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- Carnegie Mellon UniversityUS49 papers
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11 papers · 2 filters
Learning Realistic Human Reposing using Cyclic Self-Supervision with 3D Shape, Pose, and Appearance Consistency
Soubhik Sanyal, Alex Vorobiov, Timo Bolkart +5
Synthesizing images of a person in novel poses from a single image is a highly ambiguous task. Most existing approaches require paired training images; i.e. images of the same pers…
CrossCLR: Cross-modal Contrastive Learning For Multi-modal Video Representations
Mohammadreza Zolfaghari, Yi Zhu, Peter Gehler +1
Contrastive learning allows us to flexibly define powerful losses by contrasting positive pairs from sets of negative samples. Recently, the principle has also been used to learn c…
Zero-Shot Out-of-Distribution Detection Based on the Pre-trained Model CLIP
Sepideh Esmaeilpour, Bing Liu, Eric Robertson +1
In an out-of-distribution (OOD) detection problem, samples of known classes(also called in-distribution classes) are used to train a special classifier. In testing, the classifier…
Video Contrastive Learning with Global Context
Haofei Kuang, Yi Zhu, Zhi Zhang +5
Contrastive learning has revolutionized self-supervised image representation learning field, and recently been adapted to video domain. One of the greatest advantages of contrastiv…
A Unified Efficient Pyramid Transformer for Semantic Segmentation
Fangrui Zhu, Yi Zhu, Li Zhang +3
Semantic segmentation is a challenging problem due to difficulties in modeling context in complex scenes and class confusions along boundaries. Most literature either focuses on co…
Learning Hierarchical Graph Neural Networks for Image Clustering
Yifan Xing, Tong He, Tianjun Xiao +6
We propose a hierarchical graph neural network (GNN) model that learns how to cluster a set of images into an unknown number of identities using a training set of images annotated…