Improving Visual Grounding with Visual-Linguistic Verification and Iterative Reasoning (2205.00272v2)

Abstract: Visual grounding is a task to locate the target indicated by a natural language expression. Existing methods extend the generic object detection framework to this problem. They base the visual grounding on the features from pre-generated proposals or anchors, and fuse these features with the text embeddings to locate the target mentioned by the text. However, modeling the visual features from these predefined locations may fail to fully exploit the visual context and attribute information in the text query, which limits their performance. In this paper, we propose a transformer-based framework for accurate visual grounding by establishing text-conditioned discriminative features and performing multi-stage cross-modal reasoning. Specifically, we develop a visual-linguistic verification module to focus the visual features on regions relevant to the textual descriptions while suppressing the unrelated areas. A language-guided feature encoder is also devised to aggregate the visual contexts of the target object to improve the object's distinctiveness. To retrieve the target from the encoded visual features, we further propose a multi-stage cross-modal decoder to iteratively speculate on the correlations between the image and text for accurate target localization. Extensive experiments on five widely used datasets validate the efficacy of our proposed components and demonstrate state-of-the-art performance. Our code is public at https://github.com/yangli18/VLTVG.

Improving Visual Grounding with Visual-Linguistic Verification and Iterative Reasoning

The paper "Improving Visual Grounding with Visual-Linguistic Verification and Iterative Reasoning" presents a novel approach to the visual grounding task, which aims to locate an object or region within an image based on a natural language description. Traditional methods in this domain often rely on modifications of generic object detection frameworks by either generating region proposals or feature anchors and then fusing these with textual embeddings extracted from the language inputs. However, these approaches frequently encounter limitations as they do not fully leverage the visual context and attribute information present in text queries, which can hinder their effectiveness.

The authors introduce a transformer-based framework that specifically addresses these shortcomings by leveraging text-conditioned discriminative features combined with multi-stage cross-modal reasoning. A key component of their framework is the visual-linguistic verification module, which enhances visual features by focusing on areas pertinent to the text description while diminishing irrelevant elements. This targeted focus allows the model to suppress extraneous data that could create noise in the localization process. Complementing this is the language-guided context encoder, which aggregates visual context surrounding the target object to heighten its distinctiveness and saliency within the feature space.

For iterative target localization, the proposed multi-stage cross-modal decoder is employed. It sequentially deduces the correlation between the image and text through iterative refinement across stages, improving the ability to accurately localize the target object. This iterative processing allows the model to progressively refine its predictions by continuously updating its understanding of the visual and linguistic contexts.

The paper provides comprehensive experimental evaluations across five benchmark datasets: RefCOCO, RefCOCO+, RefCOCOg, ReferItGame, and Flickr30k Entities. The results demonstrate that the proposed framework achieves state-of-the-art performance, with substantial improvements over existing methods. Notable strengths of this approach include enhancements due to visual-linguistic verification which enables more discriminative and contextually relevant feature representations, as well as the iterative decoding that facilitates accurate target recognition through refined cross-modal reasoning.

The implications of this research are twofold. Practically, this work enhances the capabilities of systems that rely on the comprehension and interpretation of visual data through natural language, such as in robotics, automated surveillance, and enhanced user interaction on multimedia platforms. Theoretically, this provides a foundational framework that could be expanded upon with larger and more diverse datasets for improved generalization across different domains of language and vision tasks.

Future directions might explore integrating more sophisticated LLMs to better capture subtleties in linguistic expressions or adapting the framework for real-time visual grounding tasks, which necessitate speedy and efficient processing. Furthermore, potential expansions into 3D visual space could significantly broaden the applicability of this model. Overall, the paper contributes significantly to advancing the field of visual grounding through a blend of innovative framework design and rigorous empirical validation.