Manifold-based Incomplete Multi-view Clustering via Bi-Consistency Guidance

Incomplete multi-view clustering primarily focuses on dividing unlabeled data into corresponding categories with missing instances, and has received intensive attention due to its superiority in real applications. Considering the influence of incomplete data, the existing methods mostly attempt to recover data by adding extra terms. However, for the unsupervised methods, a simple recovery strategy will cause errors and outlying value accumulations, which will affect the performance of the methods. Broadly, the previous methods have not taken the effectiveness of recovered instances into consideration, or cannot flexibly balance the discrepancies between recovered data and original data. To address these problems, we propose a novel method termed Manifold-based Incomplete Multi-view clustering via Bi-consistency guidance (MIMB), which flexibly recovers incomplete data among various views, and attempts to achieve biconsistency guidance via reverse regularization. In particular, MIMB adds reconstruction terms to representation learning by recovering missing instances, which dynamically examines the latent consensus representation. Moreover, to preserve the consistency information among multiple views, MIMB implements a biconsistency guidance strategy with reverse regularization of the consensus representation and proposes a manifold embedding measure for exploring the hidden structure of the recovered data. Notably, MIMB aims to balance the importance of different views, and introduces an adaptive weight term for each view. Finally, an optimization algorithm with an alternating iteration optimization strategy is designed for final clustering. Extensive experimental results on 6 benchmark datasets are provided to confirm that MIMB can significantly obtain superior results as compared with several state-of-the-art baselines.

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.

Graph-Collaborated Auto-Encoder Hashing for Multi-view Binary Clustering

Unsupervised hashing methods have attracted widespread attention with the explosive growth of large-scale data, which can greatly reduce storage and computation by learning compact binary codes. Existing unsupervised hashing methods attempt to exploit the valuable information from samples, which fails to take the local geometric structure of unlabeled samples into consideration. Moreover, hashing based on auto-encoders aims to minimize the reconstruction loss between the input data and binary codes, which ignores the potential consistency and complementarity of multiple sources data. To address the above issues, we propose a hashing algorithm based on auto-encoders for multi-view binary clustering, which dynamically learns affinity graphs with low-rank constraints and adopts collaboratively learning between auto-encoders and affinity graphs to learn a unified binary code, called Graph-Collaborated Auto-Encoder Hashing for Multi-view Binary Clustering (GCAE). Specifically, we propose a multi-view affinity graphs learning model with low-rank constraint, which can mine the underlying geometric information from multi-view data. Then, we design an encoder-decoder paradigm to collaborate the multiple affinity graphs, which can learn a unified binary code effectively. Notably, we impose the decorrelation and code balance constraints on binary codes to reduce the quantization errors. Finally, we utilize an alternating iterative optimization scheme to obtain the multi-view clustering results. Extensive experimental results on $5$ public datasets are provided to reveal the effectiveness of the algorithm and its superior performance over other state-of-the-art alternatives.