Although perception systems have made remarkable advancements in recent years they still
rely on explicit human instruction or pre-defined categories to identify the target objects …

LiDAR-based 3D point cloud recognition has benefited various applications. Without
specially considering the LiDAR point distribution, most current methods suffer from …

Few-shot semantic segmentation (FSS) aims to form class-agnostic models segmenting
unseen classes with only a handful of annotations. Previous methods limited to the semantic …

The booming of 3D recognition in the 2020s began with the introduction of point cloud
transformers. They quickly overwhelmed sparse CNNs and became state-of-the-art models …

Object detection (OD) is a crucial computer vision task that has seen the development of
many algorithms and models over the years. While the performance of current OD models …

Abstract This paper introduces Unified Language-driven Zero-shot Domain Adaptation
(ULDA) a novel task setting that enables a single model to adapt to diverse target domains …

In this paper, we delve deeper into the Kullback–Leibler (KL) Divergence loss and
mathematically prove that it is equivalent to the Decoupled Kullback-Leibler (DKL) …

Unsupervised anomaly detection aims to train models with only anomaly-free images to
detect and localize unseen anomalies. Previous reconstruction-based methods have been …

A recent study has shown a phenomenon called neural collapse in that the within-class
means of features and the classifier weight vectors converge to the vertices of a simplex …

Abstract Current open-source Large Multimodal Models (LMMs) excel at tasks such as open-
vocabulary language grounding and segmentation but can suffer under false premises …