On the Audio Hallucinations in Large Audio-Video Language Models

Large audio-video language models can generate descriptions for both video and audio. However, they sometimes ignore audio content, producing audio descriptions solely reliant on visual information. This paper refers to this as audio hallucinations and analyzes them in large audio-video language models. We gather 1,000 sentences by inquiring about audio information and annotate them whether they contain hallucinations. If a sentence is hallucinated, we also categorize the type of hallucination. The results reveal that 332 sentences are hallucinated with distinct trends observed in nouns and verbs for each hallucination type. Based on this, we tackle a task of audio hallucination classification using pre-trained audio-text models in the zero-shot and fine-tuning settings. Our experimental results reveal that the zero-shot models achieve higher performance (52.2% in F1) than the random (40.3%) and the fine-tuning models achieve 87.9%, outperforming the zero-shot models.

Large-scale Vision-Language Models Learn Super Images for Efficient and High-Performance Partially Relevant Video Retrieval

In this paper, we propose an efficient and high-performance method for partially relevant video retrieval (PRVR), which aims to retrieve untrimmed long videos that contain at least one relevant moment to the input text query. In terms of both efficiency and performance, the overlooked bottleneck of previous studies is the visual encoding of dense frames. This guides researchers to choose lightweight visual backbones, yielding sub-optimal retrieval performance due to their limited capabilities of learned visual representations. However, it is undesirable to simply replace them with high-performance large-scale vision-and-language models (VLMs) due to their low efficiency. To address these issues, instead of dense frames, we focus on super images, which are created by rearranging the video frames in a $N \times N$ grid layout. This reduces the number of visual encodings to $\frac{1}{N^2}$ and compensates for the low efficiency of large-scale VLMs, allowing us to adopt them as powerful encoders. Surprisingly, we discover that with a simple query-image attention trick, VLMs generalize well to super images effectively and demonstrate promising zero-shot performance against SOTA methods efficiently. In addition, we propose a fine-tuning approach by incorporating a few trainable modules into the VLM backbones. The experimental results demonstrate that our approaches efficiently achieve the best performance on ActivityNet Captions and TVR.