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Found 14 papers, 4 papers with code
Adaptive Attention Link-based Regularization for Vision Transformers
Although transformer networks are recently employed in various vision tasks with outperforming performance, extensive training data and a lengthy training time are required to train a model to disregard an inductive bias.
A high performance globally exponentially stable sensorless observer for the IPMSM: Theoretical and experimental results
In a recent paper [18] the authors proposed the first solution to the problem of designing a {\em globally exponentially stable} (GES) flux observer for the interior permanent magnet synchronous motor.
FrePGAN: Robust Deepfake Detection Using Frequency-level Perturbations
For experiments, we design new test scenarios varying from the training settings in GAN models, color manipulations, and object categories.
Self-supervised GAN Detector
Although the recent advancement in generative models brings diverse advantages to society, it can also be abused with malicious purposes, such as fraud, defamation, and fake news.
Observations on K-image Expansion of Image-Mixing Augmentation for Classification
Image-mixing augmentations (e. g., Mixup and CutMix), which typically involve mixing two images, have become the de-facto training techniques for image classification.
MToFNet: Object Anti-Spoofing with Mobile Time-of-Flight Data
When images are recaptured on display screens, various patterns differing by the screens as known as the moir\'e patterns can be also captured in spoof images.
BiHPF: Bilateral High-Pass Filters for Robust Deepfake Detection
The advancement in numerous generative models has a two-fold effect: a simple and easy generation of realistic synthesized images, but also an increased risk of malicious abuse of those images.
VaB-AL: Incorporating Class Imbalance and Difficulty with Variational Bayes for Active Learning
We show that our method can be applied to classification tasks on multiple different datasets -- including one that is a real-world dataset with heavy data imbalance -- significantly outperforming the state of the art.
Cross-modal Variational Auto-encoder with Distributed Latent Spaces and Associators
We formulate the cross-modal association in Bayesian inference framework realized by a deep neural network with multiple variational auto-encoders and variational associators.
Backbone Can Not be Trained at Once: Rolling Back to Pre-trained Network for Person Re-Identification
Our strategy alleviates the problem of gradient vanishing in low-level layers and robustly trains the low-level layers to fit the ReID dataset, thereby increasing the performance of ReID tasks.
Context-aware Deep Feature Compression for High-speed Visual Tracking
We propose a new context-aware correlation filter based tracking framework to achieve both high computational speed and state-of-the-art performance among real-time trackers.
Ranked #14 on
Visual Object Tracking
on VOT2017/18
Attentional Correlation Filter Network for Adaptive Visual Tracking
We propose a new tracking framework with an attentional mechanism that chooses a subset of the associated correlation filters for increased robustness and computational efficiency.
Action-Decision Networks for Visual Tracking With Deep Reinforcement Learning
In contrast to the existing trackers using deep networks, the proposed tracker is designed to achieve a light computation as well as satisfactory tracking accuracy in both location and scale.
Visual Tracking Using Attention-Modulated Disintegration and Integration
In this paper, we present a novel attention-modulated visual tracking algorithm that decomposes an object into multiple cognitive units, and trains multiple elementary trackers in order to modulate the distribution of attention according to various feature and kernel types.
