1 citations · 2 across the 15 of their papers we have counts for
18 papers
OmniTaskonomy: When Does Visual Generation Improve Visual Understanding?
Jiaxin Ge, Yiming Qin, Ji Xie +13
Training a model to generate visual content can encourage it to learn rich perceptual capabilities related to geometry, spatial relationships, and objectness; yet, its benefits for…
Open-Ended CT Volume Segmentation with Weak Supervision from Language
Sanjay Subramanian, Junwei Yu, Zirui Wang +5
We introduce a method for training a text-conditioned segmentation model for CT scans, which combines voxel-level supervision with coarse but scalable slice-level supervision from…
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…
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…
Visually Prompted Benchmarks Are Surprisingly Fragile
Haiwen Feng, Long Lian, Lisa Dunlap +6
A key challenge in evaluating VLMs is testing models' ability to analyze visual content independently from their textual priors. Recent benchmarks such as BLINK probe visual percep…
ThreadWeaver: Adaptive Threading for Efficient Parallel Reasoning in Language Models
Long Lian, Sida Wang, Felix Juefei-Xu +7
Scaling inference-time computation has enabled Large Language Models (LLMs) to achieve strong reasoning performance, but their inherently sequential decoding incurs substantial lat…
Pillar-0: A New Frontier for Radiology Foundation Models
Kumar Krishna Agrawal, Longchao Liu, Long Lian +11
Radiology plays an integral role in modern medicine, yet rising imaging volumes have far outpaced workforce growth. Foundation models offer a path toward assisting with the full sp…
UnSAMv2: Self-Supervised Learning Enables Segment Anything at Any Granularity
Junwei Yu, Trevor Darrell, XuDong Wang
The Segment Anything Model (SAM) family has become a widely adopted vision foundation model, but its ability to control segmentation granularity remains limited. Users often need t…
Chain-of-Visual-Thought: Teaching VLMs to See and Think Better with Continuous Visual Tokens
Yiming Qin, Bomin Wei, Jiaxin Ge +4
Vision-Language Models (VLMs) excel at reasoning in linguistic space but struggle with perceptual understanding that requires dense visual perception, e.g., spatial reasoning and g…
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…
Reconstruction Alignment Improves Unified Multimodal Models
Ji Xie, Trevor Darrell, Luke Zettlemoyer +1
Unified multimodal models (UMMs) unify visual understanding and generation within a single architecture. However, conventional training relies on image-text pairs (or sequences) wh…
Describe Anything: Detailed Localized Image and Video Captioning
Long Lian, Yifan Ding, Yunhao Ge +8
Generating detailed and accurate descriptions for specific regions in images and videos remains a fundamental challenge for vision-language models. We introduce the Describe Anythi…
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…
Learning Adaptive Parallel Reasoning with Language Models
Jiayi Pan, Xiuyu Li, Long Lian +6
Scaling inference-time computation has substantially improved the reasoning capabilities of language models. However, existing methods have significant limitations: serialized chai…
Atlas: Multi-Scale Attention Improves Long Context Image Modeling
Kumar Krishna Agrawal, Long Lian, Longchao Liu +6
Efficiently modeling massive images is a long-standing challenge in machine learning. To this end, we introduce Multi-Scale Attention (MSA). MSA relies on two key ideas, (i) multi-…
Visual Lexicon: Rich Image Features in Language Space
XuDong Wang, Xingyi Zhou, Alireza Fathi +2
We present Visual Lexicon, a novel visual language that encodes rich image information into the text space of vocabulary tokens while retaining intricate visual details that are of…
SegLLM: Multi-round Reasoning Segmentation
XuDong Wang, Shaolun Zhang, Shufan Li +5
We present SegLLM, a novel multi-round interactive reasoning segmentation model that enhances LLM-based segmentation by exploiting conversational memory of both visual and textual…
When Does Perceptual Alignment Benefit Vision Representations?
Shobhita Sundaram, Stephanie Fu, Lukas Muttenthaler +5
Humans judge perceptual similarity according to diverse visual attributes, including scene layout, subject location, and camera pose. Existing vision models understand a wide range…