$C^3$: Compositional Counterfactual Contrastive Learning for Video-grounded Dialogues
This addresses the issue of dataset bias exploitation in video-grounded dialogues, offering a method to enhance generalization for multimodal AI systems.
The paper tackles the problem of limited generalization in video-grounded dialogue systems by proposing Compositional Counterfactual Contrastive Learning (C^3), which uses contrastive training between factual and counterfactual samples to improve multimodal reasoning, achieving promising performance gains on the AVSD benchmark.
Video-grounded dialogue systems aim to integrate video understanding and dialogue understanding to generate responses that are relevant to both the dialogue and video context. Most existing approaches employ deep learning models and have achieved remarkable performance, given the relatively small datasets available. However, the results are partly accomplished by exploiting biases in the datasets rather than developing multimodal reasoning, resulting in limited generalization. In this paper, we propose a novel approach of Compositional Counterfactual Contrastive Learning ($C^3$) to develop contrastive training between factual and counterfactual samples in video-grounded dialogues. Specifically, we design factual/counterfactual sampling based on the temporal steps in videos and tokens in dialogues and propose contrastive loss functions that exploit object-level or action-level variance. Different from prior approaches, we focus on contrastive hidden state representations among compositional output tokens to optimize the representation space in a generation setting. We achieved promising performance gains on the Audio-Visual Scene-Aware Dialogues (AVSD) benchmark and showed the benefits of our approach in grounding video and dialogue context.