https://github.com/shiyujiao/cross_view_localization_CVFT
공식 코드Optimal Feature Transport for Cross-View Image Geo-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.
Optimal Feature Transport for Cross-View Image Geo-Localization
Multispectral Detection Transformer with Infrared-Centric Sensor Fusion
Calibrated and Partially Calibrated Semi-Generalized Homographies
CAM Back Again: Large Kernel CNNs from a Weakly Supervised Object Localization Perspective
Many-Shot In-Context Learning in Multimodal Foundation Models
DAFNe: A One-Stage Anchor-Free Approach for Oriented Object Detection
Audio-Visual Grouping Network for Sound Localization from Mixtures
Re-Attention Transformer for Weakly Supervised Object Localization
Contrastive Corpus Attribution for Explaining Representations
Cascade-DETR: Delving into High-Quality Universal Object Detection
BEV-CV: Birds-Eye-View Transform for Cross-View Geo-Localisation
A Simple Single-Scale Vision Transformer for Object Localization and Instance Segmentation
CORA: Adapting CLIP for Open-Vocabulary Detection with Region Prompting and Anchor Pre-Matching
MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation
YCB-LUMA: YCB Object Dataset with Luminance Keying for Object Localization
Prismatic VLMs: Investigating the Design Space of Visually-Conditioned Language Models
Prismatic VLMs: Investigating the Design Space of Visually-Conditioned Language Models
Rethinking the Route Towards Weakly Supervised Object Localization
Boosting Segment Anything Model Towards Open-Vocabulary Learning
Distance to Center of Mass Encoding for Instance Segmentation
Localizing Objects with Self-Supervised Transformers and no Labels
Unsupervised Object Localization in the Era of Self-Supervised ViTs: A Survey
Unsupervised Object Localization: Observing the Background to Discover Objects
TS-CAM: Token Semantic Coupled Attention Map for Weakly Supervised Object Localization
CLIP the Gap: A Single Domain Generalization Approach for Object Detection
End-to-end detection-segmentation network with ROI convolution
Grounding Everything: Emerging Localization Properties in Vision-Language Transformers
Revisiting Color-Event based Tracking: A Unified Network, Dataset, and Metric
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.