2 citations · 2 across the 16 of their papers we have counts for
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Reversible Efficient Diffusion for Image Fusion
Xingxin Xu, Bing Cao, DongDong Li +2
Multi-modal image fusion aims to consolidate complementary information from diverse source images into a unified representation. The fused image is expected to preserve fine detail…
CtrlFuse: Mask-Prompt Guided Controllable Infrared and Visible Image Fusion
Yiming Sun, Yuan Ruan, Qinghua Hu +1
Infrared and visible image fusion generates all-weather perception-capable images by combining complementary modalities, enhancing environmental awareness for intelligent unmanned…
DIFF-MF: A Difference-Driven Channel-Spatial State Space Model for Multi-Modal Image Fusion
Yiming Sun, Zifan Ye, Qinghua Hu +1
Multi-modal image fusion aims to integrate complementary information from multiple source images to produce high-quality fused images with enriched content. Although existing appro…
RoomEditor++: A Parameter-Sharing Diffusion Architecture for High-Fidelity Furniture Synthesis
Qilong Wang, Xiaofan Ming, Zhenyi Lin +4
Virtual furniture synthesis, which seamlessly integrates reference objects into indoor scenes while maintaining geometric coherence and visual realism, holds substantial promise fo…
Simpler and Stronger Baselines for All-in-One Image Restoration
Hongyu Du, Wenlong Jiao, Heyang Lee +7
Current methods for all-in-one image restoration increasingly rely on complex mechanisms, such as Mixture-of-Experts or prompt learning, to handle diverse degradations.In this work…
BasicAVSR: Arbitrary-Scale Video Super-Resolution via Image Priors and Enhanced Motion Compensation
Wei Shang, Wanying Zhang, Shuhang Gu +3
Arbitrary-scale video super-resolution (AVSR) aims to enhance the resolution of video frames, potentially at various scaling factors, which presents several challenges regarding sp…
Decoupled Multi-Predictor Optimization for Inference-Efficient Model Tuning
Liwei Luo, Shuaitengyuan Li, Dongwei Ren +3
Recently, remarkable progress has been made in large-scale pre-trained model tuning, and inference efficiency is becoming more crucial for practical deployment. Early exiting in co…
Motion-Aware Adaptive Pixel Pruning for Efficient Local Motion Deblurring
Wei Shang, Dongwei Ren, Wanying Zhang +3
Local motion blur in digital images originates from the relative motion between dynamic objects and static imaging systems during exposure. Existing deblurring methods face signifi…
High-Frequency Prior-Driven Adaptive Masking for Accelerating Image Super-Resolution
Wei Shang, Dongwei Ren, Wanying Zhang +3
The primary challenge in accelerating image super-resolution lies in reducing computation while maintaining performance and adaptability. Motivated by the observation that high-fre…
Bi-directional Self-Registration for Misaligned Infrared-Visible Image Fusion
Timing Li, Bing Cao, Pengfei Zhu +2
Acquiring accurately aligned multi-modal image pairs is fundamental for achieving high-quality multi-modal image fusion. To address the lack of ground truth in current multi-modal…
VTD-CLIP: Video-to-Text Discretization via Prompting CLIP
Wencheng Zhu, Yuexin Wang, Hongxuan Li +2
Vision-language models bridge visual and linguistic understanding and have proven to be powerful for video recognition tasks. Existing approaches primarily rely on parameter-effici…
AMU-Tuning: Effective Logit Bias for CLIP-based Few-shot Learning
Yuwei Tang, Zhenyi Lin, Qilong Wang +2
Recently, pre-trained vision-language models (e.g., CLIP) have shown great potential in few-shot learning and attracted a lot of research interest. Although efforts have been made…
CKD: Contrastive Knowledge Distillation from A Sample-wise Perspective
Wencheng Zhu, Xin Zhou, Pengfei Zhu +2
In this paper, we propose a simple yet effective contrastive knowledge distillation framework that achieves sample-wise logit alignment while preserving semantic consistency. Conve…
Task-Customized Mixture of Adapters for General Image Fusion
Pengfei Zhu, Yang Sun, Bing Cao +1
General image fusion aims at integrating important information from multi-source images. However, due to the significant cross-task gap, the respective fusion mechanism varies cons…
Exploring Diverse Representations for Open Set Recognition
Yu Wang, Junxian Mu, Pengfei Zhu +1
Open set recognition (OSR) requires the model to classify samples that belong to closed sets while rejecting unknown samples during test. Currently, generative models often perform…