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Tianjiao Jiang

2 accepted papers

2025

Causal Disentanglement and Cross-Modal Alignment for Enhanced Few-Shot Learning

ICCV 2025poster

Few-shot learning (FSL) often requires effective adaptation of models using limited labeled data. However, most existing FSL methods rely on entangled representations, requiring the model to implicitly recover the unmixing process to obtain disentangled representations using only limited supervision…

2025

On the Value of Cross-Modal Misalignment in Multimodal Representation Learning

NeurIPS 2025spotlight

Multimodal representation learning, exemplified by multimodal contrastive learning (MMCL) using image-text pairs, aims to learn powerful representations by aligning cues across modalities. This approach relies on the core assumption that the exemplar image-text pairs constitute two representations o…

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