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/Skyy93/Sample4Geo
공식 코드Sample4Geo: Hard Negative Sampling For Cross-View Geo-Localisation
https://github.com/Vegeta2020/CIA-SSD
공식 코드CIA-SSD: Confident IoU-Aware Single-Stage Object Detector From Point Cloud
https://github.com/VinAIResearch/Open3DIS
공식 코드Open3DIS: Open-Vocabulary 3D Instance Segmentation with 2D Mask Guidance
https://github.com/Vision-sejin/FDCNet
공식 코드https://github.com/WinFrand/MELM
공식 코드Min-Entropy Latent Model for Weakly Supervised Object Detection
https://github.com/Winfrand/C-MIL
공식 코드C-MIL: Continuation Multiple Instance Learning for Weakly Supervised Object Detection
https://github.com/WoojuLee24/OA-DG
공식 코드https://github.com/Yangseung/NL-CCAM
공식 코드Combinational Class Activation Maps for Weakly Supervised Object Localization
https://github.com/Zengyi-Qin/MonoGRNet
공식 코드MonoGRNet: A Geometric Reasoning Network for Monocular 3D Object Localization
https://github.com/ZhangGongjie/Meta-DETR
공식 코드Meta-DETR: Image-Level Few-Shot Object Detection with Inter-Class Correlation Exploitation
https://github.com/Zhao-Tian-yi/RSDet
공식 코드Removal then Selection: A Coarse-to-Fine Fusion Perspective for RGB-Infrared Object Detection
https://github.com/ZhenyueQin/Research-Softmax-with-Mutual-Information
공식 코드Rethinking Softmax with Cross-Entropy: Neural Network Classifier as Mutual Information Estimator
https://github.com/accenture/air
공식 코드Rethinking Drone-Based Search and Rescue with Aerial Person Detection
https://github.com/adityac94/Grad_CAM_plus_plus
공식 코드Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
https://github.com/ahmdtaha/l2_caf_pytorch
공식 코드A Generic Visualization Approach for Convolutional Neural Networks
https://github.com/ahmedmagdiosman/clevr-xai
공식 코드Ground Truth Evaluation of Neural Network Explanations with CLEVR-XAI
https://github.com/ahmedmagdiosman/simply-clevr-dataset
공식 코드Ground Truth Evaluation of Neural Network Explanations with CLEVR-XAI
https://github.com/ai-4-atmosphere-remote-sensing/satellite-object-localization
공식 코드https://github.com/akhilpm/ConvSTN
공식 코드https://github.com/alexisguichemerrecode/pixelcam
공식 코드PixelCAM: Pixel Class Activation Mapping for Histology Image Classification and ROI Localization
https://github.com/allenai/grit_official
공식 코드https://github.com/amazon-research/progressive-coordinate-transforms
공식 코드Progressive Coordinate Transforms for Monocular 3D Object Detection
https://github.com/amazon-science/object-centric-multiple-object-tracking
공식 코드https://github.com/amazon-science/object-centric-vol
공식 코드https://github.com/amirbar/detreg
공식 코드DETReg: Unsupervised Pretraining with Region Priors for Object Detection
https://github.com/anguyen8/generative-attribution-methods
공식 코드Explaining image classifiers by removing input features using generative models
https://github.com/antonotnawang/hint
공식 코드https://github.com/aritrabhowmik/uoi
공식 코드Union-over-Intersections: Object Detection beyond Winner-Takes-All
https://github.com/atr-dbi/scanqa
공식 코드ScanQA: 3D Question Answering for Spatial Scene Understanding
https://github.com/baidu-research/GPN
공식 코드Detecting Lesion Bounding Ellipses With Gaussian Proposal Networks
https://github.com/bcv-uniandes/egocol
공식 코드https://github.com/billpsomas/simpool
공식 코드Keep It SimPool: Who Said Supervised Transformers Suffer from Attention Deficit?
https://github.com/blaschko/supermodularLP
공식 코드Slack and Margin Rescaling as Convex Extensions of Supermodular Functions
https://github.com/brown-palm/object-states
공식 코드https://github.com/buaavrcg/LEGaussians
공식 코드Language Embedded 3D Gaussians for Open-Vocabulary Scene Understanding
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.