https://github.com/wayne-mai/egoloc
공식 코드EgoLoc: Revisiting 3D Object Localization from Egocentric Videos with Visual Queries
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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.
EgoLoc: Revisiting 3D Object Localization from Egocentric Videos with Visual Queries
Shallow Feature Matters for Weakly Supervised Object Localization
Cascaded Parallel Filtering for Memory-Efficient Image-Based Localization
A Novel Confidence Induced Class Activation Mapping for MRI Brain Tumor Segmentation
BEVNeXt: Reviving Dense BEV Frameworks for 3D Object Detection
Background Activation Suppression for Weakly Supervised Object Localization
Spatial-Aware Token for Weakly Supervised Object Localization
Each Part Matters: Local Patterns Facilitate Cross-view Geo-localization
CLIP-DIY: CLIP Dense Inference Yields Open-Vocabulary Semantic Segmentation For-Free
Rethinking Localization Map: Towards Accurate Object Perception with Self-Enhancement Maps
Self-produced Guidance for Weakly-supervised Object Localization
Multi-class Token Transformer for Weakly Supervised Semantic Segmentation
Crafting Better Contrastive Views for Siamese Representation Learning
Simple, Effective and General: A New Backbone for Cross-view Image Geo-localization
Unsupervised Object Localization with Representer Point Selection
Bounding Box Regression with Uncertainty for Accurate Object Detection
Bounding Box Regression with Uncertainty for Accurate Object Detection
RfD-Net: Point Scene Understanding by Semantic Instance Reconstruction
EDN: Salient Object Detection via Extremely-Downsampled Network
Extending Large Vision-Language Model for Diverse Interactive Tasks in Autonomous Driving
DenseTrack: Drone-based Crowd Tracking via Density-aware Motion-appearance Synergy
Bagging Regional Classification Activation Maps for Weakly Supervised Object Localization
Bilateral Reference for High-Resolution Dichotomous Image Segmentation
TBPos: Dataset for Large-Scale Precision Visual Localization
Does Thermal Really Always Matter for RGB-T Salient Object Detection?
Meta-DETR: Image-Level Few-Shot Object Detection with Inter-Class Correlation Exploitation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.