Search Results for author: Hartwig Adam

Found 36 papers, 29 papers with code

CMT-DeepLab: Clustering Mask Transformers for Panoptic Segmentation

no code implementations CVPR 2022 Qihang Yu, Huiyu Wang, Dahun Kim, Siyuan Qiao, Maxwell Collins, Yukun Zhu, Hartwig Adam, Alan Yuille, Liang-Chieh Chen

We propose Clustering Mask Transformer (CMT-DeepLab), a transformer-based framework for panoptic segmentation designed around clustering.

Panoptic Segmentation

Adaptive Transformers for Robust Few-shot Cross-domain Face Anti-spoofing

no code implementations23 Mar 2022 Hsin-Ping Huang, Deqing Sun, Yaojie Liu, Wen-Sheng Chu, Taihong Xiao, Jinwei Yuan, Hartwig Adam, Ming-Hsuan Yang

While recent face anti-spoofing methods perform well under the intra-domain setups, an effective approach needs to account for much larger appearance variations of images acquired in complex scenes with different sensors for robust performance.

Face Anti-Spoofing

Surrogate Gap Minimization Improves Sharpness-Aware Training

1 code implementation ICLR 2022 Juntang Zhuang, Boqing Gong, Liangzhe Yuan, Yin Cui, Hartwig Adam, Nicha Dvornek, Sekhar Tatikonda, James Duncan, Ting Liu

Instead, we define a \textit{surrogate gap}, a measure equivalent to the dominant eigenvalue of Hessian at a local minimum when the radius of the neighborhood (to derive the perturbed loss) is small.

Exploring Temporal Granularity in Self-Supervised Video Representation Learning

no code implementations8 Dec 2021 Rui Qian, Yeqing Li, Liangzhe Yuan, Boqing Gong, Ting Liu, Matthew Brown, Serge Belongie, Ming-Hsuan Yang, Hartwig Adam, Yin Cui

The training objective consists of two parts: a fine-grained temporal learning objective to maximize the similarity between corresponding temporal embeddings in the short clip and the long clip, and a persistent temporal learning objective to pull together global embeddings of the two clips.

Representation Learning Self-Supervised Learning

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.

Computer Vision

2.5D Visual Relationship Detection

1 code implementation26 Apr 2021 Yu-Chuan Su, Soravit Changpinyo, Xiangning Chen, Sathish Thoppay, Cho-Jui Hsieh, Lior Shapira, Radu Soricut, Hartwig Adam, Matthew Brown, Ming-Hsuan Yang, Boqing Gong

To enable progress on this task, we create a new dataset consisting of 220k human-annotated 2. 5D relationships among 512K objects from 11K images.

Depth Estimation Visual Relationship Detection

ViP-DeepLab: Learning Visual Perception with Depth-aware Video Panoptic Segmentation

1 code implementation CVPR 2021 Siyuan Qiao, Yukun Zhu, Hartwig Adam, Alan Yuille, Liang-Chieh Chen

We name this joint task as Depth-aware Video Panoptic Segmentation, and propose a new evaluation metric along with two derived datasets for it, which will be made available to the public.

Monocular Depth Estimation Panoptic Segmentation

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

MaX-DeepLab: End-to-End Panoptic Segmentation with Mask Transformers

2 code implementations CVPR 2021 Huiyu Wang, Yukun Zhu, Hartwig Adam, Alan Yuille, Liang-Chieh Chen

As a result, MaX-DeepLab shows a significant 7. 1% PQ gain in the box-free regime on the challenging COCO dataset, closing the gap between box-based and box-free methods for the first time.

Panoptic Segmentation

Naive-Student: Leveraging Semi-Supervised Learning in Video Sequences for Urban Scene Segmentation

1 code implementation ECCV 2020 Liang-Chieh Chen, Raphael Gontijo Lopes, Bowen Cheng, Maxwell D. Collins, Ekin D. Cubuk, Barret Zoph, Hartwig Adam, Jonathon Shlens

We view this work as a notable step towards building a simple procedure to harness unlabeled video sequences and extra images to surpass state-of-the-art performance on core computer vision tasks.

Computer Vision Optical Flow Estimation +3

Fashionpedia: Ontology, Segmentation, and an Attribute Localization Dataset

3 code implementations ECCV 2020 Menglin Jia, Mengyun Shi, Mikhail Sirotenko, Yin Cui, Claire Cardie, Bharath Hariharan, Hartwig Adam, Serge Belongie

In this work we explore the task of instance segmentation with attribute localization, which unifies instance segmentation (detect and segment each object instance) and fine-grained visual attribute categorization (recognize one or multiple attributes).

Fine-Grained Visual Categorization Fine-Grained Visual Recognition +3

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

Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation

6 code implementations CVPR 2020 Bowen Cheng, Maxwell D. Collins, Yukun Zhu, Ting Liu, Thomas S. Huang, Hartwig Adam, Liang-Chieh Chen

In this work, we introduce Panoptic-DeepLab, a simple, strong, and fast system for panoptic segmentation, aiming to establish a solid baseline for bottom-up methods that can achieve comparable performance of two-stage methods while yielding fast inference speed.

Ranked #3 on Instance Segmentation on Cityscapes test (using extra training data)

Instance Segmentation Panoptic Segmentation


2 code implementations10 Oct 2019 Bowen Cheng, Maxwell D. Collins, Yukun Zhu, Ting Liu, Thomas S. Huang, Hartwig Adam, Liang-Chieh Chen

The semantic segmentation branch is the same as the typical design of any semantic segmentation model (e. g., DeepLab), while the instance segmentation branch is class-agnostic, involving a simple instance center regression.

Instance Segmentation Panoptic Segmentation

The iMaterialist Fashion Attribute Dataset

1 code implementation13 Jun 2019 Sheng Guo, Weilin Huang, Xiao Zhang, Prasanna Srikhanta, Yin Cui, Yuan Li, Matthew R. Scott, Hartwig Adam, Serge Belongie

The dataset is constructed from over one million fashion images with a label space that includes 8 groups of 228 fine-grained attributes in total.

General Classification Image Classification +1

Geo-Aware Networks for Fine-Grained Recognition

1 code implementation4 Jun 2019 Grace Chu, Brian Potetz, Weijun Wang, Andrew Howard, Yang song, Fernando Brucher, Thomas Leung, Hartwig Adam

By leveraging geolocation information we improve top-1 accuracy in iNaturalist from 70. 1% to 79. 0% for a strong baseline image-only model.

Fine-Grained Image Classification General Classification

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

NetAdapt: Platform-Aware Neural Network Adaptation for Mobile Applications

4 code implementations ECCV 2018 Tien-Ju Yang, Andrew Howard, Bo Chen, Xiao Zhang, Alec Go, Mark Sandler, Vivienne Sze, Hartwig Adam

This work proposes an algorithm, called NetAdapt, that automatically adapts a pre-trained deep neural network to a mobile platform given a resource budget.

Image Classification

Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation

70 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

Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference

21 code implementations CVPR 2018 Benoit Jacob, Skirmantas Kligys, Bo Chen, Menglong Zhu, Matthew Tang, Andrew Howard, Hartwig Adam, Dmitry Kalenichenko

The rising popularity of intelligent mobile devices and the daunting computational cost of deep learning-based models call for efficient and accurate on-device inference schemes.

General Classification Quantization

InclusiveFaceNet: Improving Face Attribute Detection with Race and Gender Diversity

1 code implementation1 Dec 2017 Hee Jung Ryu, Hartwig Adam, Margaret Mitchell

We demonstrate an approach to face attribute detection that retains or improves attribute detection accuracy across gender and race subgroups by learning demographic information prior to learning the attribute detection task.

The iNaturalist Species Classification and Detection Dataset

7 code implementations CVPR 2018 Grant Van Horn, Oisin Mac Aodha, Yang song, Yin Cui, Chen Sun, Alex Shepard, Hartwig Adam, Pietro Perona, Serge Belongie

Existing image classification datasets used in computer vision tend to have a uniform distribution of images across object categories.

Classification Computer Vision +2

Learning Unified Embedding for Apparel Recognition

no code implementations19 Jul 2017 Yang Song, Yuan Li, Bo Wu, Chao-Yeh Chen, Xiao Zhang, Hartwig Adam

To ease the training difficulty, a novel learning scheme is proposed by using the output from specialized models as learning targets so that L2 loss can be used instead of triplet loss.

BranchOut: Regularization for Online Ensemble Tracking With Convolutional Neural Networks

no code implementations CVPR 2017 Bohyung Han, Jack Sim, Hartwig Adam

We propose an extremely simple but effective regularization technique of convolutional neural networks (CNNs), referred to as BranchOut, for online ensemble tracking.

online learning Visual Tracking

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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