7 accepted papers
Token compression techniques, such as token merging and pruning, are essential for alleviating the substantial computational burden caused by the proliferation of tokens within attention mechanisms. However, current methods often rely on token-to-token distances or similarity metrics to evaluate tok…
Accurate identification and localization of objects in 3-D scenes are essential for advancing comprehensive 3-D scene understanding. Although diffusion models have demonstrated impressive capabilities across a broad spectrum of computer vision tasks, their potential in both 2-D and 3-D object detect…
We propose a unified approach to simultaneously addressing the conventional setting of binary deepfake classification and a more challenging scenario of uncovering what facial components have been forged as well as the exact order of the manipulations. To solve the former task we consider multiple i…
This paper describes an attention-driven approach to 3-D point cloud sampling. We establish our method based on a structure-aware attention discriminant analysis that explores geometric and semantic relations embodied among points and their clusters. The proposed attention discriminant sampling (ADS…
Object detection over a long-tailed large-scale dataset is practical, challenging, and comprehensively under-explored. Recently proposed methods mainly focus on eliminating the imbalanced classification problem. However, only a few attempts have been made to consider the quality of the predicted bou…
"Contrastive learning (CL) is one of the most successful paradigms for self-supervised learning (SSL). In a principled way, it considers two augmented views of the same image as positive to be pulled closer, and all other images negative to be pushed further apart. However, behind the impressive suc…
Self-supervised training that elegantly couples contrastive learning with a wide spectrum of data augmentation techniques has been shown to be a successful paradigm for representation learning. However, current methods implicitly maximize the agreement between differently augmented views of the same…