8 citations · 14 across the 17 of their papers we have counts for
10 papers · 1 filter
Video-LMM Post-Training: A Deep Dive into Video Reasoning with Large Multimodal Models
Yolo Y. Tang, Jing Bi, Pinxin Liu +24
Video understanding represents the most challenging frontier in computer vision, requiring models to reason about complex spatiotemporal relationships, long-term dependencies, and…
PromptReverb: Multimodal Room Impulse Response Generation Through Latent Rectified Flow Matching
Ali Vosoughi, Yongyi Zang, Qihui Yang +3
Room impulse response (RIR) generation remains a critical challenge for creating immersive virtual acoustic environments. Current methods suffer from two fundamental limitations: t…
Diagnosing Visual Reasoning: Challenges, Insights, and a Path Forward
Jing Bi, Guangyu Sun, Ali Vosoughi +2
Multimodal large language models (MLLMs) that integrate visual and textual reasoning leverage chain-of-thought (CoT) prompting to tackle complex visual tasks, yet continue to exhib…
OPENXRD: A Comprehensive Benchmark Framework for LLM/MLLM XRD Question Answering
Ali Vosoughi, Ayoub Shahnazari, Yufeng Xi +4
We introduce OPENXRD, a comprehensive benchmarking framework for evaluating large language models (LLMs) and multimodal LLMs (MLLMs) in crystallography question answering. The fram…
Can Sound Replace Vision in LLaVA With Token Substitution?
Ali Vosoughi, Jing Bi, Pinxin Liu +2
What happens when we push audio-visual alignment to its absolute limits? To systematically investigate this question, we needed datasets with granular alignment quality annotations…
: Generating Instructional Illustrations via Text-Conditioned Diffusion
Jing Bi, Pinxin Liu, Ali Vosoughi +3
The effective communication of procedural knowledge remains a significant challenge in natural language processing (NLP), as purely textual instructions often fail to convey comple…
MMPerspective: Do MLLMs Understand Perspective? A Comprehensive Benchmark for Perspective Perception, Reasoning, and Robustness
Yolo Y. Tang, Pinxin Liu, Zhangyun Tan +11
Understanding perspective is fundamental to human visual perception, yet the extent to which multimodal large language models (MLLMs) internalize perspective geometry remains uncle…
Caption Anything in Video: Fine-grained Object-centric Captioning via Spatiotemporal Multimodal Prompting
Yunlong Tang, Jing Bi, Chao Huang +16
We present CAT-V (Caption AnyThing in Video), a training-free framework for fine-grained object-centric video captioning that enables detailed descriptions of user-selected objects…
VERIFY: A Benchmark of Visual Explanation and Reasoning for Investigating Multimodal Reasoning Fidelity
Jing Bi, Junjia Guo, Susan Liang +8
Visual reasoning is central to human cognition, enabling individuals to interpret and abstractly understand their environment. Although recent Multimodal Large Language Models (MLL…
Quality Over Quantity? LLM-Based Curation for a Data-Efficient Audio-Video Foundation Model
Ali Vosoughi, Dimitra Emmanouilidou, Hannes Gamper
Integrating audio and visual data for training multimodal foundational models remains a challenge. The Audio-Video Vector Alignment (AVVA) framework addresses this by considering A…