HyperMix: Out-of-Distribution Detection and Classification in Few-Shot Settings

no code implementations • 22 Dec 2023 • Nikhil Mehta, Kevin J Liang, Jing Huang, Fu-Jen Chu, Li Yin, Tal Hassner

Out-of-distribution (OOD) detection is an important topic for real-world machine learning systems, but settings with limited in-distribution samples have been underexplored.

Out-of-Distribution Detection Out of Distribution (OOD) Detection

Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives

no code implementations • 30 Nov 2023 • Kristen Grauman, Andrew Westbury, Lorenzo Torresani, Kris Kitani, Jitendra Malik, Triantafyllos Afouras, Kumar Ashutosh, Vijay Baiyya, Siddhant Bansal, Bikram Boote, Eugene Byrne, Zach Chavis, Joya Chen, Feng Cheng, Fu-Jen Chu, Sean Crane, Avijit Dasgupta, Jing Dong, Maria Escobar, Cristhian Forigua, Abrham Gebreselasie, Sanjay Haresh, Jing Huang, Md Mohaiminul Islam, Suyog Jain, Rawal Khirodkar, Devansh Kukreja, Kevin J Liang, Jia-Wei Liu, Sagnik Majumder, Yongsen Mao, Miguel Martin, Effrosyni Mavroudi, Tushar Nagarajan, Francesco Ragusa, Santhosh Kumar Ramakrishnan, Luigi Seminara, Arjun Somayazulu, Yale Song, Shan Su, Zihui Xue, Edward Zhang, Jinxu Zhang, Angela Castillo, Changan Chen, Xinzhu Fu, Ryosuke Furuta, Cristina Gonzalez, Prince Gupta, Jiabo Hu, Yifei HUANG, Yiming Huang, Weslie Khoo, Anush Kumar, Robert Kuo, Sach Lakhavani, Miao Liu, Mi Luo, Zhengyi Luo, Brighid Meredith, Austin Miller, Oluwatumininu Oguntola, Xiaqing Pan, Penny Peng, Shraman Pramanick, Merey Ramazanova, Fiona Ryan, Wei Shan, Kiran Somasundaram, Chenan Song, Audrey Southerland, Masatoshi Tateno, Huiyu Wang, Yuchen Wang, Takuma Yagi, Mingfei Yan, Xitong Yang, Zecheng Yu, Shengxin Cindy Zha, Chen Zhao, Ziwei Zhao, Zhifan Zhu, Jeff Zhuo, Pablo Arbelaez, Gedas Bertasius, David Crandall, Dima Damen, Jakob Engel, Giovanni Maria Farinella, Antonino Furnari, Bernard Ghanem, Judy Hoffman, C. V. Jawahar, Richard Newcombe, Hyun Soo Park, James M. Rehg, Yoichi Sato, Manolis Savva, Jianbo Shi, Mike Zheng Shou, Michael Wray

We present Ego-Exo4D, a diverse, large-scale multimodal multiview video dataset and benchmark challenge.

Video Understanding

Relational Space-Time Query in Long-Form Videos

no code implementations • CVPR 2023 • Xitong Yang, Fu-Jen Chu, Matt Feiszli, Raghav Goyal, Lorenzo Torresani, Du Tran

In this paper, we propose to study these problems in a joint framework for long video understanding.

Video Understanding

GKNet: grasp keypoint network for grasp candidates detection

no code implementations • 16 Jun 2021 • Ruinian Xu, Fu-Jen Chu, Patricio A. Vela

Decreasing the detection difficulty by grouping keypoints into pairs boosts performance.

Keypoint Detection

Using Synthetic Data and Deep Networks to Recognize Primitive Shapes for Object Grasping

1 code implementation • 12 Sep 2019 • Yunzhi Lin, Chao Tang, Fu-Jen Chu, Patricio A. Vela

Each primitive shape is designed with parametrized grasp families, permitting the pipeline to identify multiple grasp candidates per shape primitive region.

Recognizing Object Affordances to Support Scene Reasoning for Manipulation Tasks

1 code implementation • 12 Sep 2019 • Fu-Jen Chu, Ruinian Xu, Chao Tang, Patricio A. Vela

Unfortunately, the top performing affordance recognition methods use object category priors to boost the accuracy of affordance detection and segmentation.

Affordance Detection Affordance Recognition +4

Real-world multiobject, multigrasp detection

2 code implementations • IEEE ROBOTICS AND AUTOMATION LETTERS 2018 • Fu-Jen Chu, Ruinian Xu and Patricio A. Vela

A deep learning architecture is proposed to predict graspable locations for robotic manipulation.

Object Robotic Grasping

Deep Grasp: Detection and Localization of Grasps with Deep Neural Networks

4 code implementations • 1 Feb 2018 • Fu-Jen Chu, Ruinian Xu, Patricio A. Vela

By defining the learning problem to be classification with null hypothesis competition instead of regression, the deep neural network with RGB-D image input predicts multiple grasp candidates for a single object or multiple objects, in a single shot.

Robotics

When Crowdsourcing Meets Mobile Sensing: A Social Network Perspective

no code implementations • 3 Aug 2015 • Pin-Yu Chen, Shin-Ming Cheng, Pai-Shun Ting, Chia-Wei Lien, Fu-Jen Chu

Mobile sensing is an emerging technology that utilizes agent-participatory data for decision making or state estimation, including multimedia applications.

Decision Making

Supervised Collective Classification for Crowdsourcing

no code implementations • 23 Jul 2015 • Pin-Yu Chen, Chia-Wei Lien, Fu-Jen Chu, Pai-Shun Ting, Shin-Ming Cheng

Crowdsourcing utilizes the wisdom of crowds for collective classification via information (e. g., labels of an item) provided by labelers.

Classification General Classification