Search Results for author: Florian Schroff

Found 15 papers, 13 papers with code

DeepLab2: A TensorFlow Library for Deep Labeling

1 code implementation17 Jun 2021 Mark Weber, Huiyu Wang, Siyuan Qiao, Jun Xie, Maxwell D. Collins, Yukun Zhu, Liangzhe Yuan, Dahun Kim, Qihang Yu, Daniel Cremers, Laura Leal-Taixe, Alan L. Yuille, Florian Schroff, Hartwig Adam, Liang-Chieh Chen

DeepLab2 is a TensorFlow library for deep labeling, aiming to provide a state-of-the-art and easy-to-use TensorFlow codebase for general dense pixel prediction problems in computer vision.

Learning View-Disentangled Human Pose Representation by Contrastive Cross-View Mutual Information Maximization

1 code implementation CVPR 2021 Long Zhao, Yuxiao Wang, Jiaping Zhao, Liangzhe Yuan, Jennifer J. Sun, Florian Schroff, Hartwig Adam, Xi Peng, Dimitris Metaxas, Ting Liu

To evaluate the power of the learned representations, in addition to the conventional fully-supervised action recognition settings, we introduce a novel task called single-shot cross-view action recognition.

Action Recognition Contrastive Learning +1

EEV: A Large-Scale Dataset for Studying Evoked Expressions from Video

1 code implementation15 Jan 2020 Jennifer J. Sun, Ting Liu, Alan S. Cowen, Florian Schroff, Hartwig Adam, Gautam Prasad

The ability to predict evoked affect from a video, before viewers watch the video, can help in content creation and video recommendation.

Recommendation Systems Transfer Learning +1

Modeling Uncertainty with Hedged Instance Embeddings

no code implementations ICLR 2019 Seong Joon Oh, Kevin P. Murphy, Jiyan Pan, Joseph Roth, Florian Schroff, Andrew C. Gallagher

Instance embeddings are an efficient and versatile image representation that facilitates applications like recognition, verification, retrieval, and clustering.

Metric Learning

FEELVOS: Fast End-to-End Embedding Learning for Video Object Segmentation

3 code implementations CVPR 2019 Paul Voigtlaender, Yuning Chai, Florian Schroff, Hartwig Adam, Bastian Leibe, Liang-Chieh Chen

Many of the recent successful methods for video object segmentation (VOS) are overly complicated, heavily rely on fine-tuning on the first frame, and/or are slow, and are hence of limited practical use.

Semantic Segmentation Semi-Supervised Video Object Segmentation +1

Modeling Uncertainty with Hedged Instance Embedding

1 code implementation30 Sep 2018 Seong Joon Oh, Kevin Murphy, Jiyan Pan, Joseph Roth, Florian Schroff, Andrew Gallagher

Instance embeddings are an efficient and versatile image representation that facilitates applications like recognition, verification, retrieval, and clustering.

Metric Learning

Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation

68 code implementations ECCV 2018 Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, Hartwig Adam

The former networks are able to encode multi-scale contextual information by probing the incoming features with filters or pooling operations at multiple rates and multiple effective fields-of-view, while the latter networks can capture sharper object boundaries by gradually recovering the spatial information.

Image Classification Lesion Segmentation +1

Rethinking Atrous Convolution for Semantic Image Segmentation

60 code implementations17 Jun 2017 Liang-Chieh Chen, George Papandreou, Florian Schroff, Hartwig Adam

To handle the problem of segmenting objects at multiple scales, we design modules which employ atrous convolution in cascade or in parallel to capture multi-scale context by adopting multiple atrous rates.

Ranked #5 on Semantic Segmentation on PASCAL VOC 2012 val (using extra training data)

Semantic Segmentation Thermal Image Segmentation

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