The large-scale data stream problem refers to high-speed information flow which cannot be processed in scalable manner under a traditional computing platform. This problem also imposes expensive labelling cost making the deployment of fully supervised algorithms unfeasible. On the other hand, the problem of semi-supervised large-scale data streams is little explored in the literature because most works are designed in the traditional single-node computing environments while also being fully supervised approaches. This paper offers Weakly Supervised Scalable Teacher Forcing Network (WeScatterNet) to cope with the scarcity of labelled samples and the large-scale data streams simultaneously. WeScatterNet is crafted under distributed computing platform of Apache Spark with a data-free model fusion strategy for model compression after parallel computing stage. It features an open network structure to address the global and local drift problems while integrating a data augmentation, annotation and auto-correction ($DA^3$) method for handling partially labelled data streams. The performance of WeScatterNet is numerically evaluated in the six large-scale data stream problems with only $25\%$ label proportions. It shows highly competitive performance even if compared with fully supervised learners with $100\%$ label proportions.
Searching for an available, reliable, official, and understandable information is not a trivial task due to scattered information across the internet, and the availability lack of governmental communication channels communicating with African dialects and languages. In this paper, we introduce an Artificial Intelligence Powered chatbot for crisis communication that would be omnichannel, multilingual and multi dialectal. We present our work on modified StarSpace embedding tailored for African dialects for the question-answering task along with the architecture of the proposed chatbot system and a description of the different layers. English, French, Arabic, Tunisian, Igbo,Yor\`ub\'a, and Hausa are used as languages and dialects. Quantitative and qualitative evaluation results are obtained for our real deployed Covid-19 chatbot. Results show that users are satisfied and the conversation with the chatbot is meeting customer needs.
Image-text matching plays a central role in bridging the semantic gap between vision and language. The key point to achieve precise visual-semantic alignment lies in capturing the fine-grained cross-modal correspondence between image and text. Most previous methods rely on single-step reasoning to discover the visual-semantic interactions, which lacks the ability of exploiting the multi-level information to locate the hierarchical fine-grained relevance. Different from them, in this work, we propose a step-wise hierarchical alignment network (SHAN) that decomposes image-text matching into multi-step cross-modal reasoning process. Specifically, we first achieve local-to-local alignment at fragment level, following by performing global-to-local and global-to-global alignment at context level sequentially. This progressive alignment strategy supplies our model with more complementary and sufficient semantic clues to understand the hierarchical correlations between image and text. The experimental results on two benchmark datasets demonstrate the superiority of our proposed method.
StarCraft, one of the most popular real-time strategy games, is a compelling environment for artificial intelligence research for both micro-level unit control and macro-level strategic decision making. In this study, we address an eminent problem concerning macro-level decision making, known as the 'fog-of-war', which rises naturally from the fact that information regarding the opponent's state is always provided in the incomplete form. For intelligent agents to play like human players, it is obvious that making accurate predictions of the opponent's status under incomplete information will increase its chance of winning. To reflect this fact, we propose a convolutional encoder-decoder architecture that predicts potential counts and locations of the opponent's units based on only partially visible and noisy information. To evaluate the performance of our proposed method, we train an additional classifier on the encoder-decoder output to predict the game outcome (win or lose). Finally, we designed an agent incorporating the proposed method and conducted simulation games against rule-based agents to demonstrate both effectiveness and practicality. All experiments were conducted on actual game replay data acquired from professional players.
Face attribute editing aims to generate faces with one or multiple desired face attributes manipulated while other details are preserved. Unlike prior works such as GAN inversion, which has an expensive reverse mapping process, we propose a simple feed-forward network to generate high-fidelity manipulated faces. By simply employing some existing and easy-obtainable prior information, our method can control, transfer, and edit diverse attributes of faces in the wild. The proposed method can consequently be applied to various applications such as face swapping, face relighting, and makeup transfer. In our method, we decouple identity, expression, pose, and illumination using 3D priors; separate texture and colors by using region-wise style codes. All the information is embedded into adversarial learning by our identity-style normalization module. Disentanglement losses are proposed to enhance the generator to extract information independently from each attribute. Comprehensive quantitative and qualitative evaluations have been conducted. In a single framework, our method achieves the best or competitive scores on a variety of face applications.
Unsupervised domain adaptation (UDA) becomes more and more popular in tackling real-world problems without ground truth of the target domain. Though a mass of tedious annotation work is not needed, UDA unavoidably faces the problem how to narrow the domain discrepancy to boost the transferring performance. In this paper, we focus on UDA for semantic segmentation task. Firstly, we propose a style-independent content feature extraction mechanism to keep the style information of extracted features in the similar space, since the style information plays a extremely slight role for semantic segmentation compared with the content part. Secondly, to keep the balance of pseudo labels on each category, we propose a category-guided threshold mechanism to choose category-wise pseudo labels for self-supervised learning. The experiments are conducted using GTA5 as the source domain, Cityscapes as the target domain. The results show that our model outperforms the state-of-the-arts with a noticeable gain on cross-domain adaptation tasks.
Precise localization of polyp is crucial for early cancer screening in gastrointestinal endoscopy. Videos given by endoscopy bring both richer contextual information as well as more challenges than still images. The camera-moving situation, instead of the common camera-fixed-object-moving one, leads to significant background variation between frames. Severe internal artifacts (e.g. water flow in the human body, specular reflection by tissues) can make the quality of adjacent frames vary considerately. These factors hinder a video-based model to effectively aggregate features from neighborhood frames and give better predictions. In this paper, we present Spatial-Temporal Feature Transformation (STFT), a multi-frame collaborative framework to address these issues. Spatially, STFT mitigates inter-frame variations in the camera-moving situation with feature alignment by proposal-guided deformable convolutions. Temporally, STFT proposes a channel-aware attention module to simultaneously estimate the quality and correlation of adjacent frames for adaptive feature aggregation. Empirical studies and superior results demonstrate the effectiveness and stability of our method. For example, STFT improves the still image baseline FCOS by 10.6% and 20.6% on the comprehensive F1-score of the polyp localization task in CVC-Clinic and ASUMayo datasets, respectively, and outperforms the state-of-the-art video-based method by 3.6% and 8.0%, respectively. Code is available at \url{https://github.com/lingyunwu14/STFT}.
We present a new four-pronged approach to build firefighter's situational awareness for the first time in the literature. We construct a series of deep learning frameworks built on top of one another to enhance the safety, efficiency, and successful completion of rescue missions conducted by firefighters in emergency first response settings. First, we used a deep Convolutional Neural Network (CNN) system to classify and identify objects of interest from thermal imagery in real-time. Next, we extended this CNN framework for object detection, tracking, segmentation with a Mask RCNN framework, and scene description with a multimodal natural language processing(NLP) framework. Third, we built a deep Q-learning-based agent, immune to stress-induced disorientation and anxiety, capable of making clear navigation decisions based on the observed and stored facts in live-fire environments. Finally, we used a low computational unsupervised learning technique called tensor decomposition to perform meaningful feature extraction for anomaly detection in real-time. With these ad-hoc deep learning structures, we built the artificial intelligence system's backbone for firefighters' situational awareness. To bring the designed system into usage by firefighters, we designed a physical structure where the processed results are used as inputs in the creation of an augmented reality capable of advising firefighters of their location and key features around them, which are vital to the rescue operation at hand, as well as a path planning feature that acts as a virtual guide to assist disoriented first responders in getting back to safety. When combined, these four approaches present a novel approach to information understanding, transfer, and synthesis that could dramatically improve firefighter response and efficacy and reduce life loss.
Constrained clustering has gained significant attention in the field of machine learning as it can leverage prior information on a growing amount of only partially labeled data. Following recent advances in deep generative models, we propose a novel framework for constrained clustering that is intuitive, interpretable, and can be trained efficiently in the framework of stochastic gradient variational inference. By explicitly integrating domain knowledge in the form of probabilistic relations, our proposed model (DC-GMM) uncovers the underlying distribution of data conditioned on prior clustering preferences, expressed as pairwise constraints. These constraints guide the clustering process towards a desirable partition of the data by indicating which samples should or should not belong to the same cluster. We provide extensive experiments to demonstrate that DC-GMM shows superior clustering performances and robustness compared to state-of-the-art deep constrained clustering methods on a wide range of data sets. We further demonstrate the usefulness of our approach on two challenging real-world applications.
Mobile robots have recently been deployed in public spaces such as shopping malls, airports, and urban sidewalks. Most of these robots are designed with human-aware motion planning capabilities but are not designed to communicate with pedestrians. Pedestrians that encounter these robots without prior understanding of the robots' behaviour can experience discomfort, confusion, and delayed social acceptance. In this work we designed and evaluated nonverbal robot motion legibility cues, which communicate a mobile robot's motion intention to pedestrians. We compared a motion legibility cue using Projected Arrows to one using Flashing Lights. We designed the cues to communicate path information, goal information, or both, and explored different Robot Movement Scenarios. We conducted an online user study with 229 participants using videos of the motion legibility cues. Our results show that the absence of cues was not socially acceptable, and that Projected Arrows were the more socially acceptable cue in most experimental conditions. We conclude that the presence and choice of motion legibility cues can positively influence robots' acceptance and successful deployment in public spaces.