5 citations · 11 across the 41 of their papers we have counts for
15 papers · 1 filter
Advancing Comprehensive Aesthetic Insight with Multi-Scale Text-Guided Self-Supervised Learning
Yuti Liu, Shice Liu, Junyuan Gao +4
Image Aesthetic Assessment (IAA) is a vital and intricate task that entails analyzing and assessing an image's aesthetic values, and identifying its highlights and areas for improv…
Hero-SR: One-Step Diffusion for Super-Resolution with Human Perception Priors
Jiangang Wang, Qingnan Fan, Qi Zhang +4
Owing to the robust priors of diffusion models, recent approaches have shown promise in addressing real-world super-resolution (Real-SR). However, achieving semantic consistency an…
RAP-SR: RestorAtion Prior Enhancement in Diffusion Models for Realistic Image Super-Resolution
Jiangang Wang, Qingnan Fan, Jinwei Chen +3
Benefiting from their powerful generative capabilities, pretrained diffusion models have garnered significant attention for real-world image super-resolution (Real-SR). Existing di…
CPA: Camera-pose-awareness Diffusion Transformer for Video Generation
Yuelei Wang, Jian Zhang, Pengtao Jiang +3
Despite the significant advancements made by Diffusion Transformer (DiT)-based methods in video generation, there remains a notable gap with controllable camera pose perspectives.…
Learning Adaptive Lighting via Channel-Aware Guidance
Qirui Yang, Peng-Tao Jiang, Hao Zhang +4
Learning lighting adaptation is a crucial step in achieving good visual perception and supporting downstream vision tasks. Current research often addresses individual light-related…
TSD-SR: One-Step Diffusion with Target Score Distillation for Real-World Image Super-Resolution
Linwei Dong, Qingnan Fan, Yihong Guo +5
Pre-trained text-to-image diffusion models are increasingly applied to real-world image super-resolution (Real-ISR) task. Given the iterative refinement nature of diffusion models,…
ControlSR: Taming Diffusion Models for Consistent Real-World Image Super Resolution
Yuhao Wan, Peng-Tao Jiang, Qibin Hou +4
We present ControlSR, a new method that can tame Diffusion Models for consistent real-world image super-resolution (Real-ISR). Previous Real-ISR models mostly focus on how to activ…
Improving Consistency in Diffusion Models for Image Super-Resolution
Junhao Gu, Peng-Tao Jiang, Hao Zhang +4
Recent methods exploit the powerful text-to-image (T2I) diffusion models for real-world image super-resolution (Real-ISR) and achieve impressive results compared to previous models…
High-Precision Dichotomous Image Segmentation via Probing Diffusion Capacity
Qian Yu, Peng-Tao Jiang, Hao Zhang +4
In the realm of high-resolution (HR), fine-grained image segmentation, the primary challenge is balancing broad contextual awareness with the precision required for detailed object…
SAFNet: Selective Alignment Fusion Network for Efficient HDR Imaging
Lingtong Kong, Bo Li, Yike Xiong +3
Multi-exposure High Dynamic Range (HDR) imaging is a challenging task when facing truncated texture and complex motion. Existing deep learning-based methods have achieved great suc…
Mono-ViFI: A Unified Learning Framework for Self-supervised Single- and Multi-frame Monocular Depth Estimation
Jinfeng Liu, Lingtong Kong, Bo Li +3
Self-supervised monocular depth estimation has gathered notable interest since it can liberate training from dependency on depth annotations. In monocular video training case, rece…
Scalable Visual State Space Model with Fractal Scanning
Lv Tang, HaoKe Xiao, Peng-Tao Jiang +3
Foundational models have significantly advanced in natural language processing (NLP) and computer vision (CV), with the Transformer architecture becoming a standard backbone. Howev…
Empowering Segmentation Ability to Multi-modal Large Language Models
Yuqi Yang, Peng-Tao Jiang, Jing Wang +4
Multi-modal large language models (MLLMs) can understand image-language prompts and demonstrate impressive reasoning ability. In this paper, we extend MLLMs' output by empowering M…
Multi-Task Dense Prediction via Mixture of Low-Rank Experts
Yuqi Yang, Peng-Tao Jiang, Qibin Hou +3
Previous multi-task dense prediction methods based on the Mixture of Experts (MoE) have received great performance but they neglect the importance of explicitly modeling the global…
Improving Adversarial Energy-Based Model via Diffusion Process
Cong Geng, Tian Han, Peng-Tao Jiang +4
Generative models have shown strong generation ability while efficient likelihood estimation is less explored. Energy-based models~(EBMs) define a flexible energy function to param…