Progressive Bilateral-Context Driven Model for Post-Processing Person Re-Identification

Abstract

Most existing person re-identification methods compute pairwise similarity by extracting robust visual features and learning the discriminative metric. Owing to visual ambiguities, these content-based methods that determine the pairwise relationship only based on the similarity between them, inevitably produce a suboptimal ranking list. Instead, the pairwise similarity can be estimated more accurately along the geodesic path of the underlying data manifold by exploring the rich contextual information of the sample. In this paper, we propose a lightweight post-processing person re-identification method in which the pairwise measure is determined by the relationship between the sample and the counterpart's context in an unsupervised way. We translate the point-to-point comparison into the bilateral point-to-set comparison. The sample's context is composed of its neighbor samples with two different definition ways: the first order context and the second order context, which are used to compute the pairwise similarity in sequence, resulting in a progressive post-processing model. The experiments on four large-scale person re-identification benchmark datasets indicate that (1) the proposed method can consistently achieve higher accuracies by serving as a post-processing procedure after the content-based person re-identification methods, showing its stateof-the-art results, (2) the proposed lightweight method only needs about 6 milliseconds for optimizing the ranking results of one sample, showing its high-efficiency.

A Comprehensive Survey on Video Saliency Detection With Auditory Information: The Audio-Visual Consistency Perceptual is the Key!

“…12. The methodology of the FAM task is quite similar to that of the person re-identification (ReID) [93], [94], [95], [96], while the major difference relies only on their feature modalities, where the ReID task only needs to consider the visual domain, while the FAM task needs to consider both audio and visual. Also, the key to succeeding in matching faces and voices heavily relies on the design of an appropriate audio-visual fusion.…”

Domain Camera Adaptation and Collaborative Multiple Feature Clustering for Unsupervised Person Re-ID

“…Extensive supervised methods have been developed on the widely used benchmarks [26,41,42], concentrating on discriminative feature representation learning [2,20], deep metric learning [17,36], postprocessing procedures [1,4,43], and other problems such as occlusion [14], various image resolutions [31]. Although great progress has been observed, these supervised approaches may have a significant performance drop when applied to another unseen domain due to the existence of domain shift.…”