64 citations · 205 across the 9 of their papers we have counts for
7 papers · 1 filter
Multi-label Classification with Partial Annotations using Class-aware Selective Loss
Emanuel Ben-Baruch, Tal Ridnik, Itamar Friedman +4
Large-scale multi-label classification datasets are commonly, and perhaps inevitably, partially annotated. That is, only a small subset of labels are annotated per sample. Differen…
Semantic Diversity Learning for Zero-Shot Multi-label Classification
Avi Ben-Cohen, Nadav Zamir, Emanuel Ben Baruch +2
Training a neural network model for recognizing multiple labels associated with an image, including identifying unseen labels, is challenging, especially for images that portray nu…
Asymmetric Loss For Multi-Label Classification
Emanuel Ben-Baruch, Tal Ridnik, Nadav Zamir +4
In a typical multi-label setting, a picture contains on average few positive labels, and many negative ones. This positive-negative imbalance dominates the optimization process, an…
TResNet: High Performance GPU-Dedicated Architecture
Tal Ridnik, Hussam Lawen, Asaf Noy +3
Many deep learning models, developed in recent years, reach higher ImageNet accuracy than ResNet50, with fewer or comparable FLOPS count. While FLOPs are often seen as a proxy for…
Graph Embedded Pose Clustering for Anomaly Detection
Amir Markovitz, Gilad Sharir, Itamar Friedman +2
We propose a new method for anomaly detection of human actions. Our method works directly on human pose graphs that can be computed from an input video sequence. This makes the ana…
Compact Network Training for Person ReID
Hussam Lawen, Avi Ben-Cohen, Matan Protter +2
The task of person re-identification (ReID) has attracted growing attention in recent years leading to improved performance, albeit with little focus on real-world applications. Mo…
Video Object Segmentation using Tracked Object Proposals
Gilad Sharir, Eddie Smolyansky, Itamar Friedman
We present an approach to semi-supervised video object segmentation, in the context of the DAVIS 2017 challenge. Our approach combines category-based object detection, category-ind…