4 citations · 7 across the 14 of their papers we have counts for
16 papers
Evidence-Grounded Trustworthy Multimodal Reasoning and Evaluation Benchmark in Complex Urban Scenes
Zhaoyang Wei, Bowen Jiang, Xumeng Han +6
While Multimodal Large Language Models (MLLMs) demonstrate impressive performance in benign scenarios, their cognitive reliability deteriorates significantly in complex scenes unde…
GeoBridge: Decoupled Semantic Conditioning for Generative Image Geolocalization
Zhiyang Dou, Xumeng Han, Fengde Peng +4
Multimodal large language models (MLLMs) have advanced image geolocalization mainly by improving how they reason about geographic cues. How that reasoning isdecoded into coordinate…
HeroGS: Hierarchical Guidance for Robust 3D Gaussian Splatting under Sparse Views
Jiashu Li, Xumeng Han, Zhaoyang Wei +5
3D Gaussian Splatting (3DGS) has recently emerged as a promising approach in novel view synthesis, combining photorealistic rendering with real-time efficiency. However, its succes…
SAPNet++: Evolving Point-Prompted Instance Segmentation with Semantic and Spatial Awareness
Zhaoyang Wei, Xumeng Han, Xuehui Yu +4
Single-point annotation is increasingly prominent in visual tasks for labeling cost reduction. However, it challenges tasks requiring high precision, such as the point-prompted ins…
VER-Bench: Evaluating MLLMs on Reasoning with Fine-Grained Visual Evidence
Chenhui Qiang, Zhaoyang Wei, Xumeng Han +5
With the rapid development of MLLMs, evaluating their visual capabilities has become increasingly crucial. Current benchmarks primarily fall into two main types: basic perception b…
Evidence-Grounded Trustworthy Multimodal Reasoning and Evaluation Benchmark in Complex Urban Scenes
Zhaoyang Wei, Bowen Jiang, Xumeng Han +6
While Multimodal Large Language Models (MLLMs) demonstrate impressive performance in benign scenarios, their cognitive reliability deteriorates significantly in complex scenes unde…
Mixpert: Mitigating Multimodal Learning Conflicts with Efficient Mixture-of-Vision-Experts
Xin He, Xumeng Han, Longhui Wei +2
Multimodal large language models (MLLMs) require a nuanced interpretation of complex image information, typically leveraging a vision encoder to perceive various visual scenarios.…
P2Object: Single Point Supervised Object Detection and Instance Segmentation
Pengfei Chen, Xuehui Yu, Xumeng Han +5
Object recognition using single-point supervision has attracted increasing attention recently. However, the performance gap compared with fully-supervised algorithms remains large.…
Towards Interactive Global Geolocation Assistant
Zhiyang Dou, Zipeng Wang, Xumeng Han +3
Global geolocation, which seeks to predict the geographical location of images captured anywhere in the world, is one of the most challenging tasks in the field of computer vision.…
ViMoE: An Empirical Study of Designing Vision Mixture-of-Experts
Xumeng Han, Longhui Wei, Zhiyang Dou +6
Mixture-of-Experts (MoE) models embody the divide-and-conquer concept and are a promising approach for increasing model capacity, demonstrating excellent scalability across multipl…
CPR++: Object Localization via Single Coarse Point Supervision
Xuehui Yu, Pengfei Chen, Kuiran Wang +5
Point-based object localization (POL), which pursues high-performance object sensing under low-cost data annotation, has attracted increased attention. However, the point annotatio…
P2Seg: Pointly-supervised Segmentation via Mutual Distillation
Zipeng Wang, Xuehui Yu, Xumeng Han +4
Point-level Supervised Instance Segmentation (PSIS) aims to enhance the applicability and scalability of instance segmentation by utilizing low-cost yet instance-informative annota…
Boosting Segment Anything Model Towards Open-Vocabulary Learning
Xumeng Han, Longhui Wei, Xuehui Yu +6
The recent Segment Anything Model (SAM) has emerged as a new paradigmatic vision foundation model, showcasing potent zero-shot generalization and flexible prompting. Despite SAM fi…
P2RBox: Point Prompt Oriented Object Detection with SAM
Guangming Cao, Xuehui Yu, Wenwen Yu +5
Single-point annotation in oriented object detection of remote sensing scenarios is gaining increasing attention due to its cost-effectiveness. However, due to the granularity ambi…
Point-to-Box Network for Accurate Object Detection via Single Point Supervision
Pengfei Chen, Xuehui Yu, Xumeng Han +7
Object detection using single point supervision has received increasing attention over the years. However, the performance gap between point supervised object detection (PSOD) and…
Rethinking Sampling Strategies for Unsupervised Person Re-identification
Xumeng Han, Xuehui Yu, Guorong Li +5
Unsupervised person re-identification (re-ID) remains a challenging task. While extensive research has focused on the framework design and loss function, this paper shows that samp…