111 citations · 195 across the 22 of their papers we have counts for
20 papers · 1 filter
Large Language Models are Universal Reasoners for Visual Generation
Sucheng Ren, Chen Chen, Zhenbang Wang +5
Text-to-image generation has advanced rapidly with diffusion models, progressing from CLIP and T5 conditioning to unified systems where a single LLM backbone handles both visual un…
UniGen-1.5: Enhancing Image Generation and Editing through Reward Unification in Reinforcement Learning
Rui Tian, Mingfei Gao, Haiming Gang +5
We present UniGen-1.5, a unified multimodal large language model (MLLM) for advanced image understanding, generation and editing. Building upon UniGen, we comprehensively enhance t…
CAR-Flow: Condition-Aware Reparameterization Aligns Source and Target for Better Flow Matching
Chen Chen, Pengsheng Guo, Liangchen Song +7
Conditional generative modeling aims to learn a conditional data distribution from samples containing data-condition pairs. For this, diffusion and flow-based methods have attained…
Pico-Banana-400K: A Large-Scale Dataset for Text-Guided Image Editing
Yusu Qian, Eli Bocek-Rivele, Liangchen Song +5
Recent advances in multimodal models have demonstrated remarkable text-guided image editing capabilities, with systems like GPT-4o and Nano-Banana setting new benchmarks. However,…
Autoregressive Video Generation beyond Next Frames Prediction
Sucheng Ren, Chen Chen, Zhenbang Wang +5
Autoregressive models for video generation typically operate frame-by-frame, extending next-token prediction from language to video's temporal dimension. We question that unlike wo…
AToken: A Unified Tokenizer for Vision
Jiasen Lu, Liangchen Song, Mingze Xu +5
We present AToken, the first unified visual tokenizer that achieves both high-fidelity reconstruction and semantic understanding across images, videos, and 3D assets. Unlike existi…
UniGen: Enhanced Training & Test-Time Strategies for Unified Multimodal Understanding and Generation
Rui Tian, Mingfei Gao, Mingze Xu +5
We introduce UniGen, a unified multimodal large language model (MLLM) capable of image understanding and generation. We study the full training pipeline of UniGen from a data-centr…
GIE-Bench: Towards Grounded Evaluation for Text-Guided Image Editing
Yusu Qian, Jiasen Lu, Tsu-Jui Fu +5
Editing images using natural language instructions has become a natural and expressive way to modify visual content; yet, evaluating the performance of such models remains challeng…
SlowFast-LLaVA-1.5: A Family of Token-Efficient Video Large Language Models for Long-Form Video Understanding
Mingze Xu, Mingfei Gao, Shiyu Li +7
We introduce SlowFast-LLaVA-1.5 (abbreviated as SF-LLaVA-1.5), a family of video large language models (LLMs) offering a token-efficient solution for long-form video understanding.…
STIV: Scalable Text and Image Conditioned Video Generation
Zongyu Lin, Wei Liu, Chen Chen +13
The field of video generation has made remarkable advancements, yet there remains a pressing need for a clear, systematic recipe that can guide the development of robust and scalab…
One Diffusion to Generate Them All
Duong H. Le, Tuan Pham, Sangho Lee +5
We introduce OneDiffusion, a versatile, large-scale diffusion model that seamlessly supports bidirectional image synthesis and understanding across diverse tasks. It enables condit…
MM-Ego: Towards Building Egocentric Multimodal LLMs for Video QA
Hanrong Ye, Haotian Zhang, Erik Daxberger +9
This research aims to comprehensively explore building a multimodal foundation model for egocentric video understanding. To achieve this goal, we work on three fronts. First, as th…
Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models
Matt Deitke, Christopher Clark, Sangho Lee +47
Today's most advanced vision-language models (VLMs) remain proprietary. The strongest open-weight models rely heavily on synthetic data from proprietary VLMs to achieve good perfor…
Preserving Identity with Variational Score for General-purpose 3D Editing
Duong H. Le, Tuan Pham, Aniruddha Kembhavi +3
We present Piva (Preserving Identity with Variational Score Distillation), a novel optimization-based method for editing images and 3D models based on diffusion models. Specificall…
Unified-IO 2: Scaling Autoregressive Multimodal Models with Vision, Language, Audio, and Action
Jiasen Lu, Christopher Clark, Sangho Lee +5
We present Unified-IO 2, the first autoregressive multimodal model that is capable of understanding and generating image, text, audio, and action. To unify different modalities, we…
Unified-IO: A Unified Model for Vision, Language, and Multi-Modal Tasks
Jiasen Lu, Christopher Clark, Rowan Zellers +2
We propose Unified-IO, a model that performs a large variety of AI tasks spanning classical computer vision tasks, including pose estimation, object detection, depth estimation and…
ASC me to Do Anything: Multi-task Training for Embodied AI
Jiasen Lu, Jordi Salvador, Roozbeh Mottaghi +1
Embodied AI has seen steady progress across a diverse set of independent tasks. While these varied tasks have different end goals, the basic skills required to complete them succes…
A Simple Long-Tailed Recognition Baseline via Vision-Language Model
Teli Ma, Shijie Geng, Mengmeng Wang +5
The visual world naturally exhibits a long-tailed distribution of open classes, which poses great challenges to modern visual systems. Existing approaches either perform class re-b…
Container: Context Aggregation Network
Peng Gao, Jiasen Lu, Hongsheng Li +2
Convolutional neural networks (CNNs) are ubiquitous in computer vision, with a myriad of effective and efficient variations. Recently, Transformers -- originally introduced in natu…
Multi-Modal Answer Validation for Knowledge-Based VQA
Jialin Wu, Jiasen Lu, Ashish Sabharwal +1
The problem of knowledge-based visual question answering involves answering questions that require external knowledge in addition to the content of the image. Such knowledge typica…