14 citations · 55 across the 23 of their papers we have counts for
26 papers
Decoupling Cross-Modality Manifold Discrepancy: Leveraging Visible Diffusion Priors for Infrared Super-Resolution
Yunpeng Hua, Hongwei Yu, Jiawei Li +3
Infrared image super-resolution (IISR) mitigates the limitations imposed by low spatial resolution. Existing methods have recognized that IISR should preserve consistency in global…
PACT: Proactive Asking for Continual Task Assistance in Human-Robot Collaboration
Chengbo He, Sheng Li, Chenyang Ma +6
Robotic assistants in long-term human-robot collaboration need to assist users under partial observations while leveraging cross-day interaction history. However, human traits and…
Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images
Hongyuan Liu, Bochao Zou, Qiankun Liu +10
Creating realistic and simulation-ready 3D assets is crucial for autonomous driving research and virtual environment construction. However, existing 3D vehicle generation methods a…
Video-Only ToM: Enhancing Theory of Mind in Multimodal Large Language Models
Siqi Liu, Xinyang Li, Bochao Zou +3
As large language models (LLMs) continue to advance, there is increasing interest in their ability to infer human mental states and demonstrate a human-like Theory of Mind (ToM). M…
XYZCylinder: Towards Compatible Feed-Forward 3D Gaussian Splatting for Driving Scenes via Unified Cylinder Lifting Method
Haochen Yu, Qiankun Liu, Hongyuan Liu +4
Feed-forward paradigms for 3D reconstruction have become a focus of recent research, which learn implicit, fixed view transformations to generate a single scene representation. How…
MVSMamba: Multi-View Stereo with State Space Model
Jianfei Jiang, Qiankun Liu, Hongyuan Liu +4
Robust feature representations are essential for learning-based Multi-View Stereo (MVS), which relies on accurate feature matching. Recent MVS methods leverage Transformers to capt…
InstDrive: Instance-Aware 3D Gaussian Splatting for Driving Scenes
Hongyuan Liu, Haochen Yu, Bochao Zou +4
Reconstructing dynamic driving scenes from dashcam videos has attracted increasing attention due to its significance in autonomous driving and scene understanding. While recent adv…
MonoMVSNet: Monocular Priors Guided Multi-View Stereo Network
Jianfei Jiang, Qiankun Liu, Haochen Yu +4
Learning-based Multi-View Stereo (MVS) methods aim to predict depth maps for a sequence of calibrated images to recover dense point clouds. However, existing MVS methods often stru…
Kaleidoscopic Background Attack: Disrupting Pose Estimation with Multi-Fold Radial Symmetry Textures
Xinlong Ding, Hongwei Yu, Jiawei Li +5
Camera pose estimation is a fundamental computer vision task that is essential for applications like visual localization and multi-view stereo reconstruction. In the object-centric…
From Black Boxes to Transparent Minds: Evaluating and Enhancing the Theory of Mind in Multimodal Large Language Models
Xinyang Li, Siqi Liu, Bochao Zou +2
As large language models evolve, there is growing anticipation that they will emulate human-like Theory of Mind (ToM) to assist with routine tasks. However, existing methods for ev…
AGC-Drive: A Large-Scale Dataset for Real-World Aerial-Ground Collaboration in Driving Scenarios
Yunhao Hou, Bochao Zou, Min Zhang +7
By sharing information across multiple agents, collaborative perception helps autonomous vehicles mitigate occlusions and improve overall perception accuracy. While most previous w…
DeCoDe: Defer-and-Complement Decision-Making via Decoupled Concept Bottleneck Models
Chengbo He, Bochao Zou, Junliang Xing +3
In human-AI collaboration, a central challenge is deciding whether the AI should handle a task, be deferred to a human expert, or be addressed through collaborative effort. Existin…
Enhancing LLM Reasoning with Multi-Path Collaborative Reactive and Reflection agents
Chengbo He, Bochao Zou, Xin Li +3
Agents have demonstrated their potential in scientific reasoning tasks through large language models. However, they often face challenges such as insufficient accuracy and degenera…
RNet: Adversarial Attack Resilient Network for Robust Infrared and Visible Image Fusion
Jiawei Li, Hongwei Yu, Jiansheng Chen +5
Infrared and visible image fusion (IVIF) is a crucial technique for enhancing visual performance by integrating unique information from different modalities into one fused image. E…
PIP: Detecting Adversarial Examples in Large Vision-Language Models via Attention Patterns of Irrelevant Probe Questions
Yudong Zhang, Ruobing Xie, Jiansheng Chen +2
Large Vision-Language Models (LVLMs) have demonstrated their powerful multimodal capabilities. However, they also face serious safety problems, as adversaries can induce robustness…
Exploring Information-Theoretic Metrics Associated with Neural Collapse in Supervised Training
Kun Song, Zhiquan Tan, Bochao Zou +3
In this paper, we introduce matrix entropy as an analytical tool for studying supervised learning, investigating the information content of data representations and classification…
Isolated Diffusion: Optimizing Multi-Concept Text-to-Image Generation Training-Freely with Isolated Diffusion Guidance
Jingyuan Zhu, Huimin Ma, Jiansheng Chen +1
Large-scale text-to-image diffusion models have achieved great success in synthesizing high-quality and diverse images given target text prompts. Despite the revolutionary image ge…
RhythmFormer: Extracting Patterned rPPG Signals based on Periodic Sparse Attention
Bochao Zou, Zizheng Guo, Jiansheng Chen +3
Remote photoplethysmography (rPPG) is a non-contact method for detecting physiological signals based on facial videos, holding high potential in various applications. Due to the pe…
Learning a Graph Neural Network with Cross Modality Interaction for Image Fusion
Jiawei Li, Jiansheng Chen, Jinyuan Liu +1
Infrared and visible image fusion has gradually proved to be a vital fork in the field of multi-modality imaging technologies. In recent developments, researchers not only focus on…
DomainStudio: Fine-Tuning Diffusion Models for Domain-Driven Image Generation using Limited Data
Jingyuan Zhu, Huimin Ma, Jiansheng Chen +1
Denoising diffusion probabilistic models (DDPMs) have been proven capable of synthesizing high-quality images with remarkable diversity when trained on large amounts of data. Typic…
Few-shot 3D Shape Generation
Jingyuan Zhu, Huimin Ma, Jiansheng Chen +1
Realistic and diverse 3D shape generation is helpful for a wide variety of applications such as virtual reality, gaming, and animation. Modern generative models, such as GANs and d…
MotionVideoGAN: A Novel Video Generator Based on the Motion Space Learned from Image Pairs
Jingyuan Zhu, Huimin Ma, Jiansheng Chen +1
Video generation has achieved rapid progress benefiting from high-quality renderings provided by powerful image generators. We regard the video synthesis task as generating a seque…
Gestalt-Guided Image Understanding for Few-Shot Learning
Kun Song, Yuchen Wu, Jiansheng Chen +2
Due to the scarcity of available data, deep learning does not perform well on few-shot learning tasks. However, human can quickly learn the feature of a new category from very few…
Distribution Aligned Feature Clustering for Zero-Shot Sketch-Based Image Retrieval
Yuchen Wu, Kun Song, Fangzheng Zhao +2
Zero-Shot Sketch-Based Image Retrieval (ZS-SBIR) is a challenging cross-modal retrieval task. In prior arts, the retrieval is conducted by sorting the distance between the query sk…