5 citations · 8 across the 12 of their papers we have counts for
14 papers
4D-WAM: 4D Consistent World Modeling for Autonomous Driving
Jiacheng Fu, Yibo Yuan, Meng Tian +8
Emerging World-Action Models (WAMs) have demonstrated promising performance in autonomous driving by jointly modeling future driving scene evolution and trajectory planning. Howeve…
Fine-Grained Evaluation of Large Vision-Language Models in Autonomous Driving
Yue Li, Meng Tian, Zhenyu Lin +7
Existing benchmarks for Vision-Language Model (VLM) on autonomous driving (AD) primarily assess interpretability through open-form visual question answering (QA) within coarse-grai…
Event-boosted Deformable 3D Gaussians for Dynamic Scene Reconstruction
Wenhao Xu, Wenming Weng, Yueyi Zhang +2
Deformable 3D Gaussian Splatting (3D-GS) is limited by missing intermediate motion information due to the low temporal resolution of RGB cameras. To address this, we introduce the…
Generalizable Non-Line-of-Sight Imaging with Learnable Physical Priors
Shida Sun, Yue Li, Yueyi Zhang +1
Non-line-of-sight (NLOS) imaging, recovering the hidden volume from indirect reflections, has attracted increasing attention due to its potential applications. Despite promising re…
CEIA: CLIP-Based Event-Image Alignment for Open-World Event-Based Understanding
Wenhao Xu, Wenming Weng, Yueyi Zhang +1
We present CEIA, an effective framework for open-world event-based understanding. Currently training a large event-text model still poses a huge challenge due to the shortage of pa…
ARTV: Auto-Regressive Text-to-Video Generation with Diffusion Models
Wenming Weng, Ruoyu Feng, Yanhui Wang +10
We present ARTV, an efficient framework for auto-regressive video generation with diffusion models. Unlike existing methods that generate entire videos in one-s…
Domain Adaptive Synapse Detection with Weak Point Annotations
Qi Chen, Wei Huang, Yueyi Zhang +1
The development of learning-based methods has greatly improved the detection of synapses from electron microscopy (EM) images. However, training a model for each dataset is time-co…
Deep Multi-Threshold Spiking-UNet for Image Processing
Hebei Li, Yueyi Zhang, Zhiwei Xiong +1
U-Net, known for its simple yet efficient architecture, is widely utilized for image processing tasks and is particularly suitable for deployment on neuromorphic chips. This paper…
Toward Real-World Light Field Super-Resolution
Zeyu Xiao, Ruisheng Gao, Yutong Liu +2
Deep learning has opened up new possibilities for light field super-resolution (SR), but existing methods trained on synthetic datasets with simple degradations (e.g., bicubic down…
Image Captioning with Multi-Context Synthetic Data
Feipeng Ma, Yizhou Zhou, Fengyun Rao +2
Image captioning requires numerous annotated image-text pairs, resulting in substantial annotation costs. Recently, large models (e.g. diffusion models and large language models) h…
Degradation-agnostic Correspondence from Resolution-asymmetric Stereo
Xihao Chen, Zhiwei Xiong, Zhen Cheng +3
In this paper, we study the problem of stereo matching from a pair of images with different resolutions, e.g., those acquired with a tele-wide camera system. Due to the difficulty…
Retinal Vessel Segmentation with Pixel-wise Adaptive Filters
Mingxing Li, Shenglong Zhou, Chang Chen +3
Accurate retinal vessel segmentation is challenging because of the complex texture of retinal vessels and low imaging contrast. Previous methods generally refine segmentation resul…
EDPN: Enhanced Deep Pyramid Network for Blurry Image Restoration
Ruikang Xu, Zeyu Xiao, Jie Huang +2
Image deblurring has seen a great improvement with the development of deep neural networks. In practice, however, blurry images often suffer from additional degradations such as do…
Advanced Deep Networks for 3D Mitochondria Instance Segmentation
Mingxing Li, Chang Chen, Xiaoyu Liu +3
Mitochondria instance segmentation from electron microscopy (EM) images has seen notable progress since the introduction of deep learning methods. In this paper, we propose two adv…