Papers by YiJie Huang

2 papers
JX4MEI: Multimodal Semantically-Enhanced LLM for Joint Multimodal Emotion-Intent Explanation and Classification (2026.findings-acl)

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Challenge: Existing multimodal emotion and intent recognition tasks focus on classification, not rationale and intrinsic connections between these states.
Approach: They propose a task that requires models to jointly predict emotion and intent while generating natural language explanations for why they co-occur.
Outcome: The proposed model outperforms baseline models in prediction and explanation generation.
Cat-MoD: Accelerating Multimodal Alignment via Caption Token Guided Asymmetric Mixture-of-Depths (2026.acl-long)

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Challenge: Existing query-based alignment modules enforce uniform cross-attention across all layers, leading to computational redundancy.
Approach: They propose a framework that allows for asynchronous query-based alignment with large-scale visual features.
Outcome: The proposed framework matches or surpasses baseline performance while reducing alignment FLOPs by approximately 37% during training and inference.

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