Advanced Neural Network Applications
This cluster of papers focuses on the application of deep learning, particularly convolutional neural networks, in computer vision tasks such as image recognition, object detection, and semantic segmentation. It covers various neural network architectures, model compression techniques, and their applications in fields like autonomous driving.
Papers listed on taxonomy pages are the top few works per node from the OpenAlex snapshot. That list is not exhaustive and is not an endorsement. The topic map and the journal registry remain separate: there is still no authoritative topic-to-venue or topic-to-organization edge. Search is a lexical lookup, not a claim that a venue publishes a topic.
Most cited
- ImageNet classification with deep convolutional neural networks
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
- DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
- A survey on Image Data Augmentation for Deep Learning
- Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
- Fully Convolutional Networks for Semantic Segmentation
Most recent
- From Sparse to Dense: Label-Efficient Weakly Supervised Segmentation for Images and Videos
- Enhancing Bone Marrow Lesion Segmentation Through Dual-Channel Deep Neural Networks and Test-Time Augmentation
- An improved RT-DETR algorithm for small-object detection in UAV aerial images
- Computation-bandwidth-memory trade-offs: a unified paradigm for AI infrastructure
- iSAGE: A Human-in-the-Loop Framework for Remote Sensing Semantic Segmentation via Sparse Point Supervision
- Posture-driven multistage region proposal and fast R-CNN architecture for advanced real-time autonomous vehicle object detection