17 citations · 29 across the 6 of their papers we have counts for
6 papers
Solving ImageNet: a Unified Scheme for Training any Backbone to Top Results
Tal Ridnik, Hussam Lawen, Emanuel Ben-Baruch +1
ImageNet serves as the primary dataset for evaluating the quality of computer-vision models. The common practice today is training each architecture with a tailor-made scheme, desi…
It's All in the Head: Representation Knowledge Distillation through Classifier Sharing
Emanuel Ben-Baruch, Matan Karklinsky, Yossi Biton +3
Representation knowledge distillation aims at transferring rich information from one model to another. Common approaches for representation distillation mainly focus on the direct…
ML-Decoder: Scalable and Versatile Classification Head
Tal Ridnik, Gilad Sharir, Avi Ben-Cohen +2
In this paper, we introduce ML-Decoder, a new attention-based classification head. ML-Decoder predicts the existence of class labels via queries, and enables better utilization of…
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…
PETA: Photo Albums Event Recognition using Transformers Attention
Tamar Glaser, Emanuel Ben-Baruch, Gilad Sharir +3
In recent years the amounts of personal photos captured increased significantly, giving rise to new challenges in multi-image understanding and high-level image understanding. Even…
ImageNet-21K Pretraining for the Masses
Tal Ridnik, Emanuel Ben-Baruch, Asaf Noy +1
ImageNet-1K serves as the primary dataset for pretraining deep learning models for computer vision tasks. ImageNet-21K dataset, which is bigger and more diverse, is used less frequ…