2 citations · 3 across the 9 of their papers we have counts for
9 papers
Instruction-based Image Editing: A Survey on Data, Models, Evaluation, and Applications
Xianghao Zang, Zijian Jiang, Jiarong Cheng +8
Instruction-based Image Editing (IIE) aims to transform a given image into a new one based on textual instructions. Advances in Large Language Models (LLMs) and Vision-Language Mod…
Geometric Image Editing via Effects-Sensitive In-Context Inpainting with Diffusion Transformers
Shuo Zhang, Wenzhuo Wu, Huayu Zhang +8
Recent advances in diffusion models have significantly improved image editing. However, challenges persist in handling geometric transformations, such as translation, rotation, and…
Adaptive Evidential Learning for Temporal-Semantic Robustness in Moment Retrieval
Haojian Huang, Kaijing Ma, Jin Chen +8
In the domain of moment retrieval, accurately identifying temporal segments within videos based on natural language queries remains challenging. Traditional methods often employ pr…
OmniEraser: Remove Objects and Their Effects in Images with Paired Video-Frame Data
Runpu Wei, Zijin Yin, Shuo Zhang +8
Inpainting algorithms have achieved remarkable progress in removing objects from images, yet still face two challenges: 1) struggle to handle the object's visual effects such as sh…
Trusted Unified Feature-Neighborhood Dynamics for Multi-View Classification
Haojian Huang, Chuanyu Qin, Zhe Liu +6
Multi-view classification (MVC) faces inherent challenges due to domain gaps and inconsistencies across different views, often resulting in uncertainties during the fusion process.…
Beyond Uncertainty: Evidential Deep Learning for Robust Video Temporal Grounding
Kaijing Ma, Haojian Huang, Jin Chen +8
Existing Video Temporal Grounding (VTG) models excel in accuracy but often overlook open-world challenges posed by open-vocabulary queries and untrimmed videos. This leads to unrel…
Disentangle and denoise: Tackling context misalignment for video moment retrieval
Kaijing Ma, Han Fang, Xianghao Zang +7
Video Moment Retrieval, which aims to locate in-context video moments according to a natural language query, is an essential task for cross-modal grounding. Existing methods focus…
ProTA: Probabilistic Token Aggregation for Text-Video Retrieval
Han Fang, Xianghao Zang, Chao Ban +5
Text-video retrieval aims to find the most relevant cross-modal samples for a given query. Recent methods focus on modeling the whole spatial-temporal relations. However, since vid…
Mask to reconstruct: Cooperative Semantics Completion for Video-text Retrieval
Han Fang, Zhifei Yang, Xianghao Zang +2
Recently, masked video modeling has been widely explored and significantly improved the model's understanding ability of visual regions at a local level. However, existing methods…