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Object Classification

62 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

DeepGCNs: Making GCNs Go as Deep as CNNs

15 Oct 2019lightaime/deep_gcns

We believe that the insights in this work will open a lot of avenues for future research on GCNs and transfer to further tasks not explored in this work.

NODE CLASSIFICATION OBJECT CLASSIFICATION SEMANTIC SEGMENTATION

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 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