57 citations · 104 across the 17 of their papers we have counts for
19 papers
Decoupling Spherical Reasoning from Dense Prediction for 360 Depth Estimation
Zhijie Shen, Chunyu Lin, Shuai Zheng +4
The equirectangular projection (ERP) is widely used for panoramic depth estimation, but its spatially varying distortion makes geometry-consistent feature modeling challenging. We…
SphereSOD: Geometry-Structure Coupled Learning for 360 Salient Object Detection
Junsong Zhang, Zhijie Shen, Shuai Zheng +4
360° salient object detection (SOD) aims to accurately segment salient regions across a full field of view. However, equirectangular projection (ERP) introduces severe spatial dist…
Taming Real-World Space-Time Video Super-Resolution with One-Step Diffusion
Shuoyan Wei, Feng Li, Chen Zhou +3
Diffusion models have demonstrated exceptional success in video super-resolution (VSR), exhibiting powerful capabilities for generating fine-grained details. However, their potenti…
Towards Robust and Generalizable Continuous Space-Time Video Super-Resolution with Events
Shuoyan Wei, Feng Li, Shengeng Tang +4
Continuous space-time video super-resolution (C-STVSR) has garnered increasing interest for its capability to reconstruct high-resolution and high-frame-rate videos at arbitrary sp…
EvEnhancer: Empowering Effectiveness, Efficiency and Generalizability for Continuous Space-Time Video Super-Resolution with Events
Shuoyan Wei, Feng Li, Shengeng Tang +2
Continuous space-time video super-resolution (C-STVSR) endeavors to upscale videos simultaneously at arbitrary spatial and temporal scales, which has recently garnered increasing i…
PSVMA+: Exploring Multi-granularity Semantic-visual Adaption for Generalized Zero-shot Learning
Man Liu, Huihui Bai, Feng Li +5
Generalized zero-shot learning (GZSL) endeavors to identify the unseen categories using knowledge from the seen domain, necessitating the intrinsic interactions between the visual…
Attend and Enrich: Enhanced Visual Prompt for Zero-Shot Learning
Man Liu, Huihui Bai, Feng Li +4
Zero-shot learning (ZSL) endeavors to transfer knowledge from seen categories to recognize unseen categories, which mostly relies on the semantic-visual interactions between image…
Once-for-All: Controllable Generative Image Compression with Dynamic Granularity Adaptation
Anqi Li, Feng Li, Yuxi Liu +3
Although recent generative image compression methods have demonstrated impressive potential in optimizing the rate-distortion-perception trade-off, they still face the critical cha…
BlindDiff: Empowering Degradation Modelling in Diffusion Models for Blind Image Super-Resolution
Feng Li, Yixuan Wu, Zichao Liang +4
Diffusion models (DM) have achieved remarkable promise in image super-resolution (SR). However, most of them are tailored to solving non-blind inverse problems with fixed known deg…
You Can Mask More For Extremely Low-Bitrate Image Compression
Anqi Li, Feng Li, Jiaxin Han +6
Learned image compression (LIC) methods have experienced significant progress during recent years. However, these methods are primarily dedicated to optimizing the rate-distortion…
Exploring Resolution Fields for Scalable Image Compression with Uncertainty Guidance
Dongyi Zhang, Feng Li, Man Liu +4
Recently, there are significant advancements in learning-based image compression methods surpassing traditional coding standards. Most of them prioritize achieving the best rate-di…
Progressive Semantic-Visual Mutual Adaption for Generalized Zero-Shot Learning
Man Liu, Feng Li, Chunjie Zhang +3
Generalized Zero-Shot Learning (GZSL) identifies unseen categories by knowledge transferred from the seen domain, relying on the intrinsic interactions between visual and semantic…
Bridging Component Learning with Degradation Modelling for Blind Image Super-Resolution
Yixuan Wu, Feng Li, Huihui Bai +3
Convolutional Neural Network (CNN)-based image super-resolution (SR) has exhibited impressive success on known degraded low-resolution (LR) images. However, this type of approach i…
Learning Detail-Structure Alternative Optimization for Blind Super-Resolution
Feng Li, Yixuan Wu, Huihui Bai +3
Existing convolutional neural networks (CNN) based image super-resolution (SR) methods have achieved impressive performance on bicubic kernel, which is not valid to handle unknown…
Cross-modality Discrepant Interaction Network for RGB-D Salient Object Detection
Chen Zhang, Runmin Cong, Qinwei Lin +4
The popularity and promotion of depth maps have brought new vigor and vitality into salient object detection (SOD), and a mass of RGB-D SOD algorithms have been proposed, mainly co…
Towards Fast and Accurate Real-World Depth Super-Resolution: Benchmark Dataset and Baseline
Lingzhi He, Hongguang Zhu, Feng Li +6
Depth maps obtained by commercial depth sensors are always in low-resolution, making it difficult to be used in various computer vision tasks. Thus, depth map super-resolution (SR)…
Learning Deep Interleaved Networks with Asymmetric Co-Attention for Image Restoration
Feng Li, Runmin Cong, Huihui Bai +3
Recently, convolutional neural network (CNN) has demonstrated significant success for image restoration (IR) tasks (e.g., image super-resolution, image deblurring, rain streak remo…
Deep Interleaved Network for Image Super-Resolution With Asymmetric Co-Attention
Feng Li, Runming Cong, Huihui Bai +1
Recently, Convolutional Neural Networks (CNN) based image super-resolution (SR) have shown significant success in the literature. However, these methods are implemented as single-p…
Mixed-Resolution Image Representation and Compression with Convolutional Neural Networks
Lijun Zhao, Huihui Bai, Feng Li +2
In this paper, we propose an end-to-end mixed-resolution image compression framework with convolutional neural networks. Firstly, given one input image, feature description neural…