In Defense of Lazy Visual Grounding for Open-Vocabulary Semantic Segmentation

🌈 Abstract

The paper presents "Lazy Visual Grounding (LaVG)", a two-stage approach for open-vocabulary semantic segmentation. The first stage involves unsupervised object mask discovery using Normalized Cut on DINO ViT features, which the authors call "Panoptic Cut". The second stage assigns text entities to the discovered object masks through cross-modal similarity matching. The key idea is to perform the visual object discovery without any text interaction, and then ground the text on the discovered objects in a "lazy" manner. The proposed training-free method outperforms both training-free and weakly-supervised learning-based open-vocabulary segmentation models.

🙋 Q&A

[01] Lazy Visual Grounding

1. What are the two main stages of the Lazy Visual Grounding (LaVG) approach?

• The first stage involves unsupervised object mask discovery using Normalized Cut on DINO ViT features, which the authors call "Panoptic Cut".
• The second stage assigns text entities to the discovered object masks through cross-modal similarity matching.

2. What is the key idea behind the LaVG approach? The key idea is to perform the visual object discovery without any text interaction, and then ground the text on the discovered objects in a "lazy" manner.

3. How does LaVG compare to other open-vocabulary segmentation models? LaVG, being a training-free method, outperforms both training-free and weakly-supervised learning-based open-vocabulary segmentation models.

4. What are the advantages of the Panoptic Cut approach for object mask discovery?

• Panoptic Cut leverages the DINO ViT features, which have been shown to highlight salient foreground objects.
• It is an iterative graph partitioning approach based on Normalized Cut, which can robustly identify foreground objects.
• Panoptic Cut does not require any text information or additional training, making it a purely vision-based object discovery method.

5. How does the object grounding stage in LaVG work? In the object grounding stage, LaVG uses a pre-trained CLIP image-text encoder to match the discovered object masks with the given text embeddings, assigning the most relevant text class to each object.

[02] Experimental Results

1. How does LaVG perform quantitatively compared to other open-vocabulary segmentation models? LaVG outperforms both training-free and weakly-supervised learning-based open-vocabulary segmentation models on various public benchmarks.

2. What are the qualitative advantages of LaVG's segmentation outputs? LaVG produces segmentation masks with much sharper and cleaner object boundaries compared to pixel-wise dense prediction methods like CLIP and SCLIP.

3. What are the key limitations of the LaVG approach?

• The iterative Normalized Cut process can be computationally expensive and memory-intensive, especially for high-resolution images.
• While LaVG outperforms other methods in terms of mIoU, the evaluation metric may not fully capture the qualitative improvements in object boundary delineation.
• The sliding-window inference technique used in the grounding stage can sometimes lead to part-whole ambiguity issues, especially in textureless regions.
• LaVG, being based on CLIP, may not be suitable for closed-vocabulary segmentation tasks in specialized domains.