Summary of Object-centric Temporal Consistency Via Conditional Autoregressive Inductive Biases, by Cristian Meo et al.
Object-Centric Temporal Consistency via Conditional Autoregressive Inductive Biases
by Cristian Meo, Akihiro Nakano, Mircea Lică, Aniket Didolkar, Masahiro Suzuki, Anirudh Goyal, Mengmi Zhang, Justin Dauwels, Yutaka Matsuo, Yoshua Bengio
First submitted to arxiv on: 21 Oct 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 Unsupervised object-centric learning from videos is a crucial step towards developing compositional representations for various applications. Recent advancements have shown that pre-trained Vision Transformers (ViTs) can effectively learn object-centric representations on real-world video datasets. However, these methods struggle to maintain temporal consistency across consecutive frames in a video, leading to inconsistent slot-based representations. To address this limitation, we introduce Conditional Autoregressive Slot Attention (CA-SA), a novel framework that enhances the temporal consistency of extracted object-centric representations in video-centric vision tasks. By leveraging an autoregressive prior network and a novel consistency loss function, CA-SA predicts future slot representations and imposes consistency across frames. Our proposed method outperforms state-of-the-art baselines on downstream tasks such as video prediction and visual question-answering. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine watching a movie or a home security camera feed. The objects in the scene, like people or cars, need to be recognized and tracked over time. Recently, researchers found that special computer models called Vision Transformers can learn about these objects from videos. But there was a problem – the models didn’t keep track of which object was which over time. To fix this, scientists developed a new method called Conditional Autoregressive Slot Attention (CA-SA). This approach uses past information to predict what will happen in the future and makes sure that the recognized objects stay consistent. The results show that CA-SA works better than other methods on tasks like predicting what will happen next or answering questions about what’s happening in a video. |
Keywords
» Artificial intelligence » Attention » Autoregressive » Loss function » Question answering » Unsupervised
