9 citations · 19 across the 22 of their papers we have counts for
21 papers · 1 filter
MintAct: A Unified Visual Agent for Digital Environments
Mingfei Gao, Rui Tian, Haiming Gang +11
We present MintAct, a family of vision-language models that unifies UI grounding, multi-step navigation across mobile, desktop, and web, and visual tool use, trained at 2B, 4B, and…
MM-ToolSandBox: A Unified Framework for Evaluating Visual Tool-Calling Agents
Kaixin Ma, Di Feng, Alexander Metz +3
We introduce MM-ToolSandBox, a benchmark and evaluation framework for visually grounded tool-calling agents. The framework provides a stateful execution environment spanning 500+ t…
MODUS: Decoder-Only Any-to-Any Modeling of Diverse Modalities
Mingqiao Ye, Zhaochong An, Zhitong Gao +11
Any-to-any models predict any modality from any combination of others within a single network, a formulation used in multimodal vision and vision-language models, and increasingly…
(1D) Ordered Tokens Enable Efficient Test-Time Search
Zhitong Gao, Parham Rezaei, Ali Cy +7
Tokenization is a key component of autoregressive (AR) generative models, converting raw data into more manageable units for modeling. Commonly, tokens describe local information,…
VideoFlexTok: Flexible-Length Coarse-to-Fine Video Tokenization
Andrei Atanov, Jesse Allardice, Roman Bachmann +6
Visual tokenizers map high-dimensional raw pixels into a compressed representation for downstream modeling. Beyond compression, tokenizers dictate what information is preserved and…
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…
SO-Bench: A Structural Output Evaluation of Multimodal LLMs
Di Feng, Kaixin Ma, Feng Nan +9
Multimodal large language models (MLLMs) are increasingly deployed in real-world, agentic settings where outputs must not only be correct, but also conform to predefined data schem…
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…
Rooms from Motion: Un-posed Indoor 3D Object Detection as Localization and Mapping
Justin Lazarow, Kai Kang, Afshin Dehghan
We revisit scene-level 3D object detection as the output of an object-centric framework capable of both localization and mapping using 3D oriented boxes as the underlying geometric…
TUNA: Comprehensive Fine-grained Temporal Understanding Evaluation on Dense Dynamic Videos
Fanheng Kong, Jingyuan Zhang, Hongzhi Zhang +7
Videos are unique in their integration of temporal elements, including camera, scene, action, and attribute, along with their dynamic relationships over time. However, existing ben…
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…
StreamBridge: Turning Your Offline Video Large Language Model into a Proactive Streaming Assistant
Haibo Wang, Bo Feng, Zhengfeng Lai +6
We present StreamBridge, a simple yet effective framework that seamlessly transforms offline Video-LLMs into streaming-capable models. It addresses two fundamental challenges in ad…
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.…
MM-Spatial: Exploring 3D Spatial Understanding in Multimodal LLMs
Erik Daxberger, Nina Wenzel, David Griffiths +8
Multimodal large language models (MLLMs) excel at 2D visual understanding but remain limited in their ability to reason about 3D space. In this work, we leverage large-scale high-q…
FlexTok: Resampling Images into 1D Token Sequences of Flexible Length
Roman Bachmann, Jesse Allardice, David Mizrahi +6
Image tokenization has enabled major advances in autoregressive image generation by providing compressed, discrete representations that are more efficient to process than raw pixel…
Cubify Anything: Scaling Indoor 3D Object Detection
Justin Lazarow, David Griffiths, Gefen Kohavi +2
We consider indoor 3D object detection with respect to a single RGB(-D) frame acquired from a commodity handheld device. We seek to significantly advance the status quo with respec…
MM1.5: Methods, Analysis & Insights from Multimodal LLM Fine-tuning
Haotian Zhang, Mingfei Gao, Zhe Gan +20
We present MM1.5, a new family of multimodal large language models (MLLMs) designed to enhance capabilities in text-rich image understanding, visual referring and grounding, and mu…
Understanding Alignment in Multimodal LLMs: A Comprehensive Study
Elmira Amirloo, Jean-Philippe Fauconnier, Christoph Roesmann +8
Preference alignment has become a crucial component in enhancing the performance of Large Language Models (LLMs), yet its impact in Multimodal Large Language Models (MLLMs) remains…
SlowFast-LLaVA: A Strong Training-Free Baseline for Video Large Language Models
Mingze Xu, Mingfei Gao, Zhe Gan +5
We propose SlowFast-LLaVA (or SF-LLaVA for short), a training-free video large language model (LLM) that can jointly capture detailed spatial semantics and long-range temporal cont…
4M-21: An Any-to-Any Vision Model for Tens of Tasks and Modalities
Roman Bachmann, Oğuzhan Fatih Kar, David Mizrahi +6
Current multimodal and multitask foundation models like 4M or UnifiedIO show promising results, but in practice their out-of-the-box abilities to accept diverse inputs and perform…
4M: Massively Multimodal Masked Modeling
David Mizrahi, Roman Bachmann, Oğuzhan Fatih Kar +4
Current machine learning models for vision are often highly specialized and limited to a single modality and task. In contrast, recent large language models exhibit a wide range of…