1 citations · 3 across the 22 of their papers we have counts for
24 papers
CoVA-SFT: A Large-Scale Dataset for Chain of Visual Abstractions
Tsung-Han Wu, Heekyung Lee, Anya Ji +4
Chain-of-thought (CoT) reasoning has dramatically improved large language models (LLMs) by allowing them to decompose problems into intermediate steps. While CoT is widely effectiv…
Animation2Code: Evaluating Temporal Visual Reasoning in Video-to-Code Generation
Anya Ji, Abhijith Varma Mudunuri, David M. Chan +1
While recent vision-language models (VLMs) have achieved significant improvements on static visual-to-code tasks such as generating code for webpages, charts, or SVGs, it remains u…
T-Rex: Tactile-Reactive Dexterous Manipulation
Dantong Niu, Zhuoyang Liu, Zekai Wang +31
The ability to react dynamically to tactile signals has long been considered crucial to agile human-level dexterity. Yet contemporary learning-based Vision-Language-Action (VLA) mo…
Playful Agentic Robot Learning
Junyi Zhang, Jiaxin Ge, Hanjun Yoo +17
Current agentic robot systems can write executable Code-as-Policy programs, observe feedback, and revise behavior across multiple attempts, but they remain largely task-driven: reu…
Unintended Effects of Geographic Conditioning in Large Language Models
Naz Col, David M. Chan
Modern conversational AI systems frequently rely on user metadata to localize responses, yet the unintended regional biases introduced by this hidden context remain poorly understo…
Stateful Visual Encoders for Vision-Language Models
Zirui Wang, Junwei Yu, Adam Yala +3
Vision-language models (VLMs) are increasingly used in multi-image, multi-turn agentic settings where decisions depend on visual changes. However, in existing open-weight VLMs, vis…
DigitalCoach: Communication and Grounding Gaps in Human and Agentic Computer Use Coaching
Meng Chen, Anya Ji, Tsung-Han Wu +4
Agents are increasingly capable of automating software tasks, but can they teach humans how to use software themselves? We introduce DigitalCoach, a multimodal dataset of 72 human…
ScribbleEdit: Synthetic Data for Image Editing with Scribbles and Text
Anya Ji, George Ma, Téa Wright +4
Recent progress in generative models has significantly advanced image editing capabilities, yet precise and intuitive user control remains difficult. Specifically, users often stru…
Attend Before Attention: Efficient and Scalable Video Understanding via Autoregressive Gazing
Baifeng Shi, Stephanie Fu, Long Lian +10
Multi-modal large language models (MLLMs) have advanced general-purpose video understanding but struggle with long, high-resolution videos -- they process every pixel equally in th…
VisGym: Diverse, Customizable, Scalable Environments for Multimodal Agents
Zirui Wang, Junyi Zhang, Jiaxin Ge +9
Modern Vision-Language Models (VLMs) remain poorly characterized in multi-step visual interactions, particularly in how they integrate perception, memory, and action over long hori…
Constantly Improving Image Models Need Constantly Improving Benchmarks
Jiaxin Ge, Grace Luo, Heekyung Lee +7
Recent advances in image generation, often driven by proprietary systems like GPT-4o Image Gen, regularly introduce new capabilities that reshape how users interact with these mode…
Are Large Reasoning Models Interruptible?
Tsung-Han Wu, Mihran Miroyan, David M. Chan +3
Real-world applications of Large Reasoning Models (LRMs) often require reasoning about changing prompts or environments. In this work, we challenge the frozen world assumption and…
Do What? Teaching Vision-Language-Action Models to Reject the Impossible
Wen-Han Hsieh, Elvis Hsieh, Dantong Niu +3
Recently, Vision-Language-Action (VLA) models have demonstrated strong performance on a range of robotic tasks. These models rely on multimodal inputs, with language instructions p…
LISAT: Language-Instructed Segmentation Assistant for Satellite Imagery
Jerome Quenum, Wen-Han Hsieh, Tsung-Han Wu +3
Segmentation models can recognize a pre-defined set of objects in images. However, models that can reason over complex user queries that implicitly refer to multiple objects of int…
Puzzled by Puzzles: When Vision-Language Models Can't Take a Hint
Heekyung Lee, Jiaxin Ge, Tsung-Han Wu +3
Rebus puzzles, visual riddles that encode language through imagery, spatial arrangement, and symbolic substitution, pose a unique challenge to current vision-language models (VLMs)…
REOrdering Patches Improves Vision Models
Declan Kutscher, David M. Chan, Yutong Bai +2
Sequence models such as transformers require inputs to be represented as one-dimensional sequences. In vision, this typically involves flattening images using a fixed row-major (ra…
TULIP: Towards Unified Language-Image Pretraining
Zineng Tang, Long Lian, Seun Eisape +6
Despite the recent success of image-text contrastive models like CLIP and SigLIP, these models often struggle with vision-centric tasks that demand high-fidelity image understandin…
Generate, but Verify: Reducing Hallucination in Vision-Language Models with Retrospective Resampling
Tsung-Han Wu, Heekyung Lee, Jiaxin Ge +3
Vision-Language Models (VLMs) excel at visual understanding but often suffer from visual hallucinations, where they generate descriptions of nonexistent objects, actions, or concep…
Enough Coin Flips Can Make LLMs Act Bayesian
Ritwik Gupta, Rodolfo Corona, Jiaxin Ge +4
Large language models (LLMs) exhibit the ability to generalize given few-shot examples in their input prompt, an emergent capability known as in-context learning (ICL). We investig…
Analyzing The Language of Visual Tokens
David M. Chan, Rodolfo Corona, Joonyong Park +3
With the introduction of transformer-based models for vision and language tasks, such as LLaVA and Chameleon, there has been renewed interest in the discrete tokenized representati…
CLAIR-A: Leveraging Large Language Models to Judge Audio Captions
Tsung-Han Wu, Joseph E. Gonzalez, Trevor Darrell +1
The Automated Audio Captioning (AAC) task asks models to generate natural language descriptions of an audio input. Evaluating these machine-generated audio captions is a complex ta…
Visual Haystacks: A Vision-Centric Needle-In-A-Haystack Benchmark
Tsung-Han Wu, Giscard Biamby, Jerome Quenum +4
Large Multimodal Models (LMMs) have made significant strides in visual question-answering for single images. Recent advancements like long-context LMMs have allowed them to ingest…
ALOHa: A New Measure for Hallucination in Captioning Models
Suzanne Petryk, David M. Chan, Anish Kachinthaya +4
Despite recent advances in multimodal pre-training for visual description, state-of-the-art models still produce captions containing errors, such as hallucinating objects not prese…
ANIM-400K: A Large-Scale Dataset for Automated End-To-End Dubbing of Video
Kevin Cai, Chonghua Liu, David M. Chan
The Internet's wealth of content, with up to 60% published in English, starkly contrasts the global population, where only 18.8% are English speakers, and just 5.1% consider it the…