# What can human minimal videos tell us about dynamic recognition models?

arXiv: https://arxiv.org/abs/2104.09447 · HTML: https://mlsanity.com/paper/2104.09447/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 45 references that are in the map.
- First posted on arXiv 19 Apr 2021.

## Builds on (most cited first, 10 of 10)

- [The Kinetics Human Action Video Dataset](https://mlsanity.com/paper/1705.06950/) (2017)
- [A Closer Look at Spatiotemporal Convolutions for Action Recognition](https://mlsanity.com/paper/1711.11248/) (2017)
- [Two-Stream Convolutional Networks for Action Recognition in Videos](https://mlsanity.com/paper/1406.2199/) (2014)
- [Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?](https://mlsanity.com/paper/1711.09577/) (2017)
- [Long-term Recurrent Convolutional Networks for Visual Recognition and Description](https://mlsanity.com/paper/1411.4389/) (2014)
- [Convolutional Two-Stream Network Fusion for Video Action Recognition](https://mlsanity.com/paper/1604.06573/) (2016)
- [Spatiotemporal Residual Networks for Video Action Recognition](https://mlsanity.com/paper/1611.02155/) (2016)
- [P-CNN: Pose-based CNN Features for Action Recognition](https://mlsanity.com/paper/1506.03607/) (2015)
- [Joint Optical Flow and Temporally Consistent Semantic Segmentation](https://mlsanity.com/paper/1607.07716/) (2016)
- [Sensitivity to Timing and Order in Human Visual Cortex](https://mlsanity.com/paper/1404.6420/) (2014)

## Built on by (most cited first, 3 of 3)

- [Temporal vs. Spatial: Comparing DINOv3 and V-JEPA2 Feature Representations for Video Action Analysis](https://mlsanity.com/paper/2509.21595/) (2025)
- [Human-like Relational Models for Activity Recognition in Video](https://mlsanity.com/paper/2107.05319/) (2021)
- [Human-AI Divergence in Ego-centric Action Recognition under Spatial and Spatiotemporal Manipulations](https://mlsanity.com/paper/2603.08317/) (2026)

## Abstract (arXiv)

In human vision objects and their parts can be visually recognized from purely spatial or purely temporal information but the mechanisms integrating space and time are poorly understood. Here we show that human visual recognition of objects and actions can be achieved by efficiently combining spatial and motion cues in configurations where each source on its own is insufficient for recognition. This analysis is obtained by identifying minimal videos: these are short and tiny video clips in which objects, parts, and actions can be reliably recognized, but any reduction in either space or time makes them unrecognizable. State-of-the-art deep networks for dynamic visual recognition cannot replicate human behavior in these configurations. This gap between humans and machines points to critical mechanisms in human dynamic vision that are lacking in current models.