Pseudo-Q: Generating Pseudo Language Queries for Visual Grounding (2203.08481v2)

Abstract: Visual grounding, i.e., localizing objects in images according to natural language queries, is an important topic in visual language understanding. The most effective approaches for this task are based on deep learning, which generally require expensive manually labeled image-query or patch-query pairs. To eliminate the heavy dependence on human annotations, we present a novel method, named Pseudo-Q, to automatically generate pseudo language queries for supervised training. Our method leverages an off-the-shelf object detector to identify visual objects from unlabeled images, and then language queries for these objects are obtained in an unsupervised fashion with a pseudo-query generation module. Then, we design a task-related query prompt module to specifically tailor generated pseudo language queries for visual grounding tasks. Further, in order to fully capture the contextual relationships between images and language queries, we develop a visual-LLM equipped with multi-level cross-modality attention mechanism. Extensive experimental results demonstrate that our method has two notable benefits: (1) it can reduce human annotation costs significantly, e.g., 31% on RefCOCO without degrading original model's performance under the fully supervised setting, and (2) without bells and whistles, it achieves superior or comparable performance compared to state-of-the-art weakly-supervised visual grounding methods on all the five datasets we have experimented. Code is available at https://github.com/LeapLabTHU/Pseudo-Q.

Analysis of "Pseudo-Q: Generating Pseudo Language Queries for Visual Grounding"

"Pseudo-Q: Generating Pseudo Language Queries for Visual Grounding," presents a novel approach to visual grounding, a core task in the intersection of computer vision and natural language processing. The technique introduced in this work, Pseudo-Q, aims to mitigate the dependency on expensive and labor-intensive manual annotations typically required for training deep learning models in this domain. This paper focuses on generating pseudo language queries to facilitate the supervised training of models for visual grounding without relying on task-specific human-provided annotations.

Methodology Overview

Pseudo-Q showcases an innovative approach by leveraging three major components: pseudo-query generation, query prompt module, and a sophisticated visual-LLM with multi-level cross-modality attention. The pseudo-query generation module utilizes an off-the-shelf object detector to identify candidate objects within an unlabeled image, attributing them with automatically generated pseudo-queries. This process effectively incorporates three linguistic elements—nouns, attributes, and spatial relationships—to generate comprehensive language queries.

Key to its approach is the sophisticated modeling of spatial relationships, employing heuristics to determine such relationships within the same class of objects. This alleviates the burden of manually annotating complex image contexts. The multi-level cross-modality attention mechanism enhances the fusion of visual and textual information, which is crucial for achieving effective grounding.

Experimental Results

Empirically, Pseudo-Q is shown to significantly reduce annotation costs by 31% on the RefCOCO dataset while maintaining performance integrity in a fully supervised setting. Notably, Pseudo-Q demonstrates competitive performance when compared to existing weakly-supervised methods across five benchmark datasets: RefCOCO, RefCOCO+, RefCOCOg, ReferItGame, and Flickr30K Entities. It surpasses previous unsupervised methods and achieves favorable results against weakly-supervised approaches, despite the inherent lack of task-related human annotations.

Implications and Future Directions

The implications of Pseudo-Q are substantial, presenting an avenue towards reducing costs and resource allocation in creating labeled datasets for visual grounding tasks. It lays the groundwork for further research into unsupervised and weakly-supervised learning paradigms where the generation of pseudo data could serve other vision-language tasks, such as visual question answering or visual reasoning.

Future work could explore refining the spatial relationship modeling process, which currently relies on heuristics that, while effective, may limit generalization across unseen or particularly complex scene compositions. Another promising direction is enhancing the adaptability of pseudo-query generation across more diverse and expansive datasets, ensuring robustness in a broader array of practical applications. Investigating the scalability of Pseudo-Q in real-world settings could also provide insights into its potential deployment in commercial AI systems.

In conclusion, Pseudo-Q substantially contributes to the field of visual grounding and broadly to the development of cost-effective deep learning models in visual language understanding, presenting a progressive step toward independent learning systems.