Bo Peng

2D image coding for machines (ICM) has achieved great success in coding efficiency, while less effort has been devoted to stereo image fields. To promote the efficiency of stereo image compression (SIC) and intelligent analysis, the stereo image coding for machines (SICM) is formulated and explored in this paper. More specifically, a machine vision-oriented stereo feature compression network (MVSFC-Net) is proposed for SICM, where the stereo visual features are effectively extracted, compressed, and transmitted for 3D visual task. To efficiently compress stereo visual features in MVSFC-Net, a stereo multi-scale feature compression (SMFC) module is designed to gradually transform sparse stereo multi-scale features into compact joint visual representations by removing spatial, interview, and cross-scale redundancies simultaneously. Experimental results show that the proposed MVSFC-Net obtains superior compression efficiency as well as 3D visual task performance, when compared with the existing ICM anchors recommended by MPEG and the state-of-the-art SIC method. The code is available at https://github.com/zhaob10/MVSFC-Net-for-SICM.

In recent times, sketch-based 3D shape retrieval has emerged as a pivotal theme and garnered considerable attention within the area of cross-modal retrieval. As a prevalent 3D shape modality, the exponential growth in the quantity of 3D point clouds has boosted a substantial increase in the demand for 3D point cloud retrieval. Simultaneously, due to the absence of prior knowledge about unseen classes, transferring models learned from seen classes to tackle the data from unseen classes effectively remains a significant hurdle in cross-modal retrieval. In light of this, a novel meta-learned zero-shot sketch-based point cloud retrieval (MetaZS-SBPR) network is proposed in this article for exploring cross-modal consistent feature representation from 2D sketches and 3D point clouds, while effectively transferring the knowledge from seen classes to unseen classes. Specifically, a perspective-predicted point cloud feature learning module is presented to capture discriminative features of point clouds from predicted perspectives, thereby mitigating the modal differences across point clouds and sketches. Additionally, a meta zero-shot retrieval strategy is introduced to investigate the knowledge transfer from seen classes to unseen classes harnessing meta-learning, thereby enabling the efficient retrieval of the point clouds from unseen classes. Experimental evaluations conducted on the ZS-SBPR benchmark dataset affirm the effectiveness of the proposed MetaZS-SBPR.

Multi-view clustering has achieved advanced progress over the years, which typically integrates multi-view information to learn discriminative common representations or a unified clustering distribution for clustering. However, existing methods either simply regard each view as equally important or assign fixed weights to each view, which are insufficient to dynamically assess the sample quality variations caused by noise in multi-view data. To address this issue, by effectively modeling the uncertainty of different samples across different views, this paper proposes a novel uncertainty-aware multi-view graph clustering network, termed UMGC-Net, which achieves trusted multi-view clustering in an unsupervised manner. Specifically, by measuring the clustering distribution entropy, an uncertainty-guided common feature learning mechanism is proposed to estimate the uncertainty for each sample of each view, thus learning multi-view features friendly to clustering. Besides, a cross-view trusted distribution fusion module is designed to obtain robust clustering distribution by exploring the trusted consistency among multi-view clustering distributions based on uncertainty. Finally, experimental results on four popular multi-view datasets validate the superior performance of the proposed UMGC-Net.

Recently, deep learning-based visual localization has gained significant attention and made remarkable advancements. Although previous visual localization methods have obtained promising performance on indoor or outdoor street scenes, there have been few attempts at visual localization on aerial scenes. In this article, a depth-aware aerial localization transformer (DALTR) is proposed to learn camera poses in real-world aerial scenes assisted by the depth map. To improve the ability of network to perceive on aerial scenes, a multi-level depth embedding transformer module is presented by adaptively incorporating depth information into multiple levels of transformer. In addition, to encourage the piece-wise smooth geometric characteristic of the scene coordinates, a depth-guided smoothness constraint is developed to provide additional supervision for scene coordinate regression. Extensive experimental results on aerial localization benchmark datasets demonstrate that the proposed DALTR achieves superior aerial localization performance.

The conditional coding paradigm is widely used in learned video compression, which shows superior performance in capturing redundancies within a large context space. However, existing Conditional coding-based Learned Video Compression (C-LVC) methods ignore that the predicted motion vectors usually contain large uncertainty due to complex motions, occlusions, etc., which consequently decrease the accuracy of the generated temporal contexts. In addition, existing C-LVC methods have a weak ability to mine diverse dependencies within the context space, which are closely related to the coding efficiency. To address these issues, an efficient temporal redundancy mining method is proposed to improve the coding efficiency of C-LVC in this paper. To generate accurate temporal contexts, a Long Short-Term Motion Aggregation (LSTMA) model is proposed, in which an LSTMA-based motion estimation module is developed to capture both current and aggregated long short-term motion information to reduce the uncertainty of predicted motion vectors. Based on the dual motion information, an LSTMA-based temporal context mining module is developed to exploit the aggregated long short-term motion information and increase the accuracy of the generated temporal contexts. In order to fully eliminate spatial-temporal redundancies in a video, a Global-Local Information Decorrelation Module (GLIDM)-based context codec is proposed, in which the GLIDM is designed based on the visual state space block (namely vmamba), the residual block, and the squeeze-and-excitation block to effectively capture long-range, short-range spatial-temporal dependencies and channel-wise dependencies. Experimental results demonstrate that our proposed method can effectively improve the coding performance of C-LVC, and outperforms other state-of-the-art LVC methods.