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Adaptation Across Extreme Variations using Unlabeled Bridges

BMCV 2020 | We tackle an unsupervised domain adaptation problem: when the domain discrepancy between labeled source and unlabeled target domains is large, due to many factors of inter- and intra-domain variation. We propose decomposing domain discrepancy into multiple smaller discrepancies by introducing unlabeled bridging domains that connect the source and target domains; this makes it easier to minimize each.

Shuffle & Attend: Video Domain Adaptation

ECCV 2020 | We address the problem of domain adaptation in videos for the task of human action recognition. Inspired by image-based domain adaptation, we propose to (i) learn to align important (discriminative) clips to achieve improved representation for the target domain and (ii) employ a self-supervised task that encourages the model to focus on actions rather than scene-context information in order to learn representations, which are more robust to domain shifts.

Learning to Optimize Domain-Specific Normalization for Domain Generalization

ECCV 2020 | We propose a simple but effective multi-source domain-generalization technique based on deep neural networks that incorporates optimized normalization layers that are specific to individual domains. Our approach employs multiple normalization methods while learning separate affine parameters per domain.

Domain Adaptive Semantic Segmentation Using Weak Labels

ECCV 2020 | We propose a novel framework for domain adaptation in semantic segmentation with image-level weak labels in the target domain. The weak labels may be obtained based on a model prediction for unsupervised domain adaptation (UDA), or from a human annotator in a new weakly supervised domain adaptation (WDA) paradigm for semantic segmentation.

Private-kNN: Practical Differential Privacy for Computer Vision

With increasing applications of semantic segmentation, numerous datasets have been proposed in the past few years. Yet labeling remains expensive, thus, it is desirable to jointly train models across aggregations of datasets to enhance data volume and diversity. However, label spaces differ across datasets and may even be in conflict with one another.

Unsupervised & Semi-Supervised Domain Adaptation for Action Recognition From Drones

WACV 2020 | We address the problem of human action classification in drone videos. Due to the high cost of capturing and labeling large-scale drone videos with diverse actions, we present unsupervised and semi-supervised domain adaptation approaches that leverage both the existing, fully-annotated action-recognition datasets and unannotated (or only a few annotated) videos from drones.

Adversarial Learning of Privacy-Preserving & Task-Oriented Representations

AAAI 2020 | Our aim is to learn privacy-preserving and task-oriented representations that defend against model inversion attacks. To achieve this, we propose an adversarial reconstruction-based framework for learning latent representations that cannot be decoded to recover the original input images.

Domain Adaptation for Structured Output via Discriminative Patch Representations

PAMI 2019 | We tackle domain adaptive semantic segmentation via learning discriminative feature representations of patches in the source domain by discovering multiple modes of patch-wise output distribution through the construction of a clustered space. With such guidance, we use an adversarial learning scheme to push the feature representations of target patches in the clustered space closer to the distributions of source patches.

Gotta Adapt ’Em All: Joint Pixel & Feature-Level Domain Adaptation for Recognition in the Wild

CVPR 2019 | We provide a solution that allows knowledge transfer from fully annotated source images to unlabeled target ones, which are often captured in a different condition. We adapt at multiple semantic levels from feature to pixel, with complementary insights for each type. Utilizing the proposal, we achieve better recognition accuracy of car images in an unlabeled surveillance domain by adapting the knowledge from car images on the web.

Learning to Adapt Structured Output Space for Semantic Segmentation

CVPR 2018 | We develop a semantic segmentation method for adapting source ground truth labels to the unseen target domain. To achieve it, we consider semantic segmentation as structured prediction with spatial similarities between the source and target domains and then adopt multi-level adversarial learning in the output space.

Unsupervised Domain Adaptation for Face Recognition in Unlabeled Videos

CCV 2017 | Despite rapid advances in face recognition, there remains a clear gap between the performance of still-image-based face recognition and video-based face recognition. To address this, we propose an image-to-video feature-level domain adaptation method to learn discriminative video-frame representations.

Reconstruction-Based Disentanglement for Pose-Invariant Face Recognition

ICCV 2017 | Generic data-driven deep face features might confound images of the same identity under large poses with other identities. We propose a feature reconstruction metric learning to disentangle identity and pose information in the latent feature space. The disentangled feature space encourages identity features of the same subject to be clustered together in spite of pose variation.

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NEC Laboratories America, Inc. (NEC Labs) is the US-based center for NEC Corporation’s global network of corporate research laboratories. Our diverse research groups collaborate with industry, academia and governments to provide disruptive solutions to complex problems. A leader in the integration of IT and network technologies with more than 100 years of expertise, NEC provides a combination of products and solutions that cross-utilize the company’s experience and global resources to meet the complex and ever-changing needs of its customers.