Learning Globally Optimized Object Detector via Policy Gradient
In this paper, we propose a simple yet effective method to learn globally optimized detector for object detection, which is a simple modification to the standard cross-entropy gradient inspired by the REINFORCE algorithm. In our approach, the cross-entropy gradient is adaptively adjusted according to overall mean Average Precision (mAP) of the current state for each detection candidate, which leads to more effective gradient and global optimization of detection results, and brings no computational overhead. Benefiting from more precise gradients produced by the global optimization method, our framework significantly improves state-of-the-art object detectors. Furthermore, since our method is based on scores and bounding boxes without modification on the architecture of object detector, it can be easily applied to off-the-shelf modern object detection frameworks.
Code (0)
등록된 구현이 없습니다.
Tasks
global-optimizationObjectobject-detectionObject DetectionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Pessimistic Off-Policy Multi-Objective Optimization
Multi-objective optimization is a type of decision making problems where multiple conflicting objectives are optimized. We study offline optimization of multi-objective policies from data collected by an existing policy.…
Decision MakingTrust Region Inverse Reinforcement Learning: Explicit Dual Ascent using Local Policy Updates
Inverse reinforcement learning (IRL) is typically formulated as maximizing entropy subject to matching the distribution of expert trajectories. Classical (dual-ascent) IRL guarantees monotonic performance improvement but…
Reinforcement LearningIs the Policy Gradient a Gradient?
The policy gradient theorem describes the gradient of the expected discounted return with respect to an agent's policy parameters. However, most policy gradient methods drop the discount factor from the state distributio…
Open-Ended Question AnsweringPolicy Gradient MethodsNatural Policy Gradient and Actor Critic Methods for Constrained Multi-Task Reinforcement Learning
Multi-task reinforcement learning (RL) aims to find a single policy that effectively solves multiple tasks at the same time. This paper presents a constrained formulation for multi-task RL where the goal is to maximize t…
Reinforcement Learning (RL)Pushing the Envelope of Gradient Boosting Forests via Globally-Optimized Oblique Trees
Ensemble methods based on decision trees, such as Random Forests or boosted forests, have long been established as some of the most powerful, off-the-shelf machine learning models, and have been widely used in comput…