Jie Li

Jie Li, Shengwei Tian, Long Yu et al.

Chamfer Distance (CD) comprises two components that can evaluate the global distribution and local performance of generated point clouds, making it widely utilized as a similarity measure between generated and target point clouds in point cloud completion tasks. Additionally, CD's computational efficiency has led to its frequent application as an objective function for guiding point cloud generation. However, using CD directly as an objective function with fixed equal weights for its two components can often result in seemingly high overall performance (i.e., low CD score), while failing to achieve a good global distribution. This is typically reflected in high Earth Mover's Distance (EMD) and Decomposed Chamfer Distance (DCD) scores, alongside poor human assessments. To address this issue, we propose a Flexible-Weighted Chamfer Distance (FCD) to guide point cloud generation. FCD assigns a higher weight to the global distribution component of CD and incorporates a flexible weighting strategy to adjust the balance between the two components, aiming to improve global distribution while maintaining robust overall performance. Experimental results on two state-of-the-art networks demonstrate that our method achieves superior results across multiple evaluation metrics, including CD, EMD, DCD, and F-Score, as well as in human evaluations.

Hu Han, Jie Li, Anil K. Jain et al.

The explosive growth of digital images in video surveillance and social media has led to the significant need for efficient search of persons of interest in law enforcement and forensic applications. Despite tremendous progress in primary biometric traits (e.g., face and fingerprint) based person identification, a single biometric trait alone cannot meet the desired recognition accuracy in forensic scenarios. Tattoos, as one of the important soft biometric traits, have been found to be valuable for assisting in person identification. However, tattoo search in a large collection of unconstrained images remains a difficult problem, and existing tattoo search methods mainly focus on matching cropped tattoos, which is different from real application scenarios. To close the gap, we propose an efficient tattoo search approach that is able to learn tattoo detection and compact representation jointly in a single convolutional neural network (CNN) via multi-task learning. While the features in the backbone network are shared by both tattoo detection and compact representation learning, individual latent layers of each sub-network optimize the shared features toward the detection and feature learning tasks, respectively. We resolve the small batch size issue inside the joint tattoo detection and compact representation learning network via random image stitch and preceding feature buffering. We evaluate the proposed tattoo search system using multiple public-domain tattoo benchmarks, and a gallery set with about 300K distracter tattoo images compiled from these datasets and images from the Internet. In addition, we also introduce a tattoo sketch dataset containing 300 tattoos for sketch-based tattoo search. Experimental results show that the proposed approach has superior performance in tattoo detection and tattoo search at scale compared to several state-of-the-art tattoo retrieval algorithms.