4 citations · 10 across the 19 of their papers we have counts for
18 papers · 1 filter
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…
Referring Layer Decomposition
Fangyi Chen, Yaojie Shen, Lu Xu +4
Precise, object-aware control over visual content is essential for advanced image editing and compositional generation. Yet, most existing approaches operate on entire images holis…
EchoFoley: Event-Centric Hierarchical Control for Video Grounded Creative Sound Generation
Bingxuan Li, Yiming Cui, Yicheng He +4
Sound effects build an essential layer of multimodal storytelling, shaping the emotional atmosphere and the narrative semantics of videos. Despite recent advancement in video-text-…
Structured Context Learning for Generic Event Boundary Detection
Xin Gu, Congcong Li, Xinyao Wang +5
Generic Event Boundary Detection (GEBD) aims to identify moments in videos that humans perceive as event boundaries. This paper proposes a novel method for addressing this task, ca…
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…
TGT: Text-Grounded Trajectories for Locally Controlled Video Generation
Guofeng Zhang, Angtian Wang, Jacob Zhiyuan Fang +8
Text-to-video generation has advanced rapidly in visual fidelity, whereas standard methods still have limited ability to control the subject composition of generated scenes. Prior…
CyberV: Cybernetics for Test-time Scaling in Video Understanding
Jiahao Meng, Shuyang Sun, Yue Tan +4
Current Multimodal Large Language Models (MLLMs) may struggle with understanding long or complex videos due to computational demands at test time, lack of robustness, and limited a…
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…
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…
Accurate and Fast Compressed Video Captioning
Yaojie Shen, Xin Gu, Kai Xu +3
Existing video captioning approaches typically require to first sample video frames from a decoded video and then conduct a subsequent process (e.g., feature extraction and/or capt…
Structured Context Transformer for Generic Event Boundary Detection
Congcong Li, Xinyao Wang, Dexiang Hong +4
Generic Event Boundary Detection (GEBD) aims to detect moments where humans naturally perceive as event boundaries. In this paper, we present Structured Context Transformer (or SC-…