Learning to Generate Text-grounded Mask for Open-world Semantic Segmentation from Only Image-Text Pairs (2212.00785v2)

Abstract: We tackle open-world semantic segmentation, which aims at learning to segment arbitrary visual concepts in images, by using only image-text pairs without dense annotations. Existing open-world segmentation methods have shown impressive advances by employing contrastive learning (CL) to learn diverse visual concepts and transferring the learned image-level understanding to the segmentation task. However, these CL-based methods suffer from a train-test discrepancy, since it only considers image-text alignment during training, whereas segmentation requires region-text alignment during testing. In this paper, we proposed a novel Text-grounded Contrastive Learning (TCL) framework that enables a model to directly learn region-text alignment. Our method generates a segmentation mask for a given text, extracts text-grounded image embedding from the masked region, and aligns it with text embedding via TCL. By learning region-text alignment directly, our framework encourages a model to directly improve the quality of generated segmentation masks. In addition, for a rigorous and fair comparison, we present a unified evaluation protocol with widely used 8 semantic segmentation datasets. TCL achieves state-of-the-art zero-shot segmentation performances with large margins in all datasets. Code is available at https://github.com/kakaobrain/tcl.

Insightful Overview of "Learning to Generate Text-grounded Mask for Open-world Semantic Segmentation from Only Image-Text Pairs"

The paper presents a method for tackling the challenging task of open-world semantic segmentation, where the goal is to identify and segment arbitrary concepts within an image using only image-text pairs for training. Seminal works in the domain, including those that leverage CLIP's image-text alignment capabilities, have traditionally focused on learning and transferring image-level semantic knowledge to segmentation tasks. However, these approaches have often overlooked the discrepancy between train-time image-text alignment and test-time region-text alignment.

Approach and Methodology

The paper proposes a novel framework termed Text-grounded Contrastive Learning (TCL) that directly addresses this alignment discrepancy. TCL introduces a mechanism to generate segmentation masks grounded in text, thus facilitating a direct region-text alignment learning paradigm. The method employs a bespoke segmentation mask generation technique that extracts and aligns image embeddings from masked regions with their corresponding text embeddings. This approach not only generates more precise text-grounded masks but also enhances the overall quality of the learned segmentations.

Central to the framework is the integration of TCL loss, compiled from three primary components: image-level TCL, feature-level TCL, and area-based priors, with additional smoothness regularization to bolster segmentation quality. These innovative losses are designed to optimize mutual information between the prospective text-grounded region and the corresponding textual description, thereby ensuring effective alignment.

Empirical Evaluation and Results

The authors conducted exhaustive evaluations on eight diverse semantic segmentation datasets. The results showcase a substantial performance leap over existing methods, with TCL achieving state-of-the-art zero-shot segmentation metrics uniformly across all benchmarks. This consistent outperformance underscores the efficacy of the proposed method in reconciling alignment discrepancies and delivering robust segmentation outcomes.

Implications and Future Directions

The practical and theoretical ramifications of the proposed approach are multifold. On a practical level, TCL provides a new mechanism to efficiently train segmentation models without dense annotations, broadening the application horizon in real-world scenarios. Theoretically, it establishes a clear precedent for designing segmentation models that progress beyond mere image-text alignment. As AI paradigms evolve, future research could explore further augmentation of TCL, including scaling the method to handle higher-resolution imagery or extending its capability to more complex multimodal data systems.

In conclusion, the paper effectively charts a course for open-world segmentation through minimal supervision, a haLLMark for advancing computer vision capabilities in unrestricted environments. As it stands, TCL makes a substantial contribution to the field, elucidating a pathway from image-text pairs to fine-grained, text-grounded segmentation.