In this paper, we propose a multi-attribute regression network (MARN) to investigate the problem of face reconstruction, especially in challenging cases when faces undergo large variations including severe poses, extreme expressions, and partial occlusions in unconstrained environments. The traditional 3DMM parametric regression method is absent from the learning of identity, expression, and attitude attributes, resulting in lacking geometric details in the reconstructed face. Our MARN method is to enable the network to better extract the feature information of face identity, expression, and pose attributes. We introduced identity, expression, and pose attribute loss functions to enhance the learning of details in each attribute. At the same time, we carefully design the geometric contour constraint loss function and use the constraints of sparse 2D face landmarks to improve the reconstructed geometric contour information. The experimental results show that our face reconstruction method has achieved significant results on the AFLW2000-3D and AFLW datasets compared with the most advanced methods. In addition, there has been a great improvement in dense face alignment. .

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 based 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 one of the critical problems. Second, most of the previous approaches only optimize a single vehicle detection model, ignoring the relationship between different visual perception tasks. In response to the above 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 LSVH, a dataset with large variations in vehicle scales, and achieve the state-of-the-art detection accuracy.

In this paper, we propose an occlusion-robust pose estimation method of an unknown object instance in an object category from a depth image. In a cluttered environment, objects are often occluded mutually. For estimating the pose of an object in such a situation, a method that de-occludes the unobservable area of the object would be effective. However, there are two difficulties; occlusion causes offset between the actual object center and the center of its observable area, and different instances in a category may have different shapes. To cope with these difficulties, we propose a two-stage Encoder-Decoder model to extract features with objects whose centers are aligned to the image center. In the model, we also propose the Median-shape Reconstructor as the second stage to absorb shape variations in a category. By evaluating the method with both a large-scale virtual dataset and a real dataset, we confirmed the proposed method achieves good performance on pose estimation of an occluded object from a depth image.

In recent years, learning based shadow detection and removal approaches have shown prospects and, in most cases, yielded state-of-the-art results. The performance of these approaches, however, relies heavily on the construction of training database of shadow images, shadow-free versions, and shadow maps as ground truth. This conventional data gathering method is time-consuming, expensive, or even practically intractable to realize especially for outdoor scenes with complicated shadow patterns, thus limiting the size of the data available for training. In this paper, we leverage on large high quality synthetic image database and domain adaptation to eliminate the bottlenecks resulting from insufficient training samples and domain bias. Specifically, our approach utilizes adversarial training to predict near-pixel-perfect shadow map from synthetic shadow image for downstream shadow removal steps. At inference time, we capitalize on domain adaptation via image style transfer to map the style of real- world scene to that of synthetic scene for the purpose of detecting and subsequently removing shadow. Comprehensive experiments indicate that our approach outperforms state-of-the-art methods on select benchmark datasets.

In this paper we address unsupervised machine learning for two use cases in satellite communication, which are scheduling problems: (i) Ka-band frequency plan optimization and (ii) dynamic configuration of an active antenna array satellite. We apply approaches based on the Expectation-Maximization (EM) framework to both of them. We compare against baselines of currently deployed solutions, and show that they can be significantly outperformed by the EM-based approach. In addition, the approaches can be applied incrementally, thus supporting fast adaptation to small changes in the input configuration.

In this paper, a multi-task learning framework has been proposed for solving and improving traditional Mongolian words recognition. To be specific, a sequence-to-sequence model with attention mechanism was utilized to accomplish the task of recognition. Therein, the attention mechanism is designed to fulfill the task of glyph segmentation during the process of recognition. Although the glyph segmentation is an implicit operation, the information of glyph segmentation can be integrated into the process of recognition. After that, the two tasks can be accomplished simultaneously under the framework of multi-task learning. By this way, adjacent image frames can be decoded into a glyph more precisely, which results in improving not only the performance of words recognition but also the accuracy of character segmentation. Experimental results demonstrate that the proposed multi-task learning based scheme outperforms the conventional glyph segmentation-based method and various segmentation-free (i.e. holistic recognition) methods.

Forecasting human actions and motion trajectories addresses the problem of predicting what a person is going to do next and how they will perform it. This is crucial in a wide range of applications such as assisted living and future co-robotic settings. We propose to simultaneously learn actions and action-related human motion dynamics, while existing works perform them independently. In this paper, we present a method to jointly forecast categories of human action and the pose of skeletal joints in the hope that the two tasks can help each other. As a result, our system can predict not only the future actions but also the motion trajectories that will result. To achieve this, we define a task of joint action classification and pose regression. We employ a sequence to sequence encoder-decoder model combined with multi-task learning to forecast future actions and poses progressively before the action happens. Experimental results on two public datasets, IkeaDB and OAD, demonstrate the effectiveness of the proposed method.

Aspect-Based Sentiment Analysis (ABSA) studies the extraction of sentiments and their targets. Collecting labeled data for this task in order to help neural networks generalize better can be laborious and time-consuming. As an alternative, similar data to the real-world examples can be produced artificially through an adversarial process which is carried out in the embedding space. Although these examples are not real sentences, they have been shown to act as a regularization method which can make neural networks more robust. In this work, we fine-tune the general purpose BERT and domain specific post-trained BERT (BERT-PT) using adversarial training. After improving the results of post-trained BERT with different hyperparameters, we propose a novel architecture called BERT Adversarial Training (BAT) to utilize adversarial training for the two major tasks of Aspect Extraction and Aspect Sentiment Classification in sentiment analysis. The proposed model outperforms the general BERT as well as the in-domain post-trained BERT in both tasks. To the best of our knowledge, this is the first study on the application of adversarial training in ABSA. The code is publicly available on a GitHub repository at https://github.com/IMPLabUniPr/Adversarial-Training-fo r-ABSA

In myocardium segmentation of cardiac magnetic resonance images, ambiguities often appear near the boundaries of the target domains due to tissue similarities. To address this issue, we propose a new architecture, called FOANet, which can be decomposed in three main steps: a localization step, a Gaussian-based contrast enhancement step, and a segmentation step. This architecture is supplied with a hybrid loss function that guides the FOANet to study the transformation relationship between the input image and the corresponding label in a threelevel hierarchy (pixel-, patch- and map-level), which is helpful to improve segmentation and recovery of the boundaries. We demonstrate the efficiency of our approach on two public datasets in terms of regional and boundary segmentations.

Unsupervised Domain Adaptation (UDA) aims at improving the generalization capability of a model trained on a source domain to perform well on a target domain for which no labeled data is available. In this paper, we consider the semantic segmentation of urban scenes and we propose an approach to adapt a deep neural network trained on synthetic data to real scenes addressing the domain shift between the two different data distributions. We introduce a novel UDA framework where a standard supervised loss on labeled synthetic data is supported by an adversarial module and a self-training strategy aiming at aligning the two domain distributions. The adversarial module is driven by a couple of fully convolutional discriminators dealing with different domains: the first discriminates between ground truth and generated maps, while the second between segmentation maps coming from synthetic or real world data. The self-training module exploits the confidence estimated by the discriminators on unlabeled data to select the regions used to reinforce the learning process. Furthermore, the confidence is thresholded with an adaptive mechanism based on the per-class overall confidence. Experimental results prove the effectiveness of the proposed strategy in adapting a segmentation network trained on synthetic datasets like GTA5 and SYNTHIA, to real world datasets like Cityscapes and Mapillary.

This paper presents an approach for spotting and accurately localizing identity documents in the wild. Contrary to blind solutions that often rely on borders and corners detection, the proposed approach requires a classification a priori along with a list of predefined models. The matching and accurate localization are performed using specific ID document features. This process is especially difficult due to the intrinsic variable nature of ID models (text fields, multi-pass printing with offset, unstable layouts, added artifacts, blinking security elements, non-rigid materials). We tackle the problem by putting different combinations of features in competition within a multi-hypothesis exploration where only the best document quadrilateral candidate is retained thanks to a custom visual similarity metric. The idea is to find, in a given context, at least one feature able to correctly crop the document. The proposed solution has been tested and has shown its benefits on both the MIDV-500 academic dataset and an industrial one supposedly more representative of a real-life application.

Learning how to adapt to complex and dynamic environments is one of the most important factors that contribute to our intelligence. Endowing artificial agents with this ability is not a simple task, particularly in competitive scenarios. In this paper, we present a broad study on how popular reinforcement learning algorithms can be adapted and implemented to learn and to play a real-world implementation of a competitive multiplayer card game. We propose specific training and validation routines for the learning agents, in order to evaluate how the agents learn to be competitive and explain how they adapt to each others' playing style. Finally, we pinpoint how the behavior of each agent derives from their learning style and create a baseline for future research on this scenario.

Face recognition has been widely accepted as a means of identification in applications ranging from border control to security in the banking sector. Surprisingly, while widely accepted, we still lack the understanding of the uniqueness or distinctiveness of face as a biometric characteristic. In this work, we study the impact of factors such as image resolution, feature representation, database size, age and gender on uniqueness denoted by the Kullback-Leibler divergence between genuine and impostor distributions. Towards understanding the impact, we present experimental results on the datasets AT&T, LFW, IMDb-Face, as well as ND-TWINS, with the feature extraction algorithms VGGFace, VGG16, ResNet50, InceptionV3, MobileNet and DenseNet121, that reveal the quantitative impact of the named factors. While these are early results, our findings indicate the need for a better understanding of the concept of biometric uniqueness and its implication on face recognition.

6D object pose estimation is widely applied in robotic tasks such as grasping and manipulation. Prior methods using RGB-only images are vulnerable to heavy occlusion and poor illumination, so it is important to complement them with depth information. However, existing methods using RGB-D data cannot adequately exploit consistent and complementary information between RGB and depth modalities. In this paper, we present a novel method to effectively consider the correlation within and across both modalities with attention mechanism to learn discriminative and compact multi-modal features. Then, effective fusion strategies for intra- and inter-correlation modules are explored to ensure efficient information flow between RGB and depth. To our best knowledge, this is the first work to explore effective intra- and inter-modality fusion in 6D pose estimation. The experimental results show that our method can achieve the state-of-the-art performance on LineMOD and YCBVideo dataset. We also demonstrate that the proposed method can benefit a real-world robot grasping task by providing accurate object pose estimation.

Object localization and local feature representation are key issues in fine-grained image retrieval. However, the existing unsupervised methods still need to be improved in these two aspects. For conquering these issues in a unified framework, a novel unsupervised scheme, named DSAW for short, is presented in this paper. Firstly, we proposed a dual-selection (DS) method, which achieves more accurate object localization by using adaptive threshold method to perform feature selection on local and global activation map in turn. Secondly, a novel and faster self-attention weights (AW) method is developed to weight local features by measuring their importance in the global context. Finally, we also evaluated the performance of the proposed method on five fine-grained image datasets and the results showed that our DSAW outperformed the existing best method.

Human action recognition (HAR) in videos is a fundamental research topic in computer vision. It consists mainly in understanding actions performed by humans based on a sequence of visual observations. In recent years, HAR have witnessed significant progress, especially with the emergence of deep learning models. However, most of existing approaches for action recognition rely on information that is not always relevant for the task, and are limited in the way they fuse temporal information. In this paper, we propose a novel method for human action recognition that encodes efficiently the most discriminative appearance information of an action with explicit attention on representative pose features, into a new compact grid representation. Our GRAR (Grid-based Representation for Action Recognition) method is tested on several benchmark datasets that demonstrate that our model can accurately recognize human actions, despite intra-class appearance variations and occlusion challenges.

We present a novel approach to text detection for document images. For robust text detection of noisy scanned or captured document images, the advantages of multi-task learning are adopted by adding an auxiliary task of text enhancement. Consequently, our proposed model trains reducing noise and enhancing text regions as well as detecting text. To overcome the insufficiency of document image data for text detection, train data for our model are enriched with synthesized document images that are fully labeled for text detection and enhancement. For the effective use of synthetic and real data, the proposed model is trained in two phases. The first phase is training only synthetic data in a fully-supervised manner. Then real data with only detection labels are added in the second phase. The enhancement task for real data is weakly-supervised with information from detection labels. Our methods are demonstrated on a real document dataset with performances exceeding those of other methods. Also, we conducted ablations to analyze effects of the synthetic data, multi-task, and weak-supervision. Whereas the existing text detection studies mostly focus on the text in scenes, our proposed method is optimized to the applications for the text in scanned or captured documents.

Aiming at recognizing images of the same person across distinct camera views, person re-identification (re-ID) has been among active research topics in computer vision. Most existing re-ID works require collection of a large amount of labeled image data from the scenes of interest. When the data to be recognized are different from the source-domain training ones, a number of domain adaptation approaches have been proposed. Nevertheless, one still needs to collect labeled or unlabelled target-domain data during training. In this paper, we tackle an even more challenging and practical setting, domain generalized (DG) person re-ID. That is, while a number of labeled source-domain datasets are available, we do not have access to any target-domain training data. In order to learn domain-invariant features without knowing the target domain of interest, we present an episodic learning scheme which advances meta learning strategies to exploit the observed source-domain labeled data. The learned features would exhibit sufficient domain-invariant properties while not overfitting the source-domain data or ID labels. Our experiments on four benchmark datasets confirm the superiority of our method over the state-of-the-arts.

Many image and vision applications require a large amount of data for model training. Collecting all such data at a central location can be challenging due to data privacy and communication bandwidth restrictions. Federated learning is an effective way of training a machine learning model in a distributed manner from local data collected by client devices, which does not require exchanging the raw data among clients. A challenge is that among the large variety of data collected at each client, it is likely that only a subset is relevant for a learning task while the rest of data has a negative impact on model training. Therefore, before starting the learning process, it is important to select the subset of data that is relevant to the given federated learning task. In this paper, we propose a method for distributedly selecting relevant data, where we use a benchmark model trained on a small benchmark dataset that is task-specific, to evaluate the relevance of individual data samples at each client and select the data with sufficiently high relevance. Then, each client only uses the selected subset of its data in the federated learning process. The effectiveness of our proposed approach is evaluated on multiple real-world image datasets in a simulated system with a large number of clients, showing up to 25% improvement in model accuracy compared to training with all data.

We present a public dataset for Identity Preserved Tracking (IPT) consisting of sequences of depth data recorded using an Orbbec Astra depth sensor. The dataset features sequences in ten different locations with a high amount of background variation and is designed to be applicable to a wide range of tasks. Its labeling is versatile, allowing for tracking in either 3d space or image coordinates. Next to frame-by-frame 3d and inferred bounding box labeling we provide supplementary annotation of camera poses and room layouts, split in multiple semantically distinct categories. Intended use-cases are applications where both a high level understanding of scene understanding and privacy are central points of consideration, such as active and assisted living (AAL), security and industrial safety. Compared to similar public datasets IPT distinguishes itself with its sequential data format, 3d instance labeling and room layout annotation. We present baseline object detection results in image coordinates using a YOLOv3 network architecture and implement a background model suitable for online tracking applications to increase detection accuracy. Additionally we propose a novel volumetric non-maximum suppression (V-NMS) approach, taking advantage of known room geometry. Last we provide baseline person tracking results utilizing Multiple Object Tracking Challenge (MOTChallenge) evaluation metrics of the CVPR19 benchmark.

Tracking Fast Moving Objects (FMO), which appear as blurred streaks in video sequences, is a difficult task for standard trackers, as the object position does not overlap in consecutive video frames and texture information of the objects is blurred. Up-to-date approaches tuned for this task are based on background subtraction with a static background and slow deblurring algorithms. In this article, we present a tracking-by-segmentation approach implemented using modern deep learning methods that perform near real-time tracking on real-world video sequences. We have developed a physically plausible FMO sequence generator to be a robust foundation for our training pipeline and demonstrate straightforward network adaptation for different FMO scenarios with varying foreground.