64 citations · 284 across the 15 of their papers we have counts for
8 papers · 1 filter
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…
BINAS: Bilinear Interpretable Neural Architecture Search
Niv Nayman, Yonathan Aflalo, Asaf Noy +2
Practical use of neural networks often involves requirements on latency, energy and memory among others. A popular approach to find networks under such requirements is through cons…
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…
An Image is Worth 16x16 Words, What is a Video Worth?
Gilad Sharir, Asaf Noy, Lihi Zelnik-Manor
Leading methods in the domain of action recognition try to distill information from both the spatial and temporal dimensions of an input video. Methods that reach State of the Art…
HardCoRe-NAS: Hard Constrained diffeRentiable Neural Architecture Search
Niv Nayman, Yonathan Aflalo, Asaf Noy +1
Realistic use of neural networks often requires adhering to multiple constraints on latency, energy and memory among others. A popular approach to find fitting networks is through…
A Convergence Theory Towards Practical Over-parameterized Deep Neural Networks
Asaf Noy, Yi Xu, Yonathan Aflalo +2
Deep neural networks' remarkable ability to correctly fit training data when optimized by gradient-based algorithms is yet to be fully understood. Recent theoretical results explai…