12 citations · 19 across the 31 of their papers we have counts for
31 papers · 1 filter
Destination-Conditioned Adaptation with Semantic-Geometric Alignment for Generative Inbetweening
Zhiyu Xu, Wei Shang, Yushen Xu +4
Generative inbetweening aims to synthesize temporally coherent intermediate frames between a start frame and a target frame. Although pretrained image-to-video diffusion models pro…
SAGE: Source-Anchored Guidance via Frequency Equalization for Hierarchical RGB-T Alignment and Fusion
Timing Li, Yiming Sun, Boan Tao +3
Spatial misregistration and cross-modal discrepancies often cause ghosting, structural blurring, and content imbalance in RGB-T fusion. Existing methods typically decouple appearan…
Socialized UAV Cross-Task Learning: Towards Cross-Granularity Collaboration through Hierarchical Interaction
Xinjie Yao, Ruipu Zhao, Yunqi Zhu +6
Joint learning across heterogeneous tasks is often treated as task coupling through feature sharing, distillation, or auxiliary supervision. However, in cross-task learning, mismat…
UniDiffFusion: A Unified Diffusion Framework for Multi-Task and Degradation-Robust Image Fusion
Xingxin Xu, Siqi Zhao, Xin Li +3
General image fusion aims to integrate complementary information from multiple source images, but existing methods often rely on task-specific models and struggle to maintain robus…
Socialized Detector Learning: Trajectory-Guided and Reciprocal Distillation for Heterogeneous Object Detectors
Weihao Li, Yunqi Zhu, Zhihe Fan +5
Object detection knowledge is fragmented across independently trained, heterogeneous detectors with complementary category supports. In socialized learning, this knowledge resides…
DynCur-Geo: Dynamic Curiosity Reward Shaping for Multimodal Active Geo-Localization
Yiming Sun, Yang Zhang, Pengfei Zhu
Active geo-localization enables low-altitude UAVs to search for specified targets from limited local aerial observations, supporting time-sensitive applications such as search and…
RGBX-R1: Visual Modality Chain-of-Thought Guided Reinforcement Learning for Multimodal Grounding
Jiahe Wu, Bing Cao, Qilong Wang +3
Multimodal Large Language Models (MLLM) are primarily pre-trained on the RGB modality, thereby limiting their performance on other modalities, such as infrared, depth, and event da…
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…
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…
Hyperbolic Cycle Alignment for Infrared-Visible Image Fusion
Timing Li, Bing Cao, Jiahe Feng +3
Image fusion synthesizes complementary information from multiple sources, mitigating the inherent limitations of unimodal imaging systems. Accurate image registration is essential…
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…
BackMix: Regularizing Open Set Recognition by Removing Underlying Fore-Background Priors
Yu Wang, Junxian Mu, Hongzhi Huang +3
Open set recognition (OSR) requires models to classify known samples while detecting unknown samples for real-world applications. Existing studies show impressive progress using un…
Dream-IF: Dynamic Relative EnhAnceMent for Image Fusion
Xingxin Xu, Bing Cao, Dongdong Li +2
Image fusion aims to integrate comprehensive information from images acquired through multiple sources. However, images captured by diverse sensors often encounter various degradat…
Asymmetric Reinforcing against Multi-modal Representation Bias
Xiyuan Gao, Bing Cao, Pengfei Zhu +2
The strength of multimodal learning lies in its ability to integrate information from various sources, providing rich and comprehensive insights. However, in real-world scenarios,…
Dynamic Brightness Adaptation for Robust Multi-modal Image Fusion
Yiming Sun, Bing Cao, Pengfei Zhu +1
Infrared and visible image fusion aim to integrate modality strengths for visually enhanced, informative images. Visible imaging in real-world scenarios is susceptible to dynamic e…
Conditional Controllable Image Fusion
Bing Cao, Xingxin Xu, Pengfei Zhu +2
Image fusion aims to integrate complementary information from multiple input images acquired through various sources to synthesize a new fused image. Existing methods usually emplo…
Efficient Masked AutoEncoder for Video Object Counting and A Large-Scale Benchmark
Bing Cao, Quanhao Lu, Jiekang Feng +3
The dynamic imbalance of the fore-background is a major challenge in video object counting, which is usually caused by the sparsity of target objects. This remains understudied in…