2 citations · 3 across the 11 of their papers we have counts for
11 papers
WorldBench: A Challenging and Visually Diverse Multimodal Reasoning Benchmark
Yida Yin, Harish Krishnakumar, Chung Peng Lee +9
In real-world applications, models are expected to perform reliably across diverse settings. Yet, many existing multimodal benchmarks expand task types without capturing the visual…
Guess the Unified Model: How Much Can We Recover from Generated Images?
Jasin Cekinmez, Ryo Mitsuhashi, Addison J. Wu +1
With unified model-generated images now widespread online, attributing their model of origin offers a path toward transparency and deeper insight into the characteristic behaviors…
VisionFoundry: Teaching VLMs Visual Perception with Synthetic Images
Guanyu Zhou, Yida Yin, Wenhao Chai +3
Vision-language models (VLMs) still struggle with visual perception tasks such as spatial understanding and viewpoint recognition, largely because natural image datasets provide li…
UEval: A Benchmark for Unified Multimodal Generation
Bo Li, Yida Yin, Wenhao Chai +2
We introduce UEval, a benchmark to evaluate unified models, i.e., models capable of generating both images and text. UEval comprises 1,000 expert-curated questions that require bot…
Generative Modeling of Weights: Generalization or Memorization?
Boya Zeng, Yida Yin, Zhiqiu Xu +1
Generative models have recently been explored for synthesizing neural network weights. These approaches take neural network checkpoints as training data and aim to generate high-pe…
Idiosyncrasies in Large Language Models
Mingjie Sun, Yida Yin, Zhiqiu Xu +2
In this work, we unveil and study idiosyncrasies in Large Language Models (LLMs) -- unique patterns in their outputs that can be used to distinguish the models. To do so, we consid…
Understanding Bias in Large-Scale Visual Datasets
Boya Zeng, Yida Yin, Zhuang Liu
A recent study has shown that large-scale visual datasets are very biased: they can be easily classified by modern neural networks. However, the concrete forms of bias among these…
In-Context Learning Enables Robot Action Prediction in LLMs
Yida Yin, Zekai Wang, Yuvan Sharma +3
Recently, Large Language Models (LLMs) have achieved remarkable success using in-context learning (ICL) in the language domain. However, leveraging the ICL capabilities within LLMs…
MATRIX: Multi-Agent Trajectory Generation with Diverse Contexts
Zhuo Xu, Rui Zhou, Yida Yin +3
Data-driven methods have great advantages in modeling complicated human behavioral dynamics and dealing with many human-robot interaction applications. However, collecting massive…
Initializing Models with Larger Ones
Zhiqiu Xu, Yanjie Chen, Kirill Vishniakov +5
Weight initialization plays an important role in neural network training. Widely used initialization methods are proposed and evaluated for networks that are trained from scratch.…
A Coefficient Makes SVRG Effective
Yida Yin, Zhiqiu Xu, Zhiyuan Li +2
Stochastic Variance Reduced Gradient (SVRG), introduced by Johnson & Zhang (2013), is a theoretically compelling optimization method. However, as Defazio & Bottou (2019) highlight,…