Cross-Modal Self-Attention Network for Referring Image Segmentation (1904.04745v1)

Abstract: We consider the problem of referring image segmentation. Given an input image and a natural language expression, the goal is to segment the object referred by the language expression in the image. Existing works in this area treat the language expression and the input image separately in their representations. They do not sufficiently capture long-range correlations between these two modalities. In this paper, we propose a cross-modal self-attention (CMSA) module that effectively captures the long-range dependencies between linguistic and visual features. Our model can adaptively focus on informative words in the referring expression and important regions in the input image. In addition, we propose a gated multi-level fusion module to selectively integrate self-attentive cross-modal features corresponding to different levels in the image. This module controls the information flow of features at different levels. We validate the proposed approach on four evaluation datasets. Our proposed approach consistently outperforms existing state-of-the-art methods.

Cross-Modal Self-Attention Network for Referring Image Segmentation

The paper "Cross-Modal Self-Attention Network for Referring Image Segmentation" focuses on the problem of refining image segmentation in response to natural language expressions. The objective is to accurately segment objects within an image that are described by a given natural language cue. Prior methodologies have addressed the linguistic and visual modalities separately, which often fail to capture interrelated dependencies crucial for high-fidelity segmentation.

Proposed Methodology

The authors introduce a novel Cross-Modal Self-Attention (CMSA) module designed to enhance the interaction between the linguistic and visual modalities. This approach allows the model to dynamically emphasize salient words in the referring expression and important regions within the image, thereby capturing long-range correlations across modalities. The CMSA module effectively enhances the framework's ability to leverage subtle cues within the language to refine the segmentation task.

Furthermore, this work presents a gated multi-level fusion module that selectively integrates cross-modal features across varying representational levels. Such a design facilitates the control of information flow and ensures that salient characteristics from different hierarchies in the feature space are accentuated, which is pivotal for achieving fine-grained segmentation.

Experimental Validation

The proposed model was rigorously tested on four standard benchmark datasets. The experimental results demonstrate a consistent outperformance over existing state-of-the-art methods in referring image segmentation. This performance improvement can be attributed to the CMSA module's ability to focus on contextually relevant aspects of both the linguistic expressions and image features, thus providing a nuanced understanding conducive to enhanced segmentation accuracy.

Implications and Future Directions

The implications of this research are multifaceted. Practically, the enhanced capability for precise image segmentation in response to natural language inputs can significantly benefit applications ranging from autonomous vehicles, augmented reality, to advanced human-computer interaction systems. Theoretically, this work contributes to the understanding of cross-modal attention mechanisms, offering a framework that future research can build upon or adapt.

Looking forward, this paper opens several avenues for further exploration. There is potential to extend this framework to other cross-modal tasks where mutual dependency across modalities can be better leveraged using self-attention mechanisms. Additionally, exploring how such cross-modal architectures can be generalized or adapted to handle more complex scenes featuring multiple interacting objects could yield further advancements in the field.

In conclusion, this research offers a sophisticated approach that not only advances the field of image segmentation but also provides valuable insights into cross-modal architectures, underscoring their utility in complex AI tasks.