Italy


Video stabilization is fundamental for providing good Quality of Experience for viewers and generating suitable content for video applications. In this scenario, Digital Video Stabilization (DVS) is convenient and economical for casual or amateur recording because it neither requires specific equipment nor demands knowledge of the device used for recording. Although DVS has been a research topic for decades, with a number of proposals from industry and academia, traditional methods tend to fail in a number of scenarios, e.g. with occlusion, textureless areas, parallax, dark areas, amongst others. On the other hand, defining a smooth camera path is a hard task in Deep Learning scenarios. This paper proposes a video stabilization system based on traditional and Deep Learning methods. First, we leverage Spatial Transformer Networks (STNs) to learn transformation parameters between image pairs, then utilize this knowledge to stabilize videos: we obtain the motion parameters between frame pairs and then smooth the camera path using moving averages. Our approach aims at combining the strengths of both Deep Learning and traditional methods: the ability of STNs to estimate motion parameters between two frames and the effectiveness of moving averages to smooth camera paths. Experimental results show that our system outperforms state-of-the-art proposals and a commercial solution.

ICPR 2020

Deep Homography-Based Video Stabilization​

moving average

video stabilization

homography

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. 



In this paper, we propose an automatic approach for localizing the inner eye canthus in thermal face images. We first coarsely detect 5 facial keypoints corresponding to the center of the eyes, the nose tip, and the ears. Then we compute a sparse 2D-3D points correspondence using a 3D Morphable Face Model (3DMM). This correspondence is used to project the entire 3D face onto the image, and subsequently locate the inner eye canthus. Detecting this location allows obtaining the most precise body temperature measurement for a person using a thermal camera. We evaluated the approach on a thermal face dataset provided with manually annotated landmarks. However, such manual annotations are normally conceived to identify facial parts such as eyes, nose, and mouth, and are not specifically tailored for localizing the eye canthus region. As an additional contribution, we enrich the original dataset by using the annotated landmarks to deform and project the 3DMM onto the images. Then, by manually selecting a small region corresponding to the eye canthus, we enrich the dataset with additional annotations. By using the manual landmarks, we ensure the correctness of the 3DMM projection, which can be used as ground-truth for future evaluations. Moreover, we supply the dataset with the 3D head poses and per-point visibility masks for detecting self-occlusions.

Inner Eye Canthus Localization for Human Body Temperature Screening


In this paper, we seek to extend multi-object tracking research on a relatively less explored domain, that of, underwater multi-object tracking in the wild. Multi-object fish tracking is an important task because it can provide fish monitoring systems with richer information (e.g. multiple views of the same fish) as compared to detections and it can be an invaluable input to fish behavior analysis. However, there is a lack of an annotated benchmark dataset with enough samples for this task. To circumvent the need for manual ground truth tracking annotation, we craft a synthetic dataset. Using this synthetic dataset, we train an integrated detector and tracker called SynDHN. SynDHN uses the Deep Hungarian Network (DHN), which is a differentiable approximation of the Hungarian assignment algorithm. We repurpose DHN to become the tracking component of our algorithm by performing the task of affinity estimation between detector predictions. We consider both spatial and appearance features for affinity estimation. Our results show that despite being trained on a synthetic dataset, SynDHN generalizes well to real underwater video tracking and performs better against our baseline algorithms. 


SynDHN: Multi-Object Fish Tracker Trained on Synthetic Underwater Videos


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.

End-to-end Multi-task Learning of Missing Value
Imputation and Classification in Time-Series Data


Imaging anisotropy poses a critical challenge in applying deep learning models to 3D medical image analysis. Anisotropy downgrades model performance, especially when slice spacing varies significantly between training and clinical datasets. We propose a transformer-based model to tackle the anisotropy problem. It is adaptable to different levels of anisotropy and is computationally efficient. Our model outperforms baseline models in 3D lung cancer segmentation experiments.

A Transformer-Based Network for Anisotropic 3D Medical Image Segmentation


The detection of intimacy plays a crucial role in the improvement of intimate relationship, which contributes to promote the family and social harmony. Previous studies have shown that different degrees of intimacy have significant differences in brain imaging. Recently, a few of work has emerged to recognise intimacy automatically by using machine learning technique. Moreover, considering the temporal dynamic characteristics of intimacy relationship on neural mechanism,
how to model spatio-temporal dynamics for intimacy prediction effectively is still a challenge. In this paper, we propose a novel method to explore deep spatial-temporal representations for intimacy prediction by Attention-enhanced Cascade Convolutional Recurrent Neural Network (ACCRNN). Given the advantages of time-frequency resolution in complex neuronal activities analysis, this paper utilizes functional near-infrared spectroscopy (fNIRS) to analyse and infer to intimate relationship. We collect a fNIRSbased dataset for the analysis of intimate relationship. Fortytwo-channel fNIRS signals are recorded from the 44 subjects’ prefrontal cortex when they watched a total of 18 photos of lovers, friends and strangers for 30 seconds per photo. The experimental results show that our proposed method outperforms the others in terms of accuracy with the precision of 96.5%. To the best of our knowledge, this is the first time that such a hybrid deep architecture has been employed for fNIRS-based intimacy prediction.

Exploring Spatial-Temporal Representations for fNIRS-based Intimacy Detection via Attention-enhanced Cascade Convolutional Recurrent Neural Network


Image segmentation, one of the most critical vision tasks, has been studied for many years. Most of the early algorithms are unsupervised methods, which use hand-crafted features to divide the image into many regions. Recently, owing to the great success of deep learning technology, CNNs based methods show superior performance in image segmentation. However, these methods rely on a large number of human annotations, which are expensive to collect. In this paper, we propose a deep unsupervised method for image segmentation, which contains the following two stages. First, a Superpixelwise Autoencoder (SuperAE) is designed to learn the deep embedding and reconstruct a smoothed image, then the smoothed image is passed to generate superpixels. Second, we present a novel clustering algorithm called Deep Superpixel Cut (DSC), which measures the deep similarity between superpixels and formulates image segmentation as a soft partitioning problem. Via backpropagation, DSC adaptively partitions the superpixels into perceptual regions. Experimental results on the BSDS500 dataset demonstrate the effectiveness of the proposed method.

Deep Superpixel Cut for Unsupervised Image Segmentation


Hand gesture and motion sensing offer an intuitive and natural form of human-machine interface. Air-writing systems allow users to draw alpha-numerical or linguistic characters in the virtual board in air through hand gestures. Traditionally, radar-based air-writing systems have been based on a network of radars, at least three, to localize the hand target through trilateration algorithm followed by tracking to extract the drawn trajectory, which is then followed by recognition of the drawn character by either Long-Short Term Memory (LSTM) utilizing the sensed trajectory or Deep Convolutional Neural Network (DCNN) utilizing a reconstructed 2D image from the trajectory. However, the practical deployments of such systems are limited since the detection of the finger or hand target by all three radars cannot be guaranteed leading to failure of the trilateration algorithm. Further placement of three or more radars for the air-writing solution is neither always physically plausible nor cost-effective. Furthermore, these solutions do not exploit the full potentials of deep neural networks, which are generally capable of learning features implicitly. In this paper, we propose an air-writing system based on a network of sparse radars, i.e. strictly less than three, using 1D DCNN-LSTM-1D transposed DCNN architecture to reconstruct and classify the drawn character utilizing only the range information from each radar. The paper employs real data using one and two 60 GHz milli-meter wave radar sensors to demonstrate the success of the proposed air-writing solution.

Air-Writing with Sparse Network of Radars using Spatio-Temporal Learning


The presentation includes three parts. The motivation of this work, the architecture we propose, and some results of Experiments.

Progressive Splitting and Upscaling Structure for Super-Resolution


Graph Neural Networks (GNNs) have achieved great success on graph-structured data, and their applications on traditional data structures such as natural language processing and semi-supervised text classification have been extensively explored in recent years. While previous works only consider the text information while building the graph, heterogeneous information such as labels is ignored. In this paper, we consider incorporating the label information while building the graph by adding text- label-text paths, through which the supervision information will propagate among the graph more directly. Specifically, we treat labels as nodes in the graph which also contains text and word nodes, and then connect labels with texts belonging to that label. Through graph convolutions, label embeddings are jointly learned with text embeddings in the same latent semantic space. The newly incorporated label nodes will facilitate learning more accurate text embeddings by introducing the label information, and thus benefit the downstream text classification tasks. Extensive results on several benchmark datasets show that the proposed framework outperforms baseline methods by a significant margin.


Label-Incorporated Graph neural networks for text classification

As the most commonly used communication tool, the mobile phone has become an indispensable part of our daily life. The surface of the mobile phone as the main window of human-phone interaction directly affects the user experience. It is necessary to detect surface defects on the production line in order to ensure the high quality of the mobile phone. However, the existing mobile phone surface defect detection is mainly done manually. Currently, there are few automatic defect detection methods to replace human eyes. How to quickly and accurately detect the surface defects of the mobile phone is an urgent problem to be solved. Hence, an efficient defect detection network (EDD-Net) is proposed. Firstly, EfficientNet is used as the backbone network. Then, according to the smallscale of mobile phone surface defects, a feature pyramid module named GCSA-BiFPN is proposed to obtain more discriminative features. Finally, the box/class prediction network is used to achieve effective defect detection. We also build a mobile phone surface oil stain defect (MPSOSD) dataset to alleviate the lack of dataset in this field. The performance on the relevant datasets shows that the proposed network is effective and has practical significance for industrial production.

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Inner Eye Canthus Localization for Human Body Temperature Screening

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