# Actor-Critic Instance Segmentation

arXiv: https://arxiv.org/abs/1904.05126 · HTML: https://mlsanity.com/paper/1904.05126/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Apr 2019.

## Built on by (most cited first, 4 of 4)

- [Reinforced Attention for Few-Shot Learning and Beyond](https://mlsanity.com/paper/2104.04192/) (2021)
- [Scale Optimization Using Evolutionary Reinforcement Learning for Object Detection on Drone Imagery](https://mlsanity.com/paper/2312.15219/) (2023)
- [RLCorrector: Reinforced Proofreading for Cell-level Microscopy Image Segmentation](https://mlsanity.com/paper/2106.05487/) (2021)
- [Stateless actor-critic for instance segmentation with high-level priors](https://mlsanity.com/paper/2107.02600/) (2021)

## Abstract (arXiv)

Most approaches to visual scene analysis have emphasised parallel processing of the image elements. However, one area in which the sequential nature of vision is apparent, is that of segmenting multiple, potentially similar and partially occluded objects in a scene. In this work, we revisit the recurrent formulation of this challenging problem in the context of reinforcement learning. Motivated by the limitations of the global max-matching assignment of the ground-truth segments to the recurrent states, we develop an actor-critic approach in which the actor recurrently predicts one instance mask at a time and utilises the gradient from a concurrently trained critic network. We formulate the state, action, and the reward such as to let the critic model long-term effects of the current prediction and incorporate this information into the gradient signal. Furthermore, to enable effective exploration in the inherently high-dimensional action space of instance masks, we learn a compact representation using a conditional variational auto-encoder. We show that our actor-critic model consistently provides accuracy benefits over the recurrent baseline on standard instance segmentation benchmarks.