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20152026
most citedTransferrable Prototypical Networks for Unsupervised Domain Adaptation

50 citations · 578 across the 81 of their papers we have counts for

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Showing 2025 · cs.CVShow all

9 papers · 2 filters

cs.CV2025

FreeInpaint: Tuning-free Prompt Alignment and Visual Rationality Enhancement in Image Inpainting

Chao Gong, Dong Li, Yingwei Pan +3

Text-guided image inpainting endeavors to generate new content within specified regions of images using textual prompts from users. The primary challenge is to accurately align the…

cs.CV2025

Visual Autoregressive Modeling for Instruction-Guided Image Editing

Qingyang Mao, Qi Cai, Yehao Li +5

Recent advances in diffusion models have brought remarkable visual fidelity to instruction-guided image editing. However, their global denoising process inherently entangles the ed…

cs.CV2025

DreamJourney: Perpetual View Generation with Video Diffusion Models

Bo Pan, Yang Chen, Yingwei Pan +3

Perpetual view generation aims to synthesize a long-term video corresponding to an arbitrary camera trajectory solely from a single input image. Recent methods commonly utilize a p…

cs.CV2025★ 1 cited

HiDream-I1: A High-Efficient Image Generative Foundation Model with Sparse Diffusion Transformer

Qi Cai, Jingwen Chen, Yang Chen +19

Recent advancements in image generative foundation models have prioritized quality improvements but often at the cost of increased computational complexity and inference latency. T…

cs.CV2025

Hierarchical Masked Autoregressive Models with Low-Resolution Token Pivots

Guangting Zheng, Yehao Li, Yingwei Pan +4

Autoregressive models have emerged as a powerful generative paradigm for visual generation. The current de-facto standard of next token prediction commonly operates over a single-s…

cs.CV2025

MotionPro: A Precise Motion Controller for Image-to-Video Generation

Zhongwei Zhang, Fuchen Long, Zhaofan Qiu +4

Animating images with interactive motion control has garnered popularity for image-to-video (I2V) generation. Modern approaches typically rely on large Gaussian kernels to extend m…