CLIP as RNN: Segment Countless Visual Concepts without Training Endeavor
(2312.07661)Abstract
Existing open-vocabulary image segmentation methods require a fine-tuning step on mask labels and/or image-text datasets. Mask labels are labor-intensive, which limits the number of categories in segmentation datasets. Consequently, the vocabulary capacity of pre-trained VLMs is severely reduced after fine-tuning. However, without fine-tuning, VLMs trained under weak image-text supervision tend to make suboptimal mask predictions. To alleviate these issues, we introduce a novel recurrent framework that progressively filters out irrelevant texts and enhances mask quality without training efforts. The recurrent unit is a two-stage segmenter built upon a frozen VLM. Thus, our model retains the VLM's broad vocabulary space and equips it with segmentation ability. Experiments show that our method outperforms not only the training-free counterparts, but also those fine-tuned with millions of data samples, and sets the new state-of-the-art records for both zero-shot semantic and referring segmentation. Concretely, we improve the current record by 28.8, 16.0, and 6.9 mIoU on Pascal VOC, COCO Object, and Pascal Context.
Overview
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The paper introduces a novel recurrent framework for image segmentation that uses pre-trained vision-language models (VLMs) without the need for additional fine-tuning.
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The framework, referred to as CLIP as RNN (CaR), iteratively refines the segmentation masks, improving mask quality by eliminating irrelevant text queries.
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CaR outperforms both non-fine-tuned counterparts and fine-tuned models with additional data across various benchmarks in zero-shot semantic segmentation and referring image segmentation tasks.
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The approach involves post-processing with dense conditional random fields (CRF) to further refine mask boundaries, and it has been extended to video for zero-shot baseline segmentation.
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CaR demonstrates the potential of vision-language integration without extra training data, handling a wide range of concepts and generating precise masks.
Understanding and Segmenting Visual Content Without Explicit Training
Introduction
The field of computer vision has achieved remarkable advances in image segmentation tasks, the practice of identifying and delineating objects within an image. Recent methods have been able to segment images with a wide vocabulary of concepts by leveraging pre-trained models that understand both images and their corresponding textual descriptions, called vision-language models (VLMs). However, fine-tuning these VLMs for the task of image segmentation often limits the range of recognizable concepts due to the labor-intensive nature of creating mask annotations for a large number of categories. Moreover, VLMs trained with weak supervision might struggle with optimal mask creation when dealing with text queries that refer to nonexistent concepts in an image.
Novel Approach and Advantages
A new recurrent framework, designed to bridge this gap, has been introduced. This framework sidesteps the traditional fine-tuning step, thereby preserving the extensive vocabulary acquired by VLMs during their pre-training on vast image-text data. At the core of the framework is a two-stage segmenter that operates on the principle of iterative refinement —without requiring any additional training data.
Using a fixed-weight segmenter shared across all iterations, the model progressively eliminates irrelevant text queries and thereby enhances the quality of the masks generated for visual segmentation tasks. This retained model, labelled CLIP as RNN (CaR), achieves superior performance both in the tasks of zero-shot semantic segmentation and referring image segmentation across various benchmarks including Pascal VOC, COCO Object, and Pascal Context.
Comparative Results
Car not only outperforms its counterparts that do not resort to additional training data but also surpasses models that have been fine-tuned with millions of additional data samples. In practice, this means that CaR significantly improves the previous records by noticeable margins on the mentioned benchmarks. Even when text prompts that refer to non-existing objects in images are provided, CaR efficiently filters them out and delivers refined mask proposals.
Post-Processing and Extensions
The final step involves post-processing using dense conditional random fields (CRF) to refine the mask boundaries. The method has also been extended to the domain of video, setting a new zero-shot baseline for video referring segmentation.
Car's contributions come not only from a recurrent architecture that requires no fine-tuning but also from its simplicity and ease of extension. Future research may explore the integration of trainable modules to enhance the handling of small objects or the adoption of other advanced VLMs, thus widening the method’s applicability.
Conclusion
CaR opens up new possibilities in the world of open-vocabulary segmentation by offering a method that requires no additional training data and can handle a broader range of concepts. Its effectiveness in generating precise masks and eliminating irrelevant information without the need of explicit fine-tuning stands as an excellent testament to the potential of integrating vision and language models more seamlessly in image segmentation tasks.
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