11 citations · 25 across the 18 of their papers we have counts for
19 papers
Recovering Parametric Scenes from Very Few Time-of-Flight Pixels
Carter Sifferman, Yiquan Li, Yiming Li +4
We aim to recover the geometry of 3D parametric scenes using very few depth measurements from low-cost, commercially available time-of-flight sensors. These sensors offer very low…
TimeLoc: A Unified End-to-End Framework for Precise Timestamp Localization in Long Videos
Chen-Lin Zhang, Lin Sui, Shuming Liu +3
Temporal localization in untrimmed videos, which aims to identify specific timestamps, is crucial for video understanding but remains challenging. This task encompasses several sub…
Political-LLM: Large Language Models in Political Science
Lincan Li, Jiaqi Li, Catherine Chen +44
In recent years, large language models (LLMs) have been widely adopted in political science tasks such as election prediction, sentiment analysis, policy impact assessment, and mis…
Harnessing Temporal Causality for Advanced Temporal Action Detection
Shuming Liu, Lin Sui, Chen-Lin Zhang +3
As a fundamental task in long-form video understanding, temporal action detection (TAD) aims to capture inherent temporal relations in untrimmed videos and identify candidate actio…
Ctrl-X: Controlling Structure and Appearance for Text-To-Image Generation Without Guidance
Kuan Heng Lin, Sicheng Mo, Ben Klingher +2
Recent controllable generation approaches such as FreeControl and Diffusion Self-Guidance bring fine-grained spatial and appearance control to text-to-image (T2I) diffusion models…
SnAG: Scalable and Accurate Video Grounding
Fangzhou Mu, Sicheng Mo, Yin Li
Temporal grounding of text descriptions in videos is a central problem in vision-language learning and video understanding. Existing methods often prioritize accuracy over scalabil…
Towards 3D Vision with Low-Cost Single-Photon Cameras
Fangzhou Mu, Carter Sifferman, Sacha Jungerman +5
We present a method for reconstructing 3D shape of arbitrary Lambertian objects based on measurements by miniature, energy-efficient, low-cost single-photon cameras. These cameras,…
Towards Few-Shot Adaptation of Foundation Models via Multitask Finetuning
Zhuoyan Xu, Zhenmei Shi, Junyi Wei +3
Foundation models have emerged as a powerful tool for many AI problems. Despite the tremendous success of foundation models, effective adaptation to new tasks, particularly those w…
FreeControl: Training-Free Spatial Control of Any Text-to-Image Diffusion Model with Any Condition
Sicheng Mo, Fangzhou Mu, Kuan Heng Lin +4
Recent approaches such as ControlNet offer users fine-grained spatial control over text-to-image (T2I) diffusion models. However, auxiliary modules have to be trained for each type…
Towards 4D Human Video Stylization
Tiantian Wang, Xinxin Zuo, Fangzhou Mu +2
We present a first step towards 4D (3D and time) human video stylization, which addresses style transfer, novel view synthesis and human animation within a unified framework. While…
NMS Threshold matters for Ego4D Moment Queries -- 2nd place solution to the Ego4D Moment Queries Challenge 2023
Lin Sui, Fangzhou Mu, Yin Li
This report describes our submission to the Ego4D Moment Queries Challenge 2023. Our submission extends ActionFormer, a latest method for temporal action localization. Our extensio…
SimHaze: game engine simulated data for real-world dehazing
Zhengyang Lou, Huan Xu, Fangzhou Mu +7
Deep models have demonstrated recent success in single-image dehazing. Most prior methods consider fully supervised training and learn from paired clean and hazy images, where a ha…
GLIGEN: Open-Set Grounded Text-to-Image Generation
Yuheng Li, Haotian Liu, Qingyang Wu +5
Large-scale text-to-image diffusion models have made amazing advances. However, the status quo is to use text input alone, which can impede controllability. In this work, we propos…
Where a Strong Backbone Meets Strong Features -- ActionFormer for Ego4D Moment Queries Challenge
Fangzhou Mu, Sicheng Mo, Gillian Wang +1
This report describes our submission to the Ego4D Moment Queries Challenge 2022. Our submission builds on ActionFormer, the state-of-the-art backbone for temporal action localizati…
A Simple Transformer-Based Model for Ego4D Natural Language Queries Challenge
Sicheng Mo, Fangzhou Mu, Yin Li
This report describes Badgers@UW-Madison, our submission to the Ego4D Natural Language Queries (NLQ) Challenge. Our solution inherits the point-based event representation from our…
Physics to the Rescue: Deep Non-line-of-sight Reconstruction for High-speed Imaging
Fangzhou Mu, Sicheng Mo, Jiayong Peng +5
Computational approach to imaging around the corner, or non-line-of-sight (NLOS) imaging, is becoming a reality thanks to major advances in imaging hardware and reconstruction algo…
3D Photo Stylization: Learning to Generate Stylized Novel Views from a Single Image
Fangzhou Mu, Jian Wang, Yicheng Wu +1
Visual content creation has spurred a soaring interest given its applications in mobile photography and AR / VR. Style transfer and single-image 3D photography as two representativ…
Towards Non-Line-of-Sight Photography
Jiayong Peng, Fangzhou Mu, Ji Hyun Nam +4
Non-line-of-sight (NLOS) imaging is based on capturing the multi-bounce indirect reflections from the hidden objects. Active NLOS imaging systems rely on the capture of the time of…
Gradients as Features for Deep Representation Learning
Fangzhou Mu, Yingyu Liang, Yin Li
We address the challenging problem of deep representation learning--the efficient adaption of a pre-trained deep network to different tasks. Specifically, we propose to explore gra…