Italy

Class imbalance is a widely existed and challenging problem in real-world applications such as disease diagnosis, fraud detection, network intrusion detection and so on. Due to the scarce of data, it could significantly deteriorate the accuracy of classification. To address this challenge, we propose a novel Imbalanced Data Augmentation Generative Adversarial Networks (GAN) named IDA-GAN as an augmentation tool to deal with the imbalanced dataset. This is a great challenge because it is hard to train a GAN model under this situation. We address this issue by coupling variational autoencoder along with GAN training. In this paper, specifically, we introduce the variational autoencoder to learn the majority and minority class distributions in the latent space, and use the generative model to utilize each class distribution for the subsequent GAN training. The generative model learns useful features to generate target minority-class samples. Compared with the state-of-the-art GAN model, the experimental results demonstrate that our proposed IDA-GAN could generate more diverse minority samples with better qualities, and it could benefits the imbalanced classification task in terms of several widely-used evaluation metrics on five benchmark datasets: MNIST, Fashion-MNIST, SVHN, CIFAR-10 and GTSRB.

ICPR 2020

IDA-GAN: A Novel Imbalanced Data Augmentation GAN

gan.

imbalanced learning

variational autoencoder

data augmentation

We are pleased to announce the Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI-24), which will be held in Vancouver, British Columbia at the Vancouver Convention Centre – West Building from 20-27 February, 2024.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-24 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

We expect for AAAI-24 to be an in-person conference – one author of all accepted papers will be expected to present work in person unless there are exceptional circumstances that prevent this.<br><br><br><br>

In order to access the AAAI-24 event page you need to register [here](https://aaai.org/aaai-conference/registration/)

AAAI 2024

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. 

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. 



Couples suffering from infertility issues often use
In Vitro Fertilization (IVF) treatment to give birth. Continuous
embryo monitoring with time-lapse imaging enables time-based
development metrics alongside visual features to assess
an embryo’s quality before transfer. Tracking embryonic cell
development provides valuable information about its likelihood
of leading to a positive pregnancy. Automating this task is
challenging due to cell overlap, occlusion, and variation. In this
paper, cell stage is identified by counting detected cell centroids
in early embryo time-lapse sequences. A convolutional regression
network is trained on Gaussian-annotated centroid maps to
localize cell centroids. Added network attention blocks encode
spatio-temporal relationship in time-lapse sequences to emphasize
relevant features in the current frame based on previous frame
and cell movement. The proposed approach was applied to 108
embryo sequences including 1- to 4-cell stage, achieving cell
centroid localization distance error of 3.98 pixels, cell detection
rate 80.9%, and cell counting accuracy of 80.2%.

Human Embryo Cell Centroid Localization and Counting in Time-Lapse Sequences


This paper considers the problem of sparse signal recovery where there is a structure in the signal. Efficient recovery schemes can be designed to leverage the signal structure. Following the model-based compressive sensing framework, we have developed an efficient algorithm for both head and tail approximations for the model-projection problem. The problem is modeled as a constrained graph optimization problem, which is an NP-hard optimization problem. Solving the NP-hard optimization program is then transformed to solving a linear program and finding a randomized algorithm to find an integral solution. The integral solution is optimal-in-expectation. The algorithm is proved to have the same geometric convergence as previous work. The algorithm has been tested on various compressing matrices. The proposed algorithm demonstrated improved recoverability and used fewer number of iterations to recover the signal.

A Randomized Algorithm for Sparse Recovery


We propose a deep neural network-based method to recover dynamic online trajectories from offline handwritten Japanese kanji character images. It is a challenging task since Japanese kanji characters consist of multiple strokes. Our proposed model has three main components: Convolutional Neural Network-based encoder, Long Short-Term Memory Network-based decoder with an attention layer, and Gaussian Mixture Model (GMM). The encoder focuses on feature extraction while the decoder refers to the extracted features and generates time-sequences of GMM parameters. The attention layer is the key component for trajectory recovery. The GMM provides robustness to style variations so that the proposed model does not overfit to training samples. In the experiments, the proposed method is evaluated by both visual verification and handwritten character recognition. This is the first attempt to use online recovered trajectories to help improve the performance of
offline handwriting recognition. Although the visual verification reveals some problems, the recognition experiments demonstrate the effect of trajectory recovery in improving the accuracy of offline handwritten character recognition when online recognition of the recovered trajectories are combined.

Online trajectory recovery from offline handwritten Japanese kanji characters of multiple strokes

In this paper, we have addressed the problem of automated detection of safe zone(s) for helicopter landing in hazardous environments from videos captured by an Unmanned Aerial Vehicle (UAV). The unconstrained motion of the video capturing drone (the UAV in our case) makes the problem further difficult. The solution pipeline consists of natural landmark detection and tracking, stereo-pair generation using constrained graph clustering, digital terrain map construction and safe landing zone detection. The main methodological contribution lies in mathematically formulating epipolar constraint and then using it in a Minimum Spanning Tree (MST) based graph clustering approach. We have also made publicly available AHL (Autonomous Helicopter Landing) dataset, a new aerial video dataset captured by a drone, with annotated ground-truths. Experimental comparisons with other competing clustering methods i) in terms of Dunn Index and Davies Bouldin Index as well as ii) for frame-level safe zone detection in terms of F-measure and confusion matrix clearly demonstrate the effectiveness of the proposed formulation.

Unconstrained Vision Guided UAV Based Safe Helicopter Landing

Face normalization can be a crucial step when handling generic face recognition. We propose the Pose and Illumination Normalization (PIN) framework with contrast data learning for face normalization. The PIN framework is designed to learn the transformation from a source set to a target set. The source set and the target set compose a contrastive data set for learning. The source set contains faces collected in the wild and thus covers a wide range of variation across illumination, pose, expression and other variables. The target set contains face images taken under controlled conditions and all faces are in frontal pose and balanced in illumination. The PIN framework is composed of an encoder, a decoder and two discriminators. The encoder is made of a state-of-the-art face recognition network and acts as a facial feature extractor, which is not updated during training. The decoder is trained on both the source and target sets, and aims to learn the transformation from the source set to the target set; and therefore, it can transform an arbitrary face into a illumination and pose normalized face. The discriminators are trained to ensure the photo-realistic quality of the normalized face images generated by the decoder. The loss functions employed in the decoder and discriminators are appropriately designed and weighted for yielding better normalization outcomes and recognition performance. We verify the performance of the propose framework on several benchmark databases, and compare with state-of-the-art approaches.

Contrastive Data Learning for Facial Pose and Illumination Normalization


This paper studies the problem of Person Re-Identification (ReID)
for large-scale applications. Recent research efforts have been devoted
to building complicated part models, which introduce considerably
high computational cost and memory consumption, inhibiting its practicability
in large-scale applications. This paper aims to develop a novel learning
strategy to find efficient feature embeddings while maintaining the
balance of accuracy and model complexity. More specifically, we find
by enhancing the classical triplet loss together with cross-entropy
loss, our method can explore the hard examples and build a discriminant
feature embedding yet compact enough for large-scale applications.
Our method is carried out progressively using Bayesian optimization,
and we call it the Progressive Learning Algorithm (PLA). Extensive
experiments on three large-scale datasets show that our PLA is comparable
or better than the state-of-the-arts. Especially, on the challenging
Market-1501 dataset, we achieve Rank-1=94.7%/mAP=89.4% while saving
at least 30\% parameters than strong part models.

JECL: Joint Embedding and Cluster Learning for Image-Text Pairs


As a challenging task of Natural Language Processing(NLP), zero-shot text classification has attracted more and more attention recently. It aims to detect classes that the model has never seen in the training set. For this purpose, a feasible way is to construct a connection between the seen and unseen classes by semantic extension and classify the unseen classes by information propagation over the connection. Although many related zero-shot text classification methods have been exploited, how to realize semantic extension properly and propagate information effectively are far from solved. In this paper, we propose a novel zero-shot text classification method called Semantically Extended Graph Convolutional Network (SEGCN). In the proposed method, the semantic category knowledge from ConceptNet is utilized to semantic extension for linking seen classes to unseen classes and constructing a graph of all categories. Then, we build upon Graph Convolutional Network (GCN) for predicting the textual classifier for each category, which transfers the category knowledge by the convolution operators on the constructed graph and is trained in a semi-supervised manner using the samples of the seen classes. The experimental results on Dbpedia and 20newsgroup datasets show that our method outperforms the state of the art zero-shot text classification methods.

Zero-Shot Text Classification with Semantically Extended Graph Convolutional Network

Building footprint extraction in remote sensing data
benefits many important applications, such as urban
planning and population estimation. Recently, rapid
development of Convolutional Neural Networks (CNNs) and
open-sourced high resolution satellite building image
datasets have pushed the performance boundary further for
automated building extractions. However, CNN approaches
often generate imprecise building morphologies including
noisy edges and round corners. In this paper, we leverage
the performance of CNNs, and propose a module that uses
prior knowledge of building corners to create angular and
concise building polygons from CNN segmentation outputs. We
describe a new transform, Relative Gradient Angle Transform
(RGA Transform) that converts object contours from time vs.
space to time vs. angle. We propose a new shape descriptor,
Boundary Orientation Relation Set (BORS), to describe angle
relationship between edges in RGA domain, such as
orthogonality and parallelism. Finally, we develop an
energy minimization framework that makes use of the angle
relationship in BORS to straighten edges and reconstruct
sharp corners, and the resulting corners create a polygon.
Experimental results demonstrate that our method refines
CNN output from a rounded approximation to a more clear-cut
angular shape of the building footprint.

Quantization in Relative Gradient Angle Domain For Building Polygon Estimation

For image ranking tasks with naturally continuous output, such as age and scenicness estimation, it is common to discretize the label range and apply methods from (ordered)classification analysis. In this paper, we propose a data-driven approach for simultaneous representation learning and label discretization. Compared to arbitrarily selecting thresholds, we seek to learn thresholds and image representations by minimizing a novel loss function in an end-to-end model. We demonstrate our combined approach on a variety of image ranking tasks and demonstrate that it outperforms task-specific methods. Additionally, our learned partitioning scheme can be transferred to improve methods that rely on discretization.

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Human Embryo Cell Centroid Localization and Counting in Time-Lapse Sequences

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