ReCLIP: A Strong Zero-Shot Baseline for Referring Expression Comprehension (2204.05991v2)

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%.

• The paper introduces ReCLIP, a strong zero-shot approach that repurposes CLIP by integrating isolated proposal scoring and spatial relation resolution for referring expression comprehension.
• It demonstrates up to a 29% reduction in performance gap on RefCOCOg and an 8% improvement over supervised models on RefGTA.
• The study reveals critical insights into CLIP’s spatial reasoning limitations, paving the way for enhanced zero-shot adaptation in vision-language tasks.

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

The paper "ReCLIP: A Strong Zero-Shot Baseline for Referring Expression Comprehension" introduces ReCLIP, a novel approach designed to tackle the challenge of referring expression comprehension (ReC) in a zero-shot framework. This addresses the complexity involved in recognizing and localizing objects within an image based solely on textual descriptions, a task that becomes cumbersome when transitioning across diverse visual domains.

Key Contributions

1. ReCLIP's Architecture:

The ReCLIP model is predicated on repurposing CLIP, a large-scale pre-trained vision-LLM, uniquely integrating contrastive learning to facilitate zero-shot ReC. ReCLIP is structured around two core mechanisms: - Isolated Proposal Scoring: Utilizing CLIP's contrastive pre-training paradigm, this method isolates object proposals by strategic cropping and blurring. These regions are then passed to CLIP for scoring, capitalizing on CLIP's robust image-text matching capabilities. - Spatial Relation Resolution: Addressing the shortfalls identified in CLIP's inherent spatial reasoning abilities, this component introduces rule-based heuristics to parse and resolve spatial relations mentioned in text, complementing CLIP's proposal scoring.

1. Experimental Evaluation: Through exhaustive experiments, ReCLIP demonstrated its effectiveness, notably reducing the disparity between zero-shot baselines and supervised models by up to 29% on RefCOCOg. Additionally, within the challenging domain of RefGTA, ReCLIP exhibits a remarkable 8% enhancement over supervised models trained exclusively on real images.
2. Insights into Spatial Reasoning: The paper meticulously investigates CLIP's spatial reasoning capabilities through controlled synthetic experiments, revealing deficiencies in its zero-shot spatial reasoning. This critical insight informed the development of ReCLIP's spatial relation resolver, establishing a robust framework for parsing and resolving spatial relationships between objects.

Technical Insights

ReCLIP's introduction is poised at the intersection of large-scale model efficacy and practical application in diverse domains. The employment of the Isolated Proposal Scoring technique showcases how aligning complex visual tasks with pre-trained models' native abilities can yield substantial benefits. Furthermore, the spatial relation resolver exemplifies a complementary approach that augments pre-trained models in scenarios where inherent capabilities fall short.

Evaluations and Results

ReCLIP achieves impressive accuracy levels on several datasets, demonstrating its superiority over existing zero-shot methods. Notably, the accuracy on the RefCOCOg and RefCOCO datasets presents a marked improvement, solidifying its viability as a zero-shot solution. While GradCAM and CPT-adapted methods provided competitive frames of reference, ReCLIP surpassed them especially in settings involving complex noun phrase resolutions and spatial relations.

Implications and Future Directions

The success of ReCLIP in zero-shot ReC applications suggests profound implications for both theoretical research and practical applications. It opens up pathways for exploring more efficient zero-shot adaptation strategies using existing large-scale models. Moreover, the findings underline the potential for further advancements in spatial reasoning within AI systems. Future research could focus on refining pre-training strategies to inherently encompass spatial reasoning or expand the current model's heuristic capabilities.

In conclusion, the paper underscores a pivotal step towards versatile, scalable AI models capable of complex contextual understanding across diverse domains, setting the stage for ongoing enhancements in large-scale vision-LLM applications.