35 citations · 116 across the 24 of their papers we have counts for
13 papers · 1 filter
Learning Robust Stereo Matching in the Wild with Selective Mixture-of-Experts
Yun Wang, Longguang Wang, Chenghao Zhang +6
Recently, learning-based stereo matching networks have advanced significantly. However, they often lack robustness and struggle to achieve impressive cross-domain performance due t…
Lifelong-MonoDepth: Lifelong Learning for Multi-Domain Monocular Metric Depth Estimation
Junjie Hu, Chenyou Fan, Liguang Zhou +3
With the rapid advancements in autonomous driving and robot navigation, there is a growing demand for lifelong learning models capable of estimating metric (absolute) depth. Lifelo…
Progressive Self-Distillation for Ground-to-Aerial Perception Knowledge Transfer
Junjie Hu, Chenyou Fan, Mete Ozay +3
We study a practical yet hasn't been explored problem: how a drone can perceive in an environment from different flight heights. Unlike autonomous driving, where the perception is…
Dense Depth Distillation with Out-of-Distribution Simulated Images
Junjie Hu, Chenyou Fan, Mete Ozay +2
We study data-free knowledge distillation (KD) for monocular depth estimation (MDE), which learns a lightweight model for real-world depth perception tasks by compressing it from a…
Deep Depth Completion from Extremely Sparse Data: A Survey
Junjie Hu, Chenyu Bao, Mete Ozay +4
Depth completion aims at predicting dense pixel-wise depth from an extremely sparse map captured from a depth sensor, e.g., LiDARs. It plays an essential role in various applicatio…
Boosting Light-Weight Depth Estimation Via Knowledge Distillation
Junjie Hu, Chenyou Fan, Hualie Jiang +4
Monocular depth estimation (MDE) methods are often either too computationally expensive or not accurate enough due to the trade-off between model complexity and inference performan…
Heterogeneous Memory Enhanced Multimodal Attention Model for Video Question Answering
Chenyou Fan, Xiaofan Zhang, Shu Zhang +3
In this paper, we propose a novel end-to-end trainable Video Question Answering (VideoQA) framework with three major components: 1) a new heterogeneous memory which can effectively…
Joint Person Segmentation and Identification in Synchronized First- and Third-person Videos
Mingze Xu, Chenyou Fan, Yuchen Wang +2
In a world of pervasive cameras, public spaces are often captured from multiple perspectives by cameras of different types, both fixed and mobile. An important problem is to organi…
Multi-Task Spatiotemporal Neural Networks for Structured Surface Reconstruction
Mingze Xu, Chenyou Fan, John D Paden +2
Deep learning methods have surpassed the performance of traditional techniques on a wide range of problems in computer vision, but nearly all of this work has studied consumer phot…
Forecasting Hands and Objects in Future Frames
Chenyou Fan, Jangwon Lee, Michael S. Ryoo
This paper presents an approach to forecast future presence and location of human hands and objects. Given an image frame, the goal is to predict what objects will appear in the fu…
Identifying First-person Camera Wearers in Third-person Videos
Chenyou Fan, Jangwon Lee, Mingze Xu +4
We consider scenarios in which we wish to perform joint scene understanding, object tracking, activity recognition, and other tasks in environments in which multiple people are wea…
DeepDiary: Automatic Caption Generation for Lifelogging Image Streams
Chenyou Fan, David J. Crandall
Lifelogging cameras capture everyday life from a first-person perspective, but generate so much data that it is hard for users to browse and organize their image collections effect…
Learning Latent Sub-events in Activity Videos Using Temporal Attention Filters
AJ Piergiovanni, Chenyou Fan, Michael S. Ryoo
In this paper, we newly introduce the concept of temporal attention filters, and describe how they can be used for human activity recognition from videos. Many high-level activitie…