To leverage the full potential of multimodal data, we need representations that go beyond the state-of-the-art alignment and fusion approaches and exploit all cross-modal interactions without sacrificing modality-specific information. Learning disentangled representations is a principled way to identify these underlying shared and unique factors that are hidden in observational data.
arXiv:2608. 10857v1 Announce Type: new Abstract: Determining the complexity, or Intrinsic Dimension (ID), of data is fundamental to efficient and interpretable representation learning.
By Viktoria Schuster, Sana Tonekaboni, Caroline Uhler
arXiv:2607. 16789v1 Announce Type: new Abstract: Real-world perception and decision making are inherently multimodal, integrating complementary signals across modalities.
By Sana Tonekaboni, Viktoria Schuster, Caroline Uhler
arXiv:2503. 09679v2 Announce Type: replace Abstract: Meta-learning represents a strong class of approaches for solving few-shot learning tasks.
By Wei Cui, Tongzi Wu, Jesse C. Cresswell, Yi Sui, Keyvan Golestan
Multimodal fusion learning (MFL) has shown great potential in the medical domain, where we are faced with disparate data modalities such as imaging, clinical records, and omics. However, existing MFL strategies face several major challenges.
arXiv:2606. 29888v1 Announce Type: new Abstract: Vision-language models map images and text into a joint embedding space.
By Chungpa Lee, Jihoon Kwon, Kyle Min, Jy-yong Sohn
arXiv:2601. 21688v2 Announce Type: replace-cross Abstract: Disentangled representation learning aims to map independent factors of variation to independent representation components.
By Alexandre Myara, Nicolas Bourriez, Thomas Boyer, Thomas Lemercier, Ihab Bendidi, Auguste Genovesio
arXiv:2606. 25225v1 Announce Type: cross Abstract: Self-supervised learning from large-scale video data has emerged as a dominant paradigm for visual representation learning.
By Revant Teotia, Adrien Bardes, Michael Rabbat, Sumit Chopra, Matthew J. Muckley, Nicolas Ballas
Self-supervised learning from large-scale video data has emerged as a dominant paradigm for visual representation learning. Since audio and visual streams naturally co-occur in video data, extending this success to jointly learn from both modalities is a natural next step, yet it remains challenging.
arXiv:2604. 07753v2 Announce Type: replace-cross Abstract: Empowering Large Multimodal Models (LMMs) with image generation often leads to catastrophic forgetting in understanding tasks due to severe gradient conflicts.
By Xiangyue Liu, Zijian Zhang, Miles Yang, Zhao Zhong, Liefeng Bo, Ping Tan
arXiv:2608. 08309v1 Announce Type: cross Abstract: We argue that learning visual representations without labels requires a training signal jointly complete across three non-overlapping objectives: semantic invariance across augmented views, patch-level spatial prediction, and representational non-degeneracy.
By Nikos Giakoumoglou, Paschalis Giakoumoglou, Tania Stathaki
arXiv:2608. 09986v1 Announce Type: new Abstract: Most existing multimodal sentiment analysis approaches assume access to complete multimodal inputs.
By Yuhua Wen, Yingying Zhou, Qifei Li, Yingming Gao, Zhengqi Wen, Jianhua Tao, Ya Li