10 citations · 38 across the 17 of their papers we have counts for
28 papers
VEBench:Benchmarking Large Multimodal Models for Real-World Video Editing
Andong Deng, Dawei Du, Zhenfang Chen +7
Real-world video editing demands not only expert knowledge of cinematic techniques but also multimodal reasoning to select, align, and combine footage into coherent narratives. Whi…
SuperEdit: Rectifying and Facilitating Supervision for Instruction-Based Image Editing
Ming Li, Xin Gu, Fan Chen +4
Due to the challenges of manually collecting accurate editing data, existing datasets are typically constructed using various automated methods, leading to noisy supervision signal…
Vidi: Large Multimodal Models for Video Understanding and Editing
Vidi Team, Celong Liu, Chia-Wen Kuo +20
Humans naturally share information with those they are connected to, and video has become one of the dominant mediums for communication and expression on the Internet. To support t…
Where do Large Vision-Language Models Look at when Answering Questions?
Xiaoying Xing, Chia-Wen Kuo, Li Fuxin +6
Large Vision-Language Models (LVLMs) have shown promising performance in vision-language understanding and reasoning tasks. However, their visual understanding behaviors remain und…
D-Attn: Decomposed Attention for Large Vision-and-Language Models
Chia-Wen Kuo, Sijie Zhu, Fan Chen +2
Large vision-and-language models (LVLMs) have traditionally integrated visual and textual tokens by concatenating them into a single homogeneous input for large language models (LL…
Multi-Reward as Condition for Instruction-based Image Editing
Xin Gu, Ming Li, Libo Zhang +4
High-quality training triplets (instruction, original image, edited image) are essential for instruction-based image editing. Predominant training datasets (e.g., InsPix2Pix) are c…