# Active Visual Perception: Opportunities and Challenges

arXiv: https://arxiv.org/abs/2512.03687 · HTML: https://mlsanity.com/paper/2512.03687/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 80 references that are in the map.
- First posted on arXiv 3 Dec 2025.

## Builds on (most cited first, 15 of 15)

- [Generalizing to Unseen Domains: A Survey on Domain Generalization](https://mlsanity.com/paper/2103.03097/) (2021)
- [Multi-View 3D Object Detection Network for Autonomous Driving](https://mlsanity.com/paper/1611.07759/) (2016)
- [Multi-Robot Collaborative Perception with Graph Neural Networks](https://mlsanity.com/paper/2201.01760/) (2022)
- [Deep Bayesian Unsupervised Lifelong Learning](https://mlsanity.com/paper/2106.07035/) (2021)
- [Human-robot collaboration and machine learning: a systematic review of recent research](https://mlsanity.com/paper/2110.07448/) (2021)
- [Active Learning for Deep Visual Tracking](https://mlsanity.com/paper/2110.13259/) (2021)
- [Hierarchical Program-Triggered Reinforcement Learning Agents For Automated Driving](https://mlsanity.com/paper/2103.13861/) (2021)
- [Understanding Policy and Technical Aspects of AI-Enabled Smart Video Surveillance to Address Public Safety](https://mlsanity.com/paper/2302.04310/) (2023)
- [Attention-based Active Visual Search for Mobile Robots](https://mlsanity.com/paper/1807.10744/) (2018)
- [Explainable, Domain-Adaptive, and Federated Artificial Intelligence in Medicine](https://mlsanity.com/paper/2211.09317/) (2022)
- [Resource Allocation and Service Provisioning in Multi-Agent Cloud Robotics: A Comprehensive Survey](https://mlsanity.com/paper/2104.14270/) (2021)
- [On Deep Recurrent Reinforcement Learning for Active Visual Tracking of Space Noncooperative Objects](https://mlsanity.com/paper/2212.14304/) (2022)
- [Zhuyi: Perception Processing Rate Estimation for Safety in Autonomous Vehicles](https://mlsanity.com/paper/2205.03347/) (2022)
- [Context-Aware Task Handling in Resource-Constrained Robots with Virtualization](https://mlsanity.com/paper/2104.04563/) (2021)
- [On Inherent Adversarial Robustness of Active Vision Systems](https://mlsanity.com/paper/2404.00185/) (2024)

## Abstract (arXiv)

Active visual perception enables an intelligent system to improve perception by controlling how visual evidence is acquired. However, differences in how active perception is defined and evaluated make approaches difficult to compare across tasks. This review clarifies the field's functional scope by distinguishing active observation acquisition from processing fixed inputs. It then organizes representative literature around four questions about what evidence the task requires, which actions can obtain it, how candidate observations should be valued, and how the next sensing action should be selected and executed. New observations update the perceptual state and guide subsequent decisions, forming a closed loop. To evaluate the benefits of active perception, the review examines datasets, interactive environments, physical platforms, and baseline comparisons, and develops an evaluation framework linking improvements in visual evidence to downstream task outcomes while accounting for acquisition costs and risks. The analysis concludes that predicted observation value does not guarantee that useful evidence can be acquired through feasible actions, while task success alone cannot establish the benefit of active observation selection. Further progress requires reliable value estimation and planning over longer decision sequences under physical and resource constraints. It also requires grounding semantic reasoning in acquired evidence and maintaining effective collaboration under imperfect communication, with these capabilities verified through reproducible evaluation of the complete perception and action loop.