3 citations · 12 across the 15 of their papers we have counts for
16 papers
SecOPD: Mitigating Adaptive Prompt Injections by On-Policy Distillation
Yibo Peng, Long Lian, David Wagner +1
Prompt injection is listed as the \#1 threat to AI agents. When an agent accesses external data from websites, files, or emails, an attacker may inject a prompt into the data, sayi…
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…
: Unifying Generation and Self-Verification for Parallel Reasoners
Harman Singh, Xiuyu Li, Kusha Sareen +14
Test-time scaling for complex reasoning tasks shows that leveraging inference-time compute, by methods such as independently sampling and aggregating multiple solutions, results in…
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…
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…
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-…
Rethinking Patch Dependence for Masked Autoencoders
Letian Fu, Long Lian, Renhao Wang +6
In this work, we examine the impact of inter-patch dependencies in the decoder of masked autoencoders (MAE) on representation learning. We decompose the decoding mechanism for mask…
Unsupervised Universal Image Segmentation
Dantong Niu, Xudong Wang, Xinyang Han +3
Several unsupervised image segmentation approaches have been proposed which eliminate the need for dense manually-annotated segmentation masks; current models separately handle eit…
Self-correcting LLM-controlled Diffusion Models
Tsung-Han Wu, Long Lian, Joseph E. Gonzalez +2
Text-to-image generation has witnessed significant progress with the advent of diffusion models. Despite the ability to generate photorealistic images, current text-to-image diffus…
LLM-grounded Video Diffusion Models
Long Lian, Baifeng Shi, Adam Yala +2
Text-conditioned diffusion models have emerged as a promising tool for neural video generation. However, current models still struggle with intricate spatiotemporal prompts and oft…
Q-Diffusion: Quantizing Diffusion Models
Xiuyu Li, Yijiang Liu, Long Lian +5
Diffusion models have achieved great success in image synthesis through iterative noise estimation using deep neural networks. However, the slow inference, high memory consumption,…