Search Results for author:
Found 6 papers, 5 papers with code
Reducing Domain Gap by Reducing Style Bias
Convolutional Neural Networks (CNNs) often fail to maintain their performance when they confront new test domains, which is known as the problem of domain shift.
Ranked #62 on
Domain Generalization
on PACS
SRM : A Style-based Recalibration Module for Convolutional Neural Networks
Following the advance of style transfer with Convolutional Neural Networks (CNNs), the role of styles in CNNs has drawn growing attention from a broader perspective.
Ranked #129 on
Image Classification
on CIFAR-10
Batch-Instance Normalization for Adaptively Style-Invariant Neural Networks
Real-world image recognition is often challenged by the variability of visual styles including object textures, lighting conditions, filter effects, etc.
Dual Attention Networks for Multimodal Reasoning and Matching
We propose Dual Attention Networks (DANs) which jointly leverage visual and textual attention mechanisms to capture fine-grained interplay between vision and language.
Ranked #2 on
Visual Question Answering (VQA)
on VQA v1 test-dev
Modeling and Propagating CNNs in a Tree Structure for Visual Tracking
We present an online visual tracking algorithm by managing multiple target appearance models in a tree structure.
Learning Multi-Domain Convolutional Neural Networks for Visual Tracking
Our algorithm pretrains a CNN using a large set of videos with tracking ground-truths to obtain a generic target representation.
