activity
20212023
most citedInpaint Anything: Segment Anything Meets Image Inpainting

61 citations · 145 across the 20 of their papers we have counts for

collaborators

20 papers

cs.CV20237 cited

Prompt-ICM: A Unified Framework towards Image Coding for Machines with Task-driven Prompts

Ruoyu Feng, Jinming Liu, Xin Jin +3

Image coding for machines (ICM) aims to compress images to support downstream AI analysis instead of human perception. For ICM, developing a unified codec to reduce information red…

cs.CV2023

Learned Focused Plenoptic Image Compression with Microimage Preprocessing and Global Attention

Kedeng Tong, Xin Jin, Yuqing Yang +3

Focused plenoptic cameras can record spatial and angular information of the light field (LF) simultaneously with higher spatial resolution relative to traditional plenoptic cameras…

cs.CV2023

Dynamic Video Frame Interpolation with integrated Difficulty Pre-Assessment

Ban Chen, Xin Jin, Youxin Chen +4

Video frame interpolation(VFI) has witnessed great progress in recent years. While existing VFI models still struggle to achieve a good trade-off between accuracy and efficiency: f…

cs.SD2023

An Order-Complexity Model for Aesthetic Quality Assessment of Homophony Music Performance

Xin Jin, Wu Zhou, Jinyu Wang +3

Although computational aesthetics evaluation has made certain achievements in many fields, its research of music performance remains to be explored. At present, subjective evaluati…

cs.CV202361 cited

Inpaint Anything: Segment Anything Meets Image Inpainting

Tao Yu, Runseng Feng, Ruoyu Feng +4

Modern image inpainting systems, despite the significant progress, often struggle with mask selection and holes filling. Based on Segment-Anything Model (SAM), we make the first at…

cs.CV20232 cited

[CLS] Token is All You Need for Zero-Shot Semantic Segmentation

Letian Wu, Wenyao Zhang, Tengping Jiang +3

In this paper, we propose an embarrassingly simple yet highly effective zero-shot semantic segmentation (ZS3) method, based on the pre-trained vision-language model CLIP. First, ou…