Modeling Local and Global Deformations in Deep Learning: Epitomic Convolution, Multiple Instance Learning, and Sliding Window Detection

2015-06-01 · CVPR 2015 6 · George Papandreou, Iasonas Kokkinos, Pierre-Andre Savalle

Deep Convolutional Neural Networks (DCNNs) achieve invariance to domain transformations (deformations) by using multiple 'max-pooling' (MP) layers. In this work we show that alternative methods of modeling deformations can improve the accuracy and efficiency of DCNNs. First, we introduce epitomic convolution as an alternative to the common convolution-MP cascade of DCNNs, that comes with the same computational cost but favorable learning properties. Second, we introduce a Multiple Instance Learning algorithm to accommodate global translation and scaling in image classification, yielding an efficient algorithm that trains and tests a DCNN in a consistent manner. Third we develop a DCNN sliding window detector that explicitly, but efficiently, searches over the object's position, scale, and aspect ratio. We provide competitive image classification and localization results on the ImageNet dataset and object detection results on Pascal VOC2007.

📄 PDF Abstract BibTeX

Code (0)

등록된 구현이 없습니다.

Tasks

General Classification image-classification Image Classification Multiple Instance Learning object-detection Object DetectionPositionTranslation

Untangling Local and Global Deformations in Deep Convolutional Networks for Image Classification and Sliding Window Detection

2014-11-30 · George Papandreou, Iasonas Kokkinos, Pierre-André Savalle

Deep Convolutional Neural Networks (DCNNs) commonly use generic `max-pooling' (MP) layers to extract deformation-invariant features, but we argue in favor of a more refined treatment. First, we introduce epitomic convolu…

General Classification image-classification Image Classification Multiple Instance Learning+3

Deep Epitomic Convolutional Neural Networks

2014-06-10 · George Papandreou

Deep convolutional neural networks have recently proven extremely competitive in challenging image recognition tasks. This paper proposes the epitomic convolution as a new building block for deep neural networks. An epit…

General Classification image-classification Image Classification

Mining self-similarity: Label super-resolution with epitomic representations

2020-04-24 · ECCV 2020 8 · Nikolay Malkin, Anthony Ortiz, Caleb Robinson, Nebojsa Jojic

We show that simple patch-based models, such as epitomes, can have superior performance to the current state of the art in semantic segmentation and label super-resolution, which uses deep convolutional neural networks. …

Medical Image Analysis Semantic Segmentation Super-Resolution

Neural Deformable Models for 3D Bi-Ventricular Heart Shape Reconstruction and Modeling from 2D Sparse Cardiac Magnetic Resonance Imaging

2023-07-15 · ICCV 2023 1 · Meng Ye, Dong Yang, Mikael Kanski, Leon Axel 외

We propose a novel neural deformable model (NDM) targeting at the reconstruction and modeling of 3D bi-ventricular shape of the heart from 2D sparse cardiac magnetic resonance (CMR) imaging data. We model the bi-ventricu…

SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection

2021-01-07 · Prarthana Bhattacharyya, Chengjie Huang, Krzysztof Czarnecki

Existing point-cloud based 3D object detectors use convolution-like operators to process information in a local neighbourhood with fixed-weight kernels and aggregate global context hierarchically. However, non-local neur…

3D Object Detection Object object-detection Object Detection