Search Results for author:
Found 6 papers, 3 papers with code
SR-GNN: Spatial Relation-aware Graph Neural Network for Fine-Grained Image Categorization
Over the past few years, a significant progress has been made in deep convolutional neural networks (CNNs)-based image recognition.
Ranked #1 on
Fine-Grained Image Classification
on Stanford Dogs
Attend and Guide (AG-Net): A Keypoints-driven Attention-based Deep Network for Image Recognition
We address this by proposing an end-to-end CNN model, which learns meaningful features linking fine-grained changes using our novel attention mechanism.
Ranked #1 on
Image Classification
on Caltech-256
An attention-driven hierarchical multi-scale representation for visual recognition
Convolutional Neural Networks (CNNs) have revolutionized the understanding of visual content.
Coarse Temporal Attention Network (CTA-Net) for Driver's Activity Recognition
Our model is named Coarse Temporal Attention Network (CTA-Net), in which coarse temporal branches are introduced in a trainable glimpse network.
Context-aware Attentional Pooling (CAP) for Fine-grained Visual Classification
We evaluate our approach using six state-of-the-art (SotA) backbone networks and eight benchmark datasets.
Ranked #1 on
Fine-Grained Image Classification
on Food-101
Regional Attention Network (RAN) for Head Pose and Fine-grained Gesture Recognition
Affect is often expressed via non-verbal body language such as actions/gestures, which are vital indicators for human behaviors.
Facial Expression Recognition
Facial Expression Recognition (FER)
+2
