https://github.com/xin-ran-w/capagent
공식 코드From Simple to Professional: A Combinatorial Controllable Image Captioning Agent
Papers with Code 아카이브
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태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/xinDW/RFGANM
공식 코드High-throughput, high-resolution registration-free generated adversarial network microscopy
https://github.com/xindongzhang/elan
공식 코드Efficient Long-Range Attention Network for Image Super-resolution
https://github.com/xingaoli/dp-hoi
공식 코드Disentangled Pre-training for Human-Object Interaction Detection
https://github.com/xinge008/Cylinder3D
공식 코드Cylinder3D: An Effective 3D Framework for Driving-scene LiDAR Semantic Segmentation
https://github.com/xinge008/SSN
공식 코드SSN: Shape Signature Networks for Multi-class Object Detection from Point Clouds
https://github.com/xinghaochen/DECO
공식 코드https://github.com/xinghaochen/slab
공식 코드https://github.com/xingmingyu123456/knowledgeswapping
공식 코드https://github.com/xingyi-li/icontrol3d
공식 코드iControl3D: An Interactive System for Controllable 3D Scene Generation
https://github.com/xingyizhou/CenterNet
공식 코드https://github.com/xingyizhou/CenterNet2
공식 코드https://github.com/xingyizhou/CenterTrack
공식 코드https://github.com/xingyizhou/ExtremeNet
공식 코드Bottom-up Object Detection by Grouping Extreme and Center Points
https://github.com/xingyizhou/UniDet
공식 코드https://github.com/xingyu-group/3d-density-resampling-sdg
공식 코드Domain Generalization of 3D Object Detection by Density-Resampling
https://github.com/xinliu20/mec
공식 코드https://github.com/xinntao/ESRGAN
공식 코드ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/xinntao/SFTGAN
공식 코드Recovering Realistic Texture in Image Super-resolution by Deep Spatial Feature Transform
https://github.com/xinrui014/softshadow
공식 코드SoftShadow: Leveraging Penumbra-Aware Soft Masks for Shadow Removal
https://github.com/xinshuoweng/mono3D_PLiDAR
공식 코드https://github.com/xinw1012/cycle-confusion
공식 코드Robust Object Detection via Instance-Level Temporal Cycle Confusion
https://github.com/xinzhuma/3dodi-survey
공식 코드3D Object Detection from Images for Autonomous Driving: A Survey
https://github.com/xinzhuma/monodle
공식 코드Delving into Localization Errors for Monocular 3D Object Detection
https://github.com/xiuheng-wang/Deep_gradient_HSI_superresolution
공식 코드Hyperspectral Image Super-resolution with Deep Priors and Degradation Model Inversion
https://github.com/xiuheng-wang/Sylvester_TSFN_MDC_HSI_superresolution
공식 코드Hyperspectral Image Super-Resolution via Deep Prior Regularization with Parameter Estimation
https://github.com/xiuqhou/Salience-DETR
공식 코드Salience DETR: Enhancing Detection Transformer with Hierarchical Salience Filtering Refinement
https://github.com/xiuqhou/relation-detr
공식 코드Relation DETR: Exploring Explicit Position Relation Prior for Object Detection
https://github.com/xjtlu-cvlab/3drom
공식 코드3D Random Occlusion and Multi-Layer Projection for Deep Multi-Camera Pedestrian Localization
https://github.com/xjtu-haolin/point_assisted_sample_selection
공식 코드Leveraging Anchor-based LiDAR 3D Object Detection via Point Assisted Sample Selection
https://github.com/xk-huang/segment-caption-anything
공식 코드https://github.com/xl-tang3/RCOT
공식 코드https://github.com/xmuqimingxia/dota
공식 코드Learning to Detect Objects from Multi-Agent LiDAR Scans without Manual Labels
https://github.com/xmuqimingxia/sp3d
공식 코드SP3D: Boosting Sparsely-Supervised 3D Object Detection via Accurate Cross-Modal Semantic Prompts
https://github.com/xpixelgroup/diffbir
공식 코드DiffBIR: Towards Blind Image Restoration with Generative Diffusion Prior
https://github.com/xpixelgroup/hat
공식 코드Activating More Pixels in Image Super-Resolution Transformer
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.