90 citations · 188 across the 10 of their papers we have counts for
11 papers
An Efficient Adaptive Compression Method for Human Perception and Machine Vision Tasks
Lei Liu, Zhenghao Chen, Zhihao Hu +1
While most existing neural image compression (NIC) and neural video compression (NVC) methodologies have achieved remarkable success, their optimization is primarily focused on hum…
Towards Point Cloud Compression for Machine Perception: A Simple and Strong Baseline by Learning the Octree Depth Level Predictor
Lei Liu, Zhihao Hu, Zhenghao Chen
Point cloud compression has garnered significant interest in computer vision. However, existing algorithms primarily cater to human vision, while most point cloud data is utilized…
Group-aware Parameter-efficient Updating for Content-Adaptive Neural Video Compression
Zhenghao Chen, Luping Zhou, Zhihao Hu +1
Content-adaptive compression is crucial for enhancing the adaptability of the pre-trained neural codec for various contents. Although these methods have been very practical in neur…
VideoControlNet: A Motion-Guided Video-to-Video Translation Framework by Using Diffusion Model with ControlNet
Zhihao Hu, Dong Xu
Recently, diffusion models like StableDiffusion have achieved impressive image generation results. However, the generation process of such diffusion models is uncontrollable, which…
Inversion-by-Inversion: Exemplar-based Sketch-to-Photo Synthesis via Stochastic Differential Equations without Training
Ximing Xing, Chuang Wang, Haitao Zhou +4
Exemplar-based sketch-to-photo synthesis allows users to generate photo-realistic images based on sketches. Recently, diffusion-based methods have achieved impressive performance o…
Content Adaptive Latents and Decoder for Neural Image Compression
Guanbo Pan, Guo Lu, Zhihao Hu +1
In recent years, neural image compression (NIC) algorithms have shown powerful coding performance. However, most of them are not adaptive to the image content. Although several con…
Coarse-to-fine Deep Video Coding with Hyperprior-guided Mode Prediction
Zhihao Hu, Guo Lu, Jinyang Guo +3
The previous deep video compression approaches only use the single scale motion compensation strategy and rarely adopt the mode prediction technique from the traditional standards…
SAMOT: Switcher-Aware Multi-Object Tracking and Still Another MOT Measure
Weitao Feng, Zhihao Hu, Baopu Li +3
Multi-Object Tracking (MOT) is a popular topic in computer vision. However, identity issue, i.e., an object is wrongly associated with another object of a different identity, still…
Improving Deep Video Compression by Resolution-adaptive Flow Coding
Zhihao Hu, Zhenghao Chen, Dong Xu +3
In the learning based video compression approaches, it is an essential issue to compress pixel-level optical flow maps by developing new motion vector (MV) encoders. In this work,…
A Unified End-to-End Framework for Efficient Deep Image Compression
Jiaheng Liu, Guo Lu, Zhihao Hu +1
Image compression is a widely used technique to reduce the spatial redundancy in images. Recently, learning based image compression has achieved significant progress by using the p…
Multi-Object Tracking with Multiple Cues and Switcher-Aware Classification
Weitao Feng, Zhihao Hu, Wei Wu +2
In this paper, we propose a unified Multi-Object Tracking (MOT) framework learning to make full use of long term and short term cues for handling complex cases in MOT scenes. Besid…