Vision-Language Transformer and Query Generation for Referring Segmentation (2108.05565v1)

Abstract: In this work, we address the challenging task of referring segmentation. The query expression in referring segmentation typically indicates the target object by describing its relationship with others. Therefore, to find the target one among all instances in the image, the model must have a holistic understanding of the whole image. To achieve this, we reformulate referring segmentation as a direct attention problem: finding the region in the image where the query language expression is most attended to. We introduce transformer and multi-head attention to build a network with an encoder-decoder attention mechanism architecture that "queries" the given image with the language expression. Furthermore, we propose a Query Generation Module, which produces multiple sets of queries with different attention weights that represent the diversified comprehensions of the language expression from different aspects. At the same time, to find the best way from these diversified comprehensions based on visual clues, we further propose a Query Balance Module to adaptively select the output features of these queries for a better mask generation. Without bells and whistles, our approach is light-weight and achieves new state-of-the-art performance consistently on three referring segmentation datasets, RefCOCO, RefCOCO+, and G-Ref. Our code is available at https://github.com/henghuiding/Vision-Language-Transformer.

Vision-Language Transformer and Query Generation for Referring Segmentation: An Overview

The paper "Vision-Language Transformer and Query Generation for Referring Segmentation" addresses referring segmentation, an intricate multi-modal task requiring a model to understand both linguistic and visual data to generate a segmentation mask for a target object described by natural language expressions. The authors introduce a novel approach leveraging the capabilities of transformer architectures to enhance model performance on this task.

Key contributions of the paper include the formulation of referring segmentation as a direct attention problem. The authors employ a Vision-Language Transformer (VLT) that incorporates an encoder-decoder architecture, wherein multi-head attention mechanisms facilitate the querying of images based on language inputs. This setup enables the model to process the intricacies present in natural language specifications and image details collectively.

Key Components

• Vision-Language Transformer (VLT): This method emphasizes integrating vision and language features deeply through a transformer model, moving away from the traditional Fully Convolutional Network-based approaches. This integration allows for enhanced global context understanding which is critical in resolving the complex correlations in image and language data.
• Query Generation Module (QGM): This module is pivotal in creating multiple query vectors based on linguistic features, attended to using visual cues. Unlike previous methods that use fixed or self-attending queries, this approach adapts to different interpretations of language expressions, thus offering a significant advantage in dealing with varying data randomness caused by expansive object and linguistic variety.
• Query Balance Module: It adaptively selects the output features from the queries generated by QGM, ensuring the most relevant are emphasized when generating the final segmentation mask.

Performance

Noteworthy empirical results were observed where the proposed model achieved state-of-the-art performance across several datasets: RefCOCO, RefCOCO+, and G-Ref. These datasets are recognized for their complexity and varying challenges, emphasizing the robustness and adaptability of the proposed methodology. Specifically, the paper reports an improvement in Intersection over Union (IoU) scores, demonstrating enhanced target identification accuracy.

Implications and Future Directions

• Practical Significance: The introduction of a transformer-based framework for referring segmentation sets a precedent for applying attention mechanisms more broadly within multi-modal AI tasks. The lightweight nature of the newly proposed modules also promises applications where computational resources might be a constraint.
• Theoretical Impacts: The decompositional understanding of language via query generation can inspire further research into attention-based models for multi-modal data processing, potentially impacting tasks related to multi-modal reasoning and understanding.
• Future Work: Future exploration could delve into refining the query generation process to incorporate even more sophisticated representations of contextual dependencies between language and visual inputs. Additionally, extending this framework to real-time and low-latency applications opens an exciting avenue for practical advancements.

In conclusion, the paper's approach to solving referring segmentation successfully integrates transformer architectures to improve the fusion and comprehension of multi-modal data, marking a significant step in both the theoretical and practical realms of AI research.