Zero-shot Referring Image Segmentation with Global-Local Context Features (2303.17811v2)

Abstract: Referring image segmentation (RIS) aims to find a segmentation mask given a referring expression grounded to a region of the input image. Collecting labelled datasets for this task, however, is notoriously costly and labor-intensive. To overcome this issue, we propose a simple yet effective zero-shot referring image segmentation method by leveraging the pre-trained cross-modal knowledge from CLIP. In order to obtain segmentation masks grounded to the input text, we propose a mask-guided visual encoder that captures global and local contextual information of an input image. By utilizing instance masks obtained from off-the-shelf mask proposal techniques, our method is able to segment fine-detailed Istance-level groundings. We also introduce a global-local text encoder where the global feature captures complex sentence-level semantics of the entire input expression while the local feature focuses on the target noun phrase extracted by a dependency parser. In our experiments, the proposed method outperforms several zero-shot baselines of the task and even the weakly supervised referring expression segmentation method with substantial margins. Our code is available at https://github.com/Seonghoon-Yu/Zero-shot-RIS.

Zero-shot Referring Image Segmentation with Global-Local Context Features

The paper "Zero-shot Referring Image Segmentation with Global-Local Context Features" presents a novel approach to the complex task of referring image segmentation (RIS) by leveraging the pre-trained cross-modal capabilities of the CLIP model. Unlike traditional approaches that rely on substantial labeled data, this method addresses the zero-shot segmentation scenario, which is particularly important given the practical challenges associated with acquiring extensive annotations.

Key Components and Approach

The authors divide the segmentation task into two main phases: generating mask proposals and evaluating these against textual queries. The mask proposal phase utilizes an unsupervised method such as FreeSOLO to generate potential segmentations without relying on predefined categories or labels. In the evaluation phase, the method employs both visual and textual encoders derived from CLIP, focusing on global and local contextual features.

1. Global-Local Visual Features: A two-pronged approach is introduced, combining global-context features extracted from the entire image with local-context features derived from masked regions. This integration enables the model to understand broader relationships between objects while retaining the ability to focus on specific instances described in a query.
2. Global-Local Textual Features: The textual encoder enhances comprehension by concurrently processing the entire referring expression for contextual understanding and isolating target noun phrases for specificity. This dual-level processing improves the precision of object correspondence between textual descriptions and visual input.

Results and Evaluation

The proposed zero-shot method was applied to standard datasets like RefCOCO, RefCOCO+, and RefCOCOg. In such benchmarks, the model showed significant performance gains over existing zero-shot baselines. The incorporation of global-local features in both visual and textual modalities contributed to its performance exceeding even some weakly supervised models, indicating its ability to capture complex relationships without needing extensive domain-specific training.

Theoretical and Practical Implications

The theoretical contribution of this paper lies in its innovative use of global-local feature integration within the context of zero-shot learning. This method not only advances the direct application of CLIP to dense prediction tasks but highlights a potentially scalable solution for varied tasks within computer vision and language processing domains.

From a practical perspective, the approach can alleviate the burden of annotating large datasets, offering a feasible alternative for tasks where labeled data are scarce or hard to obtain. It opens new avenues for automatically generating segmentation datasets, potentially benefitting applications in surveillance, autonomous driving, and human-computer interaction where real-time performance and adaptability to unseen scenarios are crucial.

Future Directions

The implications of this work suggest several intriguing directions for future research. Enhancing the model's robustness to various linguistic structures and improving the granularity of segmentation through more sophisticated mask proposal methods are immediate steps. Extending this methodology to other zero-shot tasks, such as video segmentation, and exploring the integration with other multi-modal models may yield further improvements and broaden applicability.

Overall, this work sets a precedent for zero-shot segmentation tasks, challenging the status quo of data reliance in image processing and broadening the horizon for future advancements in AI-driven scene understanding.