Summary of Audio-visual Segmentation Via Unlabeled Frame Exploitation, by Jinxiang Liu et al.
Audio-Visual Segmentation via Unlabeled Frame Exploitation
by Jinxiang Liu, Yikun Liu, Fei Zhang, Chen Ju, Ya Zhang, Yanfeng Wang
First submitted to arxiv on: 17 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Multimedia (cs.MM); Sound (cs.SD); Audio and Speech Processing (eess.AS)
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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 The proposed framework for audio-visual segmentation (AVS) leverages both neighboring and distant frames to tackle the underutilization issue in current methods. By dividing unlabeled frames into these two categories based on their temporal characteristics, the approach improves objectness localization using motion cues from neighboring frames and enriches data diversity through semantic cues from distant frames. Experimental results demonstrate the superiority of this method in achieving robust AVS performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The goal is to improve audio-visual segmentation by using more video frames. Right now, we’re not using all the frames we have, which limits how well our models work. To change this, we divide the extra frames into two groups: ones that are close to the labeled frame (neighboring) and ones that are far away (distant). We use the motion in the neighboring frames to help us find objects and use the distant frames to add more variety to our training data. This makes our model work better. |
