Exclusively Penalized Q-learning for Offline Reinforcement Learning

Constraint-based offline reinforcement learning (RL) involves policy constraints or imposing penalties on the value function to mitigate overestimation errors caused by distributional shift. This paper focuses on a limitation in existing offline RL methods with penalized value function, indicating the potential for underestimation bias due to unnecessary bias introduced in the value function. To address this concern, we propose Exclusively Penalized Q-learning (EPQ), which reduces estimation bias in the value function by selectively penalizing states that are prone to inducing estimation errors. Numerical results show that our method significantly reduces underestimation bias and improves performance in various offline control tasks compared to other offline RL methods

iColoriT: Towards Propagating Local Hint to the Right Region in Interactive Colorization by Leveraging Vision Transformer

Point-interactive image colorization aims to colorize grayscale images when a user provides the colors for specific locations. It is essential for point-interactive colorization methods to appropriately propagate user-provided colors (i.e., user hints) in the entire image to obtain a reasonably colorized image with minimal user effort. However, existing approaches often produce partially colorized results due to the inefficient design of stacking convolutional layers to propagate hints to distant relevant regions. To address this problem, we present iColoriT, a novel point-interactive colorization Vision Transformer capable of propagating user hints to relevant regions, leveraging the global receptive field of Transformers. The self-attention mechanism of Transformers enables iColoriT to selectively colorize relevant regions with only a few local hints. Our approach colorizes images in real-time by utilizing pixel shuffling, an efficient upsampling technique that replaces the decoder architecture. Also, in order to mitigate the artifacts caused by pixel shuffling with large upsampling ratios, we present the local stabilizing layer. Extensive quantitative and qualitative results demonstrate that our approach highly outperforms existing methods for point-interactive colorization, producing accurately colorized images with a user's minimal effort.

Deep Edge-Aware Interactive Colorization against Color-Bleeding Effects

Deep image colorization networks often suffer from the color-bleeding artifact, a problematic color spreading near the boundaries between adjacent objects. The color-bleeding artifacts debase the reality of generated outputs, limiting the applicability of colorization models on a practical application. Although previous approaches have tackled this problem in an automatic manner, they often generate imperfect outputs because their enhancements are available only in limited cases, such as having a high contrast of gray-scale value in an input image. Instead, leveraging user interactions would be a promising approach, since it can help the edge correction in the desired regions. In this paper, we propose a novel edge-enhancing framework for the regions of interest, by utilizing user scribbles that indicate where to enhance. Our method requires minimal user effort to obtain satisfactory enhancements. Experimental results on various datasets demonstrate that our interactive approach has outstanding performance in improving color-bleeding artifacts against the existing baselines.