Summary of On the Efficacy Of Text-based Input Modalities For Action Anticipation, by Apoorva Beedu et al.
On the Efficacy of Text-Based Input Modalities for Action Anticipation
by Apoorva Beedu, Harish Haresamudram, Karan Samel, Irfan Essa
First submitted to arxiv on: 23 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Image and Video Processing (eess.IV)
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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 anticipatory action recognition by leveraging text descriptions of actions and objects as contextual cues. The proposed Multi-modal Contrastive Anticipative Transformer (M-CAT) model integrates video and audio modalities with text-based information, enabling more accurate future action anticipation. The model is trained in two stages: pre-training on rich text descriptions of future actions and fine-tuning on text descriptions of detected objects and actions during modality feature fusion. Compared to existing methods, M-CAT outperforms on the EpicKitchens dataset, demonstrating the effectiveness of incorporating simple text descriptions for action anticipation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how machines can predict what will happen next. Right now, it’s hard for computers to anticipate actions because there are so many possibilities. But if we give them more information, like written descriptions of what’s happening, they can make better predictions. The researchers created a new model called M-CAT that uses video and audio, as well as text, to predict future actions. They tested it on a special dataset and found that it worked better than other methods. |
Keywords
* Artificial intelligence * Fine tuning * Multi modal * Transformer
