Zobrazeno 1 - 10
of 43
pro vyhledávání: '"Pan, Honghu"'
Data Generation Scheme for Thermal Modality with Edge-Guided Adversarial Conditional Diffusion Model
In challenging low light and adverse weather conditions,thermal vision algorithms,especially object detection,have exhibited remarkable potential,contrasting with the frequent struggles encountered by visible vision algorithms. Nevertheless,the effic
Externí odkaz:
http://arxiv.org/abs/2408.03748
Compared to visible-to-visible (V2V) person re-identification (ReID), the visible-to-infrared (V2I) person ReID task is more challenging due to the lack of sufficient training samples and the large cross-modality discrepancy. To this end, we propose
Externí odkaz:
http://arxiv.org/abs/2210.01585
The video-based person re-identification (ReID) aims to identify the given pedestrian video sequence across multiple non-overlapping cameras. To aggregate the temporal and spatial features of the video samples, the graph neural networks (GNNs) are in
Externí odkaz:
http://arxiv.org/abs/2209.11584
Existing methods for video-based person re-identification (ReID) mainly learn the appearance feature of a given pedestrian via a feature extractor and a feature aggregator. However, the appearance models would fail when different pedestrians have sim
Externí odkaz:
http://arxiv.org/abs/2209.11582
The model-based gait recognition methods usually adopt the pedestrian walking postures to identify human beings. However, existing methods did not explicitly resolve the large intra-class variance of human pose due to camera views changing. In this p
Externí odkaz:
http://arxiv.org/abs/2209.11577
Publikováno v:
In Expert Systems With Applications 15 March 2024 238 Part C
Publikováno v:
In Knowledge-Based Systems 15 February 2024 285
The constraint of neighborhood consistency or local consistency is widely used for robust image matching. In this paper, we focus on learning neighborhood topology consistent descriptors (TCDesc), while former works of learning descriptors, such as H
Externí odkaz:
http://arxiv.org/abs/2009.07036
Triplet loss is widely used for learning local descriptors from image patch. However, triplet loss only minimizes the Euclidean distance between matching descriptors and maximizes that between the non-matching descriptors, which neglects the topology
Externí odkaz:
http://arxiv.org/abs/2006.03254
Publikováno v:
In Neural Networks March 2023 160:22-33