arXiv:2606. 19411v1 Announce Type: new Abstract: Selecting a small, diverse, high-quality subset from a massive pool of candidates is a recurring primitive in modern machine learning -- data curation and coreset selection for training and fine-tuning large models, active-learning batch acquisition, prompt and exemplar selection for in-context learning, retrieval diversification, and experimental design.
By Richard Yi Da Xu
arXiv:2609.23533v1 Announce Type: new
Abstract: Multimodal classifiers can converge to modality-dominant solutions in which one modality dominates the joint prediction, suppressing the learning of ot...
By Zechang Xiong, Da Li, Rong Yin, Kexin Tang, Biao Yang, Pengyuan Li, Wenkang Kong, Yulan Hu, Shengyu Zhu, Hao Peng
Multi-query vehicle ReID aims to leverage complementary information from diverse views for robust feature learning. However, current methods suffer from simplistic feature fusion and thus easily ignores some important view information and cross-view relationships.
Phase-Consistent Magnetic Spectral Learning for Multi-View Clustering proposes a new unsupervised method that uses a complex-valued magnetic affinity to capture both magnitude and phase information across multiple data views. By deriving a confidence-directed cross-view flow from anchor assignments and combining it with a nonnegative magnitude backbone, the approach constructs a Hermitian magnetic Laplacian that yields a stable shared spectral signal for representation learning and clustering. Experiments on ten public benchmarks show the method outperforms or matches leading MVC baselines on most dataset-metric combinations.
By Mingdong Lu, Zhikui Chen, Meng Liu, Shubin Ma, Zhengyang Tang
arXiv:2608. 04234v1 Announce Type: cross Abstract: We study the problem of aligning data from multiple modalities into a shared representation space, focusing on settings where strong pretrained unimodal encoders are available but cross-modal paired data are scarce.
By Yixuan Florence Wu, Yilun Zhu, Naichen Shi
arXiv:2608. 13922v1 Announce Type: new Abstract: Detecting distributional changes in high dimension is difficult when neither the pre-change nor post-change density is parametrically specified.
By Guoqing Zhang, Zhaixin Chen