244 citations · 389 across the 28 of their papers we have counts for
6 papers · 1 filter
SimScale: Learning to Drive via Real-World Simulation at Scale
Haochen Tian, Tianyu Li, Haochen Liu +11
Achieving fully autonomous driving systems requires learning rational decisions in a wide span of scenarios, including safety-critical and out-of-distribution ones. However, such c…
Latent Sketchpad: Sketching Visual Thoughts to Elicit Multimodal Reasoning in MLLMs
Huanyu Zhang, Wenshan Wu, Chengzu Li +9
While Multimodal Large Language Models (MLLMs) excel at visual understanding, they often struggle in complex scenarios that require visual planning and imagination. Inspired by how…
MME-Unify: A Comprehensive Benchmark for Unified Multimodal Understanding and Generation Models
Wulin Xie, Yi-Fan Zhang, Chaoyou Fu +6
Existing MLLM benchmarks face significant challenges in evaluating Unified MLLMs (U-MLLMs) due to: 1) lack of standardized benchmarks for traditional tasks, leading to inconsistent…
Scaling and Beyond: Advancing Spatial Reasoning in MLLMs Requires New Recipes
Huanyu Zhang, Chengzu Li, Wenshan Wu +8
Multimodal Large Language Models (MLLMs) have demonstrated impressive performance in general vision-language tasks. However, recent studies have exposed critical limitations in the…
Aligning Multimodal LLM with Human Preference: A Survey
Tao Yu, Yi-Fan Zhang, Chaoyou Fu +14
Large language models (LLMs) can handle a wide variety of general tasks with simple prompts, without the need for task-specific training. Multimodal Large Language Models (MLLMs),…
MM-RLHF: The Next Step Forward in Multimodal LLM Alignment
Yi-Fan Zhang, Tao Yu, Haochen Tian +17
Despite notable advancements in Multimodal Large Language Models (MLLMs), most state-of-the-art models have not undergone thorough alignment with human preferences. This gap exists…