How to Understand "Support"? An Implicit-enhanced Causal Inference Approach for Weakly-supervised Phrase Grounding
This work addresses the challenge of fine-grained multimodal understanding for researchers in computer vision and natural language processing, though it appears incremental as it builds on existing causal inference techniques.
The paper tackles the problem of weakly-supervised phrase grounding by addressing the overlooked implicit phrase-region matching relations, proposing an Implicit-Enhanced Causal Inference (IECI) approach that outperforms state-of-the-art baselines and advanced multimodal LLMs by a large margin on a new implicit-enhanced dataset.
Weakly-supervised Phrase Grounding (WPG) is an emerging task of inferring the fine-grained phrase-region matching, while merely leveraging the coarse-grained sentence-image pairs for training. However, existing studies on WPG largely ignore the implicit phrase-region matching relations, which are crucial for evaluating the capability of models in understanding the deep multimodal semantics. To this end, this paper proposes an Implicit-Enhanced Causal Inference (IECI) approach to address the challenges of modeling the implicit relations and highlighting them beyond the explicit. Specifically, this approach leverages both the intervention and counterfactual techniques to tackle the above two challenges respectively. Furthermore, a high-quality implicit-enhanced dataset is annotated to evaluate IECI and detailed evaluations show the great advantages of IECI over the state-of-the-art baselines. Particularly, we observe an interesting finding that IECI outperforms the advanced multimodal LLMs by a large margin on this implicit-enhanced dataset, which may facilitate more research to evaluate the multimodal LLMs in this direction.