Tao Lu

Xiang Fang, Shihua Zhang, Hao Zhang et al.

Two-view correspondence learning aims to discern true and false correspondences between image pairs by recognizing their underlying different information. Previous methods either treat the information equally or require the explicit storage of the entire context, tending to be laborious in real-world scenarios. Inspired by Mamba's inherent selectivity, we propose \textbf{CorrMamba}, a \textbf{Corr}espondence filter leveraging \textbf{Mamba}'s ability to selectively mine information from true correspondences while mitigating interference from false ones, thus achieving adaptive focus at a lower cost. To prevent Mamba from being potentially impacted by unordered keypoints that obscured its ability to mine spatial information, we customize a causal sequential learning approach based on the Gumbel-Softmax technique to establish causal dependencies between features in a fully autonomous and differentiable manner. Additionally, a local-context enhancement module is designed to capture critical contextual cues essential for correspondence pruning, complementing the core framework. Extensive experiments on relative pose estimation, visual localization, and analysis demonstrate that CorrMamba achieves state-of-the-art performance. Notably, in outdoor relative pose estimation, our method surpasses the previous SOTA by $2.58$ absolute percentage points in AUC@20\textdegree, highlighting its practical superiority. Our code will be publicly available.

Chenghao Liu, Tao Lu, Xin Wang et al.

Efficiency is crucial to the online recommender systems. Representing users and items as binary vectors for Collaborative Filtering (CF) can achieve fast user-item affinity computation in the Hamming space, in recent years, we have witnessed an emerging research effort in exploiting binary hashing techniques for CF methods. However, CF with binary codes naturally suffers from low accuracy due to limited representation capability in each bit, which impedes it from modeling complex structure of the data. In this work, we attempt to improve the efficiency without hurting the model performance by utilizing both the accuracy of real-valued vectors and the efficiency of binary codes to represent users/items. In particular, we propose the Compositional Coding for Collaborative Filtering (CCCF) framework, which not only gains better recommendation efficiency than the state-of-the-art binarized CF approaches but also achieves even higher accuracy than the real-valued CF method. Specifically, CCCF innovatively represents each user/item with a set of binary vectors, which are associated with a sparse real-value weight vector. Each value of the weight vector encodes the importance of the corresponding binary vector to the user/item. The continuous weight vectors greatly enhances the representation capability of binary codes, and its sparsity guarantees the processing speed. Furthermore, an integer weight approximation scheme is proposed to further accelerate the speed. Based on the CCCF framework, we design an efficient discrete optimization algorithm to learn its parameters. Extensive experiments on three real-world datasets show that our method outperforms the state-of-the-art binarized CF methods (even achieves better performance than the real-valued CF method) by a large margin in terms of both recommendation accuracy and efficiency.