• Corpus ID: 2762417

Marine Animal Detection and Recognition with Advanced Deep Learning Models

@inproceedings{Zhuang2017MarineAD,
  title={Marine Animal Detection and Recognition with Advanced Deep Learning Models},
  author={Peiqin Zhuang and Linjie Xing and Yanlin Liu and Sheng Guo and Yu Qiao},
  booktitle={Conference and Labs of the Evaluation Forum},
  year={2017}
}
This paper summarizes SIATMMLAB’s contributions in SEACLEF2017 task [1. [] Key Method In Automatic Fish Identification and Species Recognition task, we exploited different frameworks to detect the proposal boxes of foreground fish, then fine-tuned a pre-trained neural network to classify the fish. In Automatic Frame-level Salmon Identification task, we utilized the BN-Inception [2] network to identify whether a video frame contains salmons or not.
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References

SHOWING 1-10 OF 15 REFERENCES

SeaCLEF 2016: Object Proposal Classification for Fish Detection in Underwater Videos

This working note describes the results of CVG Jena Fulda team for the fish recognition task in SeaCLEF 2016 and shows that classification of background subtraction proposals works much better for fish detection thanbackground subtraction on its own.

Fish Identification in Underwater Video with Deep Convolutional Neural Network: SNUMedinfo at LifeCLEF Fish task 2015

This paper describes the participation at the LifeCLEF Fish task 2015, where foreground detection method with selective search is applied and deep convolutional neural network is used to classify fish species per win- dow.

Very Deep Convolutional Networks for Large-Scale Image Recognition

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.

Knowledge Guided Disambiguation for Large-Scale Scene Classification With Multi-Resolution CNNs

A multi-resolution CNN architecture that captures visual content and structure at multiple levels is proposed and two knowledge guided disambiguation techniques to deal with the problem of label ambiguity are designed.

Locally Supervised Deep Hybrid Model for Scene Recognition

This paper proposes a novel locally supervised deep hybrid model (LS-DHM) that effectively enhances and explores the convolutional features for scene recognition, and proposes an efficient Fisher Convolutional vector (FCV) that successfully rescues the orderless mid-level semantic information of scene image.

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

This work introduces a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals and further merge RPN and Fast R-CNN into a single network by sharing their convolutionAL features.

Deep Residual Learning for Image Recognition

This work presents a residual learning framework to ease the training of networks that are substantially deeper than those used previously, and provides comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth.

LifeCLEF 2017 Lab Overview: Multimedia Species Identification Challenges

The LifeCLEF lab proposes to evaluate challenges around 3 tasks related to multimedia information retrieval and fine-grained classification problems in 3 domains based on large volumes of real-world data.

SSD: Single Shot MultiBox Detector

The approach, named SSD, discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location, which makes SSD easy to train and straightforward to integrate into systems that require a detection component.

ParseNet: Looking Wider to See Better

This work presents a technique for adding global context to deep convolutional networks for semantic segmentation, and achieves state-of-the-art performance on SiftFlow and PASCAL-Context with small additional computational cost over baselines.