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Recent Advances in Concept Drift Adaptation Methods for Deep Learning | null | In the ``Big Data'' age, the amount and distribution of data have increased wildly and changed over time in various time-series-based tasks, e.g weather prediction, network intrusion detection. However, deep learning models may become outdated facing variable input data distribution, which is called concept drift. To address this problem, large number of samples are usually required to update deep learning models, which is impractical in many realistic applications. This challenge drives researchers to explore the effective ways to adapt deep learning models to concept drift. In this paper, we first mathematically describe the categories of concept drift including abrupt drift, gradual drift, recurrent drift, incremental drift. We then divide existing studies into two categories (i.e., model parameter updating and model structure updating), and analyze the pros and cons of representative methods in each category. Finally, we evaluate the performance of these methods, and point out the future directions of concept drift adaptation for deep learning. | Liheng Yuan, Heng Li, Beihao Xia, Cuiying Gao, Mingyue Liu, Wei Yuan, Xinge You | null | null | 2,022 | ijcai |
Vision-based Intention and Trajectory Prediction in Autonomous Vehicles: A Survey | null | This survey targets intention and trajectory prediction in Autonomous Vehicles (AV), as AV companies compete to create dedicated prediction pipelines to avoid collisions. The survey starts with a formal definition of the prediction problem and highlights its challenges, to then critically compare the models proposed in the last 2-3 years in terms of how they overcome these challenges. Further, it lists the latest methodological and technical trends in the field and comments on the efficacy of different machine learning blocks in modelling various aspects of the prediction problem. It also summarises the popular datasets and metrics used to evaluate prediction models, before concluding with the possible research gaps and future directions. | Izzeddin Teeti, Salman Khan, Ajmal Shahbaz, Andrew Bradley, Fabio Cuzzolin | null | null | 2,022 | ijcai |
Problem Compilation for Multi-Agent Path Finding: a Survey | null | Multi-agent path finding (MAPF) attracts considerable attention in artificial intelligence community. The task in the standard MAPF is to find discrete paths through which agents can navigate from their starting positions to individual goal positions. The combination of two additional requirements makes the problem computationally challenging: agents must not collide with each other and the paths must be optimal with respect to some objective. Two major approaches to optimal MAPF solving include dedicated search-based methods, and compilation-based methods that reduce a MAPF instance to an instance in a different formalism, for which an efficient solver exists. In this survey, we summarize major compilation-based solvers for MAPF using CSP, SAT, and MILP formalisms. We explain the core ideas of the solvers in a simplified and unified way while preserving the merit making them more accessible for a wider audience. | Pavel Surynek | null | null | 2,022 | ijcai |
Towards Verifiable Federated Learning | null | Federated learning (FL) is an emerging paradigm of collaborative machine learning that preserves user privacy while building powerful models. Nevertheless, due to the nature of open participation by self-interested entities, it needs to guard against potential misbehaviours by legitimate FL participants. FL verification techniques are promising solutions for this problem. They have been shown to effectively enhance the reliability of FL networks and build trust among participants. Verifiable FL has become an emerging topic of research that has attracted significant interest from the academia and the industry alike. Currently, there is no comprehensive survey on the field of verifiable federated learning, which is interdisciplinary in nature and can be challenging for researchers to enter into. In this paper, we bridge this gap by reviewing works focusing on verifiable FL. We propose a novel taxonomy for verifiable FL covering both centralised and decentralised settings, summarise the commonly adopted performance evaluation approaches, and discuss promising directions towards a versatile verifiable FL framework. | Yanci Zhang, Han Yu | null | null | 2,022 | ijcai |
On the First-Order Rewritability of Ontology-Mediated Queries in Linear Temporal Logic (Extended Abstract) | null | We argue that linear temporal logic LTL in tandem with monadic first-order logic can be used as a ba- sic language for ontology-based access to tempo- ral data and obtain a classification of the resulting ontology-mediated queries according to the type of standard first-order queries they can be rewritten to. | Alessandro Artale, Roman Kontchakov, Alisa Kovtunova, Vladislav Ryzhikov, Frank Wolter, Michael Zakharyaschev | null | null | 2,022 | ijcai |
A Survey on Neural Open Information Extraction: Current Status and Future Directions | null | Open Information Extraction (OpenIE) facilitates domain-independent discovery of relational facts from large corpora. The technique well suits many open-world natural language understanding scenarios, such as automatic knowledge base construction, open-domain question answering, and explicit reasoning. Thanks to the rapid development in deep learning technologies, numerous neural OpenIE architectures have been proposed and achieve considerable performance improvement. In this survey, we provide an extensive overview of the state-of-the-art neural OpenIE models, their key design decisions, strengths and weakness. Then, we discuss limitations of current solutions and the open issues in OpenIE problem itself. Finally we list recent trends that could help expand its scope and applicability, setting up promising directions for future research in OpenIE. To our best knowledge, this paper is the first review on neural OpenIE. | Shaowen Zhou, Bowen Yu, Aixin Sun, Cheng Long, Jingyang Li, Jian Sun | null | null | 2,022 | ijcai |
Abstraction for Deep Reinforcement Learning | null | We characterise the problem of abstraction in the context of deep reinforcement learning. Various well established approaches to analogical reasoning and associative memory might be brought to bear on this issue, but they present difficulties because of the need for end-to-end differentiability. We review developments in AI and machine learning that could facilitate their adoption. | Murray Shanahan, Melanie Mitchell | null | null | 2,022 | ijcai |
A Survey on Gradient Inversion: Attacks, Defenses and Future Directions | null | Recent studies have shown that the training samples can be recovered from gradients, which are called Gradient Inversion (GradInv) attacks. However, there remains a lack of extensive surveys covering recent advances and thorough analysis of this issue. In this paper, we present a comprehensive survey on GradInv, aiming to summarize the cutting-edge research and broaden the horizons for different domains. Firstly, we propose a taxonomy of GradInv attacks by characterizing existing attacks into two paradigms: iteration- and recursion-based attacks. In particular, we dig out some critical ingredients from the iteration-based attacks, including data initialization, model training and gradient matching. Second, we summarize emerging defense strategies against GradInv attacks. We find these approaches focus on three perspectives covering data obscuration, model improvement and gradient protection. Finally, we discuss some promising directions and open problems for further research. | Rui Zhang, Song Guo, Junxiao Wang, Xin Xie, Dacheng Tao | null | null | 2,022 | ijcai |
A Survey of Risk-Aware Multi-Armed Bandits | null | In several applications such as clinical trials and financial portfolio optimization, the expected value (or the average reward) does not satisfactorily capture the merits of a drug or a portfolio. In such applications, risk plays a crucial role, and a risk-aware performance measure is preferable, so as to capture losses in the case of adverse events. This survey aims to consolidate and summarise the existing research on risk measures, specifically in the context of multi-armed bandits. We review various risk measures of interest, and comment on their properties. Next, we review existing concentration inequalities for various risk measures. Then, we proceed to defining risk-aware bandit problems, We consider algorithms for the regret minimization setting, where the exploration-exploitation tradeoff manifests, as well as the best arm identification setting, which is a pure exploration problem—both in the context of risk-sensitive measures. We conclude by commenting on persisting challenges and fertile areas for future research. | Vincent Y. F. Tan, Prashanth L.A., Krishna Jagannathan | null | null | 2,022 | ijcai |
Detecting and Understanding Harmful Memes: A Survey | null | The automatic identification of harmful content online is of major concern for social media platforms, policymakers, and society. Researchers have studied textual, visual, and audio content, but typically in isolation. Yet, harmful content often combines multiple modalities, as in the case of memes. With this in mind, here we offer a comprehensive survey with a focus on harmful memes. Based on a systematic analysis of recent literature, we first propose a new typology of harmful memes, and then we highlight and summarize the relevant state of the art. One interesting finding is that many types of harmful memes are not really studied, e.g., such featuring self-harm and extremism, partly due to the lack of suitable datasets. We further find that existing datasets mostly capture multi-class scenarios, which are not inclusive of the affective spectrum that memes can represent. Another observation is that memes can propagate globally through repackaging in different languages and that they can also be multilingual, blending different cultures. We conclude by highlighting several challenges related to multimodal semiotics, technological constraints, and non-trivial social engagement, and we present several open-ended aspects such as delineating online harm and empirically examining related frameworks and assistive interventions, which we believe will motivate and drive future research. | Shivam Sharma, Firoj Alam, Md. Shad Akhtar, Dimitar Dimitrov, Giovanni Da San Martino, Hamed Firooz, Alon Halevy, Fabrizio Silvestri, Preslav Nakov, Tanmoy Chakraborty | null | null | 2,022 | ijcai |
Data Valuation in Machine Learning: "Ingredients", Strategies, and Open Challenges | null | Data valuation in machine learning (ML) is an emerging research area that studies the worth of data in ML. Data valuation is used in collaborative ML to determine a fair compensation for every data owner and in interpretable ML to identify the most responsible, noisy, or misleading training examples. This paper presents a comprehensive technical survey that provides a new formal study of data valuation in ML through its “ingredients” and the corresponding properties, grounds the discussion of common desiderata satisfied by existing data valuation strategies on our proposed ingredients, and identifies open research challenges for designing new ingredients, data valuation strategies, and cost reduction techniques. | Rachael Hwee Ling Sim, Xinyi Xu, Bryan Kian Hsiang Low | null | null | 2,022 | ijcai |
Few-Shot Learning on Graphs | null | Graph representation learning has attracted tremendous attention due to its remarkable performance in many real-world applications. However, prevailing supervised graph representation learning models for specific tasks often suffer from label sparsity issue as data labeling is always time and resource consuming. In light of this, few-shot learning on graphs (FSLG), which combines the strengths of graph representation learning and few-shot learning together, has been proposed to tackle the performance degradation in face of limited annotated data challenge. There have been many studies working on FSLG recently. In this paper, we comprehensively survey these work in the form of a series of methods and applications. Specifically, we first introduce FSLG challenges and bases, then categorize and summarize existing work of FSLG in terms of three major graph mining tasks at different granularity levels, i.e., node, edge, and graph. Finally, we share our thoughts on some future research directions of FSLG. The authors of this survey have contributed significantly to the AI literature on FSLG over the last few years. | Chuxu Zhang, Kaize Ding, Jundong Li, Xiangliang Zhang, Yanfang Ye, Nitesh V. Chawla, Huan Liu | null | null | 2,022 | ijcai |
On the Opportunity of Causal Learning in Recommendation Systems: Foundation, Estimation, Prediction and Challenges | null | Recently, recommender system (RS) based on causal inference has gained much attention in the industrial community, as well as the states of the art performance in many prediction and debiasing tasks. Nevertheless, a unified causal analysis framework has not been established yet. Many causal-based prediction and debiasing studies rarely discuss the causal interpretation of various biases and the rationality of the corresponding causal assumptions. In this paper, we first provide a formal causal analysis framework to survey and unify the existing causal-inspired recommendation methods, which can accommodate different scenarios in RS. Then we propose a new taxonomy and give formal causal definitions of various biases in RS from the perspective of violating the assumptions adopted in causal analysis. Finally, we formalize many debiasing and prediction tasks in RS, and summarize the statistical and machine learning-based causal estimation methods, expecting to provide new research opportunities and perspectives to the causal RS community. | Peng Wu, Haoxuan Li, Yuhao Deng, Wenjie Hu, Quanyu Dai, Zhenhua Dong, Jie Sun, Rui Zhang, Xiao-Hua Zhou | null | null | 2,022 | ijcai |
Recent Advances and New Frontiers in Spiking Neural Networks | null | In recent years, spiking neural networks (SNNs) have received extensive attention in brain-inspired intelligence due to their rich spatially-temporal dynamics, various encoding methods, and event-driven characteristics that naturally fit the neuromorphic hardware. With the development of SNNs, brain-inspired intelligence, an emerging research field inspired by brain science achievements and aiming at artificial general intelligence, is becoming hot. This paper reviews recent advances and discusses new frontiers in SNNs from five major research topics, including essential elements (i.e., spiking neuron models, encoding methods, and topology structures), neuromorphic datasets, optimization algorithms, software, and hardware frameworks. We hope our survey can help researchers understand SNNs better and inspire new works to advance this field. | Duzhen Zhang, Shuncheng Jia, Qingyu Wang | null | null | 2,022 | ijcai |
On Quantifying Literals in Boolean Logic and its Applications to Explainable AI (Extended Abstract) | null | Quantified Boolean logic results from adding operators to Boolean logic for existentially and universally quantifying variables. This extends the reach of Boolean logic by enabling a variety of applications that have been explored over the decades. The existential quantification of literals (variable states) and its applications have also been studied in the literature. We complement this by studying universal literal quantification and its applications, particularly to explainable AI. We also provide a novel semantics for quantification and discuss the interplay between variable/literal and existential/universal quantification. We further identify classes of Boolean formulas and circuits that allow efficient quantification. Literal quantification is more fine-grained than variable quantification, which leads to a refinement of quantified Boolean logic with literal quantification as its primitive. | Adnan Darwiche, Pierre Marquis | null | null | 2,022 | ijcai |
Learning Realistic Patterns from Visually Unrealistic Stimuli: Generalization and Data Anonymization (Extended Abstract) | null | Good training data is a prerequisite to develop useful Machine Learning applications. However, in many domains existing data sets cannot be shared due to privacy regulations (e.g., from medical studies). This work investigates a simple yet unconventional approach for anonymized data synthesis to enable third parties to benefit from such anonymized data. We explore the feasibility of learning implicitly from visually unrealistic, task-relevant stimuli, which are synthesized by exciting the neurons of a trained deep neural network. As such, neuronal excitation can be used to generate synthetic stimuli. The stimuli data is used to train new classification models. Furthermore, we extend this framework to inhibit representations that are associated with specific individuals. Extensive comparative empirical investigation shows that different algorithms trained on the stimuli are able to generalize successfully on the same task as the original model. | Konstantinos Nikolaidis, Stein Kristiansen, Thomas Plagemann, Vera Goebel, Knut Liestøl, Mohan Kankanhalli, Gunn-Marit Traaen, Britt Øverland, Harriet Akre, Lars Aakeroy, Sigurd Steinshamn | null | null | 2,022 | ijcai |
Situation Calculus for Controller Synthesis in Manufacturing Systems with First-Order State Representation (Extended Abstract) | null | Manufacturing is transitioning from a mass production model to a service model in which facilities `bid' for previously unseen products. To decide whether to bid for a previously unseen product, a facility must be able to synthesize, on the fly, a process plan controller that delegates abstract manufacturing tasks in a supplied process recipe to the available manufacturing resources. First-order representations of the state are commonly considered in reasoning about action in AI. Here we show that we can leverage the wide literature on the Situation Calculus automatically synthesize such controllers. We identify two important decidable cases---finite domains and bounded action theories---for which we provide practical synthesis techniques. | Giuseppe De Giacomo, Paolo Felli, Brian Logan, Fabio Patrizi, Sebastian Sardiña | null | null | 2,022 | ijcai |
Abstraction for Non-Ground Answer Set Programs (Extended Abstract) | null | Abstraction is a powerful technique that has not been considered much for nonmonotonic reasoning formalisms including Answer Set Programming (ASP), apart from related simplification methods. We introduce a notion for abstracting from the domain of an ASP program that shrinks the domain size and over-approximates the set of answer sets, as well as an abstraction-&-refinement methodology that, starting from an initial abstraction, automatically yields an abstraction with an associated answer set matching an answer set of the original program if one exists. Experiments reveal the potential of the approach, by its ability to focus on the program parts that cause unsatisfiability and by achieving concrete abstract answer sets that merely reflect relevant details. | Zeynep G. Saribatur, Thomas Eiter, Peter Schüller | null | null | 2,022 | ijcai |
Overlapping Communities and Roles in Networks with Node Attributes: Probabilistic Graphical Modeling, Bayesian Formulation and Variational Inference (Extended Abstract) | null | We study the seamless integration of community discovery and behavioral role analysis, in the domain of networks with node attributes. In particular, we focus on unifying the two tasks, by explicitly harnessing node attributes and behavioral role patterns in a principled manner. To this end, we propose two Bayesian probabilistic generative models of networks, whose novelty consists in the interrelationship of overlapping communities, roles, their behavioral patterns and node attributes. The devised models allow for a variety of exploratory, descriptive and predictive tasks. These are carried out through mean-field variational inference, which is in turn mathematically derived and implemented into a coordinate-ascent algorithm.
A wide spectrum of experiments is designed, to validate the devised models against three classes of state-of-the-art competitors using various real-world benchmark data sets from different social networking services. | Gianni Costa, Riccardo Ortale | null | null | 2,022 | ijcai |
Making Sense of Raw Input (Extended Abstract) | null | How should a machine intelligence perform unsupervised structure discovery over streams of sensory input? One approach to this problem is to cast it as an apperception task. Here, the task is to construct an explicit interpretable theory that both explains the sensory sequence and also satisfies a set of unity conditions, designed to ensure that the constituents of the theory are connected in a relational structure.
However, the original formulation of the apperception task had one fundamental limitation: it assumed the raw sensory input had already been parsed using a set of discrete categories, so that all the system had to do was receive this already-digested symbolic input, and make sense of it. But what if we don't have access to pre-parsed input? What if our sensory sequence is raw unprocessed information?
The central contribution of this paper is a neuro-symbolic framework for distilling interpretable theories out of streams of raw, unprocessed sensory experience.
First, we extend the definition of the apperception task to include ambiguous (but still symbolic) input: sequences of sets of disjunctions. Next, we use a neural network to map raw sensory input to disjunctive input. Our binary neural network is encoded as a logic program, so the weights of the network and the rules of the theory can be solved jointly as a single SAT problem. This way, we are able to jointly learn how to perceive (mapping raw sensory information to concepts) and apperceive (combining concepts into declarative rules). | Richard Evans, Matko Bošnjak, Lars Buesing, Kevin Ellis, David Pfau, Pushmeet Kohli, Marek Sergot | null | null | 2,022 | ijcai |
Local and Global Explanations of Agent Behavior: Integrating Strategy Summaries with Saliency Maps (Extended Abstract) | null | With advances in reinforcement learning (RL), agents are now being developed in high-stakes application domains such as healthcare and transportation. Explaining the behavior of these agents is challenging, as they act in large state spaces, and their decision-making can be affected by delayed rewards. In this paper, we explore a combination of explanations that attempt to convey the global behavior of the agent and local explanations which provide information regarding the agent's decision-making in a particular state. Specifically, we augment strategy summaries that demonstrate the agent's actions in a range of states with saliency maps highlighting the information it attends to. Our user study shows that intelligently choosing what states to include in the summary (global information) results in an improved analysis of the agents. We find mixed results with respect to augmenting summaries with saliency maps (local information). | Tobias Huber, Katharina Weitz, Elisabeth André, Ofra Amir | null | null | 2,022 | ijcai |
Intelligence in Strategic Games (Extended Abstract) | null | If an agent, or a coalition of agents, has a strategy, knows that she has a strategy, and knows what the strategy is, then she has a know-how strategy. Several modal logics of coalition power for know-how strategies have been studied before.
The contribution of the article is three-fold. First, it proposes a new class of know-how strategies that depend on the intelligence information about the opponents' actions. Second, it shows that the coalition power modality for the proposed new class of strategies cannot be expressed through the standard know-how modality. Third, it gives a sound and complete logical system that describes the interplay between the coalition power modality with intelligence and the distributed knowledge modality in games with imperfect information. | Pavel Naumov, Yuan Yuan | null | null | 2,022 | ijcai |
Experimental Comparison and Survey of Twelve Time Series Anomaly Detection Algorithms (Extended Abstract) | null | The existence of an anomaly detection method that is optimal for all domains is a myth. Thus, there exists a plethora of anomaly detection methods which increases every year for a wide variety of domains. But a strength can also be a weakness; given this massive library of methods, how can one select the best method for their application? Current literature is focused on creating new anomaly detection methods or large frameworks for experimenting with multiple methods at the same time. However, and especially as the literature continues to expand, an extensive evaluation of every anomaly detection method is simply not feasible. To reduce this evaluation burden, we present guidelines to intelligently choose the optimal anomaly detection methods based on the characteristics the time series displays such as seasonality, trend, level change concept drift, and missing time steps. We provide a comprehensive experimental validation and survey of twelve anomaly detection methods over different time series characteristics to form guidelines based on several metrics: the AUC (Area Under the Curve), windowed F-score, and Numenta Anomaly Benchmark (NAB) scoring model. Applying our methodologies can save time and effort by surfacing the most promising anomaly detection methods instead of experimenting extensively with a rapidly expanding library of anomaly detection methods, especially in an online setting. | Cynthia Freeman, Jonathan Merriman, Ian Beaver, Abdullah Mueen | null | null | 2,022 | ijcai |
Dimensional Inconsistency Measures and Postulates in Spatio-Temporal Databases (Extended Abstract) | null | We define and investigate new inconsistency measures that are particularly suitable for dealing with inconsistent spatio-temporal information, as they explicitly take into account the spatial and temporal dimensions, as well as the dimension concerning the identifiers of the monitored objects. Specifically, we first define natural measures that look at individual dimensions (time, space, and objects), and then propose measures based on the notion of a repair. We then analyze their behavior w.r.t. common postulates defined for classical propositional knowledge bases, and find that the latter are not suitable for spatio-temporal databases, in that the proposed inconsistency measures do not often satisfy them. In light of this, we argue that also postulates should explicitly take into account the spatial, temporal, and object dimensions, and thus define ``dimension-aware'' counterparts of common postulates, which are indeed often satisfied by the new inconsistency measures. Finally, we study the complexity of the proposed inconsistency measures. | John Grant, Maria Vanina Martinez, Cristian Molinaro, Francesco Parisi | null | null | 2,022 | ijcai |
A Theoretical Perspective on Hyperdimensional Computing (Extended Abstract) | null | Hyperdimensional (HD) computing is a set of neurally inspired methods for computing on high-dimensional, low-precision, distributed representations of data. These representations can be combined with simple, neurally plausible algorithms to effect a variety of information processing tasks. HD computing has recently garnered significant interest from the computer hardware community as an energy-efficient, low-latency, and noise-robust tool for solving learning problems. We present a novel mathematical framework that unifies analysis of HD computing architectures, and provides general, non-asymptotic, sufficient conditions under which HD information processing techniques will succeed. | Anthony Thomas, Sanjoy Dasgupta, Tajana Rosing | null | null | 2,022 | ijcai |
sunny-as2: Enhancing SUNNY for Algorithm Selection (Extended Abstract) | null | SUNNY is a k-nearest neighbors based Algorithm Selection (AS) approach that schedules and runs a number of solvers for a given unforeseen problem. In this work we present sunny-as2, an enhancement of SUNNY for generic AS scenarios that advances the original approach with wrapper-based feature selection, neighborhood-size configuration and a greedy approach to speed-up the training phase. Empirical evidence shows that sunny-as2 is competitive w.r.t. state-of-the-art AS approaches. | Tong Liu, Roberto Amadini, Maurizio Gabbrielli, Jacopo Mauro | null | null | 2,022 | ijcai |
Bayesian Auctions with Efficient Queries (Extended Abstract) | null | Designing dominant-strategy incentive compatible (DSIC) mechanisms for a seller to generate (approximately) optimal revenue by selling items to players is a fundamental problem in Bayesian mechanism design. However, most existing studies assume that the seller knows the entire distribution from which the players’ values are drawn. Unfortunately, this assumption may not hold in reality: for example, when the distributions have exponentially large supports or do not have succinct representations. In this work we consider, for the first time, the query complexityof Bayesian mechanisms. The seller only has limited oracle accesses to the players’ distributions, via quantile queriesand value queries. For single-item auctions, we design mechanisms with logarithmicnumber of value or quantile queries which achieve almost optimal revenue. We then prove logarithmic lower-bounds, i.e., logarithmic number of queries are necessary for any constant approximation DSIC mechanisms, even when randomized and adaptive queries are allowed. Thus our mechanisms are almost optimal regarding query complexity. Our lower-bounds can be extended to multi-item auctions with monotone subadditive valuations, and we complement this part with constant approximation mechanisms for unit-demand or additive valuation functions. Our results are robust even if the answers to the queries contain noises. | Jing Chen, Bo Li, Yingkai Li, Pinyan Lu | null | null | 2,022 | ijcai |
Measuring the Occupational Impact of AI: Tasks, Cognitive Abilities and AI Benchmarks (Extended Abstract)* | null | We present a framework for analysing the impact of AI on occupations. This framework maps 59 generic tasks from different occupational datasets to 14 cognitive abilities and these to a comprehensive list of 328 AI benchmarks used to evaluate research intensity in AI. The use of cognitive abilities as an intermediate layer allows for an identification of potential AI exposure for tasks for which AI applications have not been explicitly programmed. We provide insights into the abilities through which AI is most likely to affect jobs, and we show how some of the abilities where AI research is currently very intense are linked to tasks with comparatively limited labour input in the labour markets of advanced economies. | Songül Tolan, Annarosa Pesole, Fernando Martínez-Plumed, Enrique Fernández-Macías, José Hernández-Orallo, Emilia Gómez | null | null | 2,022 | ijcai |
Improving the Effectiveness and Efficiency of Stochastic Neighbour Embedding with Isolation Kernel (Extended Abstract) | null | This paper presents a new insight into improving the performance of Stochastic Neighbour Embedding (t-SNE) by using Isolation kernel instead of Gaussian kernel. We show that Isolation kernel addresses two deficiencies of t-SNE that employs Gaussian kernel, and the use of Isolation kernel enables t-SNE to deal with large-scale datasets in less runtime without trading off accuracy, unlike existing methods used in speeding up t-SNE. | Ye Zhu, Kai Ming Ting | null | null | 2,022 | ijcai |
Abstraction in Data-Sparse Task Transfer (Extended Abstract) | null | When a robot adapts a learned task for a novel environment, any changes to objects in the novel environment have an unknown effect on its task execution. For example, replacing an object in a pick-and-place task affects where the robot should target its actions, but does not necessarily affect the underlying action model. In contrast, replacing a tool that the robot will use to complete a task will effectively alter its end-effector pose with respect to the robot's base coordinate system, and thus the robot's motion must be replanned accordingly.
These examples highlight the relationship among (i) differences between the source and target environments, (ii) the level of abstraction at which a robot's task model should be represented to enable transfer to the target environment, and (iii) the information needed to ground the abstracted task representation in the target environment. In this abstract, summarizing our full article [Fitzgerald et al., 2021], we present our taxonomy of transfer problems based on this relationship. We also describe a knowledge representation called the Tiered Task Abstraction (TTA) and demonstrate its applicability to a variety of transfer problems in the taxonomy. Our experimental results indicate a trade-off between the generality and data requirements of a task representation, and reinforce the need for multiple transfer methods that operate at different levels of abstraction. | Tesca Fitzgerald, Ashok Goel, Andrea Thomaz | null | null | 2,022 | ijcai |
Ethics and Governance of Artificial Intelligence: A Survey of Machine Learning Researchers (Extended Abstract) | null | Machine learning (ML) and artificial intelligence (AI) researchers play an important role in the ethics and governance of AI, including through their work, advocacy, and choice of employment. Nevertheless, this influential group's attitudes are not well understood, undermining our ability to discern consensuses or disagreements between AI/ML researchers. To examine these researchers' views, we conducted a survey of those who published in two top AI/ML conferences (N = 524). We compare these results with those from a 2016 survey of AI/ML researchers and a 2018 survey of the US public. We find that AI/ML researchers place high levels of trust in international organizations and scientific organizations to shape the development and use of AI in the public interest; moderate trust in most Western tech companies; and low trust in national militaries, Chinese tech companies, and Facebook. While the respondents were overwhelmingly opposed to AI/ML researchers working on lethal autonomous weapons, they are less opposed to researchers working on other military applications of AI, particularly logistics algorithms. A strong majority of respondents think that AI safety research should be prioritized more and a majority that ML institutions should conduct pre-publication review to assess potential harms. Being closer to the technology itself, AI/ML researchers are well placed to highlight new risks and develop technical solutions, so this novel data has broad relevance. The findings should help to improve how researchers, private sector executives, and policymakers think about regulations, governance frameworks, guiding principles, and national and international governance strategies for AI. | Baobao Zhang, Markus Anderljung, Lauren Kahn, Noemi Dreksler, Michael C. Horowitz, Allan Dafoe | null | null | 2,022 | ijcai |
Counting, Sampling, and Synthesis: The Quest for Scalability | null | The current generation of symbolic reasoning techniques excel at the qualitative tasks (i.e., when the answer is Yes or No); such techniques sufficed for traditional systems whose design sought to achieve deterministic behavior. In contrast, modern computing systems crucially rely on the statistical methods to account for the uncertainty in the environment, and to reason about behavior of these systems, there is need to look beyond qualitative symbolic reasoning techniques. We will discuss our work focused on the development of the next generation of automated reasoning techniques that can perform higher-order tasks such as quantitative measurement, sampling of representative behavior, and automated synthesis of systems.
From a core technical perspective, our work builds on the SAT revolution, which refers to algorithmic advances in combinatorial solving techniques for the fundamental problem of
satisfiability (SAT), i.e., whether it is possible to satisfy a given set of constraints. The SAT revolution offers the opportunity to develop scalable techniques for problems that lie beyond SAT from complexity perspective and, therefore, stand to benefit from the availability of powerful SAT engines.
Our work seeks to enable a Beyond SAT revolution via design of scalable techniques for three fundamental problems that lie beyond SAT:
constrained counting, constrained sampling, and automated synthesis. | Kuldeep S. Meel | null | null | 2,022 | ijcai |
Irrational, but Adaptive and Goal Oriented: Humans Interacting with Autonomous Agents | null | Autonomous agents that interact with humans are becoming more and more prominent.
Currently, such agents usually take one of the following approaches for considering human behavior. Some methods assume either a fully cooperative or a zero-sum setting; these assumptions entail that the human's goals are either identical to that of the agent, or their opposite. In both cases, the agent is not required to explicitly model the human’s goals and account for humans' adaptation nature.
Other methods first compose a model of human behavior based on observing human actions, and then optimize the agent’s actions based on this model. Such methods do not account for how the human will react to the agent's actions and thus, suffer an overestimation bias.
Finally, other methods, such as model free reinforcement learning, merely learn which actions the agent should take at which states. While such methods can, theoretically, account for human adaptation nature, since they require extensive interaction with humans, they usually run in simulation.
By not considering the human’s goals, autonomous agents act selfishly, lack generalization, require vast amounts of data, and cannot account for human’s strategic behavior. Therefore, we call for pursuing solution concepts for autonomous agents interacting with humans that consider the human’s goals and adaptive nature. | Amos Azaria | null | null | 2,022 | ijcai |
Interaction and Expressivity in Collective Decision-Making | null | Collective decisions among human and artificial agents can be enhanced by allowing for more interaction among decision-makers and by letting them express more information about their preferences.
In this paper I present ongoing research on two settings: iterative voting, which repeatedly applies a voting rule until decision-makers converge to an outcome, and delegative voting on multiple issues. | Umberto Grandi | null | null | 2,022 | ijcai |
Integrating Machine Learning and Optimization to Boost Decision Making | null | This paper presents a conceptual review of our recent advancements in the integration of machine learning and optimization. It focuses on describing new hybrid machine learning and optimization methods to predict fast, approximate, solutions to combinatorial problems and to enable structural logical inference. | Ferdinando Fioretto | null | null | 2,022 | ijcai |
Towards New Optimized Artificial Immune Recognition Systems under the Belief Function Theory | null | Artificial Immune Recognition Systems (AIRS) are powerful machine learning techniques, which aim to solve real world problems. A number of AIRS versions have produced successful prediction results. Nevertheless, these methods are unable to handle the uncertainty that could spread out at any stage of the AIRS approach. This issue is considered as a huge obstacle for having accurate and effective classification outputs. Therefore, our main objective is to handle this uncertainty using the belief function theory. We opt also in this article for an optimization over the classical AIRS approaches in order to enhance the classification performance. | Rihab Abdelkhalek | null | null | 2,022 | ijcai |
Why Bad Coffee? Explaining BDI Agent Behaviour with Valuings (Extended Abstract) | null | An important issue in deploying an autonomous system is how to enable human users and stakeholders to develop an appropriate level of trust in the system. It has been argued that a crucial mechanism to enable appropriate trust is the ability of a system to explain its behaviour. Obviously, such explanations need to be comprehensible to humans. Due to the perceived similarity in functioning between humans and autonomous systems, we argue that it makes sense to build on the results of extensive research in social sciences that explores how humans explain their behaviour. Using similar concepts for explanation is argued to help with comprehensibility, since the concepts are familiar. Following work in the social sciences, we propose the use of a folk-psychological model that utilises beliefs, desires, and ``valuings''. We propose a formal framework for constructing explanations of the behaviour of an autonomous system, present an (implemented) algorithm for giving explanations, and present evaluation results. | Michael Winikoff, Galina Sidorenko, Virginia Dignum, Frank Dignum | null | null | 2,022 | ijcai |
Mechanism Design Powered by Social Interactions: A Call to Arms | null | Mechanism design has traditionally assumed that the participants are fixed and independent. However, in reality, the participants are well-connected (e.g., via their social networks) and we can utilize their connections to power the design. One interesting trend is to incentivize the existing participants to use their connections to invite new participants. This helps to form larger games in auctions, coalitional games, matching etc., which is not achievable with the traditional solutions. The challenge is that the participants are competitors and they would not invite each other by default. Solving this is well-coupled with the existing challenges. For example, in auctions, solving it may require revenue monotonicity and false-name-proofness, which were proved impossible to achieve under certain sensible conditions. In matching, this cannot get along with standard optimality and stability. Hence, we believe there is an important theoretical value to discover and the study will stimulate many interesting applications, especially under decentralized systems with blockchain. | Dengji Zhao | null | null | 2,022 | ijcai |
Controllable Text Generation for Open-Domain Creativity and Fairness | null | Recent advances in large pre-trained language models have demonstrated strong results in generating natural languages and significantly improved performances for many natural language generation (NLG) applications such as machine translation and text summarization. However, when the generation tasks are more open-ended and the content is under-specified, existing techniques struggle to generate long-term coherent and creative content. Moreover, the models exhibit and even amplify social biases that are learned from the training corpora. This happens because the generation models are trained to capture the surface patterns (i.e. sequences of words), instead of capturing underlying semantics and discourse structures, as well as background knowledge including social norms. In this paper, I introduce our recent works on controllable text generation to enhance the creativity and fairness of language generation models. We explore hierarchical generation and constrained decoding, with applications to creative language generation including story, poetry, and figurative languages, and bias mitigation for generation models. | Nanyun (Violet) Peng | null | null | 2,022 | ijcai |
Hybrid Learning System for Large-scale Medical Image Analysis | null | Adequate annotated data cannot always be satisfied in medical imaging applications. To address such a challenge, we would explore ways to reduce the quality and quantity of annotations requirements of the deep learning model by developing a hybrid learning system. We combined self-supervised learning, semi-supervised learning and weak-supervised learning to improve annotation utilization. Our primary research work on 2D medical image detection under poor annotation conditions has found that better regularization and adversarial loss can improve the robustness and performance with poor annotation conditions. | Zehua Cheng, Lianlong Wu | null | null | 2,022 | ijcai |
Towards Theoretically Grounded Evolutionary Learning | null | Machine learning tasks are often formulated as complex optimization problems, where the objective function can be non-differentiable, non-continuous, non-unique, inaccurate, dynamic, and have many local optima, making conventional optimization algorithms fail. Evolutionary Algorithms (EAs), inspired by Darwin's theory of evolution, are general-purpose randomized heuristic optimization algorithms, mimicking variational reproduction and natural selection. EAs have yielded encouraging outcomes for solving complex optimization problems (e.g., neural architecture search) in machine learning. However, due to the heuristic nature of EAs, most outcomes to date have been empirical and lack theoretical support, encumbering their acceptance to the general machine learning community. In this paper, I will review the progress towards theoretically grounded evolutionary learning, from the aspects of analysis methodology, theoretical perspectives and learning algorithms. Due to space limit, I will include a few representative examples and highlight our contributions. I will also discuss some future challenges. | Chao Qian | null | null | 2,022 | ijcai |
KRAKEN: A Novel Semantic-Based Approach for Keyphrases Extraction | null | We propose KRAKEN, a novel approach for the extraction of keyphrases from texts. To this aim, KRAKEN makes use of distributional semantics to identify, as completely as possible, representative portions of documents, i.e. keyphrases. In addition, we define novel metrics to assess a weighted significance to the keyphrases extracted from a document, identifying the most important ones by assessing their semantic similarity with the text of the document they belong to. | Simone D'Amico | null | null | 2,022 | ijcai |
Decomposition Methods for Solving Scheduling Problem Using Answer Set Programming | null | This study proposes solving scheduling problems in industrial applications using the decomposition approach. The proposed model has been built using Multi-shot Answer Set Programming with Difference Logic. We tested our model with some benchmark instances and the results showed that our model is comparable to Constraint Programming to other heuristics in the literature. | Mohammed M. S. El-Kholany | null | null | 2,022 | ijcai |
Extending Decision Tree to Handle Multiple Fairness Criteria | null | The demand for machine learning systems that can provide both transparency and fairness is constantly growing. Since the concept of fairness depends on the context, studies in the literature have proposed various formalisation and mitigation strategies. In this work, we propose a novel, flexible, discrimination-aware classifier that allows the user to: (i) select and mitigate the desired fairness criterion from a set of available options; (ii) implement more than one fairness criterion; (iii) handle more than one sensitive attribute; and (iv) specify the desired level of fairness to meet specific business needs or regulatory requirements. Our approach is based on an optimised extension to the decision-tree classifier, and aims to provide transparent and fair rules to the final users. | Alessandro Castelnovo | null | null | 2,022 | ijcai |
Analyzing and Designing Strategic Environments in Social Domains | null | The cross-fertilization of AI and economic concepts has led to the advanced development of novel computational ideas. These ideas include models and approaches for analyzing multi-agent interaction (via game-theoretic models and solution concepts) in strategic environments and designing strategic environments (via mechanism design) to address principal decision-making problems involving multi-agent within various social contexts. In what follows, we will discuss our works on these two main topics. For analyzing multi-agent interaction, we will discuss several computational game-theoretic models to capture various agent characteristics and social (e.g., self-organization) domains. For designing strategic environments, we will discuss principal decision-making mechanism design settings in various social (e.g., facility location) contexts where the principal has to design mechanisms that elicit agent preferences over social outcomes and implement the principal's desirable social outcomes. | Hau Chan | null | null | 2,022 | ijcai |
Dynamic Bandits with Temporal Structure | null | In this work, we study a dynamic multi-armed bandit (MAB) problem, where the expected reward of each arm evolves over time following an auto-regressive model. We present an algorithm whose per-round regret upper bound almost matches the regret lower bound, and numerically demonstrate its efficacy in adapting to the changing environment. | Qinyi Chen | null | null | 2,022 | ijcai |
A Unified Framework for Intrinsic Evaluation of Word-Embedding Algorithms | null | Word embeddings are widely used in copious Natural Language Processing tasks, including semantic analysis, information retrieval, dependency parsing, question answering, and machine translation. This extensive use implies that the evaluation of the performance of such representations is crucial for choosing the best model to perform those tasks.
Though there are well-established procedures and benchmarks for intrinsic evaluation, as far as we know, a unified method of evaluation that can merge the results of those tasks to provide a comprehensive evaluation is missing. The main goal of this work is to create a pipeline to blend all major intrinsic evaluation tasks to compute such overall evaluation - the PCE - of word embeddings. | Anna Giabelli | null | null | 2,022 | ijcai |
Decentralized Autonomous Organizations and Multi-agent Systems for Artificial Intelligence Applications and Data Analysis | null | The Ph.D research project aims to explore the potential of the Decentralized Autonomous Organization paradigm in conjunction with classic software architectures for Artificial Intelligence applications. The intended goal is to investigate and formalize a possible integration path between Multi-agent System architectures and Decentralized Autonomous Organizations. Starting from the Foundation for Intelligent Physical Agents standards, we will extend basic primitives to integrate Multi-agent Systems on Distributed Ledger Technology networks. Possible deployment of services and applications in the Internet-of-Things, Artificial Intelligence and Distributed Machine Learning areas will be tested. Application of Data Analysis techniques on datasets built on such a framework will be also addressed. | Sante Dino Facchini | null | null | 2,022 | ijcai |
Early Diagnosis of Lyme Disease by Recognizing Erythema Migrans Skin Lesion from Images Utilizing Deep Learning Techniques | null | Lyme disease is one of the most common infectious vector-borne diseases in the world. We extensively studied the effectiveness of convolutional neural networks for identifying Lyme dis-ease from images. Our research contribution includes dealing with lack of data, multimodal learning incorporating expert opinion elicitation, and automation of skin hair mask generation. | Sk Imran Hossain | null | null | 2,022 | ijcai |
Scalable ML Methods to Optimize KPIs in Real-World Manufacturing Processes | null | The goal of this work is to develop novel methods to solve the semiconductor fab scheduling problem. The problem can be modeled as a flexible job-shop with large instances and specific constraints related to special machine and job characteristics. To investigate the problem, we develop a tool to simulate small to large-scale instances of the problem. Using the simulator, we aim to develop new dispatching strategies using genetic programming and reinforcement learning. | Benjamin Kovács | null | null | 2,022 | ijcai |
Transferability and Stability of Learning with Limited Labelled Data in Multilingual Text Document Classification | null | We focus on learning with limited labelled data (especially meta-learning) in conjunction with so-far under-researched multilingual textual document classification. The core principle in such learning is to achieve transferability of learned knowledge to new datasets and tasks. Currently, factors influencing the success of transfer remain mostly unclear. Their identification from experiments is challenging due to small amounts of labels making results considerably unstable. When instability of the investigated models is not explicitly taken into consideration (as is common in existing benchmarking studies), it may result in randomness possibly even invalidating the findings. We want to remedy this by in-depth exploration of factors that influence the stability and the transferability of learning with limited labelled data in multilingual textual documents classification, such as misinformation detection. | Branislav Pecher | null | null | 2,022 | ijcai |
Building a Visual Semantics Aware Object Hierarchy | null | The semantic gap is defined as the difference between the linguistic representations of the same concept, which usually leads to misunderstanding between individuals with different knowledge backgrounds. Since linguistically annotated images are extensively used for training machine learning models, semantic gap problem (SGP) also results in inevitable bias on image annotations and further leads to poor performance on current computer vision tasks. To address this problem, we propose a novel unsupervised method to build visual semantics aware object hierarchy, aiming to get a classification model by learning from pure-visual information and to dissipate the bias of linguistic representations caused by SGP. Our intuition in this paper comes from real-world knowledge representation where concepts are hierarchically organized, and each concept can be described by a set of features rather than a linguistic annotation, namely visual semantic. The evaluation consists of two parts, firstly we apply the constructed hierarchy on the object recognition task and then we compare our visual hierarchy and existing lexical hierarchies to show the validity of our method. The preliminary results reveal the efficiency and potential of our proposed method. | Xiaolei Diao | null | null | 2,022 | ijcai |
Data-Efficient Algorithms and Neural Natural Language Processing: Applications in the Healthcare Domain | null | Recently proposed pre-trained language models can be easily fine-tuned to a wide range of downstream tasks. However, fine-tuning requires a large training set. This PhD project introduces novel natural language processing (NLP) use cases in the healthcare domain where obtaining a large training dataset is difficult and expensive. To this end, we propose data-efficient algorithms to fine-tune NLP models in low-resource settings and validate their effectiveness. We expect the outcomes of this PhD project could contribute to the NLP research and low-resource application domains. | Heereen Shim | null | null | 2,022 | ijcai |
Equilibria in Strategic Nominee Selection | null | In my PhD project I explore the game-theoretic problems related to the strategic selection of party nominees. I aim at establishing the complexity of computational problems in this setting. I further study aspects of opinion diffusion protocols related to this problem. | Grzegorz Lisowski | null | null | 2,022 | ijcai |
Multivariate Times Series Classification Using Multichannel CNN | null | Multivariate time series classification is an important and demanding task in sequence data mining. We focus on the multichannel representation of the time series and its corresponding convolutional neural network (CNN) classifier. The proposed method transforms multivariate time series into multichannel analogous image and it is fed into a pretrained multichannel CNN with transfer learning. To verify the efficacy of the proposed method, we compared it with recent deep learning-based time series classification models on five datasets with small amounts of training data. The results indicate that the proposed method provides improved performance on average compared with the other methods when incorporated with transfer learning. | YongKyung Oh | null | null | 2,022 | ijcai |
Towards Contextually Sensitive Analysis of Memes: Meme Genealogy and Knowledge Base | null | As online communication grows, memes have continued to evolve and circulate as succinct multimodal forms of communication. However, computational approaches applied to meme-related tasks lack the same depth and contextual sensitivity of non-computational approaches and struggle to interpret intra-modal dynamics and referentiality. This research proposes to a ‘meme genealogy’ of key features and relationships between memes to inform a knowledge base constructed from meme-specific online sources and embed connotative meaning or contextual information in memes. The proposed methods provide a basis to train contextually sensitive computational models for analysing memes and applications in semi-automated meme annotation. | Victoria Sherratt | null | null | 2,022 | ijcai |
Engineering Socially-Oriented Autonomous Agents and Multiagent Systems | null | The emergent field of social AI is concerned with the development of autonomous agents that are able to act as part of larger community. Within this context, my research seeks to engineer meaningful social interactions among a group of agents from two different approaches. First, the societal level leverages constructs that apply to a society as a whole, like norms and social values. Second, the individual level endows agents with the ability to reason about others, by making use of Theory of Mind capabilities. | Nieves Montes | null | null | 2,022 | ijcai |
Application of Neurosymbolic AI to Sequential Decision Making | null | In the history of AI, two main paradigms have been proposed to solve Sequential Decision Making (SDM) problems: Automated Planning (AP) and Reinforcement Learning (RL). Among the many proposals to unify both fields, the one known as neurosymbolic AI has recently attracted great attention. It combines the Deep Neural Networks used in modern RL with the symbolic representations typical of AP. The main goal of this PhD is to progress the state of the art in neurosymbolic AI for SDM, developing methods for both solving these problems and learning aspects of their structure. | Carlos Núñez-Molina | null | null | 2,022 | ijcai |
Automatic Multimodal Emotion Recognition Using Facial Expression, Voice, and Text | null | It has been a long-time dream for humans to interact with a machine as we would with a person, in a way that it understands us, advises us, and looks after us with no human supervision. Despite being efficient on logical reasoning, current advanced systems lack empathy and user understanding. Estimating the user's emotion could greatly help the machine to identify the user's needs and adapt its behaviour accordingly. This research project aims to develop an automatic emotion recognition system based on facial expression, voice, and words. We expect to address the challenges related to multimodality, data complexity, and emotion representation. | Hélène Tran | null | null | 2,022 | ijcai |
Information Injection to Deep Learning Solutions in Knowledge Transfer | null | Nowadays, with the thrive of AutoML techniques, it is expected that Machine Learning algorithms will work well without any human intervention. This is why the biggest focus is on introducing new neural network architectures, especially those capable of learning to learn. Recently, the Data-Centric AI Challenge was proposed by Andrew Ng whose goal was to change the paradigm and instead of having a fixed dataset and modifying the model, now the model is fixed and the data is preprocessed so that the model results in the best performance. In my thesis, I would like to focus on another approach, where I would not modify the given data nor introduce new architectures, instead, I would like to propose new ways of injecting additional information into knowledge transfer models to increase their performance. | Paulina Tomaszewska | null | null | 2,022 | ijcai |
Anchors Selection for Cross-lingual Embedding Alignment through Time | null | In recent years, vector representations of words have proven to be extremely useful across a wide range of NLP applications. Because of the broad interest in the topic, it became essential to answer the following question: is it possible to align different embeddings, in order to compare terms belonging to different vector spaces and their relations? While embedding alignment received considerable attention in the literature, how to find the best anchors for this process is still an open problem; in this paper, we propose an unsupervised, automatic method to select words belonging to different corpora that are close from a semantic point of view, and can be used as anchors for aligning their respective embedding spaces. | Filippo Pallucchini | null | null | 2,022 | ijcai |
Adaptive Artificial Intelligence Scheduling Methods for Large-Scale, Stochastic, Industrial Applications | null | Traditional scheduling techniques suffer from a lack of flexibility.
The problem's instances need to be deterministic, and results on datasets with small benchmark instances do usually not transfer to large-scale instances.
We propose to develop adaptive algorithms that can leverage the similarities between instances of industrial scheduling problems.
In particular, we focus on applications of modern machine learning techniques to combinatorial optimization problems, an emerging and promising research area.
Traditional scheduling techniques such as constraint, mixed-integer, or answer set programming are highly generic, domain-independent, and, therefore, do not explicitly exploit the specificities of a problem domain.
However, in a production facility, the settings between two consecutive schedules are often very similar.
The machines, workers, production capacity, etc., usually stay the same or do not change significantly.
Traditional scheduling techniques do not take advantage of such similarities, while machine learning, especially deep learning, can discover and exploit relationships in the data.
Therefore, our research aims to incorporate machine learning into combinatorial optimization. | Pierre Tassel | null | null | 2,022 | ijcai |
Diffusion Incentives in Cooperative Games | null | We study a cooperative game setting where we want to gather more players through their social connections. Social connections can be modeled as a graph, and initially, only a subset of the players are in the game. We want to introduce diffusion incentives in such a cooperative game, i.e., incentivize the players to use their connections to invite more players to join the game. Our goal cannot be achieved by existing classical solutions, such as the Shapley value. Hence, to combat this problem, we have already proposed a solution called weighted permission Shapley value. Under this solution, for each player, inviting all of her neighbors is a dominant strategy in all monotone games. As one special application of the diffusion cooperative game, we also considered the diffusion incentives in query networks and the weighted permission Shapley value successfully characterizes the solution to the query network. Furthermore, we also characterize a Sybil-proof solution to the query network called the double geometric mechanism. | Yao Zhang | null | null | 2,022 | ijcai |
A Model-Oriented Approach for Lifting Symmetry-Breaking Constraints in Answer Set Programming | null | Writing correct models for combinatorial problems is relatively straightforward; however, they must be efficient to be usable with instances producing many solution candidates.
In this work, we aim to automatically generalise the discarding of symmetric solutions of Answer Set Programming instances, improving the efficiency of the programs with first-order constraints derived from propositional symmetry-breaking constraints. | Alice Tarzariol | null | null | 2,022 | ijcai |
Displaying Justifications for Collective Decisions | null | We present an online demonstration tool illustrating a general approach to computing justifications for accepting a given decision when confronted with the preferences of several agents. Such a justification consists of a set of axioms providing a normative basis for the decision, together with a step-by-step explanation of how those axioms determine the decision. Our open-source implementation may also prove useful for realising other kinds of projects in computational social choice, particularly those requiring access to a SAT solver. | Arthur Boixel, Ulle Endriss, Oliviero Nardi | null | null | 2,022 | ijcai |
Together about Dementia | null | We present ``Together about Dementia'', a mobile health app
that aims to help persons with dementia when they get lost through
the help of caregivers, relatives, and volunteering
citizens. When the app detects that a person with dementia is
disoriented, wandering, and getting lost, it triggers an alarm that
activates a relative and possibly a volunteer in the proximity. A
backend system based on a microservice and serverless architecture
performs the detection of wandering and the subsequent coordination
of users that are put on a mission to the rescue of the person with
dementia. The backend system implements an AI technique for
spatio-temporal anomaly detection based on location data recorded by
the frontend system installed on the portable device of the person
with dementia. | Nicklas Sindlev Andersen, Marco Chiarandini | null | null | 2,022 | ijcai |
Interactive Reinforcement Learning for Symbolic Regression from Multi-Format Human-Preference Feedbacks | null | In this work, we propose an interactive platform to perform grammar-guided symbolic regression using a reinforcement learning approach from human-preference feedback. To do so, a reinforcement learning algorithm iteratively generates symbolic expressions, modeled as trajectories constrained by grammatical rules, from which a user shall elicit preferences. The interface gives the user three distinct ways of stating its preferences between multiple sampled symbolic expressions: categorizing samples, comparing pairs, and suggesting improvements to a sampled symbolic expression. Learning from preferences enables users to guide the exploration in the symbolic space toward regions that are more relevant to them. We provide a web-based interface testable on symbolic regression benchmark functions and power system data. | Laure Crochepierre, Lydia Boudjeloud-Assala, Vincent Barbesant | null | null | 2,022 | ijcai |
Knowledge-Based News Event Analysis and Forecasting Toolkit | null | We present a toolkit for knowledge-based news event analysis and forecasting. The toolkit is powered by a Knowledge Graph (KG) of events curated from structured and unstructured sources of event-related knowledge. The toolkit provides functions for 1) mapping ongoing news headlines to concepts in the KG, 2) retrieval, reasoning, and visualization for causal analysis and forecasting, and 3) extraction of causal knowledge from text documents to augment the KG with additional domain knowledge. Each function has a number of implementations using a wide range of state-of-the-art neuro-symbolic techniques. We show how the toolkit enables building a human-in-the-loop explainable solution for event analysis and forecasting. | Oktie Hassanzadeh, Parul Awasthy, Ken Barker, Onkar Bhardwaj, Debarun Bhattacharjya, Mark Feblowitz, Lee Martie, Jian Ni, Kavitha Srinivas, Lucy Yip | null | null | 2,022 | ijcai |
ExplainIt!: A Tool for Computing Robust Attributions of DNNs | null | Responsible integration of deep neural networks into the design of trustworthy systems requires the ability to explain decisions made by these models. Explainability and transparency are critical for system analysis, certification, and human-machine teaming. We have recently demonstrated that neural stochastic differential equations (SDEs) present an explanation-friendly DNN architecture. In this paper, we present ExplainIt, an online tool for explaining AI decisions that uses neural SDEs to create visually sharper and more robust attributions than traditional residual neural networks. Our tool shows that the injection of noise in every layer of a residual network often leads to less noisy and less fragile integrated gradient attributions. The discrete neural stochastic differential equation model is trained on the ImageNet data set with a million images, and the demonstration produces robust attributions on images in the ImageNet validation library and on a variety of images in the wild. Our online tool is hosted publicly for educational purposes. | Sumit Jha, Alvaro Velasquez, Rickard Ewetz, Laura Pullum, Susmit Jha | null | null | 2,022 | ijcai |
CARBEN: Composite Adversarial Robustness Benchmark | null | Prior literature on adversarial attack methods has mainly focused on attacking with and defending against a single threat model, e.g., perturbations bounded in Lp ball. However, multiple threat models can be combined into composite perturbations. One such approach, composite adversarial attack (CAA), not only expands the perturbable space of the image, but also may be overlooked by current modes of robustness evaluation. This paper demonstrates how CAA's attack order affects the resulting image, and provides real-time inferences of different models, which will facilitate users' configuration of the parameters of the attack level and their rapid evaluation of model prediction. A leaderboard to benchmark adversarial robustness against CAA is also introduced. | Lei Hsiung, Yun-Yun Tsai, Pin-Yu Chen, Tsung-Yi Ho | null | null | 2,022 | ijcai |
Itero: An Online Iterative Voting Application | null | Iterative voting allows a group of agents to take a collective decision in a dynamic fashion: a series of plurality elections are staged, making the relative scores of the candidates public after each round. Voters can thus adjust their ballots at each step until the process converges (or a maximal number of steps is reached).
Research in computational social choice has shown that this method has the potential of reaching good-quality decisions while at the same time being easy to explain to voters. This paper presents our implementation of iterative voting on a voting platform accessible on the web. | Joseph Boudou, Rachael Colley, Umberto Grandi | null | null | 2,022 | ijcai |
Anomaly Explanation | null | With the surge of deep learning and laws aiming at regulating the use of artificial intelligence, providing explanations to algorithms outputs has been a hot topic in the recent years. Most works are devoted to the explanation of classifiers outputs. The explanation of unsupervised machine learning algorithms, like anomaly detection, has received less attention from the XAI community. But this little interest is not imputable to the irrelevance of the topic. In this paper, we demonstrate the importance of anomaly explanation, the areas still needing investigation based upon our previous contributions to the field, and the future directions that will be explored. | Véronne Yepmo | null | null | 2,022 | ijcai |
Real-Time Portrait Stylization on the Edge | null | In this work we demonstrate real-time portrait stylization, specifically, translating self-portrait into cartoon or anime style on mobile devices.
We propose a latency-driven differentiable architecture search method, maintaining realistic generative quality.
With our framework, we obtain 10× computation reduction on the generative model and achieve real-time video stylization on off-the-shelf smartphone using mobile GPUs. | Yanyu Li, Xuan Shen, Geng Yuan, Jiexiong Guan, Wei Niu, Hao Tang, Bin Ren, Yanzhi Wang | null | null | 2,022 | ijcai |
VMAgent: A Practical Virtual Machine Scheduling Platform | null | Virtual machine (VM) scheduling is one of the critical tasks in cloud computing. Many works have attempted to incorporate machine learning, especially reinforcement learning, to empower VM scheduling procedures. Although improved results are shown in several demo simulators, the performances in real-world scenarios are still underexploited. In this paper, we design a practical VM scheduling platform, i.e., VMAgent, to assist researchers in developing their methods on the VM scheduling problem. VMAgent consists of three components: simulator, scheduler, and visualizer. The simulator abstracts three general realistic scheduling scenarios (fading, recovering, and expansion) based on Huawei Cloud’s scheduling data, which is the core of our platform. Flexible configurations are further provided to make the simulator compatible with practical cloud computing architecture (i.e., Multi Non-Uniform Memory Access) and scenarios. Researchers then need to instantiate the scheduler to interact with the simulator, which is also pre-built in various types (e.g., heuristic, machine learning, and operations research) of scheduling algorithms to speed up the algorithm design. The visualizer, as an auxiliary component of the simulator and scheduler, facilitates researchers to conduct an in-depth analysis of the scheduling procedure and comprehensively compare different scheduling algorithms. We believe that VMAgent would shed light on the AI for the VM scheduling community, and the demo video is presented in https://bit.ly/vmagent-demo-video. | Junjie Sheng, Shengliang Cai, Haochuan Cui, Wenhao Li, Yun Hua, Bo Jin, Wenli Zhou, Yiqiu Hu, Lei Zhu, Qian Peng, Hongyuan Zha, Xiangfeng Wang | null | null | 2,022 | ijcai |
AMICA: An Argumentative Search Engine for COVID-19 Literature | null | AMICA is an argument mining-based search engine, specifically designed for the analysis of scientific literature related to Covid-19. AMICA retrieves scientific papers based on matching keywords and ranks the results based on the papers' argumentative content. An experimental evaluation conducted on a case study in collaboration with the Italian National Institute of Health shows that the AMICA ranking agrees with expert opinion, as well as, importantly, with the impartial quality criteria indicated by Cochrane Systematic Reviews. | Marco Lippi, Francesco Antici, Gianfranco Brambilla, Evaristo Cisbani, Andrea Galassi, Daniele Giansanti, Fabio Magurano, Antonella Rosi, Federico Ruggeri, Paolo Torroni | null | null | 2,022 | ijcai |
The Good, the Bad, and the Explainer: A Tool for Contrastive Explanations of Text Classifiers | null | In the last few years, we have been witnessing the increasing deployment of machine learning-based systems, which act as black boxes whose behaviour is hidden to end-users. As a side-effect, this contributes to increasing the need for explainable methods and tools to support the coordination between humans and ML models towards collaborative decision-making. In this paper, we demonstrate ContrXT, a novel tool that computes the differences in the classification logic of two distinct trained models, reasoning on their symbolic representation through Binary Decision Diagrams.
ContrXT is available as a pip package and API. | Lorenzo Malandri, Fabio Mercorio, Mario Mezzanzanica, Navid Nobani, Andrea Seveso | null | null | 2,022 | ijcai |
Sign-to-Speech Model for Sign Language Understanding: A Case Study of Nigerian Sign Language | null | Through this paper, we seek to reduce the communication barrier between the hearing-impaired community and the larger society who are usually not familiar with sign language in the sub-Saharan region of Africa with the largest occurrences of hearing disability cases, while using Nigeria as a case study. The dataset is a pioneer dataset for the Nigerian Sign Language and was created in collaboration with relevant stakeholders. We pre-processed the data in readiness for two different object detection models and a classification model and employed diverse evaluation metrics to gauge model performance on sign-language to text conversion tasks. Finally, we convert the predicted sign texts to speech and deploy the best performing model in a lightweight application that works in real-time and achieves impressive results converting sign words/phrases to text and subsequently, into speech. | Steven Kolawole, Opeyemi Osakuade, Nayan Saxena, Babatunde Kazeem Olorisade | null | null | 2,022 | ijcai |
A Speech-driven Sign Language Avatar Animation System for Hearing Impaired Applications | null | Sign language is the communication language used in hearing impaired community. Recently, the research of sign language production has made great progress but still need to cope with some critical challenges. In this paper, we propose a system-level scheme and push forward the implementation of sign language production for practical usage. We build a system capable of translating speech into sign language avatar. Different from previous approach only focusing on single technology, we systematically combine algorithms of language translation, body gesture animation and facial avatar generation. We also develop two applications: Sign Language Interpretation APP and Virtual Sign Language Anchor, to facilitate easy and clear communication for hearing impaired people. | Li Hu, Jiahui Li, Jiashuo Zhang, Qi Wang, Bang Zhang, Ping Tan | null | null | 2,022 | ijcai |
Fine-tuning Deep Neural Networks by Interactively Refining the 2D Latent Space of Ambiguous Images | null | Deep neural networks (DNNs) have achieved excellent results currently in classification, while they may still suffer from ambiguous images which are similar across classes. By contrast, humans have a relatively good ability to distinguish these categories of images. Therefore, we propose a human-in-the-loop solution to assist the network to better classify the images by leveraging human knowledge. To achieve this, we project the high-dimensional latent space trained by the network onto a two-dimensional workspace. The users can interactively modify the projected coordinates of inputs on the workspace using our designed tools, then the modified information will be fed back to the network to fine-tune it, which in turn affects the network's classification results, thereby improving the accuracy of network classification. | Jiafu Wei, Haoran Xie, Chia-Ming Chang, Xi Yang | null | null | 2,022 | ijcai |
Text/Speech-Driven Full-Body Animation | null | Due to the increasing demand in films and games, synthesizing 3D avatar animation has attracted much attention recently. In this work, we present a production-ready text/speech-driven full-body animation synthesis system. Given the text and corresponding speech, our system synthesizes face and body animations simultaneously, which are then skinned and rendered to obtain a video stream output. We adopt a learning-based approach for synthesizing facial animation and a graph-based approach to animate the body, which generates high-quality avatar animation efficiently and robustly. Our results demonstrate the generated avatar animations are realistic, diverse and highly text/speech-correlated. | Wenlin Zhuang, Jinwei Qi, Peng Zhang, Bang Zhang, Ping Tan | null | null | 2,022 | ijcai |
PillGood: Automated and Interactive Pill Dispenser Using Facial Recognition for Safe and Personalized Medication | null | Safety of taking medicine prescribed differently to each patient in hospital relies on the discernment of medical professionals who deals with measuring pill quantity, packaging, and distributing. It is difficult and time consuming to keep track of medication record of each patient. Also, medication safety is prone to be in risk due to the human error. To help patients get accurate medication following their prescription plan with minimizing human labors and mistakes, we developed PillGood, an automated smart pill dispenser system using facial recognition technique. PillGood provides real-time and personalized guidance to take the correct medicine by alarming patients and distributing exact quantity of pills at specific time following each patient's prescription table. The system notify patients through mobile app and speaker when they need to take the medicine, and detect who the patient is through the machine learning based face recognition. Then, based on each patient's prescribing information, the controller distributes pills to each patient. Results show that PillGood enable highly accurate personalized pill dispensation followed by precise face recognition, benefiting both patients and medical professionals. Videos for demonstrating the system can be found on https://youtu.be/Wx7bXxRGjXA | Jonghyeok Kim, Hosung Kwon, Jonghyeon Kim, Jinsoo Park, Soong-Un Choi, Sookyung Kim | null | null | 2,022 | ijcai |
ACTA 2.0: A Modular Architecture for Multi-Layer Argumentative Analysis of Clinical Trials | null | Evidence-based medicine aims at making decisions about the care of individual patients based on the explicit use of the best available evidence in the patient clinical history and the medical literature results. Argumentation represents a natural way of addressing this task by (i) identifying evidence and claims in text, and (ii) reasoning upon the extracted
arguments and their relations to make a decision. ACTA 2.0 is an automated tool which relies on Argument Mining methods to analyse the abstracts of clinical trials to extract argument components and relations to support evidence-based clinical decision making. ACTA 2.0 allows also for the identification of PICO (Patient, Intervention, Comparison, Outcome) elements, and the analysis of the effects of an intervention on the outcomes of the study. A REST API is also provided to exploit the tool’s functionalities. | Benjamin Molinet, Santiago Marro, Elena Cabrio, Serena Villata, Tobias Mayer | null | null | 2,022 | ijcai |
Patch Craft: Video Denoising by Deep Modeling and Patch Matching | null | The non-local self-similarity property of natural images has been exploited extensively for solving various image processing problems. When it comes to video sequences, harnessing this force is even more beneficial due to the temporal redundancy. In the context of image and video denoising, many classically-oriented algorithms employ self-similarity, splitting the data into overlapping patches, gathering groups of similar ones and processing these together somehow. With the emergence of convolutional neural networks (CNN), the patch-based framework has been abandoned. Most CNN denoisers operate on the whole image, leveraging non-local relations only implicitly by using a large receptive field. This work proposes a novel approach for leveraging self-similarity in the context of video denoising, while still relying on a regular convolutional architecture. We introduce a concept of patch-craft frames - artificial frames that are similar to the real ones, built by tiling matched patches. Our algorithm augments video sequences with patch-craft frames and feeds them to a CNN. We demonstrate the substantial boost in denoising performance obtained with the proposed approach. | Gregory Vaksman, Michael Elad, Peyman Milanfar; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 2157-2166 | null | null | 2,021 | iccv |
Image Manipulation Detection by Multi-View Multi-Scale Supervision | null | The key challenge of image manipulation detection is how to learn generalizable features that are sensitive to manipulations in novel data, whilst specific to prevent false alarms on authentic images. Current research emphasizes the sensitivity, with the specificity overlooked. In this paper we address both aspects by multi-view feature learning and multi-scale supervision. By exploiting noise distribution and boundary artifact surrounding tampered regions, the former aims to learn semantic-agnostic and thus more generalizable features. The latter allows us to learn from authentic images which are nontrivial to taken into account by current semantic segmentation network based methods. Our thoughts are realized by a new network which we term MVSS-Net. Extensive experiments on five benchmark sets justify the viability of MVSS-Net for both pixel-level and image-level manipulation detection. | Xinru Chen, Chengbo Dong, Jiaqi Ji, Juan Cao, Xirong Li; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 14185-14193 | null | null | 2,021 | iccv |
Perturbed Self-Distillation: Weakly Supervised Large-Scale Point Cloud Semantic Segmentation | null | Large-scale point cloud semantic segmentation has wide applications. Current popular researches mainly focus on fully supervised learning which demands expensive and tedious manual point-wise annotation. Weakly supervised learning is an alternative way to avoid this exhausting annotation. However, for large-scale point clouds with few labeled points, the network is difficult to extract discriminative features for unlabeled points, as well as the regularization of topology between labeled and unlabeled points is usually ignored, resulting in incorrect segmentation results. To address this problem, we propose a perturbed self-distillation (PSD) framework. Specifically, inspired by self-supervised learning, we construct the perturbed branch and enforce the predictive consistency among the perturbed branch and original branch. In this way, the graph topology of the whole point cloud can be effectively established by the introduced auxiliary supervision, such that the information propagation between the labeled and unlabeled points will be realized. Besides point-level supervision, we present a well-integrated context-aware module to explicitly regularize the affinity correlation of labeled points. Therefore, the graph topology of the point cloud can be further refined. The experimental results evaluated on three large-scale datasets show the large gain (3.0% on average) against recent weakly supervised methods and comparable results to some fully supervised methods. | Yachao Zhang, Yanyun Qu, Yuan Xie, Zonghao Li, Shanshan Zheng, Cuihua Li; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 15520-15528 | null | null | 2,021 | iccv |
Cherry-Picking Gradients: Learning Low-Rank Embeddings of Visual Data via Differentiable Cross-Approximation | null | We propose an end-to-end trainable framework that processes large-scale visual data tensors by looking at a fraction of their entries only. Our method combines a neural network encoder with a tensor train decomposition to learn a low-rank latent encoding, coupled with cross-approximation (CA) to learn the representation through a subset of the original samples. CA is an adaptive sampling algorithm that is native to tensor decompositions and avoids working with the full high-resolution data explicitly. Instead, it actively selects local representative samples that we fetch out-of-core and on demand. The required number of samples grows only logarithmically with the size of the input. Our implicit representation of the tensor in the network enables processing large grids that could not be otherwise tractable in their uncompressed form. The proposed approach is particularly useful for large-scale multidimensional grid data (e.g., 3D tomography), and for tasks that require context over a large receptive field (e.g., predicting the medical condition of entire organs). The code is available at https://github.com/aelphy/c-pic. | Mikhail Usvyatsov, Anastasia Makarova, Rafael Ballester-Ripoll, Maxim Rakhuba, Andreas Krause, Konrad Schindler; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 11426-11435 | null | null | 2,021 | iccv |
FOVEA: Foveated Image Magnification for Autonomous Navigation | null | Efficient processing of high-resolution video streams is safety-critical for many robotics applications such as autonomous driving. Image downsampling is a commonly adopted technique to ensure the latency constraint is met. However, this naive approach greatly restricts an object detector's capability to identify small objects. In this paper, we propose an attentional approach that elastically magnifies certain regions while maintaining a small input canvas. The magnified regions are those that are believed to have a high probability of containing an object, whose signal can come from a dataset-wide prior or frame-level prior computed from recent object predictions. The magnification is implemented by a KDE-based mapping to transform the bounding boxes into warping parameters, which are then fed into an image sampler with anti-cropping regularization. The detector is then fed with the warped image and we apply a differentiable backward mapping to get bounding box outputs in the original space. Our regional magnification allows algorithms to make better use of high-resolution input without incurring the cost of high-resolution processing. On the autonomous driving datasets Argoverse-HD and BDD100K, we show our proposed method boosts the detection AP over standard Faster R-CNN, with and without finetuning. Additionally, building on top of the previous state-of-the-art in streaming detection, our method sets a new record for streaming AP on Argoverse-HD (from 17.8 to 23.0 on a GTX 1080 Ti GPU), suggesting that it has achieved a superior accuracy-latency tradeoff. | Chittesh Thavamani, Mengtian Li, Nicolas Cebron, Deva Ramanan; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 15539-15548 | null | null | 2,021 | iccv |
Specificity-Preserving RGB-D Saliency Detection | null | RGB-D saliency detection has attracted increasing attention, due to its effectiveness and the fact that depth cues can now be conveniently captured. Existing works often focus on learning a shared representation through various fusion strategies, with few methods explicitly considering how to preserve modality-specific characteristics. In this paper, taking a new perspective, we propose a specificity-preserving network for RGB-D saliency detection, which benefits saliency detection performance by exploring both the shared information and modality-specific properties (e.g., specificity). Specifically, two modality-specific networks and a shared learning network are adopted to generate individual and shared saliency maps. A cross-enhanced integration module (CIM) is proposed to fuse cross-modal features in the shared learning network, which are then propagated to the next layer for integrating cross-level information. Besides, we propose a multi-modal feature aggregation (MFA) module to integrate the modality-specific features from each individual decoder into the shared decoder, which can provide rich complementary multi-modal information to boost the saliency detection performance. Further, a skip connection is used to combine hierarchical features between the encoder and decoder layers. Experiments on six benchmark datasets demonstrate that our SP-Net outperforms other state-of-the-art methods. | Tao Zhou, Huazhu Fu, Geng Chen, Yi Zhou, Deng-Ping Fan, Ling Shao; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 4681-4691 | null | null | 2,021 | iccv |
Semantic Diversity Learning for Zero-Shot Multi-Label Classification | null | Training a neural network model for recognizing multiple labels associated with an image, including identifying unseen labels, is challenging, especially for images that portray numerous semantically diverse labels. As challenging as this task is, it is an essential task to tackle since it represents many real-world cases, such as image retrieval of natural images. We argue that using a single embedding vector to represent an image, as commonly practiced, is not sufficient to rank both relevant seen and unseen labels accurately. This study introduces an end-to-end model training for multi-label zero-shot learning that supports the semantic diversity of the images and labels. We propose to use an embedding matrix having principal embedding vectors trained using a tailored loss function. In addition, during training, we suggest up-weighting in the loss function image samples presenting higher semantic diversity to encourage the diversity of the embedding matrix. Extensive experiments show that our proposed method improves the zero-shot model's quality in tag-based image retrieval achieving SoTA results on several common datasets (NUS-Wide, COCO, Open Images). | Avi Ben-Cohen, Nadav Zamir, Emanuel Ben-Baruch, Itamar Friedman, Lihi Zelnik-Manor; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 640-650 | null | null | 2,021 | iccv |
Composable Augmentation Encoding for Video Representation Learning | null | We focus on contrastive methods for self-supervised video representation learning. A common paradigm in contrastive learning is to construct positive pairs by sampling different data views for the same instance, with different data instances as negatives. These methods implicitly assume a set of representational invariances to the view selection mechanism (e.g., sampling frames with temporal shifts), which may lead to poor performance on downstream tasks which violate these invariances (fine-grained video action recognition that would benefit from temporal information). To overcome this limitation, we propose an `augmentation aware' contrastive learning framework, where we explicitly provide a sequence of augmentation parameterisations (such as the values of the time shifts used to create data views) as composable augmentation encodings (CATE) to our model when projecting the video representations for contrastive learning. We show that representations learned by our method encode valuable information about specified spatial or temporal augmentation, and in doing so also achieve state-of-the-art performance on a number of video benchmarks. | Chen Sun, Arsha Nagrani, Yonglong Tian, Cordelia Schmid; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 8834-8844 | null | null | 2,021 | iccv |
AdaSGN: Adapting Joint Number and Model Size for Efficient Skeleton-Based Action Recognition | null | Existing methods for skeleton-based action recognition mainly focus on improving the recognition accuracy, whereas the efficiency of the model is rarely considered. Recently, there are some works trying to speed up the skeleton modeling by designing light-weight modules. However, in addition to the model size, the amount of the data involved in the calculation is also an important factor for the running speed, especially for the skeleton data where most of the joints are redundant or non-informative to identify a specific skeleton.Besides, previous works usually employ one fix-sized model for all the samples regardless of the difficulty of recognition, which wastes computations for easy samples.To address these limitations, a novel approach, called AdaSGN, is proposed in this paper, which can reduce the computational cost of the inference process by adaptively controlling the input number of the joints of the skeleton on-the-fly. Moreover, it can also adaptively select the optimal model size for each sample to achieve a better trade-off between the accuracy and the efficiency. We conduct extensive experiments on three challenging datasets, namely, NTU-60, NTU-120 and SHREC, to verify the superiority of the proposed approach, where AdaSGN achieves comparable or even higher performance with much lower GFLOPs compared with the baseline method. | Lei Shi, Yifan Zhang, Jian Cheng, Hanqing Lu; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 13413-13422 | null | null | 2,021 | iccv |
C2N: Practical Generative Noise Modeling for Real-World Denoising | null | Learning-based image denoising methods have been bounded to situations where well-aligned noisy and clean images are given, or samples are synthesized from predetermined noise models, e.g., Gaussian. While recent generative noise modeling methods aim to simulate the unknown distribution of real-world noise, several limitations still exist. In a practical scenario, a noise generator should learn to simulate the general and complex noise distribution without using paired noisy and clean images. However, since existing methods are constructed on the unrealistic assumption of real-world noise, they tend to generate implausible patterns and cannot express complicated noise maps. Therefore, we introduce a Clean-to-Noisy image generation framework, namely C2N, to imitate complex real-world noise without using any paired examples. We construct the noise generator in C2N accordingly with each component of real-world noise characteristics to express a wide range of noise accurately. Combined with our C2N, conventional denoising CNNs can be trained to outperform existing unsupervised methods on challenging real-world benchmarks by a large margin. | Geonwoon Jang, Wooseok Lee, Sanghyun Son, Kyoung Mu Lee; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 2350-2359 | null | null | 2,021 | iccv |
EventHands: Real-Time Neural 3D Hand Pose Estimation From an Event Stream | null | 3D hand pose estimation from monocular videos is a long-standing and challenging problem, which is now seeing a strong upturn. In this work, we address it for the first time using a single event camera, i.e., an asynchronous vision sensor reacting on brightness changes. Our EventHands approach has characteristics previously not demonstrated with a single RGB or depth camera such as high temporal resolution at low data throughputs and real-time performance at 1000 Hz. Due to the different data modality of event cameras compared to classical cameras, existing methods cannot be directly applied to and re-trained for event streams. We thus design a new neural approach which accepts a new event stream representation suitable for learning, which is trained on newly-generated synthetic event streams and can generalise to real data. Experiments show that EventHands outperforms recent monocular methods using a colour (or depth) camera in terms of accuracy and its ability to capture hand motions of unprecedented speed. Our method, the event stream simulator and the dataset are publicly available (see https://gvv.mpi-inf.mpg.de/projects/EventHands/). | Viktor Rudnev, Vladislav Golyanik, Jiayi Wang, Hans-Peter Seidel, Franziska Mueller, Mohamed Elgharib, Christian Theobalt; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 12385-12395 | null | null | 2,021 | iccv |
Focus on the Positives: Self-Supervised Learning for Biodiversity Monitoring | null | We address the problem of learning self-supervised representations from unlabeled image collections. Unlike existing approaches that attempt to learn useful features by maximizing similarity between augmented versions of each input image or by speculatively picking negative samples, we instead also make use of the natural variation that occurs in image collections that are captured using static monitoring cameras. To achieve this, we exploit readily available context data that encodes information such as the spatial and temporal relationships between the input images. We are able to learn representations that are surprisingly effective for downstream supervised classification, by first identifying high probability positive pairs at training time, i.e. those images that are likely to depict the same visual concept. For the critical task of global biodiversity monitoring, this results in image features that can be adapted to challenging visual species classification tasks with limited human supervision. We present results on four different camera trap image collections, across three different families of self-supervised learning methods, and show that careful image selection at training time results in superior performance compared to existing baselines such as conventional self-supervised training and transfer learning. | Omiros Pantazis, Gabriel J. Brostow, Kate E. Jones, Oisin Mac Aodha; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 10583-10592 | null | null | 2,021 | iccv |
PlenOctrees for Real-Time Rendering of Neural Radiance Fields | null | We introduce a method to render Neural Radiance Fields (NeRFs) in real time using PlenOctrees, an octree-based 3D representation which supports view-dependent effects. Our method can render 800x800 images at more than 150 FPS, which is over 3000 times faster than conventional NeRFs. We do so without sacrificing quality while preserving the ability of NeRFs to perform free-viewpoint rendering of scenes with arbitrary geometry and view-dependent effects. Real-time performance is achieved by pre-tabulating the NeRF into a PlenOctree. In order to preserve view-dependent effects such as specularities, we factorize the appearance via closed-form spherical basis functions. Specifically, we show that it is possible to train NeRFs to predict a spherical harmonic representation of radiance, removing the viewing direction as an input to the neural network. Furthermore, we show that PlenOctrees can be directly optimized to further minimize the reconstruction loss, which leads to equal or better quality compared to competing methods. Moreover, this octree optimization step can be used to reduce the training time, as we no longer need to wait for the NeRF training to converge fully. Our real-time neural rendering approach may potentially enable new applications such as 6-DOF industrial and product visualizations, as well as next generation AR/VR systems. PlenOctrees are amenable to in-browser rendering as well; please visit the project page for the interactive online demo, as well as video and code: https://alexyu.net/plenoctrees. | Alex Yu, Ruilong Li, Matthew Tancik, Hao Li, Ren Ng, Angjoo Kanazawa; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 5752-5761 | null | null | 2,021 | iccv |
Panoptic Segmentation of Satellite Image Time Series With Convolutional Temporal Attention Networks | null | Unprecedented access to multi-temporal satellite imagery has opened new perspectives for a variety of Earth observation tasks. Among them, pixel-precise panoptic segmentation of agricultural parcels has major economic and environmental implications. While researchers have explored this problem for single images, we argue that the complex temporal patterns of crop phenology are better addressed with temporal sequences of images. In this paper, we present the first end-to-end, single-stage method for panoptic segmentation of Satellite Image Time Series (SITS). This module can be combined with our novel image sequence encoding network which relies on temporal self-attention to extract rich and adaptive multi-scale spatio-temporal features. We also introduce PASTIS, the first open-access SITS dataset with panoptic annotations. We demonstrate the superiority of our encoder for semantic segmentation against multiple competing network architectures, and set up the first state-of-the-art of panoptic segmentation of SITS. Our implementation and the PASTIS dataset are publicly available at (link-upon-publication). | Vivien Sainte Fare Garnot, Loic Landrieu; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 4872-4881 | null | null | 2,021 | iccv |
Ask&Confirm: Active Detail Enriching for Cross-Modal Retrieval With Partial Query | null | Text-based image retrieval has seen considerable progress in recent years. However, the performance of existing methods suffers in real life since the user is likely to provide an incomplete description of an image, which often leads to results filled with false positives that fit the incomplete description. In this work, we introduce the partial-query problem and extensively analyze its influence on text-based image retrieval. Previous interactive methods tackle the problem by passively receiving users' feedback to supplement the incomplete query iteratively, which is time-consuming and requires heavy user effort. Instead, we propose a novel retrieval framework that conducts the interactive process in an Ask-and-Confirm fashion, where AI actively searches for discriminative details missing in the current query, and users only need to confirm AI's proposal. Specifically, we propose an object-based interaction to make the interactive retrieval more user-friendly and present a reinforcement-learning-based policy to search for discriminative objects. Furthermore, since fully-supervised training is often infeasible due to the difficulty of obtaining human-machine dialog data, we present a weakly-supervised training strategy that needs no human-annotated dialogs other than a text-image dataset. Experiments show that our framework significantly improves the performance of text-based image retrieval. Code is available at https://github.com/CuthbertCai/Ask-Confirm. | Guanyu Cai, Jun Zhang, Xinyang Jiang, Yifei Gong, Lianghua He, Fufu Yu, Pai Peng, Xiaowei Guo, Feiyue Huang, Xing Sun; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 1835-1844 | null | null | 2,021 | iccv |
4D-Net for Learned Multi-Modal Alignment | null | We present 4D-Net, a 3D object detection approach, which utilizes 3D Point Cloud and RGB sensing information, both in time. We are able to incorporate the 4D information by performing a novel dynamic connection learning across various feature representations and levels of abstraction and by observing geometric constraints. Our approach outperforms the state-of-the-art and strong baselines on the Waymo Open Dataset. 4D-Net is better able to use motion cues and dense image information to detect distant objects more successfully. We will open source the code. | AJ Piergiovanni, Vincent Casser, Michael S. Ryoo, Anelia Angelova; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 15435-15445 | null | null | 2,021 | iccv |
Describing and Localizing Multiple Changes With Transformers | null | Existing change captioning studies have mainly focused on a single change. However, detecting and describing multiple changed parts in image pairs is essential for enhancing adaptability to complex scenarios. We solve the above issues from three aspects: (i) We propose a simulation-based multi-change captioning dataset; (ii) We benchmark existing state-of-the-art methods of single change captioning on multi-change captioning; (iii) We further propose Multi-Change Captioning transformers (MCCFormers) that identify change regions by densely correlating different regions in image pairs and dynamically determines the related change regions with words in sentences. The proposed method obtained the highest scores on four conventional change captioning evaluation metrics for multi-change captioning. Additionally, our proposed method can separate attention maps for each change and performs well with respect to change localization. Moreover, the proposed framework outperformed the previous state-of-the-art methods on an existing change captioning benchmark, CLEVR-Change, by a large margin (+6.1 on BLEU-4 and +9.7 on CIDEr scores), indicating its general ability in change captioning tasks. The code and dataset are available at the project page. | Yue Qiu, Shintaro Yamamoto, Kodai Nakashima, Ryota Suzuki, Kenji Iwata, Hirokatsu Kataoka, Yutaka Satoh; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 1971-1980 | null | null | 2,021 | iccv |
3DVG-Transformer: Relation Modeling for Visual Grounding on Point Clouds | null | Visual grounding on 3D point clouds is an emerging vision and language task that benefits various applications in understanding the 3D visual world. By formulating this task as a grounding-by-detection problem, lots of recent works focus on how to exploit more powerful detectors and comprehensive language features, but (1) how to model complex relations for generating context-aware object proposals and (2) how to leverage proposal relations to distinguish the true target object from similar proposals are not fully studied yet. Inspired by the well-known transformer architecture, we propose a relation-aware visual grounding method on 3D point clouds, named as 3DVG-Transformer, to fully utilize the contextual clues for relationenhanced proposal generation and cross-modal proposal disambiguation, which are enabled by a newly designed coordinate-guided contextual aggregation (CCA) module in the object proposal generation stage, and a multiplex attention (MA) module in the cross-modal feature fusion stage. We validate that our 3DVG-Transformer outperforms the state-of-the-art methods by a large margin, on two point cloud-based visual grounding datasets, ScanRefer and Nr3D/Sr3D from ReferIt3D, especially for complex scenarios containing multiple objects of the same category. | Lichen Zhao, Daigang Cai, Lu Sheng, Dong Xu; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 2928-2937 | null | null | 2,021 | iccv |
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