8 accepted papers
Domain-generalizable speech emotion recognition (DG-SER) aims to ensure the robustness of SER models across unknown domains, which is essential for real-world human-machine interaction systems. Most DG-SER approaches employ alignment or adversarial strategies with domain labels to promote generaliza
Current vision-language models (VLMs) understand complex vision-text tasks by extracting overall semantic information from large-scale cross-modal associations. However, extracting from large-scale cross-modal associations often smooths out semantic details and requires large computations, limiting…
Low-rank adaptation (LoRA) efficiently adapts LLMs to downstream tasks by decomposing LLMs’ weight update into trainable low-rank matrices for fine-tuning. However, the random low-rank matrices may introduce massive task-irrelevant information, while their recomposed form suffer from limited represe
Multi-modal sarcasm detection (MSD) identifies sarcasm and accurately understands users’ real attitudes from text-image pairs. Most MSD researches explore the incongruity of text-image pairs as sarcasm information through consistency preference methods. However, these methods prioritize consistency…
Speech emotion recognition plays a crucial role in human-computer interaction. However, data distribution of speech signals varies among individuals for emotion recognition. It may guide models to focus more on identity information rather than emotional information, which impairs the generalization…
Visual reinforcement learning (RL) has shown promise in continuous control tasks. Despite its progress, current algorithms are still unsatisfactory in virtually every aspect of the performance such as sample efficiency, asymptotic performance, and their robustness to the choice of random seeds. In t…
Black-box few-shot text classification handles text classification in limited data without accessing the parameters and gradients of language models (LMs). Existing black-box optimization methods have demonstrated strong few-shot learning capabilities. However, they still require numerous LMs’ calls…
Soft Prompt Transfer(SPT) uses well-trained soft prompts as initialization to improve prompt tuning efficiency. However, most methods in SPT learn only a single and task-specific prompt for each source task. It may not be suitable for the target task and results in poor transferability on target tas…