35 citations · 220 across the 36 of their papers we have counts for
20 papers · 1 filter
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…
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…
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…
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…
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…
ELIC: Efficient Learned Image Compression with Unevenly Grouped Space-Channel Contextual Adaptive Coding
Dailan He, Ziming Yang, Weikun Peng +3
Recently, learned image compression techniques have achieved remarkable performance, even surpassing the best manually designed lossy image coders. They are promising to be large-s…
FlowFormer: A Transformer Architecture for Optical Flow
Zhaoyang Huang, Xiaoyu Shi, Chao Zhang +5
We introduce optical Flow transFormer, dubbed as FlowFormer, a transformer-based neural network architecture for learning optical flow. FlowFormer tokenizes the 4D cost volume buil…
IDR: Self-Supervised Image Denoising via Iterative Data Refinement
Yi Zhang, Dasong Li, Ka Lung Law +3
The lack of large-scale noisy-clean image pairs restricts supervised denoising methods' deployment in actual applications. While existing unsupervised methods are able to learn ima…
HLIC: Harmonizing Optimization Metrics in Learned Image Compression by Reinforcement Learning
Baocheng Sun, Meng Gu, Dailan He +3
Learned image compression is making good progress in recent years. Peak signal-to-noise ratio (PSNR) and multi-scale structural similarity (MS-SSIM) are the two most popular evalua…
Cross-dataset Training for Class Increasing Object Detection
Yongqiang Yao, Yan Wang, Yu Guo +3
We present a conceptually simple, flexible and general framework for cross-dataset training in object detection. Given two or more already labeled datasets that target for differen…
Quantization Mimic: Towards Very Tiny CNN for Object Detection
Yi Wei, Xinyu Pan, Hongwei Qin +2
In this paper, we propose a simple and general framework for training very tiny CNNs for object detection. Due to limited representation ability, it is challenging to train very ti…
Impression Network for Video Object Detection
Congrui Hetang, Hongwei Qin, Shaohui Liu +1
Video object detection is more challenging compared to image object detection. Previous works proved that applying object detector frame by frame is not only slow but also inaccura…
Scale-Aware Face Detection
Zekun Hao, Yu Liu, Hongwei Qin +3
Convolutional neural network (CNN) based face detectors are inefficient in handling faces of diverse scales. They rely on either fitting a large single model to faces across a larg…