Italy

Building an effective automatic micro expression recognition (MER) system is becoming increasingly desirable in computer vision applications. However, it is also very challenging given the fine-grained nature of the expressions to be recognized. Hence, we investigate if amplifying micro facial muscle movements as a pre-processing phase, by employing Eulerian Video Magnification (EVM), can boost performance of Local Phase Quantization with Three Orthogonal Planes (LPQ-TOP) to achieve improved facial MER across various datasets. In addition, we examine the rate of increase for recognition to determine if it is uniform across datasets using EVM. Ultimately, we classify the extracted features using Support Vector Machines (SVM). We evaluate and compare the performance with various methods on seven different datasets namely CASME, CAS(ME)2, CASME2, SMIC-HS, SMIC-VIS, SMIC-NIR and SAMM. The results obtained demonstrate that EVM can enhance LPQ-TOP to achieve improved recognition accuracy on the majority of the datasets.

ICPR 2020

Magnifying Spontaneous Facial Micro Expressions for Improved Recognition

lpq-top

micro expression

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).

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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. 


Deep neural networks have been widely used in crowd counting that aims to give the number of objects in images and videos. The performance of crowd counting models is greatly affected by the size of datasets with high quality annotations. However, collecting and annotating large-scale crowd counting dataset is labor-intensive and time-consuming. In this work, we exploit unlabeled web images to boost the performance of crowd counting models in a semi-supervised manner. Based on the observation that the rotation and splitting operations will not change the number of object in image, we design three auxiliary tasks to improve the feature representation ability of deep models. A semi-supervised multi-task learning framework is proposed by introducing three auxiliary tasks with respect to unlabeled data and our framework can be easily extended to other crowd counting models. Experiments shows that our semi-supervised multi-task learning framework can effectively boost the performance of crowd counting models on UCF-QNRF dataset and ShanghaiTech dataset.

Learning from Web Data: Improving Crowd Counting via Semi-Supervised Learning

In this paper, we propose Object Manifold Embedding GAN (Ω-GAN) to generate images of variously shaped and arbitrarily posed objects from a noise variable sampled from a distribution defined over the pose and the shape manifolds in a vector space. We introduce Parametric Manifold Sampling to sample noise variables from a distribution over the pose manifold to conditionally generate object images in arbitrary poses by tuning the pose parameter. We also introduce Object Identity Loss for clearly disentangling the pose and shape parameters, which allows us to maintain the shape of the object instance when only the pose parameter is changed. Through evaluation, we confirmed that the proposed Ω-GAN could generate variously shaped object images in arbitrary poses by changing the pose and shape parameters independently. We also introduce an application of the proposed method for object pose estimation, through which we confirmed that the object poses in the generated images are accurate.


OMEga-GAN: Object Manifold Embedding GAN for Image Generation via Disentangling Parameters into Pose and Shape Manifolds


Learning-based methods have enabled the recovery of a video sequence from a single motion-blurred image or a single coded exposure image. Recovering video from a single motion-blurred image is a very ill-posed problem and the recovered video usually has many artifacts. In addition to this, the direction of motion is lost and it results in motion ambiguity. However, it has the advantage of fully preserving the information in the static parts of the scene. The traditional coded exposure framework is better-posed but it only samples a fraction of the space-time volume, which is at best 50% of the space-time volume. Here, we propose to use the complementary information present in the fully-exposed (blurred) image along with the coded exposure image to recover a high fidelity video without any motion ambiguity. Our framework consists of a shared encoder followed by an attention module to selectively combine the spatial information from the fully-exposed image with the temporal information from the coded image, which is then super-resolved to recover a non-ambiguous high-quality video. The input to our algorithm is a fully-exposed and coded image pair. Such an acquisition system already exists in the form of a Coded-two-bucket (C2B) camera. We demonstrate that our proposed deep learning approach using blurred-coded image pair produces much better results than those from just a blurred image or just a coded image.

Video Reconstruction by Spatio-temporal Fusion of Blurred-coded Image Pair


Music is one of the most appreciated forms of art, and generating songs has become a popular subject in the artificial intelligence community. There are various networks that can produce pleasant sounding music, but no model has been able to produce music that duplicates the style of a specific artist or artists. In this paper, we extend a previous single-instrument model: the Deep Composer -a model we believe to be capable of achieving this. Deep Composer originates from the Deep Segment Hash Learning (DSHL) single instrument model and is designed to learn how a specific artist would place individual segments of music together rather than create music similar to a specific genre. To the best of our knowledge, no other network has been designed to achieve this. For these reasons, we introduce a new field of study, Intelligence Duplication (ID). AI research generally focuses on developing techniques to mimic universal intelligence. Intelligence Duplication (ID) research focuses on techniques to artificially duplicate or clone a specific mind such as Mozart. Additionally, we present a new retrieval algorithm, Segment Barrier Retrieval (SBR), to improve retrieval accuracy within the hash-space as opposed to a more traditionally used featurespace. SBR prevents retrieval branches from entering areas of low-density within the hash-space, a phenomena we identify and label as segment sparsity. To test our Deep Composer and the effectiveness of SBR, we evaluate various models with different SBR threshold values and conduct qualitative surveys for each model. The survey results indicate that our Deep Composer model is capable of learning music generation from multiple composers. Our extended Deep Composer model provides a more suitable platform for Intelligence Duplication. Future work can apply this platform to duplicate great composers such as Mozart or allow them to collaborate in the virtual space.

Deep Composer: A Hash-Based Duplicative Neural Network For Generating Multi Instrument Songs

The study of the physical properties of porous media is crucial for
petrophysics laboratories. Even though micro computed tomography
(CT) could be useful, the appropriate evaluation of flow properties
would involve the acquisition of a large number of representative
images. That is often unfeasible.

Stochastic reconstruction methods aim to generate novel, realistic
rock images from a small sample, thus avoiding a large acquisition
process. In this contribution, we improve a previous method for
3D-to-3D reconstruction of the structure of porous media by applying
generative adversarial neural networks (GANs). We compare several
measures of pore morphology between simulated and acquired
images. Experiments include Beadpack, Berea sandstone, and Ketton
limestone images. Results show that our GANs-based method can
reconstruct three-dimensional images of porous media at different
scales that are representative of the morphology of the original
images. Furthermore, the generation of multiple images is much
faster than classical image reconstruction methods.

Stochastic 3D rock reconstruction using GANs


The way people consume sports on TV has drastically evolved in the last years, particularly under the combined effects of the legalization of sport betting and the huge increase of sport analytics. Several companies are nowadays sending observers in the stadiums to collect live data of all the events happening on the field during the match. Those data contain meaningful information providing a very detailed description of all the actions occurring during the match to feed the coaches and staff, the fans, the viewers, and the gamblers. Exploiting all these data, sport broadcasters want to generate extra content such as match highlights, match summaries, players and teams analytics, etc., to appeal subscribers.
This paper explores the problem of summarizing professional soccer matches as automatically as possible using both the aforementioned event-stream data collected from the field and the content broadcasted on TV. We have designed an architecture, introducing first (1) a Multiple Instance Learning method that takes into account the sequential dependency among events and then (2) a hierarchical multimodal attention layer that grasps the importance of each event in an action. We evaluate our approach on matches from two professional European soccer leagues, showing its capability to identify the best actions for automatic summarization by comparing with real summaries made by human operators.

Hierarchical Multimodal Attention for Deep Video Summarization

Existing networks for stereo matching usually use 2-D CNN as the feature extractor. However, objects are usually continuous in spatial, if an extracted feature contains disparity edge (the representation of this feature on original image contains disparity edge), then this feature usually not occur inside the region of an object. We propose a novel attention mechanism to suppress features containing disparity edge, named SDE-Attention (SDEA). We notice that features containing disparity edge are usually continuous in one image and discontinuous in another, which means that they usually have a greater difference in two feature maps of the same layer than features that don’t contain disparity edge. SDEA calculate the weight matrix of the intermediate feature map according to this trait, then the weight matrix is multiplied to the intermediate feature map. experimental results show that our method has a significant improvement in accuracy.

Suppressing Features That Contain Disparity Edge for Stereo Matching

Feature pyramids are widely used to improve scale invariance for object detection. Most methods just map the objects to feature maps with relevant square receptive fields, but rarely pay attention to the aspect ratio variation, which is also an important property of object instances. It will lead to a poor match between rectangular objects and assigned features with square receptive fields, thus preventing from accurate recognition and location. Besides, the information propagation among feature layers is sparse, namely, each feature in the pyramid may mainly or only contain single-level information, which is not representative enough for classification and localization sub-tasks. In this paper, Bidirectional Matrix Feature Pyramid Network (BMFPN) is proposed to address these issues. It consists of three modules: Diagonal Layer Generation Module (DLGM), Top-down Module (TDM) and Bottom-up Module (BUM). First, multi-level features extracted by backbone are fed into DLGM to produce the base features. Then these base features are utilized to construct the final feature pyramid through TDM and BUM in series. The receptive fields of the designed feature layers in BMFPN have various scales and aspect ratios. Objects can be correctly assigned to appropriate and representative feature maps with relevant receptive fields depending on its scale and aspect ratio properties. Moreover, TDM and BUM form bidirectional and reticular information flow, which effectively fuses multi-level information in top-down and bottom-up manner respectively. To evaluate the effectiveness of our proposed architecture, an end-toend anchor-free detector is designed and trained by integrating BMFPN into FCOS. And the center-ness branch in FCOS is modified with our Gaussian center-ness branch (GCB), which brings another slight improvement. Without bells and whistles, our method gains +3.3%, +2.4% and +2.6% AP on MS COCO dataset from baselines with ResNet-50, ResNet-101 and ResNeXt-101 backbones, respectively.

Bidirectional Matrix Feature Pyramid Network for Object Detection

We present a novel Siamese graph convolution network (GCN) for face sketch recognition. To build a graph from an image, we utilize a deep learning method to detect the image edges, and then use a superpixel method to segment the edge image. Each segmented superpixel region is taken as a node, and each pair of adjacent regions forms an edge of the graph. Graphs from both a face sketch and a face photo are input into the Siamese GCN for recognition. A deep graph matching method is used to share messages between cross-modal graphs in this model. Experiments show that the GCN can obtain high performance on several face photo-sketch datasets, including seen and unseen face photo-sketch datasets. It is also shown that the model performance based on the graph structure representation of the data using the Siamese GCN is more stable than a Siamese CNN model.

Siamese Graph Convolution Network for Face Sketch Recognition

As the basis of advanced visual tasks such as vehicle tracking and traffic flow analysis, vehicle detection needs to accurately predict the position and category of vehicle objects. In the past decade, deep learning methods have made great progress. However, we also notice that some existing cases are not studied thoroughly. First, false positive on the background regions is a critical problem. Second, most of the previous approaches only optimize a single vehicle detection model, ignoring the relationship between different visual perception tasks. For these two findings, we introduce a foreground segmentation branch for the first time, which can predict the pixel level of vehicles in advance. Furthermore, two attention modules are designed to guide the work of the detection branch. The proposed method can be easily grafted into the one-stage and two-stage detection framework. We evaluate the effectiveness of our model on large scale variance highway (LSVH) dataset, and achieve the state-of-the-art detection accuracy.

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Learning from Web Data: Improving Crowd Counting via Semi-Supervised Learning

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