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Referring Image Segmentation via Cross-Modal Progressive Comprehension (2010.00514v1)

Published 1 Oct 2020 in cs.CV and cs.CL

Abstract: Referring image segmentation aims at segmenting the foreground masks of the entities that can well match the description given in the natural language expression. Previous approaches tackle this problem using implicit feature interaction and fusion between visual and linguistic modalities, but usually fail to explore informative words of the expression to well align features from the two modalities for accurately identifying the referred entity. In this paper, we propose a Cross-Modal Progressive Comprehension (CMPC) module and a Text-Guided Feature Exchange (TGFE) module to effectively address the challenging task. Concretely, the CMPC module first employs entity and attribute words to perceive all the related entities that might be considered by the expression. Then, the relational words are adopted to highlight the correct entity as well as suppress other irrelevant ones by multimodal graph reasoning. In addition to the CMPC module, we further leverage a simple yet effective TGFE module to integrate the reasoned multimodal features from different levels with the guidance of textual information. In this way, features from multi-levels could communicate with each other and be refined based on the textual context. We conduct extensive experiments on four popular referring segmentation benchmarks and achieve new state-of-the-art performances.

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Authors (8)
  1. Shaofei Huang (19 papers)
  2. Tianrui Hui (15 papers)
  3. Si Liu (130 papers)
  4. Guanbin Li (177 papers)
  5. Yunchao Wei (151 papers)
  6. Jizhong Han (48 papers)
  7. Luoqi Liu (28 papers)
  8. Bo Li (1107 papers)
Citations (160)
View on Semantic Scholar

Summary

Referring Image Segmentation via Cross-Modal Progressive Comprehension

The domain of referring image segmentation (RIS) seeks to identify and segment specific entities in an image based on natural language descriptions. This paper introduces novel methodologies aimed at improving the alignment of visual and linguistic features to achieve precise entity segmentation, which has applications in interactive image editing and autonomous systems control.

Methodology

The paper proposes two key modules: Cross-Modal Progressive Comprehension (CMPC) and Text-Guided Feature Exchange (TGFE). These modules are designed to address the challenge of accurately mapping linguistic descriptions to image regions.

  1. Cross-Modal Progressive Comprehension (CMPC) Module:
    • The CMPC module operates in two stages:
      • Entity Perception (EP): This stage associates linguistic features related to entity and attribute words with visual features, thereby identifying all potential entities in the image that may be referred to by the expression.
      • Relation-Aware Reasoning (RAR): A graph-based reasoning approach is applied using relational words as a medium to establish connections between visual regions. Graph convolution is employed to enhance the representation of the referent by emphasizing relationships among entities, which assists in distinguishing the correct entity in complex scenarios.
  2. Text-Guided Feature Exchange (TGFE) Module:
    • The TGFE module facilitates communication among multi-level features influenced by textual information. Each level of visual features communicates with one another, guided by linguistic context, improving feature refinement and consequently enhancing the precision of mask prediction.

The network architecture integrates visual features extracted from multi-layer CNNs with linguistic features derived from LSTMs, enabling the model to process and relate image and text modalities effectively.

Results

Experiments demonstrate that this methodology outperforms state-of-the-art models across several benchmark datasets used for referring image segmentation, including UNC, UNC+, G-Ref, and ReferIt, as evidenced by improvements in Intersection-over-Union (IoU) metrics.

  • On the G-Ref dataset, which includes more complex expressions, the proposed approach demonstrates superior performance in managing longer and descriptive queries.
  • The results show marked improvement in distinguishing between multiple similar entities within an image, leveraging relationship awareness for context comprehension.

The approach models deeper understanding through its progressive comprehension framework, capturing intricate entity-attribute and spatial relationships within the visual field.

Implications and Future Work

The proposed method advances the RIS task by refining feature interactions across modalities and using progressive reasoning to enhance entity identification. This introduces potential improvements in systems requiring precise visual understanding, such as autonomous navigation, human-robot interaction, and advanced AI-driven image processing applications.

For future developments, investigating deeper linguistic structural analysis and alternative graph configurations could yield further performance boosts. Additionally, generalization capabilities across broader datasets and application scenarios would be an area worth exploring, along with optimizations in computational efficiency given the complexity of graph-based operations.

The paper's contributions thus lie in its innovative approach to multi-modal feature alignment and reasoning, paving the way for future research and applications in the domain of RIS and beyond.