Weicai Yan, Xinhua Ma, Wang Lin, Tao Jin Zhejiang University
Parameter-efficient fine-tuning methods introduce a small number of training parameters, enabling pre-trained models to adapt rapidly to new data distributions. While these methods have shown promising results, they exhibit notable limitations. First, most existing methods operate in the signal space domain, which results in substantial information redundancy. Second, most existing methods utilize fixed prompts or adaptation layers, failing to fully account for the multi-scale characteristics of signals. To address these challenges, we propose the Multi-Scale Frequency Adapter (FreqAdapter), which integrates textual information and performs multi-scale fine-tuning of visual signal in the frequency domain. Additionally, we introduce a multi-scale adaptation strategy to optimize receptive fields across different frequency ranges, further enhancing the model’s representational capacity. Extensive experiments on multimodal models, including CLIP and LLaVA, demonstrate that FreqAdapter significantly improves both performance and efficiency. FreqAdapter improves performance with minimal cost and fast convergence within one epoch.
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cs.CL · cs.CV
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Zequn Xie, Xin Liu, Fangming Feng, Boyun Zhang Zhejiang University, Tao Jin Zhejiang University
In recent years, CLIP-based text-video retrieval methods have developed rapidly, with research focusing on constructing diverse features and achieving effective interactions. However, the asymmetry of cross-modal information poses a challenge to accurately establishing retrieval relationships. To overcome this challenge, we propose a novel video retrieval framework, termed the Dual-Pathway and Dual-View model (DPDV), which consists of the Dual-Pathway Partitioning Module (DPPM) for constructing features at an appropriate granularity and the Dual-View Interaction Module (DVIM) for performing effective feature interactions. For DPPM, we simulate a human macro-level cognitive perspective by partitioning visual features into two categories based on their relevance to the text query and supplementing less relevant features with additional textual information. For DVIM, we simulate a human alignment strategy from macro to micro levels, focusing on local visual features while comprehensively modeling fine-grained interactions. We evaluate DPDV on five benchmark datasets, achieving leading retrieval performance.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Lujia Yang, Weicai Yan, Yongbo He, Qifei Zhang, Tao Jin Zhejiang University, Jinshan Zhang, Meng Xi, Jianwei Yin Zhejiang University
Sign language translation (SLT) is essential for bridging communication between the deaf and hearing communities, but real-world deployment suffers from domain shift such as signer variability, lighting, and background changes. Supervised fine-tuning is impractical due to limited labeled data, and existing unsupervised adaptation methods require batch statistics or long adaptation. We introduce Test-Time Adaptation (TTA) for SLT, enabling rapid adaptation to domain shift without the need for labeled data. To the best of our knowledge, this is the first study to explore TTA in SLT. Existing TTA methods predominantly focus on image classification tasks and lack a comprehensive strategy for handling domain shift in SLT. In response, we introduce SAME, a plug-and-play, signer-aware Mixture-of-Experts (MoE) TTA architecture for SLT. SAME inserts lightweight MoE modules after multiple encoder layers. Gates are conditioned on signer features and stabilized with unsupervised regularizers, effectively decoupling domain shift across encoder depths while enabling personalized adaptation. Experiments show that SAME outperforms existing TTA methods and can enhance the capabilities of multiple SLT models.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Fangming Feng, Dongjie Fu, Zequn Xie, Yu Zhang, Yangyang Wu Zhejiang University, Zhou Zhao, Tao Jin Zhejiang University
While diffusion and flow-matching models have advanced TTS, generating high-arousal emotions remains a persistent challenge due to the trade-off between stability and expressiveness. Existing systems often suffer from linguistic collapse when pursuing high intensity or fail to meet target emotional levels under stable settings. In this work, we identify that standard Gaussian initialization inevitably introduces a neutral prosody bias, while uniform Classifier-Free Guidance often distorts the acoustic manifold, leading to artifacts. To address this, we propose an inference framework that rectifies the emotional trajectory. An Emotion-Rectified Noise Prior injects a semantic gradient at initialization to align sampling with the target emotional manifold, and Likelihood-Inverse Guidance adaptively schedules guidance via a conditional/unconditional likelihood ratio, strengthening guidance only when the trajectory drifts toward a neutral fallback. Extensive experiments demonstrate that our method effectively resolves the stability bottleneck in high-intensity scenarios, achieving superior linguistic accuracy and emotional fidelity without model retraining. Audio samples are available at https://showtts.github.io/emotionTTS/.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表