Multi-Agent Reinforcement Learning Based Frame Sampling for Effective Untrimmed Video Recognition
Video Recognition has drawn great research interest and great progress has been made. A suitable frame sampling strategy can improve the accuracy and efficiency of recognition. However, mainstream solutions generally adopt hand-crafted frame sampling strategies for recognition. It could degrade the performance, especially in untrimmed videos, due to the variation of frame-level saliency. To this end, we concentrate on improving untrimmed video classification via developing a learning-based frame sampling strategy. We intuitively formulate the frame sampling procedure as multiple parallel Markov decision processes, each of which aims at picking out a frame/clip by gradually adjusting an initial sampling. Then we propose to solve the problems with multi-agent reinforcement learning (MARL). Our MARL framework is composed of a novel RNN-based context-aware observation network which jointly models context information among nearby agents and historical states of a specific agent, a policy network which generates the probability distribution over a predefined action space at each step and a classification network for reward calculation as well as final recognition. Extensive experimental results show that our MARL-based scheme remarkably outperforms hand-crafted strategies with various 2D and 3D baseline methods. Our single RGB model achieves a comparable performance of ActivityNet v1.3 champion submission with multi-modal multi-model fusion and new state-of-the-art results on YouTube Birds and YouTube Cars.
Code (0)
등록된 구현이 없습니다.
Tasks
Action RecognitionGeneral ClassificationMulti-agent Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Video ClassificationVideo RecognitionSimilar Papers 제목 키워드 기반
Coach-assisted Multi-Agent Reinforcement Learning Framework for Unexpected Crashed Agents
Multi-agent reinforcement learning is difficult to be applied in practice, which is partially due to the gap between the simulated and real-world scenarios. One reason for the gap is that the simulated systems always ass…
Multi-agent Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)+2Deep Multi-Agent Reinforcement Learning for Decentralized Active Hypothesis Testing
We consider a decentralized formulation of the active hypothesis testing (AHT) problem, where multiple agents gather noisy observations from the environment with the purpose of identifying the correct hypothesis. At each…
Multi-agent Reinforcement Learningreinforcement-learningReinforcement LearningGraphon Mean-Field Subsampling for Cooperative Heterogeneous Multi-Agent Reinforcement Learning
Coordinating large populations of interacting agents is a central challenge in multi-agent reinforcement learning (MARL), where the size of the joint state-action space scales exponentially with the number of agents. Mea…
Multi-agent Reinforcement LearningEnhancing the MADDPG Algorithm for Multi-Agent Learning via Action Inference and Importance Sampling
We investigate multi-agent deep reinforcement learning and propose two enhancements to the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm. First, we introduce a novel Action Inference mechanism that en…
Multi-agent Reinforcement LearningPosterior sampling for multi-agent reinforcement learning: solving extensive games with imperfect information
Posterior sampling for reinforcement learning (PSRL) is a useful framework for making decisions in an unknown environment. PSRL maintains a posterior distribution of the environment and then makes planning on the enviro…
counterfactualMulti-agent Reinforcement Learningreinforcement-learningReinforcement Learning+1