Boosting Audio Visual Question Answering via Key Semantic-Aware Cues

🌈 Abstract

The article discusses the Audio Visual Question Answering (AVQA) task, which aims to answer questions related to various visual objects, sounds, and their interactions in videos. The authors propose a Temporal-Spatial Perception Model (TSPM) to effectively perceive audio-visual cues relevant to the given questions. The key aspects of the TSPM are:

• Temporal Perception Module (TPM): Constructs declarative sentence prompts derived from the question template to assist in better identifying critical temporal segments relevant to the questions.
• Spatial Perception Module (SPM): Merges visual tokens from selected temporal segments to highlight key latent targets, followed by cross-modal interaction with audio to perceive potential sound-aware areas.
• The significant temporal-spatial cues from these modules are integrated to answer the question.

The authors demonstrate that their proposed TSPM framework outperforms existing methods on multiple AVQA benchmarks, showcasing its effectiveness in understanding audio-visual scenes and answering complex questions.

🙋 Q&A

[01] Temporal Perception Module

1. What is the key aspect of the Temporal Perception Module (TPM)? The key aspect of the TPM is the use of a Text Prompt Constructor (TPC) to generate declarative sentence prompts based on the input question. These declarative prompts are designed to better align with the semantic content of the video frames, facilitating the identification of critical temporal segments relevant to the questions.

2. How does the TPM utilize the declarative prompts to identify key temporal segments? The TPM uses cross-modal attention mechanisms to measure the similarity between the declarative prompts and the visual features of the video frames. This allows the model to effectively identify the temporal segments that are most relevant to the given question.

3. What are the benefits of using declarative prompts over directly using the input questions? The input questions are often in a non-declarative format, which makes it challenging to align them with the semantic content of the video frames. In contrast, the declarative prompts generated by the TPC are better aligned with the video semantics, enabling the TPM to more effectively identify the key temporal segments.

[02] Spatial Perception Module

1. What is the key objective of the Spatial Perception Module (SPM)? The key objective of the SPM is to identify the visual regions that are relevant to the key audio sources in the video, in order to establish effective associations between the audio and visual modalities.

2. How does the SPM achieve this objective? The SPM first merges similar visual tokens within the selected temporal segments to preserve the semantic information of potential visual targets. It then conducts cross-modal interaction between the merged visual tokens and the audio features to identify the potential sound-aware areas in the video.

3. What are the benefits of the token merging strategy employed by the SPM? The token merging strategy helps to enhance the semantic information contained in the visual tokens, which is crucial for establishing effective correlations between the visual and audio modalities. This is particularly important when the visual tokens lack specific semantic information about the objects in the AVQA-related datasets.

[03] Overall Model

1. How does the TSPM integrate the outputs from the TPM and SPM? The significant temporal-spatial cues obtained from the TPM and SPM are integrated to form a joint representation, which is then used to answer the input question.

2. What are the key advantages of the TSPM framework compared to existing AVQA methods? The TSPM framework outperforms existing AVQA methods on multiple benchmarks, demonstrating its effectiveness in understanding complex audio-visual scenes and answering questions accurately. This is achieved through the TSPM's ability to precisely perceive the key temporal segments and spatial sound-aware areas relevant to the given questions.

3. How does the TSPM's performance compare to other state-of-the-art AVQA methods in terms of computational efficiency? The TSPM achieves comparable or better performance than state-of-the-art AVQA methods while requiring significantly lower computational resources, in terms of both model parameters and FLOPs. This highlights the efficiency of the TSPM framework.