# Look-ahead before you leap: end-to-end active recognition by forecasting the effect of motion

arXiv: https://arxiv.org/abs/1605.00164 · HTML: https://mlsanity.com/paper/1605.00164/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2016.

## Built on by (most cited first, 18 of 18)

- [SEAL: Self-supervised Embodied Active Learning using Exploration and 3D Consistency](https://mlsanity.com/paper/2112.01001/) (2021)
- [Is ImageNet worth 1 video? Learning strong image encoders from 1 long unlabelled video](https://mlsanity.com/paper/2310.08584/) (2023)
- [Interactron: Embodied Adaptive Object Detection](https://mlsanity.com/paper/2202.00660/) (2022)
- [Learning Active Camera for Multi-Object Navigation](https://mlsanity.com/paper/2210.07505/) (2022)
- [Evidential Active Recognition: Intelligent and Prudent Open-World Embodied Perception](https://mlsanity.com/paper/2311.13793/) (2023)
- [Learning to See Physical Properties with Active Sensing Motor Policies](https://mlsanity.com/paper/2311.01405/) (2023)
- [Self-supervised visual learning from interactions with objects](https://mlsanity.com/paper/2407.06704/) (2024)
- [Active Open-Vocabulary Recognition: Let Intelligent Moving Mitigate CLIP Limitations](https://mlsanity.com/paper/2311.17938/) (2023)
- [Which Viewpoint Shows it Best? Language for Weakly Supervising View Selection in Multi-view Instructional Videos](https://mlsanity.com/paper/2411.08753/) (2024)
- [Learning to Explore Informative Trajectories and Samples for Embodied Perception](https://mlsanity.com/paper/2303.10936/) (2023)
- [Visual Attention in Imaginative Agents](https://mlsanity.com/paper/2104.00177/) (2021)
- [Next-Gen Machine Learning Supported Diagnostic Systems for Spacecraft](https://mlsanity.com/paper/2106.05659/) (2021)
- [SEA: Semantic Map Prediction for Active Exploration of Uncertain Areas](https://mlsanity.com/paper/2510.19766/) (2025)
- [Multi-View Active Fine-Grained Recognition](https://mlsanity.com/paper/2206.01153/) (2022)
- [Online Feedback Efficient Active Target Discovery in Partially Observable Environments](https://mlsanity.com/paper/2505.06535/) (2025)
- [Autonomous Curiosity for Real-Time Training Onboard Robotic Agents](https://mlsanity.com/paper/2109.00927/) (2021)
- [Active Vision Reinforcement Learning under Limited Visual Observability](https://mlsanity.com/paper/2306.00975/) (2023)
- [DiffVAS: Diffusion-Guided Visual Active Search in Partially Observable Environments](https://mlsanity.com/paper/2605.15519/) (2026)

## Abstract (arXiv)

Visual recognition systems mounted on autonomous moving agents face the challenge of unconstrained data, but simultaneously have the opportunity to improve their performance by moving to acquire new views of test data. In this work, we first show how a recurrent neural network-based system may be trained to perform end-to-end learning of motion policies suited for this "active recognition" setting. Further, we hypothesize that active vision requires an agent to have the capacity to reason about the effects of its motions on its view of the world. To verify this hypothesis, we attempt to induce this capacity in our active recognition pipeline, by simultaneously learning to forecast the effects of the agent's motions on its internal representation of the environment conditional on all past views. Results across two challenging datasets confirm both that our end-to-end system successfully learns meaningful policies for active category recognition, and that "learning to look ahead" further boosts recognition performance.