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Browse All Topics Code & Datasets Representation Learning Responsible AI Scene Understanding

Object Detection With a Unified Label Space From Multiple Datasets

ECCV 2020 | Given multiple datasets with different label spaces, the goal of this work is to train a single object detector predicting over the union of all the label spaces. The practical benefits of such an object detector are obvious and significant—application-relevant categories can be picked and merged from arbitrary existing datasets.

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.

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.

A Parametric Top-View Representation of Complex Road Scenes

CVPR 2019 | We address the problem of inferring the layout of complex road scenes given a single camera as input. We first propose a novel parameterized model of road layouts in a top-view representation, which is not only intuitive for human visualization but also provides an interpretable interface for higher-level decision making.

Deep Supervision With Shape Concepts for Occlusion-Aware 3D Object Parsing

CVPR 2017 | We propose a deep CNN architecture to localize object semantic parts in 2D images and 3D space while inferring their visibility states given a single RGB image. We exploit domain knowledge to regularize the network by deeply supervising its hidden layers. In doing so, we sequentially infer a causal sequence of intermediate concepts.

Universal Correspondence Network

NeurIPS 2016 | We present deep metric learning to obtain a feature space that preserves geometric or semantic similarity. Our visual correspondences span across rigid motions to intra-class shape or appearance variations. Our fully convolutional architecture, along with a novel correspondence contrastive loss, allows faster training by effective reuse of computations, accurate gradient computation and linear time testing instead of quadratic time for typical patch similarity methods.

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