10,901 research outputs found
Grid Loss: Detecting Occluded Faces
Detection of partially occluded objects is a challenging computer vision
problem. Standard Convolutional Neural Network (CNN) detectors fail if parts of
the detection window are occluded, since not every sub-part of the window is
discriminative on its own. To address this issue, we propose a novel loss layer
for CNNs, named grid loss, which minimizes the error rate on sub-blocks of a
convolution layer independently rather than over the whole feature map. This
results in parts being more discriminative on their own, enabling the detector
to recover if the detection window is partially occluded. By mapping our loss
layer back to a regular fully connected layer, no additional computational cost
is incurred at runtime compared to standard CNNs. We demonstrate our method for
face detection on several public face detection benchmarks and show that our
method outperforms regular CNNs, is suitable for realtime applications and
achieves state-of-the-art performance.Comment: accepted to ECCV 201
Mining Discriminative Triplets of Patches for Fine-Grained Classification
Fine-grained classification involves distinguishing between similar
sub-categories based on subtle differences in highly localized regions;
therefore, accurate localization of discriminative regions remains a major
challenge. We describe a patch-based framework to address this problem. We
introduce triplets of patches with geometric constraints to improve the
accuracy of patch localization, and automatically mine discriminative
geometrically-constrained triplets for classification. The resulting approach
only requires object bounding boxes. Its effectiveness is demonstrated using
four publicly available fine-grained datasets, on which it outperforms or
achieves comparable performance to the state-of-the-art in classification
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