Italy

Image classification is a fundamental task in computer vision. A variety of deep learning models based on the Convolutional Neural Network (CNN) architecture have proven to be an efficient solution. Numerous improvements have been proposed over the years, where broader, deeper, and denser networks have been constructed. However, the atomic operation for these models has remained a linear unit (single neuron). In this work, we pursue an alternative dimension by hypothesizing the atomic operation to be performed by a quadratic unit. We construct convolutional layers using quadratic neurons for feature extraction and subsequently use dense layers for classification. We perform analysis to quantify the implication of replacing linear neurons with quadratic units. Results show a keen improvement in classification accuracy with quadratic neurons over linear neurons.

ICPR 2020

CQNN: Convolutional Quadratic Neural Networks

convolutional quadratic neural networks

image classification

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. 


Due to the recent breakthroughs of deep generative models, the fake faces, also known as deepfake which has been abused to deceive the general public, can be easily produced at scale and in very high fidelity. Many works focus on exploring various network architectures or various artifacts produced by deep generative models. Instead, in this work, we focus on the loss functions which have been shown to play a significant role in the context of face recognition. We perform a thorough study of several recent state-of-the-art losses commonly used in face recognition task for deepfake classification and detection since the current deepfake is highly related to face generation. With extensive experiments on the challenging FaceForensic++ and Celeb-DF datasets, the evaluation results provide a clear overview of the performance comparisons of different loss functions and generalization capability across different deepfake data.

An Experimental Evaluation of Recent Face Recognition Losses for Deepfake Detection

Recent advances in deep learning have made available
large, powerful convolutional neural networks (CNN) with
state-of-the-art performance in several real-world applications.
Unfortunately, these large-sized models have millions of parameters,
thus they are not deployable on resource-limited
platforms (e.g., where RAM is limited). Compression of CNNs
becomes therefore a critical problem to achieve memory-efficient
and possibly computationally faster model representations. In
this paper, we investigate the impact of lossy compression of
CNNs by weight pruning and quantization, and lossless weight
matrix representations based on source coding. We tested several
combinations of these techniques on four benchmark datasets
for classification and regression problems, achieving compression
rates up to 165 times, while preserving or improving the model
performance.

Compression strategies and space-conscious representations for deep neural networks

We introduce two methods of improving real-time object grasping performance from monocular colour images in an end-to-end CNN architecture. The first is the addition of an auxiliary task during model training (multi- task learning). Our multi-task CNN model improves grasping performance from a baseline average of 72.04% to 78.14% on the large Jacquard grasping dataset when performing a supplementary depth reconstruction task. The second is introducing a positional loss function that emphasises loss per pixel for secondary parameters (gripper angle and width) only on points of an object where a successful grasp can take place. This increases performance from a baseline average of 72.04% to 78.92% as well as reducing the number of training epochs required. These methods can be also performed in tandem resulting in a further performance increase to 79.12%, while maintaining sufficient inference speed to afford real-time grasp processing.

Improving Robotic Grasping on Monocular Images Via Multi-Task Learning and Positional Loss

This paper proposes a sketch-based approach to the community detection problem which clusters the full graph through the use of an informative and concise sketch. The reduced sketch is built through an effective sampling approach which selects few nodes that best represent the complete graph and operates on a pairwise node similarity measure based on the average commute time. After sampling, the proposed algorithm clusters the nodes in the sketch, and then infers the cluster membership of the remaining nodes in the full graph based on their aggregate similarity to nodes in the partitioned sketch. By sampling nodes with strong representation power, our approach can improve the success rates over full graph clustering. In challenging cases with large node degree variation, our approach not only maintains competitive accuracy with full graph clustering despite using a small sketch, but also outperforms existing sampling methods. The use of a small sketch allows considerable storage savings, and computational and timing improvements for further analysis such as clustering and visualization. We provide numerical results on synthetic data based on the homogeneous, heterogeneous and degree corrected versions of the stochastic block model, as well as experimental results on real-world data.

Sketch-based Community Detection via Representative Node Sampling


The aggressive growth of digital data threatens to exceed the capacity of conventional storage devices. The need for new means to store digital information has brought great interest in novel solutions as it is DNA, whose biological properties allow the storage of information at a high density and preserve it without any information loss for hundreds of years when stored under speciﬁc conditions. Despite being a promising solution, DNA storage faces two major obstacles: the large cost of synthesis and the high error rate introduced during sequencing. While most of the works focus on adding redundancy aiming for effective error correction, this work combines noise resistance to minimize the impact of the errors in the decoded data and post-processing to further improve the quality of the decoding.

Robust image coding on synthetic DNA: Reducing sequencing noise with inpainting


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.

Exploiting Elasticity in Tensor Ranks for Compressing Neural Networks

The deep learning models, which include attention mechanisms, are shown to enhance the performance and efficiency of the various computer vision tasks such as pattern recognition, object detection, face recognition, etc. Although the visual attention mechanism is the source of inspiration for these models, recent attention models consider `attention' as a pure machine vision optimization problem, and visual attention remains the most neglected aspect. Therefore, this paper presents a collaborative human and machine attention module which considers both visual and network's attention. The proposed module is inspired by the dorsal (`where') pathways of visual processing and can be integrated with any convolutional neural network (CNN) model. First, the module computes the spatial attention map from the input feature maps, which is then combined with the visual attention maps. The visual attention maps are created using eye-fixations obtained by performing an eye-tracking experiment with human participants. The visual attention map covers the highly salient and discriminating image regions as humans tend to focus on such regions, whereas the other relevant image regions are processed by spatial attention map. The combination of these two maps results in finer refinement in feature maps, resulting in improved performance. The comparative analysis reveals that our model not only shows significant improvement over the baseline model but also outperforms the other models. We hope that our findings using a collaborative human-machine attention module will be helpful in other computer vision tasks as well.

Collaborative Human Machine Attention Module
for Character Recognition

Given the overwhelming impact of machine learning on the last decade, several libraries and frameworks have been developed in recent years to simplify the design and training of neural networks, providing array-based programming, automatic differentiation and user-friendly access to hardware accelerators. None of those tools, however, was designed with native and transparent support for Cloud Computing or heterogeneous High-Performance Computing (HPC). The DeepHealth Toolkit is an open source Deep Learning toolkit aimed at boosting productivity of data scientists operating in the medical field by
providing a unified framework for the distributed training of neural networks, which is able to leverage hybrid HPC and cloud environments in a transparent way for the user. The toolkit is composed of a Computer Vision library, a Deep Learning library, and a front-end for non-expert users; all of the components are focused on the medical domain, but they are general purpose and can be applied to any other field. In this paper, the principles driving the design of the DeepHealth libraries are described, along with details about the implementation and the interaction between the different elements composing the toolkit. Finally, experiments on common benchmarks prove the efficiency of each separate component and of the DeepHealth Toolkit overall.

The DeepHealth Toolkit: A Unified Framework to Boost Biomedical Applications

Automatic detection for threat object items is an increasing emerging area of future application in X-ray security imagery. Although modern X-ray security scanners can provide two or more views, the integration of such object detectors across the views has not been widely explored with rigour. Therefore, we investigate the application of geometric constraints using the epipolar nature of multi-view imagery to improve object detection performance. Furthermore, we assume that images come from uncalibrated views, such that a method to estimate the fundamental matrix using ground truth bounding box centroids from multiple view object labels is proposed. In addition, detections are given a confidence probability based on its similarity with respect to the distribution of the distance to the epipolar line. This probability is used as confidence weights for merging duplicated predictions using non-maximum suppression. Using a standard object detector (YOLOv3), our technique increases the average precision of detection by 2.8% on a dataset composed of firearms, laptops, knives and cameras. These results indicate that the integration of images at different views significantly improves the detection performance of threat items of cluttered X-ray security images.

Multi-view Object Detection Using Epipolar Constraints within Cluttered X-ray Security Imagery

Quantized low-precision neural networks are very popular because they require less computational resources for inference and can provide high performance, which is vital for real-time and embedded recognition systems. However, their advantages are apparent for FPGA and ASIC devices, while general-purpose processor architectures are not always able to perform low-bit integer computations efficiently. 
The most frequently used low-precision neural network model for mobile central processors is an 8-bit quantized network. However, in a number of cases, it is possible to use fewer bits for weights and activations, and the only problem is the difficulty of efficient implementation.
We introduce an efficient implementation of 4-bit matrix multiplication for quantized neural networks and perform time measurements on a mobile ARM processor. It shows 2.9 times speedup compared to standard floating-point multiplication and is 1.5 times faster than 8-bit quantized one. We also demonstrate a 4-bit quantized neural network for OCR recognition on the MIDV-500 dataset. 4-bit quantization gives 95.0% accuracy and 48% overall inference speedup, while an 8-bit quantized network gives 95.4% accuracy and 39% speedup. The results show that 4-bit quantization perfectly suits mobile devices, yielding good enough accuracy and low inference time.

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CQNN: Convolutional Quadratic Neural Networks

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An Experimental Evaluation of Recent Face Recognition Losses for Deepfake Detection

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