Summary of Instance-aware Generalized Referring Expression Segmentation, by E-ro Nguyen et al.
Instance-Aware Generalized Referring Expression Segmentation
by E-Ro Nguyen, Hieu Le, Dimitris Samaras, Michael Ryoo
First submitted to arxiv on: 22 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes InstAlign, a method that addresses the challenges of Generalized Referring Expression Segmentation (GRES) by incorporating object-level reasoning into the segmentation process. The model leverages both text and image inputs to extract object-level tokens that capture semantic information from the input prompt and objects within the image. By modeling the text-object alignment via instance-level supervision, each token represents an object segment in the image while aligning with relevant semantic information from the text. This approach significantly advances state-of-the-art performance on gRefCOCO and Ref-ZOM benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary InstAlign is a new method for Generalized Referring Expression Segmentation (GRES). It helps computers understand complex expressions that refer to multiple objects. Most current methods struggle with this task because they don’t have a way to tell apart different object instances in an image. InstAlign uses both text and images to find objects and their descriptions. This allows it to accurately identify objects in an image and match them to the correct description from the text. The results on two benchmarks show that InstAlign is much better than previous methods. |
Keywords
» Artificial intelligence » Alignment » Prompt » Token
