Image-Text Co-Decomposition for Precise Text-Supervised Semantic Segmentation

核心概念

The proposed Image-Text Co-Decomposition (CoDe) framework jointly decomposes image-text pairs into corresponding regions and word segments, enabling direct region-word alignment and alleviating the discrepancy between training and testing for text-supervised semantic segmentation.

要約

The paper addresses the task of text-supervised semantic segmentation, which aims to segment arbitrary visual concepts within images using only image-text pairs without dense annotations.
The key insights are:

Existing methods have demonstrated that contrastive learning on image-text pairs can effectively align visual segments with the meanings of texts. However, there is a discrepancy between text alignment and semantic segmentation, as a text often consists of multiple semantic concepts, whereas semantic segmentation strives to create semantically homogeneous segments.
To address this issue, the authors propose a novel framework called Image-Text Co-Decomposition (CoDe), where the paired image and text are jointly decomposed into a set of image regions and a set of word segments, respectively, and contrastive learning is developed to enforce region-word alignment.
The authors also present a prompt learning mechanism that derives an extra representation to highlight an image segment or a word segment of interest, which helps extract more effective features from that segment.
Comprehensive experimental results demonstrate that the proposed CoDe framework performs favorably against existing text-supervised semantic segmentation methods on six benchmark datasets.

統計

The paper does not provide any specific numerical data or statistics. The focus is on the proposed framework and its performance evaluation.

引用

None.

抽出されたキーインサイト

Image-Text Co-Decomposition for Text-Supervised Semantic Segmentation

by Ji-Jia Wu,An... 場所 arxiv.org 04-08-2024

https://arxiv.org/pdf/2404.04231.pdf

深掘り質問

How can the proposed CoDe framework be extended to handle more complex language structures beyond single nouns, such as multi-word phrases or sentences?

To extend the CoDe framework to handle more complex language structures, such as multi-word phrases or sentences, several modifications can be implemented:

Phrase Segmentation: Instead of focusing solely on single nouns, the text segmenter can be enhanced to identify and segment multi-word phrases or even full sentences. This would involve adjusting the input processing and segmentation mechanisms to handle longer textual inputs.
Hierarchical Decomposition: Implement a hierarchical decomposition approach where the text is segmented at different levels of granularity. This would allow for the extraction of both individual words and phrases, enabling a more comprehensive alignment with visual regions.
Contextual Understanding: Incorporate contextual understanding mechanisms to capture the relationships between words and phrases within a sentence. This can involve leveraging pre-trained language models to extract contextual embeddings that capture the meaning of the entire text segment.
Attention Mechanisms: Integrate attention mechanisms that can focus on relevant parts of the text when aligning with visual regions. This would enable the model to attend to specific words or phrases that are most relevant for the segmentation task.

How can the region-word alignment be further improved to better capture the semantic relationships between visual and textual concepts?

To enhance the region-word alignment for better capturing semantic relationships between visual and textual concepts, the following strategies can be employed:

Fine-grained Alignment: Implement a finer-grained alignment mechanism that considers not only the overall region and word segments but also their internal structures. This can involve exploring sub-regions within visual segments and sub-words within textual segments for more precise alignment.
Semantic Embeddings: Utilize advanced semantic embedding techniques to represent visual and textual concepts in a shared semantic space. By mapping both modalities into a common embedding space, the model can capture more nuanced semantic relationships.
Multi-Modal Fusion: Incorporate multi-modal fusion techniques to combine information from visual and textual modalities effectively. Fusion mechanisms like cross-modal attention or fusion transformers can help integrate information from both modalities at different levels of abstraction.
Adaptive Alignment: Implement adaptive alignment mechanisms that dynamically adjust the alignment strategy based on the complexity and context of the input. This can involve learning to weigh different parts of the visual and textual inputs based on their relevance to the segmentation task.

What other applications beyond semantic segmentation could benefit from the image-text co-decomposition approach proposed in this work?

The image-text co-decomposition approach proposed in this work can benefit various other applications beyond semantic segmentation, including:

Image Captioning: By aligning image regions with textual descriptions, the approach can enhance image captioning systems by improving the understanding of visual content and generating more accurate and descriptive captions.
Visual Question Answering (VQA): The alignment between visual regions and textual concepts can aid in answering questions about images more effectively by enabling a deeper understanding of the content and context depicted in the images.
Content-Based Image Retrieval: The region-word alignment can improve content-based image retrieval systems by enabling more accurate matching between textual queries and visual content, leading to better retrieval results.
Interactive Image Editing: The approach can facilitate interactive image editing tools by allowing users to provide textual descriptions of desired edits, which can then be aligned with specific visual regions for targeted editing operations.
Medical Image Analysis: In the field of medical imaging, the approach can assist in tasks such as image annotation, diagnosis, and treatment planning by aligning medical images with textual descriptions provided by healthcare professionals.