2 citations · 4 across the 9 of their papers we have counts for
9 papers
Your Embedding Model is SMARTer Than You Think
Jianrui Zhang, Hyun Jung Lee, Sukanta Ganguly +3
Multimodal retrieval relies heavily on single-vector retrievers, which compress rich, sequential token sequences into one single global representation. While efficient, they discar…
Unified Spatio-Temporal Token Scoring for Efficient Video VLMs
Jianrui Zhang, Yue Yang, Rohun Tripathi +5
Token pruning is essential for enhancing the computational efficiency of vision-language models (VLMs), particularly for video-based tasks where temporal redundancy is prevalent. P…
Reasoning-Augmented Representations for Multimodal Retrieval
Jianrui Zhang, Anirudh Sundara Rajan, Brandon Han +3
Universal Multimodal Retrieval (UMR) seeks any-to-any search across text and vision, yet modern embedding models remain brittle when queries require latent reasoning (e.g., resolvi…
Molmo2: Open Weights and Data for Vision-Language Models with Video Understanding and Grounding
Christopher Clark, Jieyu Zhang, Zixian Ma +18
Today's strongest video-language models (VLMs) remain proprietary. The strongest open-weight models either rely on synthetic data from proprietary VLMs, effectively distilling from…
TemporalBench: Benchmarking Fine-grained Temporal Understanding for Multimodal Video Models
Mu Cai, Reuben Tan, Jianrui Zhang +12
Understanding fine-grained temporal dynamics is crucial for multimodal video comprehension and generation. Due to the lack of fine-grained temporal annotations, existing video benc…
Vinoground: Scrutinizing LMMs over Dense Temporal Reasoning with Short Videos
Jianrui Zhang, Mu Cai, Yong Jae Lee
There has been growing sentiment recently that modern large multimodal models (LMMs) have addressed most of the key challenges related to short video comprehension. As a result, bo…
VGBench: Evaluating Large Language Models on Vector Graphics Understanding and Generation
Bocheng Zou, Mu Cai, Jianrui Zhang +1
In the realm of vision models, the primary mode of representation is using pixels to rasterize the visual world. Yet this is not always the best or unique way to represent visual c…
CHARTOM: A Visual Theory-of-Mind Benchmark for LLMs on Misleading Charts
Shubham Bharti, Shiyun Cheng, Jihyun Rho +5
We introduce CHARTOM, a visual theory-of-mind benchmark designed to evaluate multimodal large language models' capability to understand and reason about misleading data visualizati…
CounterCurate: Enhancing Physical and Semantic Visio-Linguistic Compositional Reasoning via Counterfactual Examples
Jianrui Zhang, Mu Cai, Tengyang Xie +1
We propose CounterCurate, a framework to comprehensively improve the visio-linguistic compositional reasoning capability for both contrastive and generative multimodal models. In p…