75 citations · 157 across the 13 of their papers we have counts for
13 papers
STEERER: Resolving Scale Variations for Counting and Localization via Selective Inheritance Learning
Tao Han, Lei Bai, Lingbo Liu +1
Scale variation is a deep-rooted problem in object counting, which has not been effectively addressed by existing scale-aware algorithms. An important factor is that they typically…
Few-Shot Continual Learning via Flat-to-Wide Approaches
Muhammad Anwar Ma'sum, Mahardhika Pratama, Edwin Lughofer +3
Existing approaches on continual learning call for a lot of samples in their training processes. Such approaches are impractical for many real-world problems having limited samples…
Exploring Effective Mask Sampling Modeling for Neural Image Compression
Lin Liu, Mingming Zhao, Shanxin Yuan +5
Image compression aims to reduce the information redundancy in images. Most existing neural image compression methods rely on side information from hyperprior or context models to…
Joint Channel Estimation and Feedback with Masked Token Transformers in Massive MIMO Systems
Mingming Zhao, Lin Liu, Lifu Liu +2
The downlink channel state information (CSI) estimation and low overhead acquisition are the major challenges for massive MIMO systems in frequency division duplex to enable high M…
CLIP-Count: Towards Text-Guided Zero-Shot Object Counting
Ruixiang Jiang, Lingbo Liu, Changwen Chen
Recent advances in visual-language models have shown remarkable zero-shot text-image matching ability that is transferable to downstream tasks such as object detection and segmenta…
Visual Tuning
Bruce X. B. Yu, Jianlong Chang, Haixin Wang +9
Fine-tuning visual models has been widely shown promising performance on many downstream visual tasks. With the surprising development of pre-trained visual foundation models, visu…
Towards a Unified View on Visual Parameter-Efficient Transfer Learning
Bruce X. B. Yu, Jianlong Chang, Lingbo Liu +2
Parameter efficient transfer learning (PETL) aims at making good use of the representation knowledge in the pre-trained large models by fine-tuning a small number of parameters. Re…
Low-Light Video Enhancement with Synthetic Event Guidance
Lin Liu, Junfeng An, Jianzhuang Liu +6
Low-light video enhancement (LLVE) is an important yet challenging task with many applications such as photographing and autonomous driving. Unlike single image low-light enhanceme…
NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods and Results
Eduardo Pérez-Pellitero, Sibi Catley-Chandar, Richard Shaw +85
This paper reviews the challenge on constrained high dynamic range (HDR) imaging that was part of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conj…
TAPE: Task-Agnostic Prior Embedding for Image Restoration
Lin Liu, Lingxi Xie, Xiaopeng Zhang +5
Learning a generalized prior for natural image restoration is an important yet challenging task. Early methods mostly involved handcrafted priors including normalized sparsity, l_0…
SiamTrans: Zero-Shot Multi-Frame Image Restoration with Pre-Trained Siamese Transformers
Lin Liu, Shanxin Yuan, Jianzhuang Liu +3
We propose a novel zero-shot multi-frame image restoration method for removing unwanted obstruction elements (such as rains, snow, and moire patterns) that vary in successive frame…
Self-Adaptively Learning to Demoire from Focused and Defocused Image Pairs
Lin Liu, Shanxin Yuan, Jianzhuang Liu +3
Moire artifacts are common in digital photography, resulting from the interference between high-frequency scene content and the color filter array of the camera. Existing deep lear…
Wavelet-Based Dual-Branch Network for Image Demoireing
Lin Liu, Jianzhuang Liu, Shanxin Yuan +4
When smartphone cameras are used to take photos of digital screens, usually moire patterns result, severely degrading photo quality. In this paper, we design a wavelet-based dual-b…