Cross-Modality Paired-Images Generation for RGB-Infrared Person Re-Identification

RGB-Infrared (IR) person re-identification is very challenging due to the large cross-modality variations between RGB and IR images. The key solution is to learn aligned features to the bridge RGB and IR modalities. However, due to the lack of correspondence labels between every pair of RGB and IR images, most methods try to alleviate the variations with set-level alignment by reducing the distance between the entire RGB and IR sets. However, this set-level alignment may lead to misalignment of some instances, which limits the performance for RGB-IR Re-ID. Different from existing methods, in this paper, we propose to generate cross-modality paired-images and perform both global set-level and fine-grained instance-level alignments. Our proposed method enjoys several merits. First, our method can perform set-level alignment by disentangling modality-specific and modality-invariant features. Compared with conventional methods, ours can explicitly remove the modality-specific features and the modality variation can be better reduced. Second, given cross-modality unpaired-images of a person, our method can generate cross-modality paired images from exchanged images. With them, we can directly perform instance-level alignment by minimizing distances of every pair of images. Extensive experimental results on two standard benchmarks demonstrate that the proposed model favourably against state-of-the-art methods. Especially, on SYSU-MM01 dataset, our model can achieve a gain of 9.2% and 7.7% in terms of Rank-1 and mAP. Code is available at https://github.com/wangguanan/JSIA-ReID.Comment: accepted by AAAI'2

Faster Person Re-Identification

Fast person re-identification (ReID) aims to search person images quickly and accurately. The main idea of recent fast ReID methods is the hashing algorithm, which learns compact binary codes and performs fast Hamming distance and counting sort. However, a very long code is needed for high accuracy (e.g. 2048), which compromises search speed. In this work, we introduce a new solution for fast ReID by formulating a novel Coarse-to-Fine (CtF) hashing code search strategy, which complementarily uses short and long codes, achieving both faster speed and better accuracy. It uses shorter codes to coarsely rank broad matching similarities and longer codes to refine only a few top candidates for more accurate instance ReID. Specifically, we design an All-in-One (AiO) framework together with a Distance Threshold Optimization (DTO) algorithm. In AiO, we simultaneously learn and enhance multiple codes of different lengths in a single model. It learns multiple codes in a pyramid structure, and encourage shorter codes to mimic longer codes by self-distillation. DTO solves a complex threshold search problem by a simple optimization process, and the balance between accuracy and speed is easily controlled by a single parameter. It formulates the optimization target as a $F_{\beta}$ score that can be optimised by Gaussian cumulative distribution functions. Experimental results on 2 datasets show that our proposed method (CtF) is not only 8% more accurate but also 5x faster than contemporary hashing ReID methods. Compared with non-hashing ReID methods, CtF is $50\times$ faster with comparable accuracy. Code is available at https://github.com/wangguanan/light-reid.Comment: accepted by ECCV2020, https://github.com/wangguanan/light-rei

Domain Adaptive Person Search via GAN-based Scene Synthesis for Cross-scene Videos

Person search has recently been a challenging task in the computer vision domain, which aims to search specific pedestrians from real cameras.Nevertheless, most surveillance videos comprise only a handful of images of each pedestrian, which often feature identical backgrounds and clothing. Hence, it is difficult to learn more discriminative features for person search in real scenes. To tackle this challenge, we draw on Generative Adversarial Networks (GAN) to synthesize data from surveillance videos. GAN has thrived in computer vision problems because it produces high-quality images efficiently. We merely alter the popular Fast R-CNN model, which is capable of processing videos and yielding accurate detection outcomes. In order to appropriately relieve the pressure brought by the two-stage model, we design an Assisted-Identity Query Module (AIDQ) to provide positive images for the behind part. Besides, the proposed novel GAN-based Scene Synthesis model that can synthesize high-quality cross-id person images for person search tasks. In order to facilitate the feature learning of the GAN-based Scene Synthesis model, we adopt an online learning strategy that collaboratively learns the synthesized images and original images. Extensive experiments on two widely used person search benchmarks, CUHK-SYSU and PRW, have shown that our method has achieved great performance, and the extensive ablation study further justifies our GAN-synthetic data can effectively increase the variability of the datasets and be more realistic