activity
20232026
most citedExtracting Motion and Appearance via Inter-Frame Attention for Efficient Video Frame Interpolation

4 citations · 11 across the 11 of their papers we have counts for

collaborators
Showing 2024Show all

6 papers · 1 filter

cs.CV2024

Dynamic and Compressive Adaptation of Transformers From Images to Videos

Guozhen Zhang, Jingyu Liu, Shengming Cao +4

Recently, the remarkable success of pre-trained Vision Transformers (ViTs) from image-text matching has sparked an interest in image-to-video adaptation. However, most current appr…

cs.CV2024★ 1 cited

Efficient Test-Time Prompt Tuning for Vision-Language Models

Yuhan Zhu, Guozhen Zhang, Chen Xu +4

Vision-language models have showcased impressive zero-shot classification capabilities when equipped with suitable text prompts. Previous studies have shown the effectiveness of te…

cs.CV2024

VFIMamba: Video Frame Interpolation with State Space Models

Guozhen Zhang, Chunxu Liu, Yutao Cui +3

Inter-frame modeling is pivotal in generating intermediate frames for video frame interpolation (VFI). Current approaches predominantly rely on convolution or attention-based model…

cs.CV2024

Sparse Global Matching for Video Frame Interpolation with Large Motion

Chunxu Liu, Guozhen Zhang, Rui Zhao +1

Large motion poses a critical challenge in Video Frame Interpolation (VFI) task. Existing methods are often constrained by limited receptive fields, resulting in sub-optimal perfor…

cs.CV2024★ 1 cited

Dual DETRs for Multi-Label Temporal Action Detection

Yuhan Zhu, Guozhen Zhang, Jing Tan +2

Temporal Action Detection (TAD) aims to identify the action boundaries and the corresponding category within untrimmed videos. Inspired by the success of DETR in object detection,…

cs.CV2024★ 2 cited

StableDrag: Stable Dragging for Point-based Image Editing

Yutao Cui, Xiaotong Zhao, Guozhen Zhang +3

Point-based image editing has attracted remarkable attention since the emergence of DragGAN. Recently, DragDiffusion further pushes forward the generative quality via adapting this…