arXiv:2606. 11614v1 Announce Type: cross Abstract: Multimodal learning hinges on capturing redundant, unique, and synergistic information across modalities, which collectively constitute multimodal interactions.
By Zequn Yang, Yake Wei, Haotian Ni, Zhihao Xu, Di Hu
Multimodal representation learning has largely relied on contrastive models like CLIP that produce a single embedding per sample, which limits their ability to capture relation-dependent relevance. The proposed Relation-Conditioned Multimodal Learning (RCML) framework explicitly conditions embeddings on natural‑language relation descriptions, enabling the same sample to be represented differently under various relational contexts. RCML builds relation‑aware training pairs, incorporates a relation‑conditioned module, and uses a unified contrastive objective to jointly model cross‑modal alignment and relation‑induced structure, achieving superior performance on retrieval and classification tasks across zero‑shot, fine‑tuned, and out‑of‑domain settings.
By Yang Qiao, Yuntong Hu, Bowen Zhu, Hasibul Haque, Liang Zhao
Humans possess an innate ability to understand fine-grained interpersonal relationships, which is central to everyday social interactions. Although such reasoning is inherently multimodal, it remains largely unexplored by existing multimodal large language models (MLLMs).
The survey titled "Tackling Multimodal Learning Challenges with Mixture-of-Expert: A Survey" reviews how Mixture-of-Experts (MoE) frameworks address multimodal learning problems. It categorizes MoE’s contributions into three roles: an efficient multimodal engine that scales without excessive computation, a representation learner that fuses diverse expert knowledge, and an adapter that handles imperfect data such as missing modalities. The paper also highlights research gaps like interpretable routing, expert communication, and lifelong multimodal learning.
By Liangwei Nathan Zheng, Wenhao Liang, Wei Emma Zhang, Lin Yue, Weitong Chen
The paper introduces CLEAR, a framework for Multimodal Emotion Recognition in Conversation that couples contextual representations with local discriminative cues. It replaces emotion label generation with continuous internal representations from a vision‑language model and trains a classifier on these representations. Sample‑adaptive residual coupling is used to integrate textual and acoustic evidence from the target utterance into the contextual representation, improving emotion recognition performance on IEMOCAP and MELD datasets.
By Linan Zhu, Zihao Zhai, Mengfei Xu, Xiao Han, Yuqian Fu, Xiangfan Chen, Zihang Zhang
arXiv:2609.09628v1 Announce Type: cross
Abstract: Inferring speaker relationships from spoken conversations is an important step towards socially aware speech understanding. However, this task remain...
By Yaohan Guan, Yen-Ju Lu, Yuzhe Wang, Junhyeok Lee, Jesus Villalba, Laureano Moro Velazquez, Thomas Thebaud, Najim Dehak