Analyzing the Performance of Multilayer Neural Networks for Object Recognition

Random Forests

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Histograms of Oriented Gradients for Human Detection

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The pascal visual object classes (VOC) challenge

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Backpropagation Applied to Handwritten Zip Code Recognition

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Caffe: Convolutional Architecture for Fast Feature Embedding

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Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories

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Selective Search for Object Recognition

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Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position

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CNN Features Off-the-Shelf: An Astounding Baseline for Recognition

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DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition

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Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Psychological and Biological Models

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SUN database: Large-scale scene recognition from abbey to zoo

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Unbiased look at dataset bias

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Spectral Hashing

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Invariant visual representation by single neurons in the human brain

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Single Units and Sensation: A Neuron Doctrine for Perceptual Psychology?

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Building high-level features using large scale unsupervised learning

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Deep Neural Networks for Object Detection

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Iterative quantization: A procrustean approach to learning binary codes

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Building high-level features using large scale unsupervised learning

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Visualizing and Understanding Convolutional Networks

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Unsupervised Discovery of Mid-Level Discriminative Patches

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Blocks That Shout: Distinctive Parts for Scene Classification

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Learning Local Feature Descriptors Using Convex Optimisation

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Joint induction of shape features and tree classifiers

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Deep convolutional neural networks for computer-aided detection: cnn architectures, dataset characteristics and transfer learning

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Region-based convolutional networks for accurate object detection and segmentation

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Deep Learning for Generic Object Detection: A Survey

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Unsupervised Visual Representation Learning by Context Prediction

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Deep learning for visual understanding: A review

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Network Dissection: Quantifying Interpretability of Deep Visual Representations

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Do Better ImageNet Models Transfer Better?

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Exploring the Limits of Weakly Supervised Pretraining

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Learning Without Forgetting

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Object Detection Networks on Convolutional Feature Maps

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Radimagenet: an open Radiologic deep learning research Dataset for effective transfer learning

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Factors of Transferability for a Generic ConvNet Representation

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Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time

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Deep learning for tactile understanding from visual and haptic data

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Growing a Brain: Fine-Tuning by Increasing Model Capacity

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Performance Improvement of SIFT-based Copy-move Forgery Detection Using CSLBP Descriptor

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Implementation of Image Matching Algorithm Based on SIFT Features

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Improvements of Local Descriptor in HOG/SIFT by BOF Approach

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Survey of Feature Points Detection and Matching using SURF, SIFT and PCA-SIFT

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Application of multiple-layer SIFT to semantic concept detection

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