35 citations · 220 across the 36 of their papers we have counts for
37 papers
Language-Conditioned Representations and Mixture-of-Experts Policy for Robust Multi-Task Robotic Manipulation
Xiucheng Zhang, Yang Jiang, Hongwei Qing +1
Perceptual ambiguity and task conflict limit multitask robotic manipulation via imitation learning. We propose a framework combining a Language-Conditioned Visual Representation (L…
Task-Aware Encoder Control for Deep Video Compression
Xingtong Ge, Jixiang Luo, Xinjie Zhang +7
Prior research on deep video compression (DVC) for machine tasks typically necessitates training a unique codec for each specific task, mandating a dedicated decoder per task. In c…
Boosting Neural Representations for Videos with a Conditional Decoder
Xinjie Zhang, Ren Yang, Dailan He +5
Implicit neural representations (INRs) have emerged as a promising approach for video storage and processing, showing remarkable versatility across various video tasks. However, ex…
Super-High-Fidelity Image Compression via Hierarchical-ROI and Adaptive Quantization
Jixiang Luo, Yan Wang, Hongwei Qin
Learned Image Compression (LIC) has achieved dramatic progress regarding objective and subjective metrics. MSE-based models aim to improve objective metrics while generative models…
Consistency Model is an Effective Posterior Sample Approximation for Diffusion Inverse Solvers
Tongda Xu, Ziran Zhu, Jian Li +8
Diffusion Inverse Solvers (DIS) are designed to sample from the conditional distribution , with a predefined diffusion model , an operator , and a m…
GaussianImage: 1000 FPS Image Representation and Compression by 2D Gaussian Splatting
Xinjie Zhang, Xingtong Ge, Tongda Xu +6
Implicit neural representations (INRs) recently achieved great success in image representation and compression, offering high visual quality and fast rendering speeds with 10-1000…
Motion-I2V: Consistent and Controllable Image-to-Video Generation with Explicit Motion Modeling
Xiaoyu Shi, Zhaoyang Huang, Fu-Yun Wang +9
We introduce Motion-I2V, a novel framework for consistent and controllable image-to-video generation (I2V). In contrast to previous methods that directly learn the complicated imag…
Idempotence and Perceptual Image Compression
Tongda Xu, Ziran Zhu, Dailan He +8
Idempotence is the stability of image codec to re-compression. At the first glance, it is unrelated to perceptual image compression. However, we find that theoretically: 1) Conditi…
Unified learning-based lossy and lossless JPEG recompression
Jianghui Zhang, Yuanyuan Wang, Lina Guo +5
JPEG is still the most widely used image compression algorithm. Most image compression algorithms only consider uncompressed original image, while ignoring a large number of alread…
Efficient Learned Lossless JPEG Recompression
Lina Guo, Yuanyuan Wang, Tongda Xu +6
JPEG is one of the most popular image compression methods. It is beneficial to compress those existing JPEG files without introducing additional distortion. In this paper, we propo…
Conditional Perceptual Quality Preserving Image Compression
Tongda Xu, Qian Zhang, Yanghao Li +7
We propose conditional perceptual quality, an extension of the perceptual quality defined in \citet{blau2018perception}, by conditioning it on user defined information. Specificall…
FlowFormer: A Transformer Architecture and Its Masked Cost Volume Autoencoding for Optical Flow
Zhaoyang Huang, Xiaoyu Shi, Chao Zhang +6
This paper introduces a novel transformer-based network architecture, FlowFormer, along with the Masked Cost Volume AutoEncoding (MCVA) for pretraining it to tackle the problem of…
KBNet: Kernel Basis Network for Image Restoration
Yi Zhang, Dasong Li, Xiaoyu Shi +5
How to aggregate spatial information plays an essential role in learning-based image restoration. Most existing CNN-based networks adopt static convolutional kernels to encode spat…
FlowFormer++: Masked Cost Volume Autoencoding for Pretraining Optical Flow Estimation
Xiaoyu Shi, Zhaoyang Huang, Dasong Li +6
FlowFormer introduces a transformer architecture into optical flow estimation and achieves state-of-the-art performance. The core component of FlowFormer is the transformer-based c…
VideoFlow: Exploiting Temporal Cues for Multi-frame Optical Flow Estimation
Xiaoyu Shi, Zhaoyang Huang, Weikang Bian +7
We introduce VideoFlow, a novel optical flow estimation framework for videos. In contrast to previous methods that learn to estimate optical flow from two frames, VideoFlow concurr…
Correcting the Sub-optimal Bit Allocation
Tongda Xu, Han Gao, Yuanyuan Wang +4
In this paper, we investigate the problem of bit allocation in Neural Video Compression (NVC). First, we reveal that a recent bit allocation approach claimed to be optimal is, in f…
Spatial Moment Pooling Improves Neural Image Assessment
Tongda Xu, Yifan Shao, Yan Wang +1
In recent years, there has been widespread attention drawn to convolutional neural network (CNN) based blind image quality assessment (IQA). A large number of works start by extrac…
Multi-Sample Training for Neural Image Compression
Tongda Xu, Yan Wang, Dailan He +4
This paper considers the problem of lossy neural image compression (NIC). Current state-of-the-art (sota) methods adopt uniform posterior to approximate quantization noise, and sin…
Flexible Neural Image Compression via Code Editing
Chenjian Gao, Tongda Xu, Dailan He +2
Neural image compression (NIC) has outperformed traditional image codecs in rate-distortion (R-D) performance. However, it usually requires a dedicated encoder-decoder pair for eac…
Bit Allocation using Optimization
Tongda Xu, Han Gao, Chenjian Gao +10
In this paper, we consider the problem of bit allocation in Neural Video Compression (NVC). First, we reveal a fundamental relationship between bit allocation in NVC and Semi-Amort…
Learning Degradation Representations for Image Deblurring
Dasong Li, Yi Zhang, Ka Chun Cheung +3
In various learning-based image restoration tasks, such as image denoising and image super-resolution, the degradation representations were widely used to model the degradation pro…
Evaluating the Practicality of Learned Image Compression
Hongjiu Yu, Qiancheng Sun, Jin Hu +9
Learned image compression has achieved extraordinary rate-distortion performance in PSNR and MS-SSIM compared to traditional methods. However, it suffers from intensive computation…
A Simple Baseline for Video Restoration with Grouped Spatial-temporal Shift
Dasong Li, Xiaoyu Shi, Yi Zhang +5
Video restoration, which aims to restore clear frames from degraded videos, has numerous important applications. The key to video restoration depends on utilizing inter-frame infor…
PO-ELIC: Perception-Oriented Efficient Learned Image Coding
Dailan He, Ziming Yang, Hongjiu Yu +7
In the past years, learned image compression (LIC) has achieved remarkable performance. The recent LIC methods outperform VVC in both PSNR and MS-SSIM. However, the low bit-rate re…