arXiv:2407.20257v1 Announce Type: new
Abstract: Video Question Answering is a challenging task, which requires the model to reason over multiple frames and understand the interaction between different objects to answer questions based on the context provided within the video, especially in datasets like NExT-QA (Xiao et al., 2021a) which emphasize on causal and temporal questions. Previous approaches leverage either sub-sampled information or causal intervention techniques along with complete video features to tackle the NExT-QA task. In this work we elicit the limitations of these approaches and propose solutions along four novel directions of improvements on theNExT-QA dataset. Our approaches attempts to compensate for the shortcomings in the previous works by systematically attacking each of these problems by smartly sampling frames, explicitly encoding actions and creating interventions that challenge the understanding of the model. Overall, for both single-frame (+6.3%) and complete-video (+1.1%) based approaches, we obtain the state-of-the-art results on NExT-QA dataset.
Analysis of Video Question Answering and the NExT-QA Dataset
Video Question Answering (VQA) is a complex task that requires models to not only analyze multiple frames of a video but also understand the interactions between different objects within the video. The NExT-QA dataset, introduced by Xiao et al. in 2021, places a strong emphasis on causal and temporal questions, making it an even more challenging benchmark for VQA models. Previous approaches to tackle the NExT-QA task have utilized sub-sampled information or causal intervention techniques along with complete video features. However, these approaches have their limitations, and this work aims to address and overcome these limitations through four novel directions of improvements.
1. Smart Frame Sampling
One of the limitations of previous approaches was their reliance on sub-sampled information, which could potentially miss crucial frames that provide important context for answering the questions. The proposed approach attempts to compensate for this shortcoming by adopting smart frame sampling techniques. By strategically selecting frames that contain relevant information, the model can have a more comprehensive understanding of the video and improve its performance in answering questions.
2. Explicit Action Encoding
Understanding actions and their relationships is crucial for accurately answering questions about a video. Previous approaches might have overlooked the explicit encoding of actions, which could lead to incomplete comprehension of the video content. This work recognizes the importance of explicit action encoding and proposes methods to incorporate it into the VQA model. By explicitly representing actions, the model can better reason about the temporal dynamics and causal relationships within the video, resulting in more accurate answers to temporal and causal questions.
3. Challenging Interventions
To truly test the understanding of the model, it is necessary to introduce interventions that challenge its comprehension. By creating interventions in the video that disrupt the normal course of events, the model’s ability to reason and answer questions based on causal relationships is put to the test. The proposed approach includes interventions that deliberately challenge the model’s understanding, allowing for a more robust evaluation of its capabilities.
4. State-of-the-Art Results
Through the implementation of the aforementioned improvements, this work achieves state-of-the-art results on the NExT-QA dataset for both single-frame and complete-video based approaches. This highlights the effectiveness of the proposed solutions and their ability to overcome the limitations of previous approaches. The multi-disciplinary nature of the concepts involved in this work, such as computer vision, natural language processing, and causal reasoning, underscores the complexity of the VQA task and the need for a holistic approach that incorporates insights from various fields.
In conclusion, this study addresses the challenges of video question answering, particularly in the context of the NExT-QA dataset. By strategically addressing the limitations of previous approaches and introducing novel improvements, the proposed solutions enhance the model’s reasoning ability, leading to improved performance. The multi-disciplinary nature of the concepts tackled in this work further emphasizes the need for collaboration and integration of knowledge from different domains to advance the field of video question answering.