Italy

State-of-the-art, high capacity deep neural networks not only require large amounts of labelled training data, they are also highly susceptible to labelling errors in this data, typically resulting in large efforts and costs and therefore limiting the applicability of deep learning. To alleviate this issue, we propose a novel meta training and labelling scheme that is able to use inexpensive unlabelled data by taking advantage of the generalization power of deep neural networks. We show experimentally that by solely relying on one network architecture and our proposed scheme of combining self-training with pseudolabels, both label quality and resulting model accuracy, can be improved significantly. Our method achieves state-of-the-art results, while being architecture agnostic and therefore broadly applicable. Compared to other methods dealing with erroneous labels, our approach does neither require another network to be trained, nor does it necessarily need an additional, highly accurate reference label set. Instead of removing samples from a labelled set, our technique uses additional sensor data without the need for manual labelling. Furthermore, our approach can be used for semi-supervised learning.

ICPR 2020

Iterative Label Improvement: Robust Training by Confidence Based Filtering and Dataset Partitioning

noisy labels

image processing

deep learning

### Welcome to the 7th annual workshop on Naval Applications of Machine Learning!

The NAML workshop is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team.

The goals of NAML are:

* To spread awareness of current machine learning research relevant to Naval applications

* To connect machine learning researchers with experts in military requirements

* To build and strengthen collaborations within the defense research community

Naval Information Warfare Center Pacific is partnering with AFCEA San Diego chapter to host the workshop.

**General Chairs:**

Katie Rainey, Naval Information Warfare Center Pacific

Nicole Isoda, Naval Information Warfare Center Pacific<br><br>

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NAML 2023

The 7th annual workshop on Naval Applications of Machine Learning is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team. The goals of NAML are to spread awareness of current machine learning research relevant to Naval applications, to connect machine learning researchers with experts in military requirements and to build and strengthen collaborations within the defense research community.


poster

ICPR 2020 is the premier international conference on Pattern Recognition, gathering leading researchers from around the world to present their latest achievements in Pattern Analysis and Recognition, applied Artificial Intelligence and Machine Learning. The topic of the year is " Putting Artificial intelligence to work on Patterns". 
For the first time, ICPR has employed a two-round review process similar to journal submissions, with new papers allowed to be submitted in either the first or second round and papers submitted in the first round and not accepted allowed to be revised and re-submitted for second round review. 

In this edition of the **ICPR 2020** Conference, there are 1407 Oral/Poster presentations organized in five Tracks: 
- Artificial Intelligence, Machine Learning for Pattern Analysis; 
- Biometrics, Human Analysis and Behavior Understanding; Computer Vision, Robotics and Intelligent Systems; 
- Document and Media Analysis; and 
- Image and Signal Processing. 

The **ICPR 2020** Main Conference program also includes 7 Keynotes from prominent world-renowned scientists, 8 Tutorials, 28 Demos, 8 Challenges, 2 Panels, as well as an Industry/Sponsor Exhibition. In addition, 41 Workshops on a range of important current and emerging research topics complete the ICPR 2020 program. 



Localizing page elements/objects such as tables, figures, equations, etc. is the primary step in extracting information from document images. We propose a novel end-to-end trainable deep network, (CDeC-Net) for detecting tables present in the documents. The proposed network consists of a multistage extension of Mask R-CNN with a dual backbone having deformable convolution for detecting tables varying in scale with high detection accuracy at higher IoU threshold. We empirically evaluate CDeC-Net on the publicly available benchmark datasets with extensive experiments.
Our solution has three important properties: (i) a single trained model CDeC-Net that performs well across all the popular benchmark datasets; (ii) we report excellent performances across multiple, including higher, thresholds of IoU; (iii) by following the same protocol of the recent papers for each of the benchmarks, we consistently demonstrate the superior quantitative performance. Our code and models are publicly available at https://github.com/mdv3101/CDeCNet for enabling reproducibility of the results.

CDeC-Net: Composite Deformable Cascade Network for Table Detection in Document Images


We propose a novel scheme for improving the word recognition accuracy using word image embeddings. We use a trained text recognizer, which can predict multiple text hypothesis for a given word image. Our fusion scheme improves the recognition process by utilizing the word image and text embeddings obtained from a trained word image embedding network. We propose EmbedNet, which is trained using a triplet loss for learning a suitable embedding space where the embedding of the word image lies closer to the embedding of the corresponding text transcription. The updated embedding space thus helps in choosing the correct prediction with higher confidence. To further improve the accuracy, we propose a plug-and-play module called Confidence based Accuracy Booster (CAB). The CAB module takes in the confidence scores obtained from the text recognizer and Euclidean distances between the embeddings to generate an updated distance vector. The updated distance vector has lower distance values for the correct words and higher distance values for the incorrect words. We rigorously evaluate our proposed method systematically on a collection of books in the Hindi language. Our method achieves an absolute improvement of around 10% in terms of word recognition accuracy.

Improving Word Recognition using Multiple Hypotheses and Deep Embeddings


Recently, with the increasing number of large-scale remote sensing images, the demand for large-scale remote sensing image object classification is growing and attracting the interest of many researchers. Hashing, because of its low memory requirements and high time efficiency, has been widely solve the problem of large-scale remote sensing image. Supervised hashing methods mainly leverage the label information of remote sensing image to learn hash function, however, the similarity of the original feature space cannot be well preserved, which can not meet the accurate requirements for object classification of remote sensing image. To solve the mentioned problem, we propose a novel method named Optimized Projection Supervised Discrete Hashing(OPSDH), which jointly learns a discrete binary codes generation and optimized projection constraint model. It uses an effective optimized projection method to further constraint the supervised hash learning and generated hash codes preserve the similarity based on the data label while retaining the similarity of the original feature space. The experimental results show that OPSDH reaches improved performance compared with the existing hash learning methods and demonstrate that the proposed method is more efficient for operational applications.

Object Classification of Remote Sensing Images Based on Optimized Projection Supervised Discrete Hashing


In this paper we introduce a novel public dataset for developing and benchmarking indoor localization systems. We have selected and 3D mapped a set of representative indoor environments including a large office building, a conference room, a workshop, an exhibition area and a restaurant. Our acquisition pipeline is based on a portable LiDAR SLAM backpack to map the buildings and to accurately track the pose of the user as it moves freely inside them. We introduce the calibration procedures that enable us to acquire and geo-reference live data coming from different independent sensors rigidly attached to the backpack. This has allowed us to collect long sequences of spherical and stereo images, together with all the sensor readings coming from a consumer smartphone and locate them inside the map with centimetre accuracy. The dataset addresses many of the limitations of existing indoor localization datasets regarding the scale and diversity of the mapped buildings; the number of acquired sequences under varying conditions; the accuracy of the ground-truth trajectory; the availability of a detailed 3D model and the availability of different sensor types. It enables the benchmarking of existing and the development of new indoor localization approaches, in particular for deep learning based systems that require large amounts of labeled training data.

RISEdb: a Novel Indoor Localization Dataset


A poster of a robust text recognizer for irregular text.

IBN-STR: A Robust Text Recognizer for Irregular Text in Natural Scenes


This paper presents a parametric variational autoencoder-based human target detection and localization framework working directly with the raw analog-to-digital converter data from the frequency modulated continuous wave radar. We propose a parametrically constrained variational autoencoder, with residual and skip connections, capable of generating the clustered and localized target detections on the range-angle image. Furthermore, to circumvent the problem of training the proposed neural network on all possible scenarios using real radar data, we propose domain adaptation strategies whereby we first train the neural network using ray tracing based model data and then adapt the network to work on real sensor data. This strategy ensures better generalization and scalability of the proposed neural network even though it is trained with limited radar data. We demonstrate the superior detection and localization performance of our proposed solution compared to the conventional signal processing pipeline and earlier state-of-art deep U-Net architecture with range-doppler images as inputs.


Radar Image Reconstruction from Raw ADC Data using a Parametric Variational Autoencoder with Domain Adaptation

We propose insights into feature engineering for musical onset detection, namely about the impact of different window sizes and standardization methods.
Furthermore, we present a universal way of stacking different neural networks to improve the output of a single neural network.

Feature Engineering and Stacked Echo State Networks for Musical Onset Detection

For tree counting tasks, since traditional image processing methods require expensive feature engineering and are not end-to-end frameworks, this will cause additional noise and cannot be optimized overall, so this method has not been widely
used in recent trends of tree counting application. Recently, many deep learning based approaches are designed for this task because of the powerful feature extracting ability. The representative way is bounding box based supervised method, but time-consuming annotations are indispensable for them. Moreover, these methods are difficult to overcome the occlusion or overlap. To solve this problem, we propose a weakly tree counting network (WTCNet) based on deep segmentation network with only point supervision. It can simultaneously complete tree counting with localization and output mask of each tree at the same time. We first adopt
a novel feature extractor network (FENet) to get features of input images, and then an effective strategy is introduced to deal with different mask predictions. In the end, we propose a basic localization guidance accompany with rectification guidance to train the network. We create two different datasets and select an existing challenging plant dataset to evaluate our method on three different tasks. Experimental results show the good performance improvement of our method compared with other existing methods.

Point in: Counting Trees with Weakly Supervised Segmentation Network

Network pruning has become the de facto tool
to accelerate deep neural networks for mobile and edge applications.
Recently, feature-map discriminant based channel
pruning has shown promising results, as it aligns well with the
CNN’s objective of differentiating multiple classes and offers
better interpretability of the pruning decision. However, existing
discriminant-based methods are challenged by computation inefficiency,
as there is a lack of theoretical guidance on quantifying
the feature-map discriminant power. In this paper, we develop a
mathematical formulation to accurately and efficiently quantify
the feature-map discriminativeness, which gives rise to a novel
criterion, Discriminant Information (DI). We analyze the theoretical
property of DI, specifically the non-decreasing property,
that makes DI a valid channel selection criterion. By measuring
the differential discriminant, we can identify and remove those
channels with minimum influence to the discriminant power. The
versatility of DI criterion also enables an intra-layer mixed precision
quantization to further compress the network. Moreover,
we propose a DI-based greedy pruning algorithm and structure
distillation technique to automatically decide the pruned structure
that satisfies certain resource budget, which is a common
requirement in reality. Extensive experiments demonstrate the
effectiveness of our method: our pruned ResNet50 on ImageNet
achieves 44% FLOPs reduction without any Top-1 accuracy loss
compared to unpruned model.

A Discriminant Information Approach to Deep Neural Network Pruning

Edge devices use neural network models learnt on cloud to predict labels of its data records, which may lead to incorrect predictions especially for records that are different from the data involved in the training process, i.e., out-of-distribution (OOD) records. However, recent efforts in OOD detection either require the retraining of the model or assume the existence of a certain amount of OOD records, thus limiting their application in practice.
In this work, we propose a novel OOD detection method (named as NeuralFP) without requiring any access to OOD records, which constructs non-linear fingerprints of neural network models memorizing the information of data observed during training. The key idea of NeuralFP is to exploit the difference in how the neural network model responds to data records in its training set versus data records that are anomalous. Specifically, NeuralFP builds autoencoders for each layer of the neural network model and then carefully analyzes the error distribution of the autocoders in reconstructing the training set to identify OOD records. Through extensive experiments on multiple real-world datasets, we show the effectiveness of NeuralFP in detecting OOD records as well as its advantages over previous approaches. Furthermore, we provide useful guidelines for parameter selection in the practical adoption of NeuralFP.

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CDeC-Net: Composite Deformable Cascade Network for Table Detection in Document Images

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