparse4d

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Sparse4D: Multi-view 3D Object Detection with Sparse Spatial-Temporal Fusion

https://github.com/liruilong940607/OCHumanApi

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Pose2Seg: Detection Free Human Instance Segmentation

https://github.com/liruilong940607/Pose2Seg

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Pose2Seg: Detection Free Human Instance Segmentation

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