https://github.com/callsys/genpromp
공식 코드Generative Prompt Model for Weakly Supervised Object Localization
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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
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Generative Prompt Model for Weakly Supervised Object Localization
Vote2Cap-DETR++: Decoupling Localization and Describing for End-to-End 3D Dense Captioning
Context-Aware Entity Grounding with Open-Vocabulary 3D Scene Graphs
OCDet: Object Center Detection via Bounding Box-Aware Heatmap Prediction on Edge Devices with NPUs
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
Evaluating Weakly Supervised Object Localization Methods Right
IllusionVQA: A Challenging Optical Illusion Dataset for Vision Language Models
Few-Shot Object Detection via Variational Feature Aggregation
Prototypical VoteNet for Few-Shot 3D Point Cloud Object Detection
A Low-Shot Object Counting Network With Iterative Prototype Adaptation
Faraway-Frustum: Dealing with Lidar Sparsity for 3D Object Detection using Fusion
Revisiting Color-Event based Tracking: A Unified Network, Dataset, and Metric
MambaEVT: Event Stream based Visual Object Tracking using State Space Model
QUB-PHEO: A Visual-Based Dyadic Multi-View Dataset for Intention Inference in Collaborative Assembly
Context-Aware 3D Object Localization from Single Calibrated Images: A Study of Basketballs
CV-Cities: Advancing Cross-View Geo-Localization in Global Cities
Class-aware Sounding Objects Localization via Audiovisual Correspondence
LocNet: Improving Localization Accuracy for Object Detection
Object detection via a multi-region & semantic segmentation-aware CNN model
Global-Local Collaborative Inference with LLM for Lidar-Based Open-Vocabulary Detection
Learning to Segment from Scribbles using Multi-scale Adversarial Attention Gates
PEEKABOO: Hiding parts of an image for unsupervised object localization
DynaMem: Online Dynamic Spatio-Semantic Memory for Open World Mobile Manipulation
Dual Progressive Transformations for Weakly Supervised Semantic Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.