标题： TEn-CATS：具有多尺度类别感知时间图的文本增强音视频视频解析 显示英文标题

标题： TEn-CATS: Text-Enriched Audio-Visual Video Parsing with Multi-Scale Category-Aware Temporal Graph

摘要： 音频-视觉视频解析（AVVP）任务旨在通过弱监督标签识别给定视频中的事件类别及其发生时间。 现有方法通常分为两类：(i) 基于注意力机制设计增强架构以实现更好的时间建模，以及 (ii) 生成更丰富的伪标签以弥补帧级标注的缺失。 然而，第一类方法将噪声段级伪标签视为可靠的监督，第二类方法让无差别的注意力在所有帧中扩散，初始错误在训练过程中被反复放大。 为了解决这个问题，我们提出了一种结合双向文本融合（BiT）模块和类别感知时间图（CATS）模块的方法。 具体而言，我们整合了前两种研究方向的优势和互补性。 我们首先通过BiT模块对音频和视觉模态特征进行语义注入和动态校准，以定位并净化更清洁、更丰富的语义线索。 然后，我们利用CATS模块进行语义传播和连接，以实现时间上的精确语义信息传播。 实验结果表明，我们的方法在两个基准数据集LLP和UnAV-100的多个关键指标上达到了最先进的性能（SOTA）。 显示英文摘要

摘要： Audio-Visual Video Parsing (AVVP) task aims to identify event categories and their occurrence times in a given video with weakly supervised labels. Existing methods typically fall into two categories: (i) designing enhanced architectures based on attention mechanism for better temporal modeling, and (ii) generating richer pseudo-labels to compensate for the absence of frame-level annotations. However, the first type methods treat noisy segment-level pseudo labels as reliable supervision and the second type methods let indiscriminate attention spread them across all frames, the initial errors are repeatedly amplified during training. To address this issue, we propose a method that combines the Bi-Directional Text Fusion (BiT) module and Category-Aware Temporal Graph (CATS) module. Specifically, we integrate the strengths and complementarity of the two previous research directions. We first perform semantic injection and dynamic calibration on audio and visual modality features through the BiT module, to locate and purify cleaner and richer semantic cues. Then, we leverage the CATS module for semantic propagation and connection to enable precise semantic information dissemination across time. Experimental results demonstrate that our proposed method achieves state-of-the-art (SOTA) performance in multiple key indicators on two benchmark datasets, LLP and UnAV-100.