# Switch-a-View: View Selection Learned from Unlabeled In-the-wild Videos

arXiv: https://arxiv.org/abs/2412.18386 · HTML: https://mlsanity.com/paper/2412.18386/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 39 of its 67 references that are in the map.
- First posted on arXiv 24 Dec 2024.

## Builds on (most cited first, 30 of 39)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [The Kinetics Human Action Video Dataset](https://mlsanity.com/paper/1705.06950/) (2017)
- [SlowFast Networks for Video Recognition](https://mlsanity.com/paper/1812.03982/) (2018)
- [A Closer Look at Spatiotemporal Convolutions for Action Recognition](https://mlsanity.com/paper/1711.11248/) (2017)
- [HowTo100M: Learning a Text-Video Embedding by Watching Hundred Million Narrated Video Clips](https://mlsanity.com/paper/1906.03327/) (2019)
- [Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives](https://mlsanity.com/paper/2311.18259/) (2023)
- [Masked Autoencoders As Spatiotemporal Learners](https://mlsanity.com/paper/2205.09113/) (2022)
- [COIN: A Large-scale Dataset for Comprehensive Instructional Video Analysis](https://mlsanity.com/paper/1903.02874/) (2019)
- [InternVideo2: Scaling Foundation Models for Multimodal Video Understanding](https://mlsanity.com/paper/2403.15377/) (2024)
- [X-CLIP: End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval](https://mlsanity.com/paper/2207.07285/) (2022)
- [Charades-Ego: A Large-Scale Dataset of Paired Third and First Person Videos](https://mlsanity.com/paper/1804.09626/) (2018)
- [Actor and Observer: Joint Modeling of First and Third-Person Videos](https://mlsanity.com/paper/1804.09627/) (2018)
- [Ego-Exo: Transferring Visual Representations from Third-person to First-person Videos](https://mlsanity.com/paper/2104.07905/) (2021)
- [Learning To Recognize Procedural Activities with Distant Supervision](https://mlsanity.com/paper/2201.10990/) (2022)
- [Video Summarization Using Fully Convolutional Sequence Networks](https://mlsanity.com/paper/1805.10538/) (2018)
- [Align and Attend: Multimodal Summarization with Dual Contrastive Losses](https://mlsanity.com/paper/2303.07284/) (2023)
- [A Dataset for Developing and Benchmarking Active Vision](https://mlsanity.com/paper/1702.08272/) (2017)
- [Procedure-Aware Pretraining for Instructional Video Understanding](https://mlsanity.com/paper/2303.18230/) (2023)
- [Learning Procedure-aware Video Representation from Instructional Videos and Their Narrations](https://mlsanity.com/paper/2303.17839/) (2023)
- [TL;DW? Summarizing Instructional Videos with Task Relevance & Cross-Modal Saliency](https://mlsanity.com/paper/2208.06773/) (2022)
- [Video-Mined Task Graphs for Keystep Recognition in Instructional Videos](https://mlsanity.com/paper/2307.08763/) (2023)
- [Learning to Ground Instructional Articles in Videos through Narrations](https://mlsanity.com/paper/2306.03802/) (2023)
- [Summary Transfer: Exemplar-based Subset Selection for Video Summarization](https://mlsanity.com/paper/1603.03369/) (2016)
- [Pano2Vid: Automatic Cinematography for Watching 360circ Videos](https://mlsanity.com/paper/1612.02335/) (2016)
- [Deep 360 Pilot: Learning a Deep Agent for Piloting through 360deg Sports Video](https://mlsanity.com/paper/1705.01759/) (2017)
- [Adaptive Video Highlight Detection by Learning from User History](https://mlsanity.com/paper/2007.09598/) (2020)

## Built on by (most cited first, 1 of 1)

- [MistExit: Learning to Exit for Early Mistake Detection in Procedural Videos](https://mlsanity.com/paper/2603.14252/) (2026)

## Abstract (arXiv)

We introduce SWITCH-A-VIEW, a model that learns to automatically select the viewpoint to display at each timepoint when creating a how-to video. The key insight of our approach is how to train such a model from unlabeled -- but human-edited -- video samples. We pose a pretext task that pseudo-labels segments in the training videos for their primary viewpoint (egocentric or exocentric), and then discovers the patterns between the visual and spoken content in a how-to video on the one hand and its view-switch moments on the other hand. Armed with this predictor, our model can be applied to new multi-view video settings for orchestrating which viewpoint should be displayed when, even when such settings come with limited labels. We demonstrate our idea on a variety of real-world videos from HowTo100M and Ego-Exo4D, and rigorously validate its advantages. Project: https://vision.cs.utexas.edu/projects/switch_a_view/.