37 citations · 106 across the 10 of their papers we have counts for
23 papers
Detector-Free Weakly Supervised Grounding by Separation
Assaf Arbelle, Sivan Doveh, Amit Alfassy +14
Nowadays, there is an abundance of data involving images and surrounding free-form text weakly corresponding to those images. Weakly Supervised phrase-Grounding (WSG) deals with th…
AdaFuse: Adaptive Temporal Fusion Network for Efficient Action Recognition
Yue Meng, Rameswar Panda, Chung-Ching Lin +5
Temporal modelling is the key for efficient video action recognition. While understanding temporal information can improve recognition accuracy for dynamic actions, removing tempor…
A Maximal Correlation Approach to Imposing Fairness in Machine Learning
Joshua Lee, Yuheng Bu, Prasanna Sattigeri +4
As machine learning algorithms grow in popularity and diversify to many industries, ethical and legal concerns regarding their fairness have become increasingly relevant. We explor…
Optimizing Mode Connectivity via Neuron Alignment
N. Joseph Tatro, Pin-Yu Chen, Payel Das +3
The loss landscapes of deep neural networks are not well understood due to their high nonconvexity. Empirically, the local minima of these loss functions can be connected by a lear…
not-so-BigGAN: Generating High-Fidelity Images on Small Compute with Wavelet-based Super-Resolution
Seungwook Han, Akash Srivastava, Cole Hurwitz +2
State-of-the-art models for high-resolution image generation, such as BigGAN and VQVAE-2, require an incredible amount of compute resources and/or time (512 TPU-v3 cores) to train,…
OnlineAugment: Online Data Augmentation with Less Domain Knowledge
Zhiqiang Tang, Yunhe Gao, Leonid Karlinsky +3
Data augmentation is one of the most important tools in training modern deep neural networks. Recently, great advances have been made in searching for optimal augmentation policies…