Papers by Haolin He

2 papers
FineLAP: Taming Heterogeneous Supervision for Fine-grained Language-Audio Pretraining (2026.acl-long)

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Challenge: Existing audio-language models excel at clip-level understanding but struggle with frame-level tasks.
Approach: They propose a novel training paradigm that advances both clip- and frame-level alignment in CLAP with heterogeneous data.
Outcome: The proposed training paradigm improves both clip- and frame-level alignment in CLAP with heterogeneous data.
APEX: Learning Adaptive Priorities for Multi-Objective Alignment in Vision-Language Generation (2026.findings-acl)

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Challenge: APEX optimizes for text-to-image generation by combining learning potential, conflict penalty, and progress need.
Approach: They propose an algorithm that stabilizes heterogeneous rewards and dynamically schedules objectives . they propose a method that achieves better Pareto trade-offs across four heterogenous objectives based on P3 Adaptive Priorities .
Outcome: The proposed algorithm achieves better pareto trade-offs across four heterogeneous objectives while maintaining competitive OCR accuracy.

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