23 citations · 80 across the 38 of their papers we have counts for
21 papers · 1 filter
Understanding Degradation with Vision Language Model
Guanzhou Lan, Chenyi Liao, Yuqi Yang +5
Understanding visual degradations is a critical yet challenging problem in computer vision. While recent Vision-Language Models (VLMs) excel at qualitative description, they often…
Cross from Left to Right Brain: Adaptive Text Dreamer for Vision-and-Language Navigation
Pingrui Zhang, Yifei Su, Pengyuan Wu +7
Vision-and-Language Navigation (VLN) requires the agent to navigate by following natural instructions under partial observability, making it difficult to align perception with lang…
AerialVG: A Challenging Benchmark for Aerial Visual Grounding by Exploring Positional Relations
Junli Liu, Qizhi Chen, Zhigang Wang +6
Visual grounding (VG) aims to localize target objects in an image based on natural language descriptions. In this paper, we propose AerialVG, a new task focusing on visual groundin…
Exploring the Potential of Encoder-free Architectures in 3D LMMs
Yiwen Tang, Zoey Guo, Zhuhao Wang +8
Encoder-free architectures have been preliminarily explored in the 2D Large Multimodal Models (LMMs), yet it remains an open question whether they can be effectively applied to 3D…
Night-to-Day Translation via Illumination Degradation Disentanglement
Guanzhou Lan, Yuqi Yang, Zhigang Wang +3
Night-to-Day translation (Night2Day) aims to achieve day-like vision for nighttime scenes. However, processing night images with complex degradations remains a significant challeng…
Open-Vocabulary Octree-Graph for 3D Scene Understanding
Zhigang Wang, Yifei Su, Chenhui Li +4
Open-vocabulary 3D scene understanding is indispensable for embodied agents. Recent works leverage pretrained vision-language models (VLMs) for object segmentation and project them…
Efficient Diffusion as Low Light Enhancer
Guanzhou Lan, Qianli Ma, Yuqi Yang +4
The computational burden of the iterative sampling process remains a major challenge in diffusion-based Low-Light Image Enhancement (LLIE). Current acceleration methods, whether tr…
Semi-LLIE: Semi-supervised Contrastive Learning with Mamba-based Low-light Image Enhancement
Guanlin Li, Ke Zhang, Ting Wang +3
Despite the impressive advancements made in recent low-light image enhancement techniques, the scarcity of paired data has emerged as a significant obstacle to further advancements…
Learning 2D Invariant Affordance Knowledge for 3D Affordance Grounding
Xianqiang Gao, Pingrui Zhang, Delin Qu +4
3D Object Affordance Grounding aims to predict the functional regions on a 3D object and has laid the foundation for a wide range of applications in robotics. Recent advances tackl…
LiveScene: Language Embedding Interactive Radiance Fields for Physical Scene Rendering and Control
Delin Qu, Qizhi Chen, Pingrui Zhang +5
This paper scales object-level reconstruction to complex scenes, advancing interactive scene reconstruction. We introduce two datasets, OmniSim and InterReal, featuring 28 scenes w…
Any2Point: Empowering Any-modality Large Models for Efficient 3D Understanding
Yiwen Tang, Ray Zhang, Jiaming Liu +8
Large foundation models have recently emerged as a prominent focus of interest, attaining superior performance in widespread scenarios. Due to the scarcity of 3D data, many efforts…
HPL-ESS: Hybrid Pseudo-Labeling for Unsupervised Event-based Semantic Segmentation
Linglin Jing, Yiming Ding, Yunpeng Gao +7
Event-based semantic segmentation has gained popularity due to its capability to deal with scenarios under high-speed motion and extreme lighting conditions, which cannot be addres…
Motion-Aware Video Frame Interpolation
Pengfei Han, Fuhua Zhang, Bin Zhao +1
Video frame interpolation methodologies endeavor to create novel frames betwixt extant ones, with the intent of augmenting the video's frame frequency. However, current methods are…
Vehicle Perception from Satellite
Bin Zhao, Pengfei Han, Xuelong Li
Satellites are capable of capturing high-resolution videos. It makes vehicle perception from satellite become possible. Compared to street surveillance, drive recorder or other equ…
GS-SLAM: Dense Visual SLAM with 3D Gaussian Splatting
Chi Yan, Delin Qu, Dan Xu +4
In this paper, we introduce \textbf{GS-SLAM} that first utilizes 3D Gaussian representation in the Simultaneous Localization and Mapping (SLAM) system. It facilitates a better bala…
Implicit Event-RGBD Neural SLAM
Delin Qu, Chi Yan, Dong Wang +4
Implicit neural SLAM has achieved remarkable progress recently. Nevertheless, existing methods face significant challenges in non-ideal scenarios, such as motion blur or lighting v…
Disentangled Contrastive Image Translation for Nighttime Surveillance
Guanzhou Lan, Bin Zhao, Xuelong Li
Nighttime surveillance suffers from degradation due to poor illumination and arduous human annotations. It is challengable and remains a security risk at night. Existing methods re…
One-Shot High-Fidelity Talking-Head Synthesis with Deformable Neural Radiance Field
Weichuang Li, Longhao Zhang, Dong Wang +7
Talking head generation aims to generate faces that maintain the identity information of the source image and imitate the motion of the driving image. Most pioneering methods rely…
Propagate And Calibrate: Real-time Passive Non-line-of-sight Tracking
Yihao Wang, Zhigang Wang, Bin Zhao +3
Non-line-of-sight (NLOS) tracking has drawn increasing attention in recent years, due to its ability to detect object motion out of sight. Most previous works on NLOS tracking rely…
Towards Nonlinear-Motion-Aware and Occlusion-Robust Rolling Shutter Correction
Delin Qu, Yizhen Lao, Zhigang Wang +3
This paper addresses the problem of rolling shutter correction in complex nonlinear and dynamic scenes with extreme occlusion. Existing methods suffer from two main drawbacks. Firs…
ViewRefer: Grasp the Multi-view Knowledge for 3D Visual Grounding with GPT and Prototype Guidance
Zoey Guo, Yiwen Tang, Ray Zhang +4
Understanding 3D scenes from multi-view inputs has been proven to alleviate the view discrepancy issue in 3D visual grounding. However, existing methods normally neglect the view c…