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Object Classification

57 papers with code · Computer Vision

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M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network

12 Nov 2018qijiezhao/M2Det

Finally, we gather up the decoder layers with equivalent scales (sizes) to develop a feature pyramid for object detection, in which every feature map consists of the layers (features) from multiple levels.

OBJECT CLASSIFICATION OBJECT DETECTION

And the Bit Goes Down: Revisiting the Quantization of Neural Networks

12 Jul 2019facebookresearch/kill-the-bits

In this paper, we address the problem of reducing the memory footprint of ResNet-like convolutional network architectures.

OBJECT CLASSIFICATION QUANTIZATION

SBNet: Sparse Blocks Network for Fast Inference

CVPR 2018 uber/sbnet

Conventional deep convolutional neural networks (CNNs) apply convolution operators uniformly in space across all feature maps for hundreds of layers - this incurs a high computational cost for real-time applications.

3D OBJECT DETECTION OBJECT CLASSIFICATION SEMANTIC SEGMENTATION

SoundNet: Learning Sound Representations from Unlabeled Video

NeurIPS 2016 cvondrick/soundnet

We learn rich natural sound representations by capitalizing on large amounts of unlabeled sound data collected in the wild.

OBJECT CLASSIFICATION

CNN Features off-the-shelf: an Astounding Baseline for Recognition

23 Mar 2014baldassarreFe/deep-koalarization

We report on a series of experiments conducted for different recognition tasks using the publicly available code and model of the \overfeat network which was trained to perform object classification on ILSVRC13.

IMAGE CLASSIFICATION IMAGE RETRIEVAL OBJECT CLASSIFICATION SCENE RECOGNITION

Adversarial Discriminative Domain Adaptation

CVPR 2017 erictzeng/adda

Adversarial learning methods are a promising approach to training robust deep networks, and can generate complex samples across diverse domains.

OBJECT CLASSIFICATION UNSUPERVISED DOMAIN ADAPTATION UNSUPERVISED IMAGE-TO-IMAGE TRANSLATION

Volumetric and Multi-View CNNs for Object Classification on 3D Data

CVPR 2016 charlesq34/3dcnn.torch

Empirical results from these two types of CNNs exhibit a large gap, indicating that existing volumetric CNN architectures and approaches are unable to fully exploit the power of 3D representations.

3D OBJECT RECOGNITION OBJECT CLASSIFICATION