124 citations · 133 across the 17 of their papers we have counts for
11 papers · 1 filter
Unleashing Hierarchical Reasoning: An LLM-Driven Framework for Training-Free Referring Video Object Segmentation
Bingrui Zhao, Lin Yuanbo Wu, Xiangtian Fan +5
Referring Video Object Segmentation (RVOS) aims to segment an object of interest throughout a video based on a language description. The prominent challenge lies in aligning static…
Self-Navigated Residual Mamba for Universal Industrial Anomaly Detection
Hanxi Li, Jingqi Wu, Lin Yuanbo Wu +4
In this paper, we propose Self-Navigated Residual Mamba (SNARM), a novel framework for universal industrial anomaly detection that leverages ``self-referential learning'' within te…
Mitigating Information Loss under High Pruning Rates for Efficient Large Vision Language Models
Mingyu Fu, Wei Suo, Ji Ma +3
Despite the great success of Large Vision Language Models (LVLMs), their high computational cost severely limits their broad applications. The computational cost of LVLMs mainly st…
A Deep Semantic Segmentation Network with Semantic and Contextual Refinements
Zhiyan Wang, Deyin Liu, Lin Yuanbo Wu +3
Semantic segmentation is a fundamental task in multimedia processing, which can be used for analyzing, understanding, editing contents of images and videos, among others. To accele…
UIFormer: A Unified Transformer-based Framework for Incremental Few-Shot Object Detection and Instance Segmentation
Chengyuan Zhang, Yilin Zhang, Lei Zhu +6
This paper introduces a novel framework for unified incremental few-shot object detection (iFSOD) and instance segmentation (iFSIS) using the Transformer architecture. Our goal is…
Towards Efficient Pixel Labeling for Industrial Anomaly Detection and Localization
Hanxi Li, Jingqi Wu, Lin Yuanbo Wu +3
In the realm of practical Anomaly Detection (AD) tasks, manual labeling of anomalous pixels proves to be a costly endeavor. Consequently, many AD methods are crafted as one-class c…