2 citations · 4 across the 13 of their papers we have counts for
14 papers · 1 filter
RSure-Agent: Reliable Use of Tool Observations for Remote Sensing Agents
Fuyuan Liu, Nayu Liu, Wenhao Yu +8
Remote sensing agents rely on perception, measurement, and raster analysis tools to solve Earth observation tasks. We refer to their judgments and quantitative results about ground…
RegionDet: A Benchmark for Region Detection Beyond Object Instances
Liang Wan, Yuhan Wang, Yupeng Zhang +4
Object detection is a fundamental task in computer vision and has achieved remarkable progress on standard benchmarks by localizing discrete and well-bounded object instances. Howe…
ECAD: Expanding Class-Agnostic Detection Beyond Thing-Centric Objectness
Liang Wan, Zixin Ren, Yupeng Zhang +2
Object detection is a fundamental task in visual perception, providing structured region representations for recognition, grounding, reasoning, and interaction. However, existing d…
CoLT: Teaching Multi-Modal Models to Think with Chain of Latent Thoughts
Lianyu Hu, Shengqian Qin, Zeqin Liao +4
Chain-of-thought (CoT) reasoning has enabled multi-modal large language models (MLLMs) to tackle complex visual reasoning tasks by generating explicit intermediate reasoning steps…
RT-SDGOD: Real-Time Single-Domain Generalized Object Detection
Yupeng Zhang, Fangzhuo Gao, Ruize Han +2
In real-world deployment under strict real-time constraints, weather and imaging variations induce significant distribution shifts, severely degrading detectors. Single-Domain Gene…
ExDet: Open-Domain Open-Vocabulary Detection with Cross-modal Extrapolation and Rectification
Yupeng Zhang, Yuzhong Feng, Ruize Han +3
Open-domain open-vocabulary detection (ODOVD) requires detectors to generalize to both novel categories and unseen domains, making it more challenging than open-vocabulary detectio…
LV-OSD: Language-Vision-Complementary Open-Set Object Detection
Yupeng Zhang, Ruize Han, Wei Feng +2
Object detection is an important task in computer vision, which aims to detect the objects of interest. through the given category list or query images. In this work, we propose a…
VFMSDG: Unveiling the Power of VFMs for Single-Domain Generalized Object Detection
Yupeng Zhang, Ruize Han, Ningnan Guo +3
Real-world weather, illumination, and imaging variations often induce severe domain shifts, degrading single-source detectors in unseen environments. Existing single-domain general…
NoOVD: Novel Category Discovery and Embedding for Open-Vocabulary Object Detection
Yupeng Zhang, Ruize Han, Zhiwei Chen +2
Despite the remarkable progress in open-vocabulary object detection (OVD), a significant gap remains between the training and testing phases. During training, the RPN and RoI heads…
ODOV: Benchmark the Open-Domain Open-Vocabulary Object Detection
Yupeng Zhang, Ruize Han, Fangnan Zhou +2
Existing studies typically investigate domain shift and category shift as independent problems, however, in real-world scenarios, the two types of shifts often occur simultaneously…
Casual Inference via Style Bias Deconfounding for Domain Generalization
Jiaxi Li, Di Lin, Hao Chen +3
Deep neural networks (DNNs) often struggle with out-of-distribution data, limiting their reliability in diverse realworld applications. To address this issue, domain generalization…
CorrNet+: Sign Language Recognition and Translation via Spatial-Temporal Correlation
Lianyu Hu, Wei Feng, Liqing Gao +2
In sign language, the conveyance of human body trajectories predominantly relies upon the coordinated movements of hands and facial expressions across successive frames. Despite th…
Long-Tailed Learning as Multi-Objective Optimization
Weiqi Li, Fan Lyu, Fanhua Shang +2
Real-world data is extremely imbalanced and presents a long-tailed distribution, resulting in models that are biased towards classes with sufficient samples and perform poorly on r…
Open Compound Domain Adaptation with Object Style Compensation for Semantic Segmentation
Tingliang Feng, Hao Shi, Xueyang Liu +4
Many methods of semantic image segmentation have borrowed the success of open compound domain adaptation. They minimize the style gap between the images of source and target domain…