Summary of Leveraging Transformers For Weakly Supervised Object Localization in Unconstrained Videos, by Shakeeb Murtaza et al.
Leveraging Transformers for Weakly Supervised Object Localization in Unconstrained Videos
by Shakeeb Murtaza, Marco Pedersoli, Aydin Sarraf, Eric Granger
First submitted to arxiv on: 8 Jul 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 proposes a novel approach to Weakly-Supervised Video Object Localization (WSVOL), which involves localizing objects in videos using only video-level labels, also referred to as tags. The proposed method, called TrCAM-V, builds upon the Temporal CAM (TCAM) model but addresses limitations of existing methods by incorporating a DeiT backbone with two heads for classification and localization. The classification head is trained using standard classification loss, while the localization head is trained using pseudo-labels extracted from a pre-trained CLIP model. The method also employs conditional random field (CRF) loss to align object boundaries with foreground maps. Experimental results on YouTube-Objects datasets show that TrCAM-V achieves state-of-the-art performance in terms of classification and localization accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it possible to find objects in videos using only a few labels, without knowing where the objects are exactly. The new method, called TrCAM-V, is an improvement over previous approaches because it can handle complex video scenes better. It uses two types of “heads” (parts) on top of a special kind of neural network called DeiT. One head helps identify what’s happening in the scene, and the other head helps find where specific objects are. The method also uses some clever math tricks to make sure the object boundaries look good. The results show that this new method is better than previous methods at finding objects in videos. |
Keywords
» Artificial intelligence » Classification » Neural network » Supervised
