Search Results for author:
Found 16 papers, 5 papers with code
Uncertainty Calibration with Energy Based Instance-wise Scaling in the Wild Dataset
In this paper, we investigate robust post-hoc uncertainty calibration methods for DNNs within the context of multi-class classification tasks.
Spherical Transformer
Second, we demonstrate that the transformer architecture can achieve rotation equivariance on specific rotations.
Style Transfer with Target Feature Palette and Attention Coloring
In this paper, to solve these problems, a novel artistic stylization method with target feature palettes is proposed, which can transfer key features accurately.
Neural Markov Controlled SDE: Stochastic Optimization for Continuous-Time Data
We propose a novel probabilistic framework for modeling stochastic dynamics with the rigorous use of stochastic optimal control theory.
Wasserstein Distributional Normalization
We propose a novel Wasserstein distributional normalization (WDN) algorithm to handle noisy labels for accurate classification.
Deep Diffusion-Invariant Wasserstein Distributional Classification
In this paper, we present a novel classification method called deep diffusion-invariant Wasserstein distributional classification (DeepWDC).
DALE : Dark Region-Aware Low-light Image Enhancement
In this paper, we present a novel low-light image enhancement method called dark region-aware low-light image enhancement (DALE), where dark regions are accurately recognized by the proposed visual attention module and their brightness are intensively enhanced.
Visual Tracking by TridentAlign and Context Embedding
However, extensive scale variations of the target object and distractor objects with similar categories have consistently posed challenges in visual tracking.
Deep ensemble network with explicit complementary model for accuracy-balanced classification
The average accuracy is one of major evaluation metrics for classification systems, while the accuracy deviation is another important performance metric used to evaluate various deep neural networks.
Adaptive Regularization via Residual Smoothing in Deep Learning Optimization
We present an adaptive regularization algorithm that can be effectively applied to the optimization problem in deep learning framework.
LED2Net: Deep Illumination-aware Dehazing with Low-light and Detail Enhancement
We present a novel dehazing and low-light enhancement method based on an illumination map that is accurately estimated by a convolutional neural network (CNN).
3D Point Cloud Generative Adversarial Network Based on Tree Structured Graph Convolutions
In this paper, we propose a novel generative adversarial network (GAN) for 3D point clouds generation, which is called tree-GAN.
Deep Meta Learning for Real-Time Target-Aware Visual Tracking
In this paper, we propose a novel on-line visual tracking framework based on the Siamese matching network and meta-learner network, which run at real-time speeds.
Real-time visual tracking by deep reinforced decision making
In this paper, we introduce a novel real-time visual tracking algorithm based on a template selection strategy constructed by deep reinforcement learning methods.
Interval Tracker: Tracking by Interval Analysis
By minimizing the interval of the posterior, our method can reduce the modeling uncertainty in the posterior.
Minimum Uncertainty Gap for Robust Visual Tracking
The uncertainty of the likelihood is estimated by obtaining the gap between the lower and upper bounds of the likelihood.
