Summary of Spatio-temporal Prompting Network For Robust Video Feature Extraction, by Guanxiong Sun et al.
Spatio-temporal Prompting Network for Robust Video Feature Extraction
by Guanxiong Sun, Chi Wang, Zhaoyu Zhang, Jiankang Deng, Stefanos Zafeiriou, Yang Hua
First submitted to arxiv on: 4 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 tackles the issue of frame quality deterioration in video understanding, which hinders the extraction of robust and accurate features. Current approaches employ transformer-based integration modules to compensate for this loss, but these modules are heavy and complex, making it challenging to generalize across multiple tasks. The proposed Spatio-Temporal Prompting Network (STPN) framework addresses this issue by dynamically adjusting input features in the backbone network. STPN predicts video prompts containing spatio-temporal information of neighboring frames, which are then prepended to patch embeddings for feature extraction. This approach allows for easy generalization across various tasks without requiring task-specific modules. The paper demonstrates state-of-the-art performance on three benchmark datasets: ImageNetVID for object detection, YouTubeVIS for instance segmentation, and GOT-10k for visual object tracking. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make videos better understood by computers! When video quality gets poor, it’s hard to get good results. Some people use special modules that are really powerful but complicated. They’re not very good at doing different tasks though. The new idea is called STPN (Spatio-Temporal Prompting Network). It makes the computer look at previous and next frames to help figure out what’s happening in the current frame. This helps a lot, and it works well for lots of different things like finding objects or tracking them. It even beats other computers on some tests! |
Keywords
* Artificial intelligence * Feature extraction * Generalization * Instance segmentation * Object detection * Object tracking * Prompting * Tracking * Transformer
