16 citations · 28 across the 25 of their papers we have counts for
25 papers · 1 filter
InfoAgent: Traceable Generation and Repair of Evidence-Grounded Infographics
Yifan Li, Tong Li, Qi Zeng +6
Reliable infographic generation requires facts, symbols, and visual relations to remain consistent through rendering and revision. Correcting one element also requires tracking its…
PixelUMM: Encoder-Free Unified Image and Video Understanding and Generation
Cong Wei, Xuanchi Ren, Bryan Chu +8
Unified Multimodal Models (UMMs) often rely on separate visual representations for understanding and generation, increasing visual context length and complicating integration with…
VGI-Bench: Probing Visual Intelligence in Video Generation Models
Xuan He, Cong Wei, Yuhao Cheng +20
Recent studies suggest that video generation models can exhibit certain forms of zero-shot visual reasoning through generated frames. Yet reliable evaluation remains challenging: b…
Search Beyond What Can Be Taught: Evolving the Knowledge Boundary in Agentic Visual Generation
Haozhe Wang, Weijia Feng, Jinpeng Yu +8
Visual generators excel at rendering, but they confidently fabricate what they do not know. User requests are unbounded, evolving, and deeply long-tailed: new characters, trending…
Starve to Perceive: Taming Lazy Perception in VLMs with Constrained Visual Bandwidth
Yuhuan Wu, Cong Wei, Fangzhen Lin +2
Vision-Language Models (VLMs) deployed as situated agents in high-resolution visual environments require active perception -- the ability to dynamically decide where to look throug…
Context Forcing: Consistent Autoregressive Video Generation with Long Context
Shuo Chen, Cong Wei, Sun Sun +5
Recent approaches to real-time long video generation typically employ streaming tuning strategies, attempting to train a long-context student using a short-context (memoryless) tea…
Visual-Aware CoT: Achieving High-Fidelity Visual Consistency in Unified Models
Zixuan Ye, Quande Liu, Cong Wei +5
Recently, the introduction of Chain-of-Thought (CoT) has largely improved the generation ability of unified models. However, it is observed that the current thinking process during…
UFVideo: Towards Unified Fine-Grained Video Cooperative Understanding with Large Language Models
Hewen Pan, Cong Wei, Dashuang Liang +8
With the advancement of multi-modal Large Language Models (LLMs), Video LLMs have been further developed to perform on holistic and specialized video understanding. However, existi…
UniVideo: Unified Understanding, Generation, and Editing for Videos
Cong Wei, Quande Liu, Zixuan Ye +5
Unified multimodal models have shown promising results in multimodal content generation and editing but remain largely limited to the image domain. In this work, we present UniVide…
Advancing Visual Large Language Model for Multi-granular Versatile Perception
Wentao Xiang, Haoxian Tan, Cong Wei +3
Perception is a fundamental task in the field of computer vision, encompassing a diverse set of subtasks that can be systematically categorized into four distinct groups based on t…
MoCha: Towards Movie-Grade Talking Character Synthesis
Cong Wei, Bo Sun, Haoyu Ma +10
Recent advancements in video generation have achieved impressive motion realism, yet they often overlook character-driven storytelling, a crucial task for automated film, animation…
Vamba: Understanding Hour-Long Videos with Hybrid Mamba-Transformers
Weiming Ren, Wentao Ma, Huan Yang +3
State-of-the-art transformer-based large multimodal models (LMMs) struggle to handle hour-long video inputs due to the quadratic complexity of the causal self-attention operations,…
InstructSeg: Unifying Instructed Visual Segmentation with Multi-modal Large Language Models
Cong Wei, Yujie Zhong, Haoxian Tan +4
Boosted by Multi-modal Large Language Models (MLLMs), text-guided universal segmentation models for the image and video domains have made rapid progress recently. However, these me…
HyperSeg: Towards Universal Visual Segmentation with Large Language Model
Cong Wei, Yujie Zhong, Haoxian Tan +4
This paper aims to address universal segmentation for image and video perception with the strong reasoning ability empowered by Visual Large Language Models (VLLMs). Despite signif…
VISTA: Enhancing Long-Duration and High-Resolution Video Understanding by Video Spatiotemporal Augmentation
Weiming Ren, Huan Yang, Jie Min +2
Current large multimodal models (LMMs) face significant challenges in processing and comprehending long-duration or high-resolution videos, which is mainly due to the lack of high-…
OmniEdit: Building Image Editing Generalist Models Through Specialist Supervision
Cong Wei, Zheyang Xiong, Weiming Ren +3
Instruction-guided image editing methods have demonstrated significant potential by training diffusion models on automatically synthesized or manually annotated image editing pairs…
MANTIS: Interleaved Multi-Image Instruction Tuning
Dongfu Jiang, Xuan He, Huaye Zeng +4
Large multimodal models (LMMs) have shown great results in single-image vision language tasks. However, their abilities to solve multi-image visual language tasks is yet to be impr…
LaSagnA: Language-based Segmentation Assistant for Complex Queries
Cong Wei, Haoxian Tan, Yujie Zhong +2
Recent advancements have empowered Large Language Models for Vision (vLLMs) to generate detailed perceptual outcomes, including bounding boxes and masks. Nonetheless, there are two…
AnyV2V: A Tuning-Free Framework For Any Video-to-Video Editing Tasks
Max Ku, Cong Wei, Weiming Ren +2
In the dynamic field of digital content creation using generative models, state-of-the-art video editing models still do not offer the level of quality and control that users desir…
Towards Artwork Explanation in Large-scale Vision Language Models
Kazuki Hayashi, Yusuke Sakai, Hidetaka Kamigaito +2
Large-scale Vision-Language Models (LVLMs) output text from images and instructions, demonstrating capabilities in text generation and comprehension. However, it has not been clari…
ConsistI2V: Enhancing Visual Consistency for Image-to-Video Generation
Weiming Ren, Huan Yang, Ge Zhang +4
Image-to-video (I2V) generation aims to use the initial frame (alongside a text prompt) to create a video sequence. A grand challenge in I2V generation is to maintain visual consis…
VIEScore: Towards Explainable Metrics for Conditional Image Synthesis Evaluation
Max Ku, Dongfu Jiang, Cong Wei +2
In the rapidly advancing field of conditional image generation research, challenges such as limited explainability lie in effectively evaluating the performance and capabilities of…
UniIR: Training and Benchmarking Universal Multimodal Information Retrievers
Cong Wei, Yang Chen, Haonan Chen +5
Existing information retrieval (IR) models often assume a homogeneous format, limiting their applicability to diverse user needs, such as searching for images with text description…
DreamEdit: Subject-driven Image Editing
Tianle Li, Max Ku, Cong Wei +1
Subject-driven image generation aims at generating images containing customized subjects, which has recently drawn enormous attention from the research community. However, the prev…