Yongbiao Gao | Search arXiv

arXiv2026

Learning Adaptive and Visually Aligned Conditions for Generative Zero-Shot Learning

Generative zero-shot learning (ZSL) synthesizes visual features for unseen classes by learning a semantic-conditioned generator from seen classes. Since semantic conditions determine the knowledge transfer from seen to unseen classes, their effectiveness is critical for generating plausible features. However, existing class-level semantic prototypes lack semantic diversity to capture intra-class variations, while exhibiting inconsistent inter-class structures with the visual space due to the semantic-visual gap. To address these issues, we propose Adaptive Attribute Distribution and Visual Structure Alignment (AAVS), a unified semantic condition learning framework for generative ZSL. Specifically, the Adaptive Attribute Distribution (AAD) learns dimension-adaptive attribute distributions with discriminability-guided variation calibration to capture transferable semantic diversity, while Visual Structure Alignment (VSA) aligns the diverse attributes with refined visual prototypes to learn visual inter-class structures. By jointly modeling semantic diversity and visual structural consistency, AAVS provides more effective generation conditions for unseen classes to generate visual features. Extensive quantitative and qualitative evaluations on three benchmarks demonstrate that AAVS outperforms existing generative ZSL methods and validate the effectiveness of the learned generation conditions.

cs.CV↗

arXiv2024

Reinforced Label Denoising for Weakly-Supervised Audio-Visual Video Parsing

Audio-visual video parsing (AVVP) aims to recognize audio and visual event labels with precise temporal boundaries, which is quite challenging since audio or visual modality might include only one event label with only the overall video labels available. Existing label denoising models often treat the denoising process as a separate preprocessing step, leading to a disconnect between label denoising and AVVP tasks. To bridge this gap, we present a novel joint reinforcement learning-based label denoising approach (RLLD). This approach enables simultaneous training of both label denoising and video parsing models through a joint optimization strategy. We introduce a novel AVVP-validation and soft inter-reward feedback mechanism that directly guides the learning of label denoising policy. Extensive experiments on AVVP tasks demonstrate the superior performance of our proposed method compared to label denoising techniques. Furthermore, by incorporating our label denoising method into other AVVP models, we find that it can further enhance parsing results.

cs.CV↗