20 citations · 46 across the 42 of their papers we have counts for
10 papers · 1 filter
OpenVLThinkerV2: A Generalist Multimodal Reasoning Model for Multi-domain Visual Tasks
Wenbo Hu, Xin Chen, Yan Gao-Tian +3
Group Relative Policy Optimization (GRPO) has emerged as the de facto Reinforcement Learning (RL) objective driving recent advancements in Multimodal Large Language Models. However…
3DLLM-Mem: Long-Term Spatial-Temporal Memory for Embodied 3D Large Language Model
Wenbo Hu, Yining Hong, Yanjun Wang +7
Humans excel at performing complex tasks by leveraging long-term memory across temporal and spatial experiences. In contrast, current Large Language Models (LLMs) struggle to effec…
PARTONOMY: Large Multimodal Models with Part-Level Visual Understanding
Ansel Blume, Jeonghwan Kim, Hyeonjeong Ha +7
Real-world objects are composed of distinctive, object-specific parts. Identifying these parts is key to performing fine-grained, compositional reasoning-yet, large multimodal mode…
OpenVLThinker: Complex Vision-Language Reasoning via Iterative SFT-RL Cycles
Yihe Deng, Hritik Bansal, Fan Yin +3
We introduce OpenVLThinker, one of the first open-source large vision-language models (LVLMs) to exhibit sophisticated chain-of-thought reasoning, achieving notable performance gai…
SYNTHIA: Novel Concept Design with Affordance Composition
Hyeonjeong Ha, Xiaomeng Jin, Jeonghwan Kim +7
Text-to-image (T2I) models enable rapid concept design, making them widely used in AI-driven design. While recent studies focus on generating semantic and stylistic variations of g…
Contrastive Visual Data Augmentation
Yu Zhou, Bingxuan Li, Mohan Tang +6
Large multimodal models (LMMs) often struggle to recognize novel concepts, as they rely on pre-trained knowledge and have limited ability to capture subtle visual details. Domain-s…
METAL: A Multi-Agent Framework for Chart Generation with Test-Time Scaling
Bingxuan Li, Yiwei Wang, Jiuxiang Gu +2
Chart generation aims to generate code to produce charts satisfying the desired visual properties, e.g., texts, layout, color, and type. It has great potential to empower the autom…
MRAG-Bench: Vision-Centric Evaluation for Retrieval-Augmented Multimodal Models
Wenbo Hu, Jia-Chen Gu, Zi-Yi Dou +4
Existing multimodal retrieval benchmarks primarily focus on evaluating whether models can retrieve and utilize external textual knowledge for question answering. However, there are…
GenEARL: A Training-Free Generative Framework for Multimodal Event Argument Role Labeling
Hritik Bansal, Po-Nien Kung, P. Jeffrey Brantingham +2
Multimodal event argument role labeling (EARL), a task that assigns a role for each event participant (object) in an image is a complex challenge. It requires reasoning over the en…
ConTextual: Evaluating Context-Sensitive Text-Rich Visual Reasoning in Large Multimodal Models
Rohan Wadhawan, Hritik Bansal, Kai-Wei Chang +1
Many real-world tasks require an agent to reason jointly over text and visual objects, (e.g., navigating in public spaces), which we refer to as context-sensitive text-rich visual…