2 citations · 3 across the 15 of their papers we have counts for
17 papers
VlogReward: Learning Multi-Dimensional Evaluation for Vlog Editing
Yexiang Liu, Wen Zhong, Sijie Zhu +6
The rapid rise of vlogs as a personalized storytelling medium has created a demand for automated systems to evaluate and refine vlog editing plans. However, vlog assessment is high…
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…
Granular Ball Guided Stable Latent Domain Discovery for Domain-General Crowd Counting
Fan Chen, Shuyin Xia, Yi Wang
Single-source domain generalization for crowd counting is highly challenging because a single labeled source domain may contain heterogeneous latent domains, while unseen target do…
SAMA: Factorized Semantic Anchoring and Motion Alignment for Instruction-Guided Video Editing
Xinyao Zhang, Wenkai Dong, Yuxin Song +10
Current instruction-guided video editing models struggle to simultaneously balance precise semantic modifications with faithful motion preservation. While existing approaches rely…
Thinking With Bounding Boxes: Enhancing Spatio-Temporal Video Grounding via Reinforcement Fine-Tuning
Xin Gu, Haoji Zhang, Qihang Fan +7
Spatio-temporal video grounding (STVG) requires localizing a target object in untrimmed videos both temporally and spatially from natural language descriptions. Despite their stron…
Vidi2.5: Large Multimodal Models for Video Understanding and Creation
Vidi Team, Chia-Wen Kuo, Chuang Huang +31
Video has emerged as the primary medium for communication and creativity on the Internet, driving strong demand for scalable, high-quality video production. Vidi models continue to…
The Coverage Principle: How Pre-Training Enables Post-Training
Fan Chen, Audrey Huang, Noah Golowich +5
Language models demonstrate remarkable abilities when pre-trained on large text corpora and fine-tuned for specific tasks, but how and why pre-training shapes the success of the fi…
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…
DiffLM: Controllable Synthetic Data Generation via Diffusion Language Models
Ying Zhou, Xinyao Wang, Yulei Niu +6
Recent advancements in large language models (LLMs) have significantly enhanced their knowledge and generative capabilities, leading to a surge of interest in leveraging LLMs for h…
AIPO: Improving Training Objective for Iterative Preference Optimization
Yaojie Shen, Xinyao Wang, Yulei Niu +5
Preference Optimization (PO), is gaining popularity as an alternative choice of Proximal Policy Optimization (PPO) for aligning Large Language Models (LLMs). Recent research on ali…
Beyond Raw Videos: Understanding Edited Videos with Large Multimodal Model
Lu Xu, Sijie Zhu, Chunyuan Li +7
The emerging video LMMs (Large Multimodal Models) have achieved significant improvements on generic video understanding in the form of VQA (Visual Question Answering), where the ra…
CuMo: Scaling Multimodal LLM with Co-Upcycled Mixture-of-Experts
Jiachen Li, Xinyao Wang, Sijie Zhu +6
Recent advancements in Multimodal Large Language Models (LLMs) have focused primarily on scaling by increasing text-image pair data and enhancing LLMs to improve performance on mul…
Edit3K: Universal Representation Learning for Video Editing Components
Xin Gu, Libo Zhang, Fan Chen +4
This paper focuses on understanding the predominant video creation pipeline, i.e., compositional video editing with six main types of editing components, including video effects, a…