Summary of Ref-avs: Refer and Segment Objects in Audio-visual Scenes, by Yaoting Wang et al.
Ref-AVS: Refer and Segment Objects in Audio-Visual Scenes
by Yaoting Wang, Peiwen Sun, Dongzhan Zhou, Guangyao Li, Honggang Zhang, Di Hu
First submitted to arxiv on: 15 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 introduces Reference Audio-Visual Segmentation (Ref-AVS), a novel task that segments objects in visual scenes based on expressions containing multimodal cues. The Ref-AVS benchmark provides pixel-level annotations for objects described in multimodal-cue expressions, allowing researchers to tackle this task with existing methods. To address the challenge of segmenting objects using multimodal-cue expressions, the authors propose a new method that effectively utilizes these cues. Experimental results on three test subsets demonstrate the effectiveness of the proposed approach, outperforming existing methods from related tasks. The Ref-AVS dataset is publicly available. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about teaching computers to understand and separate objects in pictures based on words and sounds. Right now, most computer vision research focuses on looking at still images, but this paper shows that using sounds and movements can help improve object detection. The authors create a new task called Reference Audio-Visual Segmentation (Ref-AVS) and develop a method to solve it. They test their approach with different sets of data and show that it works better than previous methods. The dataset used in the research is available online for others to use. |
Keywords
* Artificial intelligence * Object detection
