Italy



In this presentation, we will discuss the utilization of visual attention (saliency) for object detection and tracking and briefly summarise 1) How a visual attention map such as subjectness attention or saliency map and an objectness attention map can facilitate region proposal generation in a 2-stage object detector; 2) How a visual attention map can be used for tracking multiple objects using a sequential Monte Carlo probability hypothesis density (PHD) filter. The proposed neural network for simultaneously detect objects and generate objectness and subjectness maps to save computational power is also discussed in this presentation. The experiments are conducted on KITTI and DETRAC datasets. The use of visual attention and hierarchical features has shown a considerable improvement of ≈8% in object detection which effectively increased tracking performance by ≈4% on KITTIdataset.

ICPR 2020

Utilising Visual Attention Cues for Vehicle Detection and Tracking

visual attention map

particle-phd filter

visual saliency

object tracking

object detection

Naval Information Warfare Center Pacific is partnering with AFCEA San Diego chapter to host the workshop.

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

**Watch this video to hear about the AFCEA purpose.**

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NAML 2021

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. 



Text segmentation, the task of dividing a document into sections, is often a prerequisite for performing additional natural language processing tasks. Existing text segmentation methods have typically been developed and tested using clean, narrative-style text with segments containing distinct topics. Here we consider a challenging text segmentation task: dividing newspaper marriage announcement lists into units of one announcement each. In many cases the information is not structured into sentences, and adjacent segments are not topically distinct from each other. In addition, the text of the announcements, which is derived from images of historical newspapers via optical character recognition, contains many typographical errors. As a result, these announcements are not amenable to segmentation with existing techniques. We present a novel deep learning-based model for segmenting such text and show that it significantly outperforms an existing state-of-the-art method on our task.

Segmenting Messy Text: Detecting Boundaries in Text Derived from Historical Newspaper Images


A Lite BERT (ALBERT) has been introduced to scale up deep bidirectional representation learning for natural languages. Due to the lack of pretrained ALBERT models for Korean language, the best available practice is the multilingual model or resorting back to the any other BERT-based model. In this paper, we develop and pretrain KoreALBERT, a monolingual ALBERT model specifically for Korean language understanding. We introduce a new training objective, namely Word Order Prediction (WOP), and use alongside the existing MLM and SOP criteria to the same architecture and model parameters. Despite having significantly fewer model parameters (thus, quicker to train), our pretrained KoreALBERT outperforms its BERT counterpart on 6 different NLU tasks. Consistent with the empirical results in English by Lan et al., KoreALBERT seems to improve downstream task performance involving multi-sentence encoding for Korean language. The pretrained KoreALBERT is publicly available to encourage research and application development for Korean NLP.

KoreALBERT: Pretraining a Lite BERT Model for Korean Language Understanding


VR sickness is one of the main reasons that hinder the growth of VR. Since the level of VR sickness is in proportion to the degree of motion in VR content, it would be benefitted if the level of VR sickness can be predicted based on VR contents. In this paper, we propose a novel method with perception prior and hybrid temporal features to predict the occurrence of VR sickness.

VR Sickness Assessment with Perception Prior and Hybrid Temporal Features

In this paper, we tackle the task of automatically analyzing 3D volumetric scans obtained from computed tomography(CT) devices. In particular, we address a particular task for which data is very limited: the segmentation of ancient Egyptian mummies CT scans.We aim at digitally unwrapping the mummy and identify different segments such as body, bandages and jewelry. The problem is complex because of the lack of annotated data for the different semantic regions to segment, thus discouraging the use of strongly supervised approaches. We, therefore, propose a weakly supervised and efficient interactive segmentation method to solve this challenging problem. After segmenting the wrapped mummy from its exterior region using histogram analysis and template matching, we first design a voxel distance measure to find an approximate solution for the body and bandage segments. Here, we use geodesic distances since voxel features as well as spatial relationship among voxels is incorporated in this measure. Next, we refine the solution using a GrabCut based segmentation together with a tracking method on the slices of the scan that assigns labels to different regions in the volume, using limited supervision in the form of scribbles drawn by the user. The efficiency of the proposed method is demonstrated using visualizations and validated through quantitative measures and qualitative unwrapping of the mummy

Weakly Supervised Geodesic Segmentation of Egyptian Mummy CT Scans

Obtaining a meaningful, interpretable yet compact representation of the immediate surroundings of an autonomous vehicle is paramount for effective operation as well as safety. This paper proposes a solution to this by representing semantically important objects from a top-down, ego-centric bird’s eye view. The novelty in this work is from formulating this problem as an adversarial learning task, tasking a generator model to produce bird’s eye view representations which are plausible enough to be mistaken as a ground truth sample. This is achieved by using a Wasserstein Generative Adversarial Network based model conditioned on object detections from monocular RGB images and the corresponding bounding boxes. Extensive experiments show our model is more robust to novel data compared to strictly supervised benchmark models, while being a fraction of the size of the next best.

DeepBEV: A Conditional Adversarial Network for Bird's Eye View Generation

Active learning emerged as an alternative to alleviate
the effort to label huge amount of data for data-hungry applications (such as image/video indexing and retrieval, autonomous
driving, etc.). The goal of active learning is to automatically select
a number of unlabeled samples for annotation (according to a
budget), based on an acquisition function, which indicates how
valuable a sample is for training the model. The learning loss
method is a task-agnostic approach which attaches a module
to learn to predict the target loss of unlabeled data, and
select data with the highest loss for labeling. In this work,
we follow this strategy but we define the acquisition function
as a learning to rank problem and rethink the structure of
the loss prediction module, using a simple but effective listwise
approach. Experimental results on four datasets demonstrate that
our method outperforms recent state-of-the-art active learning
approaches for both image classification and regression tasks.

Learning to Rank for Active Learning: A Listwise
Approach


This paper addresses the problem of selecting appearance features for multiple object tracking (MOT) in urban scenes. Over the years, a large number of features has been used for MOT. However, it is not clear whether some of them are better than others. Commonly used features are color histograms, histograms of oriented gradients, deep features from convolutional neural networks and re-identification (ReID) features. In this study, we assess how good these features are at discriminating objects enclosed by a bounding box in urban scene tracking scenarios. Several affinity measures, namely the L1, L2 and the Bhattacharyya distances, Rank-1 counts and the cosine similarity, are also assessed for their impact on the discriminative power of the features. Results on several datasets show that features from ReID networks are the best for discriminating instances from one another regardless of the quality of the detector. If a ReID model is not available, color histograms may be selected if the detector has a good recall and there are few occlusions; otherwise, deep features are more robust to detectors with lower recall.

An Empirical Analysis of Visual Features for Multiple Object Tracking in Urban Scenes


Zero-Shot Learning(ZSL) techniques could classify
a completely unseen class, which it has never seen before
during training. Thus, making it more apt for any real-life
classification problem, where it is not possible to train a system
with annotated data for all possible class types. This work
investigates recognition of word images written in Bengali Script
in a ZSL framework. The proposed approach performs Zero-Shot
word recognition by coupling deep learned features procured
from various CNN architectures along with 13 basic shapes/stroke
primitives commonly observed in Bengali script characters. As
per the notion of ZSL framework those 13 basic shapes are
termed as “Signature/Semantic Attributes”. The obtained results
are promising while evaluation was carried out in a Five-
Fold cross-validation setup dealing with samples from 250 word
classes.

Recognizing Bengali Word Images - A Zero-Shot
Learning Perspective


Object detection based on convolutional neural networks is a hot research topic in computer vision. The illumination component in the image has a great impact on object detection, and it will cause a sharp decline in detection performance under low-light conditions. Using low-light image enhancement technique as a pre-processing mechanism can improve image quality and obtain better detection results. However, due to the complexity of low-light environments, the existing enhancement methods may have negative effects on some samples. Therefore, it is difficult to improve the overall detection performance in low-light conditions. In this paper, our goal is to use image enhancement to improve object detection performance rather than perceptual quality for humans. We propose a novel framework that combines low-light enhancement and object detection for end-to-end training. The framework can dynamically select different enhancement subnetworks for each sample to improve the performance of the detector. Our proposed method consists of two stage: the enhancement stage and the detection stage. The enhancement stage dynamically enhances the low-light images under the supervision of several enhancement methods and output corresponding weights. During the detection stage, the weights offers information on object classification to generate high-quality region proposals and in turn result in accurate detection. Our experiments present promising results, which show that the proposed method can significantly improve the detection performance in low-light environment.

Dynamic Low-Light Image Enhancement for Object Detection via End-to-End Training

We present several extensions to an existing two-stage architecture for segmentation-based industrial defect detection. We enable end-to-end training by careful balancing of both losses and by manipulating gradient flow through the networks during learning. Employing weighted segmentation loss by applying the distance transform algorithm to the coarse region-based labels allows us to reduce the annotation requirements. Frequency-of-use based sampling scheme is used to address the issues of sampling in heavily unbalanced datasets. Combining all the extensions allows the proposed method to achieve a 100% detection rate on two surface defect detection tasks. We perform an extensive ablation study of our contributions on three datasets.

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