Enhancing Video Captioning with Rare Objects and Diverse Language

In this paper, the authors explore the use of KL regularization to improve the performance of reinforcement learning agents in generating diverse outputs. They propose a novel approach that combines KL regularization with reward shaping to encourage the agent to produce high-quality captions. The proposed method is evaluated on several benchmark datasets, showing improved performance compared to existing methods.
The authors begin by highlighting the challenges of training reinforcement learning agents to generate diverse outputs, particularly in the context of image captioning. They explain that KL regularization can be used to encourage the agent to produce high-quality captions, but it can also lead to mode collapse and over-optimization of the reward function. To address these issues, the authors propose a novel approach that combines KL regularization with reward shaping.
The proposed method consists of two main components: (1) a KL regularization term, which encourages the agent to produce captions that are similar to its initial policy, and (2) a reward shaping term, which encourages the agent to produce high-quality captions that are relevant to the input image. The authors show that by combining these two components, they can improve the performance of the reinforcement learning agent in generating diverse and high-quality captions.
The authors evaluate their proposed method on several benchmark datasets, including COCO and Flickr30k. They show that their approach outperforms existing methods in terms of both diversity and quality of the generated captions. The authors also provide a thorough analysis of the effectiveness of their approach, including an evaluation of its impact on the diversity of the generated captions.
In conclusion, this paper presents a novel approach to improving the performance of reinforcement learning agents in generating diverse outputs. By combining KL regularization with reward shaping, the authors are able to improve the quality and diversity of the generated captions. The proposed method is evaluated on several benchmark datasets, showing improved performance compared to existing methods. This work provides a valuable contribution to the field of image captioning and reinforcement learning, and demonstrates the potential of KL regularization in improving the performance of these agents.