Large language models have demonstrated impressive universal capabilities across a wide
range of open-ended tasks and have extended their utility to encompass multimodal …

This paper presents ClusterFormer, a universal vision model that is based on the Clustering
paradigm with TransFormer. It comprises two novel designs: 1) recurrent cross-attention …

Multimodal Large Language Models (MLLMs) have shown promising progress in
understanding and analyzing video content. However, processing long videos remains a …

Abstract Information inside visual and LiDAR data is well complementary derived from the
fine-grained texture of images and massive geometric information in point clouds. However …

Over the past decade, deep learning models have exhibited considerable advancements,
reaching or even exceeding human-level performance in a range of visual perception tasks …

RGB-T semantic segmentation is a key technique for autonomous driving scenes
understanding. For the existing RGB-T semantic segmentation methods, however, the …

We investigate a fundamental aspect of machine vision: the measurement of features by
revisiting clustering one of the most classic approaches in machine learning and data …

Large vision and language models learned directly through image-text associations often
lack detailed visual substantiation, whereas image segmentation tasks are treated …

Recent advances in Scene Graph Generation (SGG) typically model the relationships
among entities utilizing box-level features from pre-defined detectors. We argue that an …

While convolution and self-attention are extensively used in learned image compression
(LIC) for transform coding, this paper proposes an alternative called Contextual Clustering …