Italy

Neural Architecture Search (NAS) has gained attraction due
to superior classification performance. Differential
Architecture Search (DARTS) is a computationally light
method. To limit computational resources DARTS makes
numerous approximations. These approximations result in
inferior performance. We propose to fine-tune DARTS using
fixed operations as they are independent of these
approximations. Our method offers a good trade-off between
the number of parameters and classification accuracy. Our
approach improves the top-1 accuracy on Fashion-MNIST,
CompCars and MIO-TCD datasets by 0.56%, 0.50%, and 0.39%,
respectively compared to the state-of-the-art approaches.
Our approach performs better than DARTS, improving the
accuracy by 0.28%, 1.64%, 0.34%, 4.5%, and 3.27% compared
to DARTS, on CIFAR-10, CIFAR-100, Fashion-MNIST, CompCars,
and MIO-TCD datasets, respectively.

ICPR 2020

Fine-Tuning DARTS for Image Classification

fine- grained image classification.

attention module

image classification

neural architecture search

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. 


Deep neural networks forget previously learnt tasks when they are faced with learning new tasks. This is called catastrophic forgetting. Rehearsing the neural network with the training data of the previous task can protect the network from catastrophic forgetting. Since rehearsing requires the storage of entire previous data, Pseudo rehearsal was proposed, where samples belonging to the previous data are generated synthetically for rehearsal. In an image classification setting, while current techniques try to generate synthetic data that is photo-realistic, we demonstrated that Neural networks can be rehearsed on data that is not photo-realistic and still achieve good retention of the previous task. We also demonstrated that forgoing the constraint of having photo realism in the generated data can result in a significant reduction in the consumption of computational and memory resources for pseudo rehearsal.

Pseudo rehearsal using non photo-realistic images


Environmental protection is one of the main challenges facing society nowadays. Even with constantly growing awareness, not all of the sorting can be done by people themselves - the differences between materials are not visible to the human eye. For that reason, we present the use of a hyperspectral camera as a capture device, which allows us to obtain the full spectrum of the material. In this work, we propose a method for efficient recognition of the substance of an item. We conducted several experiments and analysis of the spectra of different materials in different conditions on a special measuring stand. That enabled the identification of the best features, which can later be used during classification, which was confirmed during the extensive testing procedure.

Hyperspectral Imaging for Analysis and Classification of Plastic Waste


We present a new encoding method LODEC and a new model LODENet for Chinese/Japanese OCR.
The results achieve SOTA results on CASIA, SCUT-EPT, and a Japanese dataset.

LODENet: A Holistic Approach to Offline Handwritten Chinese and Japanese Text Line Recognition

Several typical types of retinopathy are major causes
of blindness. However, early detection of retinopathy is quite not easy since few symptoms are observable in the early stage, attributing to the development of non-mydriatic retinal cameras, these cameras produce high-resolution retinal fundus images that provide the possibility of Computer-Aided-Diagnosis (CAD) via deep learning to assist diagnosing retinopathy. Deep learning algorithms usually rely on a large number of labeled images that are expensive and time-consuming to obtain in the medical imaging area. Moreover, the random distribution of various lesions that often vary greatly in size also brings significant challenges to learn discriminative information from high-resolution fundus images. In this paper, we present generative adversarial networks simultaneously equipped with a ”good” generator and a ”bad” generator (GBGANs) to make up for the incomplete data distribution given limited fundus images. To improve the generative feasibility of the generator, we introduce a pre-trained feature extractor to acquire condensed features for each fundus image in advance. Experimental results on integrated three public iChallenge datasets show that the proposed GBGANs could fully utilize the available fundus images to identify retinopathy with little label cost.

Semi-Supervised Generative Adversarial Networks with a Pair of Complementary Generators for Retinopathy Screening


The recent progress of deep convolutional neural networks has enabled great success in single image super-resolution (SISR) and many other vision tasks. Their performances are also being increased by deepening the networks and developing more sophisticated network structures. However, finding an optimal structure for the given problem is a difficult task, even for human experts. For this reason, neural architecture search (NAS) methods have been introduced, which automate the procedure of constructing the structures. In this paper, we expand the NAS to the super-resolution domain and find a lightweight densely connected network named DeCoNASNet. We use a hierarchical search strategy to find the best connection with local and global features. In this process, we define a complexity-based penalty for solving image super-resolution, which can be considered a multi-objective problem. Experiments show that our DeCoNASNet outperforms the state-of-the-art lightweight super-resolution networks designed by handcraft methods and existing NAS-based design.

1325 - Neural Architecture Search for Image Super-Resolution Using Densely Connected Search Space: DeCoNAS

The “2019 Automatic Speaker Verification Spoofing And Countermeasures Challenge” (ASVspoof) competition aimed to facilitate the design of highly accurate voice spoofing attack detection systems. the competition did not emphasize model
complexity and latency requirements; such constraints are strict and integral in real-world deployment. Hence, most of the top performing solutions from the competition all used an ensemble approach, and combined multiple complex deep learning models to maximize detection accuracy – this kind of approach would sit uneasily with real-world deployment constraints. To design a lightweight system, we combined the notions of skip connection (from ResNet) and max feature map (from Light CNN), and evaluated the accuracy of the system using the ASVspoof 2019
dataset. With an optimized constant Q transform (CQT) feature, our single model achieved a replay attack detection equal error rate (EER) of 0.37% on the evaluation set, surpassing the top ensemble system from the competition that achieved an EER of 0.39%

ResMax: Detecting Voice Spoofing Attacks with Residual Network and Max Feature Map


In recent years, cross-media retrieval has drawn
considerable attention due to the exponential growth of multimedia
data. Many hashing approaches have been proposed for the
cross-media search task. However, there are still open problems
that warrant investigation. For example, most existing supervised
hashing approaches employ a binary label matrix, which achieves
small margins between wrong labels (0) and true labels (1). This
may affect the retrieval performance by generating many false
negatives and false positives. In addition, some methods adopt
a relaxation scheme to solve the binary constraints, which may
cause large quantization errors. There are also some discrete
hashing methods that have been presented, but most of them are
time-consuming. To conquer these problems, we present a label
relaxation and discrete matrix factorization method (LRMF) for
cross-modal retrieval. It offers a number of innovations. First
of all, the proposed approach employs a novel label relaxation
scheme to control the margins adaptively, which has the benefit of
reducing the quantization error. Second, by virtue of the proposed
discrete matrix factorization method designed to learn the binary
codes, large quantization errors caused by relaxation can be
avoided. The experimental results obtained on two widely-used
databases demonstrate that LRMF outperforms state-of-the-art
cross-media methods.

Fast Discrete Cross-Modal Hashing Based on Label Relaxation and Matrix Factorization

Visual voice activity detection (V-VAD) uses visual features to predict whether a person is speaking or not. VVAD is useful whenever audio VAD (A-VAD) is inefficient either because the acoustic signal is difficult to analyze or because it is simply missing. We propose two deep architectures for V-VAD, one based on facial landmarks and one based on optical flow. Moreover, available datasets, used for learning and for testing VVAD, lack content variability. We introduce a novel methodology to automatically create and annotate very large datasets inthe-wild – WildVVAD – based on combining A-VAD with face detection and tracking. A thorough empirical evaluation showsthe advantage of training the proposed deep V-VAD models with this dataset.

Learning Visual Voice Activity Detection with an Automatically
Annotated Dataset


In this paper we benchmark different types of
cameras and evaluate their performance in terms of reliable
localization reliability and precision in Visual Simultaneous
Localization and Mapping (vSLAM). Such benchmarking is
merely found for visual odometry, but never for vSLAM. Existing
studies usually compare several algorithms for a given camera.
The evaluation methodology we propose is applied to the recent
OpenVSLAM framework. The latter is versatile enough to natively
deal with perspective, fisheye, 360 cameras in a monocular or
stereoscopic setup, an in RGB or RGB-D modalities. Results
in various sequences containing light variation and scenery
modifications in the scene assess quantitatively the maximum
localization rate for 360 vision. In the contrary, RGB-D vision
shows the lowest localization rate, but highest precision when
localization is possible. Stereo-fisheye trades-off with localization
rates and precision between 360 vision and RGB-D vision.
The dataset with ground truth will be made available in open
access to allow evaluating other/future vSLAM algorithms with
respect to these camera types.

Benchmarking Cameras for OpenVSLAM Indoors​


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.

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Pseudo rehearsal using non photo-realistic images

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