Italy


Image stitching describes the process of reconstruction of a high-resolution image by combining multiple images. Using a scanning electron microscope as the image source, individual images will show patterns in a nm dimension, whereas the combined image may cover an area of several mm$^2$. The recovery of the physical layout of modern semiconductor products manufactured in advanced technologies nodes down to 22~nm requires a perfect stitching process with no deviation with respect to the original design data, as any stitching error will result in failures during the reconstruction of the electrical design. In addition, the recovery of the complete design requires the acquisition of all individually layers of a semiconductor device. The layers represent a 3D structure with interconnections defining error limits on the stitching error for each individual scanned image mosaic. An advanced stitching and alignment process is presented, enabling a true geometrical layout recovery in nanoscale dimensions, which is also applied to and evaluated for other use cases from biological applications.

ICPR 2020

Recovery of 2D and 3D layout information through an advanced image stitching algorithm using scanning electron microscope images

electron microscope

semiconductor technology

image stitching

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-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
AAAI is providing you with a conference planner, which you can use to help organize your itinerary of activities. This includes talks to attend in person, talks to attend remotely, breaks with colleagues and your site seeing activities. To access this conference planner, please go to [https://aaai-2023.takemobi.io](https://aaai-2023.takemobi.io).

In order to access this site, you need to register. If you haven't already, please register [here](https://aaai.org/Conferences/AAAI-23/registration/).


AAAI 2023

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. 


In recent years, Artificial Neural Networks (ANNs) pruning has become the focal point of many researches, due to the extreme overparametrization of such models. This has urged the scientific world to investigate methods for the simplification of the structure of weights in ANNs, mainly in an effort to reduce time for both training and inference. Frankle and Carbin, and later Renda, Frankle, and Carbin introduced and refined an iterative pruning method which is able to effectively prune the network of a great portion of its parameters with little to no loss in performance. On the downside, this method requires a large amount of time for its application, since, for each iteration, the network has to be trained for (almost) the same amount of epochs of the unpruned network. In this work, we show that, for a limited setting, if targeting high overall sparsity rates, this time can be effectively reduced for each iteration, save for the last one, by more than 50%, while yielding a final product (i.e., final pruned network) whose performance is comparable to the ANN obtained using the existing method.

Speeding-up pruning for Artificial Neural Networks:
Introducing Accelerated Iterative Magnitude Pruning

Scene flow estimation based on stereo sequences is a comprehensive task relevant to disparity and optical flow. Some existing methods are time-consuming and often fail in the presence of reflective surfaces. In this paper, we propose a two-stage adaptive object scene flow estimation method using a hybrid CNN-CRF model (ACOSF), which benefits from high-quality features and the structured modelling capability. Meanwhile, in order to balance the computational efficiency and accuracy, we employ adaptive iteration for energy function optimization, which is flexible and efficient for various scenes. Besides, we utilize high-quality pixel selection to reduce the computation time with only a slight decrease in accuracy. Our method achieves competitive results with the state-of-the-art, which ranks second on the challenging KITTI 2015 scene flow benchmark.

Two-Stage Adaptive Object Scene Flow Using Hybrid CNN-CRF Model


Most of the existing methods to localize (sub) image relations – a subclass of near-duplicate retrieval techniques – rely on the distinctiveness of matched features of the images being compared. These sets of matching features usually include a proportion of outliers, i.e. features linking non-matching regions. In approaches that are designed for retrieval purposes only, these false matches usually have a minor impact on the final ranking. However, if also a localization of regions and corresponding image transformations should be computed, these false matches often have a more significant impact. We present a novel outlier filtering approach "ECOTA" to classify outliers and describe the correlation between image regions without any prior knowledge about the content of images. The comparison with the state of the art approaches indicates that our approach is more robust than those approaches and is able to detect correlation even when most matches are outliers. Moreover, ECOTA reduces significantly the pre-processing time to filter the matches.

Localization and Transformation Reconstruction of Image Regions: An Extended Congruent Triangles Approach

Any high-dimensional data arising from practical applications usually contains irrelevant features that may impact on the performance of existing subspace
clustering methods. This paper proposes a novel subspace clustering method which reconstructs the feature matrix by the means of unsupervised feature selection (UFS) to achieve a better dictionary for subspace clustering (SC). Different from most existing clustering methods, the proposed approach uses the reconstructed feature matrix as the dictionary rather than the original data matrix. As the feature matrix reconstructed by representative features is more discriminative and closer to the ground-truth, it results in improved performance. The corresponding non-convex optimization problem is effectively solved using the half-quadratic and augmented Lagrange multiplier methods. Extensive experiments on four real datasets demonstrate the effectiveness of the proposed method.

Subspace Clustering via Joint Unsupervised Feature Selection


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.

A Multi-Task Neural Network for Action Recognition with 3D Key-Points


Signal Generation using 1d Deep Convolutional Generative Adversarial Networks for Fault Diagnosis of Electrical Machines


The main topic of this work is on the detection of counterfeit documents and especially banknotes. We propose an end-to-end learning model using a deep learning approach based on Adapnet++ which manages feature extraction at multiple scale levels using several residual units. Unlike previous models based on regions of interest (ROI) and high-resolution documents, our network is feed with simple input images (i.e., a single patch) and we do not need high resolution images. Besides, discriminative regions can be visualized at different scales. Our network learns by itself which regions of interest predict the better results. Experimental results show that we are competitive compared with the state-of-the-art and our deep neural network has good ability to generalize and can be applied to other kind of documents like identity or administrative one.

Documents Counterfeit Detection Through a Deep Learning Approach

The garment transfer problem comprises two tasks: learning to separate a person's body (pose, shape, color) from their clothing (garment type, shape, style) and then generating new images of the wearer dressed in arbitrary garments. We present GarmentGAN, a new algorithm that performs image-based garment transfer through generative adversarial methods. The GarmentGAN framework allows users to virtually try-on items before purchase and generalizes to various apparel types. GarmentGAN takes two images as input: a picture of the target fashion item and an image containing the customer. The output is a synthetic image wherein the customer is wearing the target apparel. To make the generated image look photo-realistic, we employ the use of novel generative adversarial techniques. GarmentGAN improves on existing methods in the realism of generated imagery and solves various problems related to self-occlusions. Our proposed model incorporates additional information during training, utilizing both segmentation maps and body keypoint information. We demonstrate GarmentGAN outperforms current fashion transfer techniques through qualitative evaluation and standard image synthesis metrics.

GarmentGAN: Photo-realistic Adversarial Fashion Transfer

At the end of content based image search


Semantic segmentation requires both a large receptive field and accurate spatial information. Although existing methods based on a fully convolutional network have greatly improved the accuracy, the prediction results still do not show
satisfactory when parsing small objects and boundary regions. We propose a refinement algorithm to improve the result generated by the front network. Our method takes a modified double-branches network to generate both segmentation masks and semantic boundaries, which serve as refinement algorithms’ input. We creatively introduce information entropy to represent the confidence of the neural network’s prediction corresponding to each pixel. The information entropy combined with the semantic boundary can capture those unpredictable pixels with
low-confidence through Monte Carlo sampling. Each selected pixel will serve as the initial seed for directed local search and refinement. According to the initial seed, our purpose is tantamount to searching the neighbor high-confidence regions,
and the re-labelling approach is based on high-confidence results. Remarkably, our method adopts a directed regional search strategy based on gradient descent to find the high-confidence region effectively. Our method can be flexibly embedded into
the existing encoder backbone and further improve the state of the art method’s accuracy both on Cityscapes and PASCAL VOC datasets. In evaluating some small objects, our method surpasses most of the state of the art methods.

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Recovery of 2D and 3D layout information through an advanced image stitching algorithm using scanning electron microscope images

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Speeding-up pruning for Artificial Neural Networks: Introducing Accelerated Iterative Magnitude Pruning

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