Improving One-stage Visual Grounding by Recursive Sub-query Construction (2008.01059v1)

Abstract: We improve one-stage visual grounding by addressing current limitations on grounding long and complex queries. Existing one-stage methods encode the entire language query as a single sentence embedding vector, e.g., taking the embedding from BERT or the hidden state from LSTM. This single vector representation is prone to overlooking the detailed descriptions in the query. To address this query modeling deficiency, we propose a recursive sub-query construction framework, which reasons between image and query for multiple rounds and reduces the referring ambiguity step by step. We show our new one-stage method obtains 5.0%, 4.5%, 7.5%, 12.8% absolute improvements over the state-of-the-art one-stage baseline on ReferItGame, RefCOCO, RefCOCO+, and RefCOCOg, respectively. In particular, superior performances on longer and more complex queries validates the effectiveness of our query modeling.

• The paper introduces a recursive sub-query construction method to break down complex queries, enhancing the precision of visual grounding.
• It achieves significant performance gains, with improvements ranging from 5.0% to 12.8% on key visual grounding benchmark datasets.
• The approach maintains a real-time inference speed of 26 ms per image, making it practical for time-sensitive applications.

Improving One-stage Visual Grounding by Recursive Sub-query Construction

The paper "Improving One-stage Visual Grounding by Recursive Sub-query Construction" addresses a significant challenge in the domain of visual grounding: the accurate localization of visual objects based on complex and lengthy natural language queries in one-stage models. Traditional one-stage approaches have struggled with such queries due to their reliance on encoding the entire query into a single embedding vector. This simplistic approach often overlooks critical detailed descriptions necessary for precise object localization.

Key Contributions

1. Recursive Sub-query Construction Framework: The authors propose a novel recursive sub-query construction framework to improve query modeling. This method involves breaking down complex language queries into manageable sub-queries that enhance the interaction between visual and textual data over multiple rounds of reasoning. This iterative method aims to systematically reduce ambiguities and result in more precise grounding predictions.
2. Significant Improvements in Performance: The introduced framework leads to substantial improvements in benchmark datasets. Specifically, the approach achieves 5.0%, 4.5%, 7.5%, and 12.8% absolute improvements on ReferItGame, RefCOCO, RefCOCO+, and RefCOCOg datasets, respectively, when compared to current state-of-the-art one-stage methods.
3. Evaluating with Speed: The proposed approach maintains a real-time inference speed of 26 milliseconds per image, highlighting its practicality for applications requiring quick processing, such as during real-time video processing tasks.

Theoretical and Practical Implications

The recursive sub-query construction addresses the inherent limitations in query encoding by providing a mechanism to focus on individual descriptive components of a query independently. Each sub-query is crafted by referencing the state of the text-conditional visual feature, which evolves across iterations. This process effectively resolves ambiguities in referring expressions by emphasizing specific query sub-components in each iteration.

Theoretically, this framework challenges the conventional one-stage grounding approach by suggesting that a nuanced decomposition of language inputs can harmonize the advantages of two-stage and one-stage methods. Practically, this indicates potential improvements in applications involving complex scene understanding or interactive AI systems where understanding natural language references rapidly and accurately is critical.

Future Directions

The findings discussed in this paper pave the way towards several future research directions:

• Refinement of Sub-query Generation: Improvements in how sub-queries are generated and dynamically adjusted could enhance the model's precision further.
• Cross-Modal Data Integration: As sub-query creation and processing become more sophisticated, exploring cross-modal data integration techniques could yield additional insights and improve the synthesis between language and vision.
• Deployment in Constrained Environments: Given its real-time performance, further exploration is required in deploying this methodology in resource-constrained environments such as mobile applications or edge computing setups.

This work has contributed valuable insights and a robust enhancement to the field of visual grounding, particularly in leveraging textual complexity to improve one-stage model performance without significantly sacrificing speed.