Papers with Code 아카이브
Object Localization
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Object Localization is the task of locating an instance of a particular object category in an image, typically by specifying a tightly cropped bounding box centered on the instance. An object proposal specifies a candidate bounding box, and an object proposal is said to be a correct localization if it sufficiently overlaps a human-labeled “ground-truth” bounding box for the given object. In the literature, the “Object Localization” task is to locate one instance of an object category, whereas “object detection” focuses on locating all instances of a category in a given image.
Source: Fast On-Line Kernel Density Estimation for Active Object Localization
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/164140757/scm
공식 코드Weakly Supervised Object Localization via Transformer with Implicit Spatial Calibration
https://github.com/4dvlab/wildrefer
공식 코드https://github.com/82magnolia/event_localization
공식 코드https://github.com/AlexisGuichemerreCode/survey_hist_wsol_sfda
공식 코드Source-Free Domain Adaptation of Weakly-Supervised Object Localization Models for Histology
https://github.com/AmirooR/Pairwise-Similarity-knowledge-Transfer-WSOL
공식 코드Pairwise Similarity Knowledge Transfer for Weakly Supervised Object Localization
https://github.com/CAMMA-public/ConvLSTM-Surgical-Tool-Tracker
공식 코드Weakly Supervised Convolutional LSTM Approach for Tool Tracking in Laparoscopic Videos
https://github.com/CQUlearningsystemgroup/YicongPeng
공식 코드MoNet3D: Towards Accurate Monocular 3D Object Localization in Real Time
https://github.com/CurryYuan/InstanceRefer
공식 코드https://github.com/DTaoo/Discriminative-Sounding-Objects-Localization
공식 코드Discriminative Sounding Objects Localization via Self-supervised Audiovisual Matching
https://github.com/DanceTrack/DanceTrack
공식 코드DanceTrack: Multi-Object Tracking in Uniform Appearance and Diverse Motion
https://github.com/DongSky/lbba_boosted_wsod
공식 코드Boosting Weakly Supervised Object Detection via Learning Bounding Box Adjusters
https://github.com/Earth-Intelligence-Lab/vleo-bench
공식 코드Good at captioning, bad at counting: Benchmarking GPT-4V on Earth observation data
https://github.com/GeorgeDu/vision-based-robotic-grasping
공식 코드https://github.com/GradientSpaces/CrossOver
공식 코드https://github.com/GuoleiSun/MCIS_wsss
공식 코드Mining Cross-Image Semantics for Weakly Supervised Semantic Segmentation
https://github.com/HCI-LMC/VLN-SUSA
공식 코드https://github.com/HYOJINPARK/TTVOS
공식 코드https://github.com/HanjiangHu/DASGIL
공식 코드DASGIL: Domain Adaptation for Semantic and Geometric-aware Image-based Localization
https://github.com/HanjiangHu/DISAM
공식 코드https://github.com/HensoldtOptronicsCV/MultispectralPedestrianDetection
공식 코드https://github.com/Jeff-Zilence/VIGOR
공식 코드VIGOR: Cross-View Image Geo-localization beyond One-to-one Retrieval
https://github.com/KieDani/SpinAndTrajectoryTableTennis
공식 코드https://github.com/KieDani/Towards_3D_Object_Localization
공식 코드Towards Learning Monocular 3D Object Localization From 2D Labels using the Physical Laws of Motion
https://github.com/LWHYC/RPR-Loc
공식 코드https://github.com/Liumouliu/deepIBL
공식 코드Stochastic Attraction-Repulsion Embedding for Large Scale Image Localization
https://github.com/MIT-SPARK/Kimera
공식 코드Kimera: from SLAM to Spatial Perception with 3D Dynamic Scene Graphs
https://github.com/MathLee/CMWNet
공식 코드Cross-Modal Weighting Network for RGB-D Salient Object Detection
https://github.com/MathLee/OLBPNet4PFOS
공식 코드Personal Fixations-Based Object Segmentation with Object Localization and Boundary Preservation
https://github.com/MeioJane/SIXray
공식 코드https://github.com/MoonBlvd/tad-IROS2019
공식 코드Unsupervised Traffic Accident Detection in First-Person Videos
https://github.com/MurpheyLab/FLS
공식 코드https://github.com/PStelldinger/PuzzleBoard
공식 코드PuzzleBoard: A New Camera Calibration Pattern with Position Encoding
https://github.com/Panxjia/SPA_CVPR2021
공식 코드Unveiling the Potential of Structure Preserving for Weakly Supervised Object Localization
https://github.com/PruneTruong/DenseMatching
공식 코드PDC-Net+: Enhanced Probabilistic Dense Correspondence Network
https://github.com/Scalsol/RepPointsV2
공식 코드RepPoints V2: Verification Meets Regression for Object Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.