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 …

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 …

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 …

Although extensive research has been conducted on 3D point cloud segmentation,
effectively adapting generic models to novel categories remains a formidable challenge …

Vision-language pre-training (VLP) aims to learn joint representations of vision and
language modalities. The contrastive paradigm is currently dominant in this field. However …

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 …

Few-shot segmentation remains challenging due to the limitations of its labeling information
for unseen classes. Most previous approaches rely on extracting high-level feature maps …

Vanilla pixel-level classifiers for semantic segmentation are based on a certain paradigm,
involving the inner product of fixed prototypes obtained from the training set and pixel …