MCCE-REC: MLLM-Driven Cross-Modal Contrastive Entropy Model for Zero-Shot Referring Expression Comprehension

Zero-shot referring expression comprehension (zero-shot REC) is a crucial yet challenging task in the field of multi-modal understanding, which aims to locate an object described by a referring expression without training on task-specific datasets. Existing methods take advantage of a pre-trained CLIP model to align cropped proposal regions with referring expressions. However, our analysis reveals that this aligning way heavily biases toward certain salient visual regions due to CLIP focusing on global-level image-text matching. To mitigate this bias, we propose MCCE-REC, an MLLM-driven cross-modal contrastive entropy model for training-free zero-shot REC. Benefiting from the remarkable in-context comprehension ability of the multi-modal large language model (MLLM), we design a set of referring prompts for MLLM to generate diverse detailed informative, and contrastive cues related to referring objects. Based on these cues, on the one hand, we propose a multi-cues cross-modal interaction network, which associates the visual features and referring object textual features from multiple perspectives and perceives surrounding context object information in a parameter-free manner, avoiding bias towards salient features. On the other hand, we introduce a contrastive similarity entropy selection mechanism that compares the positive and negative cues to suppress biased regions with high similarity scores and emphasizes accurate regions correlating with referring descriptions. Extensive experiments demonstrate our MCCE-REC outperforms existing zero-shot methods by a significant margin on various REC datasets.