ReCLIP: A Strong Zero-Shot Baseline for Referring Expression Comprehension

• URL: http://arxiv.org/abs/2204.05991v1
• Date: Tue, 12 Apr 2022 17:55:38 GMT
• Title: ReCLIP: A Strong Zero-Shot Baseline for Referring Expression Comprehension
• Authors: Sanjay Subramanian, Will Merrill, Trevor Darrell, Matt Gardner, Sameer Singh, Anna Rohrbach
• Abstract summary: Large-scale pre-trained models are useful for image classification across domains. We present ReCLIP, a simple but strong zero-shot baseline that repurposes CLIP, a state-of-the-art large-scale model, for ReC.
• Score: 114.85628613911713
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Training a referring expression comprehension (ReC) model for a new visual domain requires collecting referring expressions, and potentially corresponding bounding boxes, for images in the domain. While large-scale pre-trained models are useful for image classification across domains, it remains unclear if they can be applied in a zero-shot manner to more complex tasks like ReC. We present ReCLIP, a simple but strong zero-shot baseline that repurposes CLIP, a state-of-the-art large-scale model, for ReC. Motivated by the close connection between ReC and CLIP's contrastive pre-training objective, the first component of ReCLIP is a region-scoring method that isolates object proposals via cropping and blurring, and passes them to CLIP. However, through controlled experiments on a synthetic dataset, we find that CLIP is largely incapable of performing spatial reasoning off-the-shelf. Thus, the second component of ReCLIP is a spatial relation resolver that handles several types of spatial relations. We reduce the gap between zero-shot baselines from prior work and supervised models by as much as 29% on RefCOCOg, and on RefGTA (video game imagery), ReCLIP's relative improvement over supervised ReC models trained on real images is 8%.

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