1 citations · 3 across the 12 of their papers we have counts for
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FlowSeg: Dynamic Semantic Guidance for LLM-Conditioned Segmentation
Zekang Zhang, Guangyu Gao, Youyun Tang +7
LLM-conditioned segmentation has recently advanced rapidly by coupling large language models with iterative mask generation frameworks. However, we identify a persistent failure mo…
Self-Prompting Diffusion Transformer for Open-Vocabulary Scene Text Editing via In-Context Learning
Hongxi Li, Tong Wang, Chengjing Wu +6
Scene text editing aims to modify text in a target region of an image while preserving surrounding background style and texture. Existing methods rely solely on image background in…
MiVE: Multiscale Vision-language features for reference-guided video Editing
Tong Wang, Meng Zou, Chengjing Wu +4
Reference-guided video editing takes a source video, a text instruction, and a reference image as inputs, requiring the model to faithfully apply the instructed edits while preserv…
Learning Stochastic Bridges for Video Object Removal via Video-to-Video Translation
Zijie Lou, Xiangwei Feng, Jiaxin Wang +7
Existing video object removal methods predominantly rely on diffusion models following a noise-to-data paradigm, where generation starts from uninformative Gaussian noise. This app…
GlyphMastero: A Glyph Encoder for High-Fidelity Scene Text Editing
Tong Wang, Ting Liu, Xiaochao Qu +3
Scene text editing, a subfield of image editing, requires modifying texts in images while preserving style consistency and visual coherence with the surrounding environment. While…
Multimodal Music Generation with Explicit Bridges and Retrieval Augmentation
Baisen Wang, Le Zhuo, Zhaokai Wang +7
Multimodal music generation aims to produce music from diverse input modalities, including text, videos, and images. Existing methods use a common embedding space for multimodal fu…
SAM-REF: Introducing Image-Prompt Synergy during Interaction for Detail Enhancement in the Segment Anything Model
Chongkai Yu, Ting Liu, Anqi Li +4
Interactive segmentation is to segment the mask of the target object according to the user's interactive prompts. There are two mainstream strategies: early fusion and late fusion.…
PVUW 2024 Challenge on Complex Video Understanding: Methods and Results
Henghui Ding, Chang Liu, Yunchao Wei +34
Pixel-level Video Understanding in the Wild Challenge (PVUW) focus on complex video understanding. In this CVPR 2024 workshop, we add two new tracks, Complex Video Object Segmentat…
2nd Place Solution for MOSE Track in CVPR 2024 PVUW workshop: Complex Video Object Segmentation
Zhensong Xu, Jiangtao Yao, Chengjing Wu +2
Complex video object segmentation serves as a fundamental task for a wide range of downstream applications such as video editing and automatic data annotation. Here we present the…
Semantic Segmentation on VSPW Dataset through Masked Video Consistency
Chen Liang, Qiang Guo, Chongkai Yu +3
Pixel-level Video Understanding requires effectively integrating three-dimensional data in both spatial and temporal dimensions to learn accurate and stable semantic information fr…
3rd Place Solution for PVUW Challenge 2024: Video Panoptic Segmentation
Ruipu Wu, Jifei Che, Han Li +3
Video panoptic segmentation is an advanced task that extends panoptic segmentation by applying its concept to video sequences. In the hope of addressing the challenge of video pano…