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20242026
most citedClass Balance Matters to Active Class-Incremental Learning

8 citations · 15 across the 23 of their papers we have counts for

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cs.CV2026

ZoomDiff: A High-Fidelity Diffusion Model for Dual-Camera Smooth Zooming

Jiayi Zhang, Renlong Wu, Yukang Ding +2

Digital zoom transitions between dual cameras often exhibit conspicuous discontinuities in geometric structure and chromatic consistency, degrading the user experience. While recen…

cs.CV2026

SATB-VR: Training Few-Step Video Restoration Diffusion Model using SNR-Aware Trajectory Blending

Haoran Bai, Xiaoxu Chen, Xiaoyu Liu +4

While diffusion models excel in video restoration, their reliance on extensive iterative steps limits efficiency. Conversely, aggressive single-step distillation often compromises…

cs.CV2025

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…

cs.CV2025

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation

Jiahui Yang, Yongjia Ma, Donglin Di +6

Existing text-to-image models often rely on parameter fine-tuning techniques such as Low-Rank Adaptation (LoRA) to customize visual attributes. However, when combining multiple LoR…

cs.CV2025

Rethinking Cross-Modal Interaction in Multimodal Diffusion Transformers

Zhengyao Lv, Tianlin Pan, Chenyang Si +4

Multimodal Diffusion Transformers (MM-DiTs) have achieved remarkable progress in text-driven visual generation. However, even state-of-the-art MM-DiT models like FLUX struggle with…

cs.CV2025

LoViC: Efficient Long Video Generation with Context Compression

Jiaxiu Jiang, Wenbo Li, Jingjing Ren +5

Despite recent advances in diffusion transformers (DiTs) for text-to-video generation, scaling to long-duration content remains challenging due to the quadratic complexity of self-…

cs.CV2025

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…

cs.CV2025

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…

cs.CV2025★ 1 cited

Bridging Geometry-Coherent Text-to-3D Generation with Multi-View Diffusion Priors and Gaussian Splatting

Feng Yang, Wenliang Qian, Wangmeng Zuo +1

Score Distillation Sampling (SDS) leverages pretrained 2D diffusion models to advance text-to-3D generation but neglects multi-view correlations, being prone to geometric inconsist…

cs.CV2025

FramePainter: Endowing Interactive Image Editing with Video Diffusion Priors

Yabo Zhang, Xinpeng Zhou, Yihan Zeng +3

Interactive image editing allows users to modify images through visual interaction operations such as drawing, clicking, and dragging. Existing methods construct such supervision s…

cs.CV2024

Generative Inbetweening through Frame-wise Conditions-Driven Video Generation

Tianyi Zhu, Dongwei Ren, Qilong Wang +2

Generative inbetweening aims to generate intermediate frame sequences by utilizing two key frames as input. Although remarkable progress has been made in video generation models, g…

cs.CV2024★ 8 cited

Class Balance Matters to Active Class-Incremental Learning

Zitong Huang, Ze Chen, Yuanze Li +6

Few-Shot Class-Incremental Learning has shown remarkable efficacy in efficient learning new concepts with limited annotations. Nevertheless, the heuristic few-shot annotations may…

cs.CV2024

Seeing Beyond Views: Multi-View Driving Scene Video Generation with Holistic Attention

Hannan Lu, Xiaohe Wu, Shudong Wang +5

Generating multi-view videos for autonomous driving training has recently gained much attention, with the challenge of addressing both cross-view and cross-frame consistency. Exist…

cs.CV2024

Combining Generative and Geometry Priors for Wide-Angle Portrait Correction

Lan Yao, Chaofeng Chen, Xiaoming Li +2

Wide-angle lens distortion in portrait photography presents a significant challenge for capturing photo-realistic and aesthetically pleasing images. Such distortions are especially…

cs.CV2024

VitaGlyph: Vitalizing Artistic Typography with Flexible Dual-branch Diffusion Models

Kailai Feng, Yabo Zhang, Haodong Yu +4

Artistic typography is a technique to visualize the meaning of input character in an imaginable and readable manner. With powerful text-to-image diffusion models, existing methods…

cs.CV2024

Reblurring-Guided Single Image Defocus Deblurring: A Learning Framework with Misaligned Training Pairs

Dongwei Ren, Xinya Shu, Yu Li +3

For single image defocus deblurring, acquiring well-aligned training pairs (or training triplets), i.e., a defocus blurry image, an all-in-focus sharp image (and a defocus blur map…

cs.CV2024

SelfDRSC++: Self-Supervised Dual Reversed Rolling Shutter Correction via Video Interpolation

Wei Shang, Dongwei Ren, Wanying Zhang +3

Modern consumer cameras often use rolling shutter, capturing scenes row-by-row and causing distortion in dynamic scenes. Existing correction methods rely on supervised learning wit…

cs.CV2024★ 3 cited

Evaluation of Text-to-Video Generation Models: A Dynamics Perspective

Mingxiang Liao, Hannan Lu, Xinyu Zhang +6

Comprehensive and constructive evaluation protocols play an important role in the development of sophisticated text-to-video (T2V) generation models. Existing evaluation protocols…

cs.CV2024★ 1 cited

Diffusion Models in Low-Level Vision: A Survey

Chunming He, Yuqi Shen, Chengyu Fang +6

Deep generative models have garnered significant attention in low-level vision tasks due to their generative capabilities. Among them, diffusion model-based solutions, characterize…

cs.CV2024★ 2 cited

MC: Multi-concept Guidance for Customized Multi-concept Generation

Jiaxiu Jiang, Yabo Zhang, Kailai Feng +5

Customized text-to-image generation, which synthesizes images based on user-specified concepts, has made significant progress in handling individual concepts. However, when extende…

cs.CV2024

ShoeModel: Learning to Wear on the User-specified Shoes via Diffusion Model

Binghui Chen, Wenyu Li, Yifeng Geng +2

With the development of the large-scale diffusion model, Artificial Intelligence Generated Content (AIGC) techniques are popular recently. However, how to truly make it serve our d…

cs.CV2024

A self-supervised CNN for image watermark removal

Chunwei Tian, Menghua Zheng, Tiancai Jiao +3

Popular convolutional neural networks mainly use paired images in a supervised way for image watermark removal. However, watermarked images do not have reference images in the real…

cs.CV2024

VideoElevator: Elevating Video Generation Quality with Versatile Text-to-Image Diffusion Models

Yabo Zhang, Yuxiang Wei, Xianhui Lin +5

Text-to-image diffusion models (T2I) have demonstrated unprecedented capabilities in creating realistic and aesthetic images. On the contrary, text-to-video diffusion models (T2V)…