Proceedings Article•
Very Deep Convolutional Networks for Large-Scale Image Recognition
04 Sep 2014-
TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
Abstract: In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision.
Citations
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TL;DR: The proposed 3D DSN is capable of conducting volume‐to‐volume learning and inference, which can eliminate redundant computations and alleviate the risk of over‐fitting on limited training data, and the3D deep supervision mechanism can effectively cope with the optimization problem of gradients vanishing or exploding when training a 3D deep model.
507 citations
Cites methods from "Very Deep Convolutional Networks fo..."
...3D end-to-end learning for volumetric segmentation In a classic CNN such as the AlexNet ( Krizhevsky et al., 2012 ) nd the VGGNet ( Simonyan and Zisserman, 2014 ), the last several ayers are typically fully-connected ones, which restrict the net- ork to take fixed-sized input image and generate…...
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TL;DR: A simple approach which consists of two homogeneous branches to predict pixel-wise objectness scores and re-ID features allows \emph{FairMOT} to obtain high levels of detection and tracking accuracy and outperform previous state-of-the-arts by a large margin on several public datasets.
Abstract: There has been remarkable progress on object detection and re-identification (re-ID) in recent years which are the key components of multi-object tracking. However, little attention has been focused on jointly accomplishing the two tasks in a single network. Our study shows that the previous attempts ended up with degraded accuracy mainly because the re-ID task is not fairly learned which causes many identity switches. The unfairness lies in two-fold: (1) they treat re-ID as a secondary task whose accuracy heavily depends on the primary detection task. So training is largely biased to the detection task but ignores the re-ID task; (2) they use ROI-Align to extract re-ID features which is directly borrowed from object detection. However, this introduces a lot of ambiguity in characterizing objects because many sampling points may belong to disturbing instances or background. To solve the problems, we present a simple approach \emph{FairMOT} which consists of two homogeneous branches to predict pixel-wise objectness scores and re-ID features. The achieved fairness between the tasks allows \emph{FairMOT} to obtain high levels of detection and tracking accuracy and outperform previous state-of-the-arts by a large margin on several public datasets. The source code and pre-trained models are released at this https URL.
507 citations
Cites methods from "Very Deep Convolutional Networks fo..."
..., 2019) use a large private pedestrian detection dataset to train the Faster R-CNN detector with VGG-16 (Simonyan and Zisserman, 2014) as backbone, which obtain better detection performance....
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...Some works such as (Yu et al., 2016; Wojke et al., 2017; Zhou et al., 2018; Mahmoudi et al., 2019) use a large private pedestrian detection dataset to train the Faster R-CNN detector with VGG-16 (Simonyan and Zisserman, 2014) as backbone, which obtain better detection performance....
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07 Jun 2015
TL;DR: This work proposes a method for fine-grained recognition that uses no part annotations, based on generating parts using co-segmentation and alignment, which is combined in a discriminative mixture.
Abstract: Scaling up fine-grained recognition to all domains of fine-grained objects is a challenge the computer vision community will need to face in order to realize its goal of recognizing all object categories. Current state-of-the-art techniques rely heavily upon the use of keypoint or part annotations, but scaling up to hundreds or thousands of domains renders this annotation cost-prohibitive for all but the most important categories. In this work we propose a method for fine-grained recognition that uses no part annotations. Our method is based on generating parts using co-segmentation and alignment, which we combine in a discriminative mixture. Experimental results show its efficacy, demonstrating state-of-the-art results even when compared to methods that use part annotations during training.
507 citations
Cites background or methods from "Very Deep Convolutional Networks fo..."
...In all cases, using a VGGNet significantly improves results, so we present the remainder of the results using a VGGNet for feature extraction....
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...2% with fine-tuning), CNN Used Method [23] [40]...
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...All results not reported by prior work use the VG- GNet [40] architecture, with an architecture comparison in the supplementary material....
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...Despite using fewer annotations, our method is state-of-the-art on the competitive CUB-2011 dataset [44] when using a VGGNet [40] for feature extraction, is on par with current state-of-the-art even when using a weaker CaffeNet [23] architecture, and is furthermore able to generalize to fine-grained domains which do not have part annotations, establishing a new state-of-the-art on the cars196 [27] dataset by a large margin....
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...GNet [40] architecture, with an architecture comparison in the supplementary material....
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Posted Content•
TL;DR: An end-to-end trainable fast scene text detector, named TextBoxes, which detects scene text with both high accuracy and efficiency in a single network forward pass, involving no post-process except for a standard non-maximum suppression.
Abstract: This paper presents an end-to-end trainable fast scene text detector, named TextBoxes, which detects scene text with both high accuracy and efficiency in a single network forward pass, involving no post-process except for a standard non-maximum suppression. TextBoxes outperforms competing methods in terms of text localization accuracy and is much faster, taking only 0.09s per image in a fast implementation. Furthermore, combined with a text recognizer, TextBoxes significantly outperforms state-of-the-art approaches on word spotting and end-to-end text recognition tasks.
506 citations
Cites background from "Very Deep Convolutional Networks fo..."
...It inherits the popular VGG-16 architecture (Simonyan and Zisserman 2014), keeping the layers from conv1 1 through conv4 3....
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01 Jun 2018
TL;DR: This paper proposes a semantic segmentation neural network, named D-LinkNet, which adopts encoderdecoder structure, dilated convolution and pretrained encoder for road extraction task, built with LinkNet architecture and has dilated Convolution layers in its center part.
Abstract: Road extraction is a fundamental task in the field of remote sensing which has been a hot research topic in the past decade. In this paper, we propose a semantic segmentation neural network, named D-LinkNet, which adopts encoderdecoder structure, dilated convolution and pretrained encoder for road extraction task. The network is built with LinkNet architecture and has dilated convolution layers in its center part. Linknet architecture is efficient in computation and memory. Dilation convolution is a powerful tool that can enlarge the receptive field of feature points without reducing the resolution of the feature maps. In the CVPR DeepGlobe 2018 Road Extraction Challenge, our best IoU scores on the validation set and the test set are 0.6466 and 0.6342 respectively.
506 citations
Cites background from "Very Deep Convolutional Networks fo..."
...In this paper, we handling the road extraction task as a binary semantic segmentation task to generate pixel-level labeling of roads,. Recently, deep convolutional neural networks (DCNN) [8, 9, 10, 11] have shown their dominance on many visual recognition tasks....
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...In this paper, we follow these methods, using DCNN to handle road segmentation task....
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...Recently, deep convolutional neural networks (DCNN) [8, 9, 10, 11] have shown their dominance on many visual recognition tasks....
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References
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TL;DR: A new database called “ImageNet” is introduced, a large-scale ontology of images built upon the backbone of the WordNet structure, much larger in scale and diversity and much more accurate than the current image datasets.
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TL;DR: This paper demonstrates how constraints from the task domain can be integrated into a backpropagation network through the architecture of the network, successfully applied to the recognition of handwritten zip code digits provided by the U.S. Postal Service.
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TL;DR: A review of the Pascal Visual Object Classes challenge from 2008-2012 and an appraisal of the aspects of the challenge that worked well, and those that could be improved in future challenges.
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6,061 citations