arXiv AI July 7, 2026 By Lorenzo Caselli, Marco Mistretta, Simone Magistri, Andrew D. Bagdanov

SpectralGCD: Spectral Concept Selection and Cross-modal Representation Learning for Generalized Category Discovery

arXiv:2602. 17395v2 Announce Type: replace-cross Abstract: Generalized Category Discovery (GCD) aims to identify novel categories in unlabeled data while leveraging a small labeled subset of known classes.

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arXiv AI

Jun 10

Cross-Modal Knowledge Distillation without Paired Data: Theoretical Foundation and Algorithm

arXiv:2606. 10504v1 Announce Type: new Abstract: Cross-modal knowledge distillation (CMKD) studies how a (large) teacher model trained on one type of data (e.

By Trong Khiem Tran, Anh Duc Chu, Quang Hung Pham, Phi Le Nguyen, Trong Nghia Hoang

efficiency multimodal benchmarks safety

Hugging Face Trending Papers

Jun 9

Cross-Modal Knowledge Distillation without Paired Data: Theoretical Foundation and Algorithm

Cross-modal knowledge distillation (CMKD) studies how a (large) teacher model trained on one type of data (e. g.

efficiency multimodal benchmarks safety

Hugging Face Trending Papers

Sep 15

Hub-Spectral Activation of Latent Multimodal Knowledge

Multimodal representation learning seeks shared representations for cross-modal retrieval and knowledge transfer. Hub-based binding reduces pairwise supervision costs, but separate hub connections can...

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arXiv AI

Jun 30

Multimodal Representation Alignment for Cross-modal Information Retrieval

arXiv:2506. 08774v2 Announce Type: replace-cross Abstract: Different machine learning models can represent the same underlying concept in different ways.

By Fan Xu, Luis A. Leiva

llms rag multimodal benchmarks safety

arXiv Computer Vision

Sep 16

Hub-Spectral Activation of Latent Multimodal Knowledge

arXiv:2609.17094v1 Announce Type: new Abstract: Multimodal representation learning seeks shared representations for cross-modal retrieval and knowledge transfer. Hub-based binding reduces pairwise su...

By Ying Guo, Haidong Chen, Linrui Xu, Xiaohao Liu, Chuancheng Shi, Canran Xiao, Dan Zhang, Fei Shen, Li Shen, Tat-Seng Chua

multimodal safety

arXiv Machine Learning

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Multiplicity is an Inevitable and Inherent Challenge in Multimodal Learning

arXiv:2505. 19614v2 Announce Type: replace Abstract: Multimodal learning has seen remarkable progress, particularly with large-scale pre-training across various modalities.

By Sanghyuk Chun, Olga Russakovsky

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Cross-Modal Knowledge Distillation without Paired Data: Theoretical Foundation and Algorithm

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