OpenRaiser/Intern-Atlas huggingface dataset:
Video Coding for Machine: Compact Visual Representation Compression for Intelligent Collaborative Analytics
Venue: arXiv cs.CV 2021
Year: 2021

Video Coding for Machines (VCM) is committed to bridging to an extent separate research tracks of video/image compression and feature compression, and attempts to optimize compactness and efficiency jointly from a unified perspective of high accuracy machine vision and full fidelity human vision. In this paper, we summarize VCM methodology and philosophy based on existing academia and industrial efforts. The development of VCM follows a general rate-distortion optimization, and the categorization of key modules or techniques is established. From previous works, it is demonstrated that, although existing works attempt to reveal the nature of scalable representation in bits when dealing with machine and human vision tasks, there remains a rare study in the generality of low bit rate representation, and accordingly how to support a variety of visual analytic tasks. Therefore, we investigate a novel visual information compression for the analytics taxonomy problem to strengthen the capability of compact visual representations extracted from multiple tasks for visual analytics. A new perspective of task relationships versus compression is revisited. By keeping in mind the transferability among different machine vision tasks (e.g. high-level semantic and mid-level geometry-related), we aim to support multiple tasks jointly at low bit rates. In particular, to narrow the dimensionality gap between neural network generated features extracted from pixels and a variety of machine vision features/labels (e.g. scene class, segmentation labels), a codebook hyperprior is designed to compress the neural network-generated features. As demonstrated in our experiments, this new hyperprior model is expected to improve feature compression efficiency by estimating the signal entropy more accurately, which enables further investigation of the granularity of abstracting compact features among different tasks.
Demographic Biases of Crowd Workers in Key Opinion Leaders Finding
Venue: arXiv cs.IR 2021
Year: 2021

Key Opinion Leaders (KOLs) are people that have a strong influence and their opinions are listened to by people when making important decisions. Crowdsourcing provides an efficient and cost-effective means to gather data for the KOL finding task. However, data collected through crowdsourcing is affected by the inherent demographic biases of crowd workers. To avoid such demographic biases, we need to measure how biased each crowd worker is. In this paper, we propose a simple yet effective approach based on demographic information of candidate KOLs and their counterfactual value. We argue that it is effectiveness because of the extra information that we can consider together with labeled data to curate a less biased dataset.
A Unified Framework for Generalized Low-Shot Medical Image Segmentation with Scarce Data
Venue: arXiv cs.CV 2021
Year: 2021

Medical image segmentation has achieved remarkable advancements using deep neural networks (DNNs). However, DNNs often need big amounts of data and annotations for training, both of which can be difficult and costly to obtain. In this work, we propose a unified framework for generalized low-shot (one- and few-shot) medical image segmentation based on distance metric learning (DML). Unlike most existing methods which only deal with the lack of annotations while assuming abundance of data, our framework works with extreme scarcity of both, which is ideal for rare diseases. Via DML, the framework learns a multimodal mixture representation for each category, and performs dense predictions based on cosine distances between the pixels' deep embeddings and the category representations. The multimodal representations effectively utilize the inter-subject similarities and intraclass variations to overcome overfitting due to extremely limited data. In addition, we propose adaptive mixing coefficients for the multimodal mixture distributions to adaptively emphasize the modes better suited to the current input. The representations are implicitly embedded as weights of the fc layer, such that the cosine distances can be computed efficiently via forward propagation. In our experiments on brain MRI and abdominal CT datasets, the proposed framework achieves superior performances for low-shot segmentation towards standard DNN-based (3D U-Net) and classical registration-based (ANTs) methods, e.g., achieving mean Dice coefficients of 81%/69% for brain tissue/abdominal multiorgan segmentation using a single training sample, as compared to 52%/31% and 72%/35% by the U-Net and ANTs, respectively.
SafeAccess+: An Intelligent System to make Smart Home Safer and Americans with Disability Act Compliant
Venue: arXiv cs.CY 2021
Year: 2021

Smart homes are becoming ubiquitous, but they are not Americans with Disability Act (ADA) compliant. Smart homes equipped with ADA compliant appliances and services are critical for people with disabilities (i.e., visual impairments and limited mobility) to improve independence, safety, and quality of life. Despite all advancements in smart home technologies, some fundamental design and implementation issues remain. For example, people with disabilities often feel insecure to respond when someone knocks on the door or rings the doorbell. In this paper, we present an intelligent system called "SafeAccess+" to build safer and ADA compliant premises (e.g. smart homes, offices). The key functionalities of the SafeAccess+ are: 1) Monitoring the inside/outside of premises and identifying incoming people; 2) Providing users relevant information to assess incoming threats (e.g., burglary, robbery) and ongoing crimes 3) Allowing users to grant safe access to homes for friends/family members. We have addressed several technical and research challenges: - developing models to detect and recognize person/activity, generating image descriptions, designing ADA compliant end-end system. In addition, we have designed a prototype smart door showcasing the proof-of-concept. The premises are expected to be equipped with cameras placed in strategic locations that facilitate monitoring the premise 24/7 to identify incoming persons and to generate image descriptions. The system generates a pre-structured message from the image description to assess incoming threats and immediately notify the users. The completeness and generalization of models have been ensured through a rigorous quantitative evaluation. The users' satisfaction and reliability of the system has been measured using PYTHEIA scale and was rated excellent (Internal Consistency-Cronbach's alpha is 0.784, Test-retest reliability is 0.939 )
pygrank: A Python Package for Graph Node Ranking
Venue: arXiv cs.LG 2021
Year: 2021

We introduce pygrank, an open source Python package to define, run and evaluate node ranking algorithms. We provide object-oriented and extensively unit-tested algorithm components, such as graph filters, post-processors, measures, benchmarks and online tuning. Computations can be delegated to numpy, tensorflow or pytorch backends and fit in back-propagation pipelines. Classes can be combined to define interoperable complex algorithms. Within the context of this paper we compare the package with related alternatives and demonstrate its flexibility and ease of use with code examples.
Prediction of Occurrence of Extreme Events using Machine Learning
Venue: arXiv cs.LG 2021
Year: 2021

Machine learning models play a vital role in the prediction task in several fields of study. In this work, we utilize the ability of machine learning algorithms to predict the occurrence of extreme events in a nonlinear mechanical system. Extreme events are rare events that occur ubiquitously in nature. We consider four machine learning models, namely Logistic Regression, Support Vector Machine, Random Forest and Multi-Layer Perceptron in our prediction task. We train these four machine learning models using training set data and compute the performance of each model using the test set data. We show that the Multi-Layer Perceptron model performs better among the four models in the prediction of extreme events in the considered system. The persistent behaviour of the considered machine learning models is cross-checked with randomly shuffled training set and test set data.
Toward a multimodal multitask model for neurodegenerative diseases diagnosis and progression prediction
Venue: arXiv cs.CY 2021
Year: 2021

Recent studies on modelling the progression of Alzheimer's disease use a single modality for their predictions while ignoring the time dimension. However, the nature of patient data is heterogeneous and time dependent which requires models that value these factors in order to achieve a reliable diagnosis, as well as making it possible to track and detect changes in the progression of patients' condition at an early stage. This article overviews various categories of models used for Alzheimer's disease prediction with their respective learning methods, by establishing a comparative study of early prediction and detection Alzheimer's disease progression. Finally, a robust and precise detection model is proposed.
Automatic Learning of Subword Dependent Model Scales
Venue: arXiv cs.CL 2021
Year: 2021

To improve the performance of state-of-the-art automatic speech recognition systems it is common practice to include external knowledge sources such as language models or prior corrections. This is usually done via log-linear model combination using separate scaling parameters for each model. Typically these parameters are manually optimized on some held-out data. In this work we propose to optimize these scaling parameters via automatic differentiation and stochastic gradient decent similar to the neural network model parameters. We show on the LibriSpeech (LBS) and Switchboard (SWB) corpora that the model scales for a combination of attentionbased encoder-decoder acoustic model and language model can be learned as effectively as with manual tuning. We further extend this approach to subword dependent model scales which could not be tuned manually which leads to 7% improvement on LBS and 3% on SWB. We also show that joint training of scales and model parameters is possible and gives additional 6% improvement on LBS.
FAST3D: Flow-Aware Self-Training for 3D Object Detectors
Venue: arXiv cs.CV 2021
Year: 2021

In the field of autonomous driving, self-training is widely applied to mitigate distribution shifts in LiDAR-based 3D object detectors. This eliminates the need for expensive, high-quality labels whenever the environment changes (e.g., geographic location, sensor setup, weather condition). State-of-the-art self-training approaches, however, mostly ignore the temporal nature of autonomous driving data. To address this issue, we propose a flow-aware self-training method that enables unsupervised domain adaptation for 3D object detectors on continuous LiDAR point clouds. In order to get reliable pseudo-labels, we leverage scene flow to propagate detections through time. In particular, we introduce a flow-based multi-target tracker, that exploits flow consistency to filter and refine resulting tracks. The emerged precise pseudo-labels then serve as a basis for model re-training. Starting with a pre-trained KITTI model, we conduct experiments on the challenging Waymo Open Dataset to demonstrate the effectiveness of our approach. Without any prior target domain knowledge, our results show a significant improvement over the state-of-the-art.
Neuro-Symbolic Forward Reasoning
Venue: arXiv cs.AI 2021
Year: 2021

Reasoning is an essential part of human intelligence and thus has been a long-standing goal in artificial intelligence research. With the recent success of deep learning, incorporating reasoning with deep learning systems, i.e., neuro-symbolic AI has become a major field of interest. We propose the Neuro-Symbolic Forward Reasoner (NSFR), a new approach for reasoning tasks taking advantage of differentiable forward-chaining using first-order logic. The key idea is to combine differentiable forward-chaining reasoning with object-centric (deep) learning. Differentiable forward-chaining reasoning computes logical entailments smoothly, i.e., it deduces new facts from given facts and rules in a differentiable manner. The object-centric learning approach factorizes raw inputs into representations in terms of objects. Thus, it allows us to provide a consistent framework to perform the forward-chaining inference from raw inputs. NSFR factorizes the raw inputs into the object-centric representations, converts them into probabilistic ground atoms, and finally performs differentiable forward-chaining inference using weighted rules for inference. Our comprehensive experimental evaluations on object-centric reasoning data sets, 2D Kandinsky patterns and 3D CLEVR-Hans, and a variety of tasks show the effectiveness and advantage of our approach.
How Far Two UAVs Should Be subject to Communication Uncertainties
Venue: arXiv cs.RO 2021
Year: 2021

Unmanned aerial vehicles are now becoming increasingly accessible to amateur and commercial users alike. A safety air traffic management system is needed to help ensure that every newest entrant into the sky does not collide with others. Much research has been done to design various methods to perform collision avoidance with obstacles. However, how to decide the safety radius subject to communication uncertainties is still suspended. Based on assumptions on communication uncertainties and supposed control performance, a separation principle of the safety radius design and controller design is proposed. With it, the safety radius corresponding to the safety area in the design phase (without uncertainties) and flight phase (subject to uncertainties) are studied. Furthermore, the results are extended to multiple obstacles. Simulations and experiments are carried out to show the effectiveness of the proposed methods.
Don't Judge Me by My Face : An Indirect Adversarial Approach to Remove Sensitive Information From Multimodal Neural Representation in Asynchronous Job Video Interviews
Venue: arXiv cs.CV 2021
Year: 2021

se of machine learning for automatic analysis of job interview videos has recently seen increased interest. Despite claims of fair output regarding sensitive information such as gender or ethnicity of the candidates, the current approaches rarely provide proof of unbiased decision-making, or that sensitive information is not used. Recently, adversarial methods have been proved to effectively remove sensitive information from the latent representation of neural networks. However, these methods rely on the use of explicitly labeled protected variables (e.g. gender), which cannot be collected in the context of recruiting in some countries (e.g. France). In this article, we propose a new adversarial approach to remove sensitive information from the latent representation of neural networks without the need to collect any sensitive variable. Using only a few frames of the interview, we train our model to not be able to find the face of the candidate related to the job interview in the inner layers of the model. This, in turn, allows us to remove relevant private information from these layers. Comparing our approach to a standard baseline on a public dataset with gender and ethnicity annotations, we show that it effectively removes sensitive information from the main network. Moreover, to the best of our knowledge, this is the first application of adversarial techniques for obtaining a multimodal fair representation in the context of video job interviews. In summary, our contributions aim at improving fairness of the upcoming automatic systems processing videos of job interviews for equality in job selection.
The complexity of the Quantified CSP having the polynomially generated powers property
Venue: arXiv cs.CC 2021
Year: 2021

It is known that if an algebra of polymorphisms of the constraint language has the Polynomially Generated Powers (PGP) Property then the Quantified CSP can be reduced to the CSP over the same constraint language with constants. The only limitation of this reduction is that it is applicable only for the constraint languages with constants. We drastically simplified the reduction and generalized it for constraint languages without constants. As a result, we completely classified the complexity of the QCSP for constraint languages having the PGP property.
Provable Hierarchy-Based Meta-Reinforcement Learning
Venue: arXiv cs.LG 2021
Year: 2021

Hierarchical reinforcement learning (HRL) has seen widespread interest as an approach to tractable learning of complex modular behaviors. However, existing work either assume access to expert-constructed hierarchies, or use hierarchy-learning heuristics with no provable guarantees. To address this gap, we analyze HRL in the meta-RL setting, where a learner learns latent hierarchical structure during meta-training for use in a downstream task. We consider a tabular setting where natural hierarchical structure is embedded in the transition dynamics. Analogous to supervised meta-learning theory, we provide "diversity conditions" which, together with a tractable optimism-based algorithm, guarantee sample-efficient recovery of this natural hierarchy. Furthermore, we provide regret bounds on a learner using the recovered hierarchy to solve a meta-test task. Our bounds incorporate common notions in HRL literature such as temporal and state/action abstractions, suggesting that our setting and analysis capture important features of HRL in practice.
TransFusion: Cross-view Fusion with Transformer for 3D Human Pose Estimation
Venue: arXiv cs.CV 2021
Year: 2021

Estimating the 2D human poses in each view is typically the first step in calibrated multi-view 3D pose estimation. But the performance of 2D pose detectors suffers from challenging situations such as occlusions and oblique viewing angles. To address these challenges, previous works derive point-to-point correspondences between different views from epipolar geometry and utilize the correspondences to merge prediction heatmaps or feature representations. Instead of post-prediction merge/calibration, here we introduce a transformer framework for multi-view 3D pose estimation, aiming at directly improving individual 2D predictors by integrating information from different views. Inspired by previous multi-modal transformers, we design a unified transformer architecture, named TransFusion, to fuse cues from both current views and neighboring views. Moreover, we propose the concept of epipolar field to encode 3D positional information into the transformer model. The 3D position encoding guided by the epipolar field provides an efficient way of encoding correspondences between pixels of different views. Experiments on Human 3.6M and Ski-Pose show that our method is more efficient and has consistent improvements compared to other fusion methods. Specifically, we achieve 25.8 mm MPJPE on Human 3.6M with only 5M parameters on 256 x 256 resolution.
On-the-fly Code Activation for Attack Surface Reduction
Venue: arXiv cs.CR 2021
Year: 2021

Modern code reuse attacks are taking full advantage of bloated software. Attackers piece together short sequences of instructions in otherwise benign code to carry out malicious actions. Eliminating these reusable code snippets, known as gadgets, has become one of the prime concerns of attack surface reduction. The aim is to break these chains of gadgets, thereby making such code reuse attacks impossible or substantially less common. Previous work on attack surface reduction has typically tried to eliminate such attacks by subsetting the application, e.g. via user-specified inputs, configurations, or features, or by focusing on third-party libraries to achieve high gadget reductions with minimal interference to the application. In this work we present a general, whole-program attack surface reduction technique called OCA that significantly reduces gadgets and has minor performance degradation. OCA requires no user inputs and leaves all features intact. OCA identifies specific program points and through analysis determines key function sets to enable/disable at runtime. The runtime system, thus, controls the set of enabled functions during execution, thereby significantly reducing the set of active gadgets an attacker can use, and by extension, cutting down the set of active gadget chains dramatically. On SPEC CPU 2017, our framework achieves 73.2% total gadget reduction with only 4% average slowdown. On 10 GNU coreutils applications, it achieves 87.2% reduction. On the nginx server it achieves 80.3% reduction with 2% slowdown. We also provide a gadget chain-breaking study across all applications, and show that our framework breaks the shell-spawning chain in all cases.
BGaitR-Net: Occluded Gait Sequence reconstructionwith temporally constrained model for gait recognition
Venue: arXiv cs.CV 2021
Year: 2021

Recent advancements in computational resources and Deep Learning methodologies has significantly benefited development of intelligent vision-based surveillance applications. Gait recognition in the presence of occlusion is one of the challenging research topics in this area, and the solutions proposed by researchers to date lack in robustness and also dependent of several unrealistic constraints, which limits their practical applicability. We improve the state-of-the-art by developing novel deep learning-based algorithms to identify the occluded frames in an input sequence and next reconstruct these occluded frames by exploiting the spatio-temporal information present in the gait sequence. The multi-stage pipeline adopted in this work consists of key pose mapping, occlusion detection and reconstruction, and finally gait recognition. While the key pose mapping and occlusion detection phases are done %using Constrained KMeans Clustering and via a graph sorting algorithm, reconstruction of occluded frames is done by fusing the key pose-specific information derived in the previous step along with the spatio-temporal information contained in a gait sequence using a Bi-Directional Long Short Time Memory. This occlusion reconstruction model has been trained using synthetically occluded CASIA-B and OU-ISIR data, and the trained model is termed as Bidirectional Gait Reconstruction Network BGait-R-Net. Our LSTM-based model reconstructs occlusion and generates frames that are temporally consistent with the periodic pattern of a gait cycle, while simultaneously preserving the body structure.
Fair and Efficient Allocations of Chores under Bivalued Preferences
Venue: arXiv cs.GT 2021
Year: 2021

We study the problem of fair and efficient allocation of a set of indivisible chores to agents with additive cost functions. We consider the popular fairness notion of envy-freeness up to one good (EF1) with the efficiency notion of Pareto-optimality (PO). While it is known that an EF1+PO allocation exists and can be computed in pseudo-polynomial time in the case of goods, the same problem is open for chores. Our first result is a strongly polynomial-time algorithm for computing an EF1+PO allocation for bivalued instances, where agents have (at most) two disutility values for the chores. To the best of our knowledge, this is the first non-trivial class of indivisible chores to admit an EF1+PO allocation and an efficient algorithm for its computation. We also study the problem of computing an envy-free (EF) and PO allocation for the case of divisible chores. While the existence of an EF+PO allocation is known via competitive equilibrium with equal incomes, its efficient computation is open. Our second result shows that for bivalued instances, an EF+PO allocation can be computed in strongly polynomial-time.