3 citations · 4 across the 11 of their papers we have counts for
13 papers
Urban Boundaries, Social Barriers: A Benchmark and Vision-Centric Framework for Mapping Gated Communities and Equity Implications
Minwei Zhao, Weiming Zhang, Jiawang Du +4
Communities are fundamental spatial units that shape urban form and social life. Whether a residential compound is spatially open or enclosed affects mobility, access to public ser…
AdapToPASS: Ambiguity-aware Adaptive Spherical Transformer for Panoramic Semantic Segmentation
Soumyaratna Debnath, Weiming Zhang, Shriram Damodaran +2
Spherical Transformers have emerged as a promising framework for panoramic semantic segmentation (PASS) by operating directly on spherical geometry and alleviating projection-induc…
From Boundaries to Semantics: Prompt-Guided Multi-Task Learning for Petrographic Thin-section Segmentation
Yili Ren, Shiqi Wen, Li Hou +8
Grain-edge segmentation (GES) and lithology semantic segmentation (LSS) are two pivotal tasks for quantifying rock fabric and composition. However, these two tasks are often treate…
PanoSAM2: Lightweight Distortion- and Memory-aware Adaptions of SAM2 for 360 Video Object Segmentation
Dingwen Xiao, Weiming Zhang, Shiqi Wen +1
360 video object segmentation (360VOS) aims to predict temporally-consistent masks in 360 videos, offering full-scene coverage, benefiting applications, such as VR/AR and embodied…
Tarot-SAM3: Training-free SAM3 for Any Referring Expression Segmentation
Weiming Zhang, Dingwen Xiao, Songyue Guo +5
Referring Expression Segmentation (RES) aims to segment image regions described by natural-language expressions, serving as a bridge between vision and language understanding. Exis…
Reference-Free Omnidirectional Stereo Matching via Multi-View Consistency Maximization
Lehuai Xu, Weiming Zhang, Yang Li +2
Reliable omnidirectional depth estimation from multi-fisheye stereo matching is pivotal to many applications, such as embodied robotics. Existing approaches either rely on spherica…
OpenSubject: Leveraging Video-Derived Identity and Diversity Priors for Subject-driven Image Generation and Manipulation
Yexin Liu, Manyuan Zhang, Yueze Wang +8
Despite the promising progress in subject-driven image generation, current models often deviate from the reference identities and struggle in complex scenes with multiple subjects.…
Leader360V: The Large-scale, Real-world 360 Video Dataset for Multi-task Learning in Diverse Environment
Weiming Zhang, Dingwen Xiao, Aobotao Dai +5
360 video captures the complete surrounding scenes with the ultra-large field of view of 360X180. This makes 360 scene understanding tasks, eg, segmentation and tracking, crucial f…
E-SAM: Training-Free Segment Every Entity Model
Weiming Zhang, Dingwen Xiao, Lei Chen +1
Entity Segmentation (ES) aims at identifying and segmenting distinct entities within an image without the need for predefined class labels. This characteristic makes ES well-suited…
GoodSAM++: Bridging Domain and Capacity Gaps via Segment Anything Model for Panoramic Semantic Segmentation
Weiming Zhang, Yexin Liu, Xu Zheng +1
This paper presents GoodSAM++, a novel framework utilizing the powerful zero-shot instance segmentation capability of SAM (i.e., teacher) to learn a compact panoramic semantic segm…
PanDA: Towards Panoramic Depth Anything with Unlabeled Panoramas and Mobius Spatial Augmentation
Zidong Cao, Jinjing Zhu, Weiming Zhang +4
Recently, Depth Anything Models (DAMs) - a type of depth foundation models - have demonstrated impressive zero-shot capabilities across diverse perspective images. Despite its succ…
Unsupervised Visible-Infrared ReID via Pseudo-label Correction and Modality-level Alignment
Yexin Liu, Weiming Zhang, Athanasios V. Vasilakos +1
Unsupervised visible-infrared person re-identification (UVI-ReID) has recently gained great attention due to its potential for enhancing human detection in diverse environments wit…
GoodSAM: Bridging Domain and Capacity Gaps via Segment Anything Model for Distortion-aware Panoramic Semantic Segmentation
Weiming Zhang, Yexin Liu, Xu Zheng +1
This paper tackles a novel yet challenging problem: how to transfer knowledge from the emerging Segment Anything Model (SAM) -- which reveals impressive zero-shot instance segmenta…