36 citations · 188 across the 36 of their papers we have counts for
9 papers · 1 filter
Direct Classification of Emotional Intensity
Jacob Ouyang, Isaac R Galatzer-Levy, Vidya Koesmahargyo +1
In this paper, we present a model that can directly predict emotion intensity score from video inputs, instead of deriving from action units. Using a 3d DNN incorporated with dynam…
LID 2020: The Learning from Imperfect Data Challenge Results
Yunchao Wei, Shuai Zheng, Ming-Ming Cheng +32
Learning from imperfect data becomes an issue in many industrial applications after the research community has made profound progress in supervised learning from perfectly annotate…
Dual-constrained Deep Semi-Supervised Coupled Factorization Network with Enriched Prior
Yan Zhang, Zhao Zhang, Yang Wang +4
Nonnegative matrix factorization is usually powerful for learning the "shallow" parts-based representation, but it clearly fails to discover deep hierarchical information within bo…
Zero-Shot Heterogeneous Transfer Learning from Recommender Systems to Cold-Start Search Retrieval
Tao Wu, Ellie Ka-In Chio, Heng-Tze Cheng +15
Many recent advances in neural information retrieval models, which predict top-K items given a query, learn directly from a large training set of (query, item) pairs. However, they…
Egocentric Action Recognition by Video Attention and Temporal Context
Juan-Manuel Perez-Rua, Antoine Toisoul, Brais Martinez +4
We present the submission of Samsung AI Centre Cambridge to the CVPR2020 EPIC-Kitchens Action Recognition Challenge. In this challenge, action recognition is posed as the problem o…
Neural Collaborative Filtering vs. Matrix Factorization Revisited
Steffen Rendle, Walid Krichene, Li Zhang +1
Embedding based models have been the state of the art in collaborative filtering for over a decade. Traditionally, the dot product or higher order equivalents have been used to com…
Instance Credibility Inference for Few-Shot Learning
Yikai Wang, Chengming Xu, Chen Liu +2
Few-shot learning (FSL) aims to recognize new objects with extremely limited training data for each category. Previous efforts are made by either leveraging meta-learning paradigm…
What Deep CNNs Benefit from Global Covariance Pooling: An Optimization Perspective
Qilong Wang, Li Zhang, Banggu Wu +4
Recent works have demonstrated that global covariance pooling (GCP) has the ability to improve performance of deep convolutional neural networks (CNNs) on visual classification tas…
Semantic Discord: Finding Unusual Local Patterns for Time Series
Li Zhang, Yifeng Gao, Jessica Lin
Finding anomalous subsequence in a long time series is a very important but difficult problem. Existing state-of-the-art methods have been focusing on searching for the subsequence…