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Evaluating Approval-Based Multiwinner Voting in Terms of Robustness to Noise
| null |
Approval-based multiwinner voting rules have recently received much attention in the Computational Social Choice literature. Such rules aggregate approval ballots and determine a winning committee of alternatives. To assess effectiveness, we propose to employ new noise models that are specifically tailored for approval votes and committees. These models take as input a ground truth committee and return random approval votes to be thought of as noisy estimates of the ground truth. A minimum robustness requirement for an approval-based multiwinner voting rule is to return the ground truth when applied to profiles with sufficiently many noisy votes. Our results indicate that approval-based multiwinner voting can indeed be robust to reasonable noise. We further refine this finding by presenting a hierarchy of rules in terms of how robust to noise they are.
|
Ioannis Caragiannis, Christos Kaklamanis, Nikos Karanikolas, George A. Krimpas
| null | null | 2,020 |
ijcai
|
Speeding Up Incomplete GDL-based Algorithms for Multi-agent Optimization with Dense Local Utilities
| null |
Incomplete GDL-based algorithms including Max-sum and its variants are important methods for multi-agent optimization. However, they face a significant scalability challenge as the computational overhead grows exponentially with respect to the arity of each utility function. Generic Domain Pruning (GDP) technique reduces the computational effort by performing a one-shot pruning to filter out suboptimal entries. Unfortunately, GDP could perform poorly when dealing with dense local utilities and ties which widely exist in many domains. In this paper, we present several novel sorting-based acceleration algorithms by alleviating the effect of densely distributed local utilities. Specifically, instead of one-shot pruning in GDP, we propose to integrate both search and pruning to iteratively reduce the search space. Besides, we cope with the utility ties by organizing the search space of tied utilities into AND/OR trees to enable branch-and-bound. Finally, we propose a discretization mechanism to offer a tradeoff between the reconstruction overhead and the pruning efficiency. We demonstrate the superiorities of our algorithms over the state-of-the-art from both theoretical and experimental perspectives.
|
Yanchen Deng, Bo An
| null | null | 2,020 |
ijcai
|
Formalizing Group and Propagated Trust in Multi-Agent Systems
| null |
We present a formal framework that allows individual and group of agents to reason about their trust toward other agents. In particular, we propose a branching time temporal logic BT which includes operators that express concepts such as everyone trust, distributed trust and propagated trust. We analyze the satisfiability and model checking problems of this logic using a reduction technique.
|
Nagat Drawel, Jamal Bentahar, Amine Laarej, Gaith Rjoub
| null | null | 2,020 |
ijcai
|
Maximum Nash Welfare and Other Stories About EFX
| null |
We consider the classic problem of fairly allocating indivisible goods among agents with additive valuation functions and explore the connection between two prominent fairness notions: maximum Nash welfare (MNW) and envy-freeness up to any good (EFX). We establish that an MNW allocation is always EFX as long as there are at most two possible values for the goods, whereas this implication is no longer true for three or more distinct values. As a notable consequence, this proves the existence of EFX allocations for these restricted valuation functions. While the efficient computation of an MNW allocation for two possible values remains an open problem, we present a novel algorithm for directly constructing EFX allocations in this setting. Finally, we study the question of whether an MNW allocation implies any EFX guarantee for general additive valuation functions under a natural new interpretation of approximate EFX allocations.
|
Georgios Amanatidis, Georgios Birmpas, Aris Filos-Ratsikas, Alexandros Hollender, Alexandros A. Voudouris
| null | null | 2,020 |
ijcai
|
Intention Progression under Uncertainty
| null |
A key problem in Belief-Desire-Intention agents is how an agent progresses its intentions, i.e., which plans should be selected and how the execution of these plans should be interleaved so as to achieve the agent’s goals. Previous approaches to the intention progression problem assume the agent has perfect information about the state of the environment. However, in many real-world applications, an agent may be uncertain about whether an environment condition holds, and hence whether a particular plan is applicable or an action is executable. In this paper, we propose SAU, a Monte-Carlo Tree Search (MCTS)-based scheduler for intention progression problems where the agent’s beliefs are uncertain. We evaluate the performance of our approach experimentally by varying the degree of uncertainty in the agent’s beliefs. The results suggest that SAU is able to successfully achieve the agent’s goals even in settings where there is significant uncertainty in the agent’s beliefs.
|
Yuan Yao, Natasha Alechina, Brian Logan, John Thangarajah
| null | null | 2,020 |
ijcai
|
Fine-Grained View on Bribery for Group Identification
| null |
Given a set of individuals qualifying or disqualifying each other, group identification is the task of identifying a socially qualified subgroup of individuals. Social qualification depends on the specific rule used to aggregate individual qualifications. The bribery problem in this context asks how many agents need to change their qualifications in order to change the outcome.
Complementing previous results showing polynomial-time solvability or NP-hardness of bribery for various social rules in the constructive (aiming at making specific individuals socially qualified) or destructive (aiming at making specific individuals socially disqualified) setting, we provide a comprehensive picture of the parameterized computational complexity landscape. Conceptually, we also consider a more fine-grained concept of bribery cost, where we ask how many single qualifications need to be changed, and a more general bribery goal that combines the constructive and destructive setting.
|
Niclas Boehmer, Robert Bredereck, Dušan Knop, Junjie Luo
| null | null | 2,020 |
ijcai
|
Social Ranking Manipulability for the CP-Majority, Banzhaf and Lexicographic Excellence Solutions
| null |
We investigate the issue of manipulability for social ranking rules, where the goal is to rank individuals given the ranking of coalitions formed by them and each individual prefers to reach the highest positions in the social ranking. This problem lies at the intersection of computational social choice and the algorithmic theory of power indices. Different social ranking rules have been recently proposed and studied from an axiomatic point of view. In this paper, we focus on rules representing three classical approaches in social choice theory: the marginal contribution approach, the lexicographic approach and the (ceteris paribus) majority one. We first consider some particular members of these families analysing their resistance to a malicious behaviour of individuals. Then, we analyze the computational complexity of manipulation, and complete our theoretical results with simulations in order to analyse the manipulation frequencies and to assess the effects of manipulations.
|
Tahar Allouche, Bruno Escoffier, Stefano Moretti, Meltem Öztürk
| null | null | 2,020 |
ijcai
|
Biased Opinion Dynamics: When the Devil is in the Details
| null |
We investigate opinion dynamics in multi-agent networks when there
exists a bias toward one of two possible opinions; for example, reflecting a status quo vs a
superior alternative.
Starting with all agents sharing an initial opinion representing the status
quo, the system evolves in steps. In each step, one agent selected uniformly at
random adopts with some probability a the superior opinion, and with
probability 1 - a it follows an underlying update rule to revise its
opinion on the basis of those held by its neighbors.
We analyze the convergence of the resulting process under two well-known update
rules, namely majority and voter.
The framework we propose exhibits a rich structure, with a nonobvious
interplay between topology and underlying update rule.
For example, for the voter rule we show that the speed of convergence
bears no significant dependence on the underlying topology,
whereas the picture changes completely under the majority rule,
where network density negatively affects convergence.
We believe that the model we propose is at the same time simple, rich, and modular,
affording mathematical characterization of the interplay between bias,
underlying opinion dynamics, and social structure in a unified setting.
|
Aris Anagnostopoulos, Luca Becchetti, Emilio Cruciani, Francesco Pasquale, Sara Rizzo
| null | null | 2,020 |
ijcai
|
Strategic Campaign Management in Apportionment Elections
| null |
In parliamentary elections, parties compete for a limited, typically fixed number of seats. We study the complexity of the following bribery-style problem: Given the distribution of votes among the parties, what is the smallest number of voters that need to be convinced to vote for our party, so that it gets a desired number of seats. We also run extensive experiments on real-world election data and measure the effectiveness of our method.
|
Robert Bredereck, Piotr Faliszewski, Michal Furdyna, Andrzej Kaczmarczyk, Martin Lackner
| null | null | 2,020 |
ijcai
|
Decentralized MCTS via Learned Teammate Models
| null |
Decentralized online planning can be an attractive paradigm for cooperative
multi-agent systems, due to improved scalability and robustness.
A key difficulty of such approach lies in making accurate
predictions about the decisions of other agents.
In this paper, we present a trainable online decentralized planning algorithm
based on decentralized Monte Carlo Tree Search, combined with
models of teammates learned from previous episodic runs.
By only allowing one agent to adapt its models at a time,
under the assumption of ideal policy approximation,
successive iterations of our method are guaranteed to improve joint
policies, and eventually lead to convergence to a Nash equilibrium.
We test the efficiency of the algorithm by performing experiments
in several scenarios of the spatial task
allocation environment introduced in [Claes et al., 2015]. We show that
deep learning and convolutional neural networks can be employed
to produce accurate policy approximators which exploit the spatial features of the
problem, and that the proposed algorithm improves over the baseline
planning performance for particularly challenging domain configurations.
|
Aleksander Czechowski, Frans A. Oliehoek
| null | null | 2,020 |
ijcai
|
Uniform Welfare Guarantees Under Identical Subadditive Valuations
| null |
We study the problem of allocating indivisible goods among agents that have an identical subadditive valuation over the goods. The extent of fair- ness and efficiency of allocations is measured by the generalized means of the values that the alloca- tions generate among the agents. Parameterized by an exponent term p, generalized-mean welfares en- compass multiple well-studied objectives, such as social welfare, Nash social welfare, and egalitarian welfare. We establish that, under identical subadditive valu- ations and in the demand oracle model, one can efficiently find a single allocation that approximates the optimal generalized-mean welfare—to within a factor of 40—uniformly for all p ∈ (−∞,1]. Hence, by way of a constant-factor approximation algorithm, we obtain novel results for maximizing Nash social welfare and egalitarian welfare for identical subadditive valuations.
|
Siddharth Barman, Ranjani G. Sundaram
| null | null | 2,020 |
ijcai
|
Stable Roommate Problem with Diversity Preferences
| null |
In the multidimensional stable roommate problem, agents have to be allocated to rooms and have preferences over sets of potential roommates. We study the complexity of finding good allocations of agents to rooms under the assumption that agents have diversity preferences (Bredereck, Elkind, Igarashi, AAMAS'19): each agent belongs to one of the two types (e.g., juniors and seniors, artists and engineers), and agents’ preferences over rooms depend solely on the fraction of agents of their own type among their potential roommates. We consider various solution concepts for this setting, such as core and exchange stability, Pareto optimality and envy-freeness. On the negative side, we prove that envy-free, core stable or (strongly) exchange stable outcomes may fail to exist and that the associated decision problems are NP-complete. On the positive side, we show that these problems are in FPT with respect to the room size, which is not the case for the general stable roommate problem.
|
Niclas Boehmer, Edith Elkind
| null | null | 2,020 |
ijcai
|
Assume-Guarantee Synthesis for Prompt Linear Temporal Logic
| null |
Prompt-LTL extends Linear Temporal Logic with a bounded version of the ``eventually'' operator to express temporal requirements such as bounding waiting times. We study assume-guarantee synthesis for prompt-LTL: the goal is to construct a system such that for all environments satisfying a first prompt-LTL formula (the assumption) the system composed with this environment satisfies a second prompt-LTL formula (the guarantee). This problem has been open for a decade. We construct an algorithm for solving it and show that, like classical LTL synthesis, it is 2-EXPTIME-complete.
|
Nathanaël Fijalkow, Bastien Maubert, Aniello Murano, Moshe Vardi
| null | null | 2,020 |
ijcai
|
Ethics, Prosperity, and Society: Moral Evaluation Using Virtue Ethics and Utilitarianism
| null |
Modelling ethics is critical to understanding and analysing social phenomena. However, prior literature either incorporates ethics into agent strategies or uses it for evaluation of agent behaviour. This work proposes a framework that models both, ethical decision making as well as evaluation using virtue ethics and utilitarianism. In an iteration, agents can use either the classical Continuous Prisoner's Dilemma or a new type of interaction called moral interaction, where agents donate or steal from other agents. We introduce moral interactions to model ethical decision making. We also propose a novel agent type, called virtue agent, parametrised by the agent's level of ethics. Virtue agents' decisions are based on moral evaluations of past interactions. Our simulations show that unethical agents make short term gains but are less prosperous in the long run. We find that in societies with positivity bias, unethical agents have high incentive to become ethical. The opposite is true of societies with negativity bias. We also evaluate the ethicality of existing strategies and compare them with those of virtue agents.
|
Aditya Hegde, Vibhav Agarwal, Shrisha Rao
| null | null | 2,020 |
ijcai
|
Maximizing Welfare with Incentive-Aware Evaluation Mechanisms
| null |
Motivated by applications such as college admission and insurance rate determination, we study a classification problem where the inputs are controlled by strategic individuals who can modify their features at a cost. A learner can only partially observe the features, and aims to classify individuals with respect to a quality score. The goal is to design a classification mechanism that maximizes the overall quality score in the population, taking any strategic updating into account.
When scores are linear and mechanisms can assign their own scores to agents, we show that the optimal classifier is an appropriate projection of the quality score. For the more restrictive task of binary classification via linear thresholds, we construct a (1/4)-approximation to the optimal classifier when the underlying feature distribution is sufficiently smooth and admits an oracle for finding dense regions. We extend our results to settings where the prior distribution is unknown and must be learned from samples.
|
Nika Haghtalab, Nicole Immorlica, Brendan Lucier, Jack Z. Wang
| null | null | 2,020 |
ijcai
|
Concentration of Distortion: The Value of Extra Voters in Randomized Social Choice
| null |
We study higher statistical moments of Distortion for randomized social choice in a metric implicit utilitarian model. The Distortion of a social choice mechanism is the expected approximation factor with respect to the optimal utilitarian social cost (OPT). The k'th moment of Distortion is the expected approximation factor with respect to the k'th power of OPT. We consider mechanisms that elicit alternatives by randomly sampling voters for their favorite alternative. We design two families of mechanisms that provide constant (with respect to the number of voters and alternatives) k'th moment of Distortion using just k samples if all voters can then participate in a vote among the proposed alternatives, or 2k-1 samples if only the sampled voters can participate. We also show that these numbers of samples are tight. Such mechanisms deviate from a constant approximation to OPT with probability that drops exponentially in the number of samples, independent of the total number of voters and alternatives. We conclude with simulations on real-world Participatory Budgeting data to qualitatively complement our theoretical insights.
|
Brandon Fain, William Fan, Kamesh Munagala
| null | null | 2,020 |
ijcai
|
Flow-Based Network Creation Games
| null |
Network Creation Games(NCGs) model the creation of decentralized communication networks like the Internet. In such games strategic agents corresponding to network nodes selfishly decide with whom to connect to optimize some objective function. Past research intensively analyzed models where the agents strive for a central position in the network. This models agents optimizing the network for low-latency applications like VoIP. However, with today's abundance of streaming services it is important to ensure that the created network can satisfy the increased bandwidth demand. To the best of our knowledge, this natural problem of the decentralized strategic creation of networks with sufficient bandwidth has not yet been studied.
We introduce Flow-Based NCGs where the selfish agents focus on bandwidth instead of latency. In essence, budget-constrained agents create network links to maximize their minimum or average network flow value to all other network nodes. Equivalently, this can also be understood as agents who create links to increase their connectivity and thus also the robustness of the network.
For this novel type of NCG we prove that pure Nash equilibria exist, we give a simple algorithm for computing optimal networks, we show that the Price of Stability is 1 and we prove an (almost) tight bound of 2 on the Price of Anarchy. Last but not least, we show that our models do not admit a potential function.
|
Hagen Echzell, Tobias Friedrich, Pascal Lenzner, Anna Melnichenko
| null | null | 2,020 |
ijcai
|
Stable Matchings with Diversity Constraints: Affirmative Action is beyond NP
| null |
We investigate the following many-to-one stable matching problem with diversity constraints (SMTI-DIVERSE): Given a set of students and a set of colleges which have preferences over each other, where the students have overlapping types, and the colleges each have a total capacity as well as quotas for individual types (the diversity constraints), is there a matching satisfying all diversity constraints such that no unmatched student-college pair has an incentive to deviate?
SMTI-DIVERSE is known to be NP-hard. However, as opposed to the NP-membership claims in the literature [Aziz et al., AAMAS 2019; Huang,SODA 2010], we prove that it is beyond NP: it is complete for the complexity class Σ^P_2. In addition, we provide a comprehensive analysis of the problem’s complexity from the viewpoint of natural restrictions to inputs and obtain new algorithms for the problem.
|
Jiehua Chen, Robert Ganian, Thekla Hamm
| null | null | 2,020 |
ijcai
|
Mechanism Design for School Choice with Soft Diversity Constraints
| null |
We study the controlled school choice problem where students may belong to overlapping types and schools have soft target quotas for each type. We formalize fairness concepts for the setting that extend fairness concepts considered for restricted settings without overlapping types. Our central contribution is presenting a new class of algorithms that takes into account the representations of combinations of student types. The algorithms return matchings that are non-wasteful and satisfy fairness for same types. We further prove that the algorithms are strategyproof for the students and yield a fair outcome with respect to the induced quotas for type combinations. We experimentally compare our algorithms with two existing approaches in terms of achieving diversity goals and satisfying fairness.
|
Haris Aziz, Serge Gaspers, Zhaohong Sun
| null | null | 2,020 |
ijcai
|
Logics of Allies and Enemies: A Formal Approach to the Dynamics of Social Balance Theory
| null |
We combine social balance theory with temporal logic to obtain a Logic of Allies and Enemies (LAE), which formally describes the likely changes to a social network due to social pressure. We demonstrate how the rich language of LAE can be used to describe various interesting concepts, and show that both model checking and validity checking are PSPACE-complete.
|
Wiebe Van der Hoek, Louwe Kuijer, Yì Wáng
| null | null | 2,020 |
ijcai
|
Proportionality in Approval-Based Elections With a Variable Number of Winners
| null |
We study proportionality in approval-based multiwinner elections with a variable number of winners, where both the size and identity of the winning committee are informed by voters' opinions. While proportionality has been studied in multiwinner elections with a fixed number of winners, it has not been considered in the variable number of winners setting. The measure of proportionality we consider is average satisfaction (AS), which intuitively measures the number of agreements on average between sufficiently large and cohesive groups of voters and the output of the voting rule. First, we show an upper bound on AS that any deterministic rule can provide, and that straightforward adaptations of deterministic rules from the fixed number of winners setting do not achieve better than a 1/2 approximation to AS even for large numbers of candidates. We then prove that a natural randomized rule achieves a 29/32 approximation to AS.
|
Rupert Freeman, Anson Kahng, David M. Pennock
| null | null | 2,020 |
ijcai
|
On the Complexity of Winner Verification and Candidate Winner for Multiwinner Voting Rules
| null |
The Chamberlin-Courant and Monroe rules are fundamental and well-studied rules in the literature of multi-winner elections. The problem of determining if there exists a committee of size k that has a Chamberlin-Courant (respectively, Monroe) dissatisfaction score of at most r is known to be NP-complete. We consider the following natural problems in this setting: a) given a committee S of size k as input, is it an optimal k-sized committee?, and b) given a candidate c and a committee size k, does there exist an optimal k-sized committee that contains c? In this work, we resolve the complexity of both problems for the Chamberlin-Courant and Monroe voting rules in the settings of rankings as well as approval ballots. We show that verifying if a given committee is optimal is coNP-complete whilst the latter problem is complete for Theta_2^P. Our contribution fills an essential gap in the literature for these important multi-winner rules.
|
Chinmay Sonar, Palash Dey, Neeldhara Misra
| null | null | 2,020 |
ijcai
|
Peer-Prediction in the Presence of Outcome Dependent Lying Incentives
| null |
We derive conditions under which a peer-consistency mechanism can be used to elicit truthful data from non-trusted rational agents when an aggregate statistic of the collected data affects the amount of their incentives to lie. Furthermore, we discuss the relative saving that can be achieved by the mechanism, compared to the rational outcome, if no such mechanism was implemented. Our work is motivated by distributed platforms, where decentralized data oracles collect information about real-world events, based on the aggregate information provided by often self-interested participants. We compare our theoretical observations with numerical simulations on two public real datasets.
|
Naman Goel, Aris Filos-Ratsikas, Boi Faltings
| null | null | 2,020 |
ijcai
|
Prophet Inequalities for Bayesian Persuasion
| null |
We study an information-structure design problem (i.e., a Bayesian persuasion problem) in an online scenario. Inspired by the classic gambler's problem, consider a set of candidates who arrive sequentially and are evaluated by one agent (the sender). This agent learns the value from hiring the candidate to herself as well as the value to another agent, the receiver. The sender provides a signal to the receiver who, in turn, makes an irrevocable decision on whether or not to hire the candidate. A-priori, for each agent the distribution of valuation is independent across candidates but may not be identical. We design good online signaling schemes for the sender. To assess the performance, we compare the expected utility to that of an optimal offline scheme by a prophet sender who knows all candidate realizations in advance. We show an optimal prophet inequality for online Bayesian persuasion, with a 1/2-approximation when the instance satisfies a "satisfactory-status-quo" assumption. Without this assumption, there are instances without any finite approximation factor. We extend the results to combinatorial domains and obtain prophet inequalities for matching with multiple hires and multiple receivers.
|
Niklas Hahn, Martin Hoefer, Rann Smorodinsky
| null | null | 2,020 |
ijcai
|
Fair Division of Time: Multi-layered Cake Cutting
| null |
We initiate the study of multi-layered cake cutting with the goal of fairly allocating multiple divisible resources (layers of a cake) among a set of agents. The key requirement is that each agent can only utilize a single resource at each time interval. Several real-life applications exhibit such restrictions on overlapping pieces, for example, assigning time intervals over multiple facilities and resources or assigning shifts to medical professionals. We investigate the existence and computation of envy-free and proportional allocations. We show that envy-free allocations that are both feasible and contiguous are guaranteed to exist for up to three agents with two types of preferences, when the number of layers is two. We further devise an algorithm for computing proportional allocations for any number of agents when the number of layers is factorable to three and/or some power of two.
|
Hadi Hosseini, Ayumi Igarashi, Andrew Searns
| null | null | 2,020 |
ijcai
|
Well-Structured Committees
| null |
In the standard model of committee selection, we are given a set of ordinal votes over a set of candidates and a desired committee size, and the task is to select a committee that relates to the given votes.
Motivated by possible interactions and dependencies between candidates, we study a generalization of committee selection in which the candidates are connected via a network and the task is to select a committee that relates to the given votes while also satisfy certain properties with respect to this candidate network. To accommodate certain correspondences to the voter preferences, we consider three standard voting rules (in particular, $k$-Borda, Chamberlin-Courant, and Gehrlein stability); to model different aspects of interactions and dependencies between candidates, we consider two graph properties (in particular, Independent Set and Connectivity). We study the parameterized complexity of the corresponding combinatorial problems and discuss certain implications of our algorithmic results.
|
Sushmita Gupta, Pallavi Jain, Saket Saurabh
| null | null | 2,020 |
ijcai
|
Learning Optimal Temperature Region for Solving Mixed Integer Functional DCOPs
| null |
Distributed Constraint Optimization Problems (DCOPs) are an important framework for modeling coordinated decision-making problems in multi-agent systems with a set of discrete variables. Later works have extended DCOPs to model problems with a set of continuous variables, named Functional DCOPs (F-DCOPs). In this paper, we combine both of these frameworks into the Mixed Integer Functional DCOP (MIF-DCOP) framework that can deal with problems regardless of their variables' type. We then propose a novel algorithm - Distributed Parallel Simulated Annealing (DPSA), where agents cooperatively learn the optimal parameter configuration for the algorithm while also solving the given problem using the learned knowledge. Finally, we empirically evaluate our approach in DCOP, F-DCOP, and MIF-DCOP settings and show that DPSA produces solutions of significantly better quality than the state-of-the-art non-exact algorithms in their corresponding settings.
|
Saaduddin Mahmud, Md. Mosaddek Khan, Moumita Choudhury, Long Tran-Thanh, Nicholas R. Jennings
| null | null | 2,020 |
ijcai
|
The Complexity of Election Problems with Group-Separable Preferences
| null |
We analyze the complexity of several NP-hard election-related problems under the assumptions that the voters have group-separable preferences. We show that under this assumption our problems typically remain NP-hard, but we provide more efficient algorithms if additionally the clone decomposition tree is of moderate height.
|
Piotr Faliszewski, Alexander Karpov, Svetlana Obraztsova
| null | null | 2,020 |
ijcai
|
Strategyproof Mechanism for Two Heterogeneous Facilities with Constant Approximation Ratio
| null |
In this paper, we study the two-facility location game with optional preference where the acceptable set of facilities for each agent could be different and an agent's cost is his distance to the closest facility within his acceptable set. The objective is to minimize the total cost of all agents while achieving strategyproofness. For general metrics, we design a deterministic strategyproof mechanism for the problem with approximation ratio of 1+2alpha, where alpha is the approximation ratio of the optimization version. In particular, for the setting on a line, we improve the earlier best ratio of n/2+1 to a ratio of 2.75.
|
Minming Li, Pinyan Lu, Yuhao Yao, Jialin Zhang
| null | null | 2,020 |
ijcai
|
Convexity of b-matching Games
| null |
The b-matching game is a cooperative game defined on a graph.
The game generalizes the matching game to allow each individual to have more than one partner.
The game has several applications, such as the roommate assignment, the multi-item version of the seller-buyer assignment, and the international kidney exchange.
Compared with the standard matching game, the b-matching game is computationally hard.
In particular, the core non-emptiness problem and the core membership problem are co-NP-hard.
Therefore, we focus on the convexity of the game, which is a sufficient condition of the core non-emptiness and often more tractable concept than the core non-emptiness.
It also has several additional benefits.
In this study, we give a necessary and sufficient condition of the convexity of the b-matching game.
This condition also gives an O(n log n + m α(n)) time algorithm to determine whether a given game is convex or not, where n and m are the number of vertices and edges of a given graph, respectively, and α(・) is the inverse-Ackermann function.
Using our characterization, we also give a polynomial-time algorithm to compute the Shapley value of a convex b-matching game.
|
Soh Kumabe, Takanori Maehara
| null | null | 2,020 |
ijcai
|
Evaluating Committees for Representative Democracies: the Distortion and Beyond
| null |
We study a model where a group of representatives is elected to make a series of decisions on behalf of voters. The quality of such a representative committee is judged based on the extent to which the decisions it makes are consistent with the voters' preferences. We assume the set of issues on which the committee will make the decisions is unknown---a committee is elected based on the preferences of the voters over the candidates, which only reflect how similar are the preferences of the voters and candidates regarding the issues. In this model we theoretically and experimentally assess qualities of various multiwinner election rules.
|
Michał Jaworski, Piotr Skowron
| null | null | 2,020 |
ijcai
|
Partial Adversarial Behavior Deception in Security Games
| null |
Learning attacker behavior is an important research topic in security games as security agencies are often uncertain about attackers' decision making. Previous work has focused on developing various behavioral models of attackers based on historical attack data. However, a clever attacker can manipulate its attacks to fail such attack-driven learning, leading to ineffective defense strategies. We study attacker behavior deception with three main contributions. First, we propose a new model, named partial behavior deception model, in which there is a deceptive attacker (among multiple attackers) who controls a portion of attacks. Our model captures real-world security scenarios such as wildlife protection in which multiple poachers are present. Second, we introduce a new scalable algorithm, GAMBO, to compute an optimal deception strategy of the deceptive attacker. Our algorithm employs the projected gradient descent and uses the implicit function theorem for the computation of gradient. Third, we conduct a comprehensive set of experiments, showing a significant benefit for the attacker and loss for the defender due to attacker deception.
|
Thanh H. Nguyen, Arunesh Sinha, He He
| null | null | 2,020 |
ijcai
|
Incentive-Compatible Diffusion Auctions
| null |
Diffusion auction is a new model in auction design. It can incentivize the buyers who have already joined in the auction to further diffuse the sale information to others via social relations, whereby both the seller's revenue and the social welfare can be improved. Diffusion auctions are essentially non-typical multidimensional mechanism design problems and agents' social relations are complicatedly involved with their bids. In such auctions, incentive-compatibility (IC) means it is best for every agent to honestly report her valuation and fully diffuse the sale information to all her neighbors. Existing work identified some specific mechanisms for diffusion auctions, while a general theory characterizing all incentive-compatible diffusion auctions is still missing. In this work, we identify a sufficient and necessary condition for all dominant-strategy incentive-compatible (DSIC) diffusion auctions. We formulate the monotonic allocation policies in such multidimensional problems and show that any monotonic allocation policy can be implemented in a DSIC diffusion auction mechanism. Moreover, given any monotonic allocation policy, we obtain the optimal payment policy to maximize the seller's revenue.
|
Bin Li, Dong Hao, Dengji Zhao
| null | null | 2,020 |
ijcai
|
Selecting Voting Locations for Fun and Profit
| null |
While manipulative attacks on elections have been well-studied, only recently has attention turned to attacks that account for geographic information, which are extremely common in the real world. The most well known in the media is gerrymandering, in which district border-lines are changed to increase a party's chance to win, but a different geographical manipulation involves influencing the election by selecting the location of polling places, as many people are not willing to go to any distance to vote. In this paper we initiate the study of this manipulation. We find that while it is easy to manipulate the selection of polling places on the line, it becomes difficult already on the plane or in the case of more than two candidates. Moreover, we show that for more than two candidates the problem is inapproximable. However, we find a few restricted cases on the plane where some algorithms perform well. Finally, we discuss how existing results for standard control actions hold in the geographic setting, consider additional control actions in the geographic setting, and suggest directions for future study.
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Zack Fitzsimmons, Omer Lev
| null | null | 2,020 |
ijcai
|
Dinkelbach-Type Algorithm for Computing Quantal Stackelberg Equilibrium
| null |
Stackelberg security games (SSGs) have been deployed in many real-world situations to optimally allocate scarce resource to protect targets against attackers. However, actual human attackers are not perfectly rational and there are several behavior models that attempt to predict subrational behavior. Quantal response is among the most commonly used such models and Quantal Stackelberg Equilibrium (QSE) describes the optimal strategy to commit to when facing a subrational opponent. Non-concavity makes computing QSE computationally challenging and while there exist algorithms for computing QSE for SSGs, they cannot be directly used for solving an arbitrary game in the normal form. We (1) present a transformation of the primal problem for computing QSE using a Dinkelbach's method for any general-sum normal-form game, (2) provide a gradient-based and a MILP-based algorithm, give the convergence criteria, and bound their error, and finally (3) we experimentally demonstrate that using our novel transformation, a QSE can be closely approximated several orders of magnitude faster.
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Jakub Cerny, Viliam Lisý, Branislav Bošanský, Bo An
| null | null | 2,020 |
ijcai
|
Synthesis of Deceptive Strategies in Reachability Games with Action Misperception
| null |
We consider a class of two-player turn-based zero-sum games on graphs with reachability objectives, known as reachability games, where the objective of Player 1 (P1) is to reach a set of goal states, and that of Player 2 (P2) is to prevent this. In particular, we consider the case where the players have asymmetric information about each other's action capabilities: P2 starts with an incomplete information (misperception) about P1's action set, and updates the misperception when P1 uses an action previously unknown to P2. When P1 is made aware of P2's misperception, the key question is whether P1 can control P2's perception so as to deceive P2 into selecting actions to P1's advantage? To answer this question, we introduce a dynamic hypergame model to capture the reachability game with evolving misperception of P2. Then, we present a fixed-point algorithm to compute the deceptive winning region and strategy for P1 under almost-sure winning condition. Finally, we show that the synthesized deceptive winning strategy is at least as powerful as the (non-deceptive) winning strategy in the game in which P1 does not account for P2's misperception. We illustrate our algorithm using a robot motion planning in an adversarial environment.
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Abhishek N. Kulkarni, Jie Fu
| null | null | 2,020 |
ijcai
|
A Penny for Your Thoughts: The Value of Communication in Ad Hoc Teamwork
| null |
In ad hoc teamwork, multiple agents need to collaborate without having knowledge about their teammates or their plans a priori. A common assumption in this research area is that the agents cannot communicate. However, just as two random people may speak the same language, autonomous teammates may also happen to share a communication protocol. This paper considers how such a shared protocol can be leveraged, introducing a means to reason about Communication in Ad Hoc Teamwork (CAT). The goal of this work is enabling improved ad hoc teamwork by judiciously leveraging the ability of the team to communicate.
We situate our study within a novel CAT scenario, involving tasks with multiple steps, where teammates' plans are unveiled over time. In this context, the paper proposes methods to reason about the timing and value of communication and introduces an algorithm for an ad hoc agent to leverage these methods. Finally, we introduces a new multiagent domain, the tool fetching domain, and we study how varying this domain's properties affects the usefulness of communication. Empirical results show the benefits of explicit reasoning about communication content and timing in ad hoc teamwork.
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Reuth Mirsky, William Macke, Andy Wang, Harel Yedidsion, Peter Stone
| null | null | 2,020 |
ijcai
|
Approximate Pareto Set for Fair and Efficient Allocation: Few Agent Types or Few Resource Types
| null |
In fair division of indivisible goods, finding an allocation that satisfies fairness and efficiency simultaneously is highly desired but
computationally hard. We solve this problem approximately in polynomial time by modeling it as a bi-criteria optimization problem
that can be solved efficiently by determining an approximate Pareto set of bounded size. We focus on two criteria: max-min fairness and
utilitarian efficiency, and study this problem for the setting when there are only a few item types or a few agent types. We show in both cases that one can construct an approximate Pareto set in time polynomial in the input size, either by designing a dynamic programming scheme, or a linear-programming algorithm. Our techniques strengthen known methods and can be potentially applied to other notions of fairness and efficiency as well.
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Trung Thanh Nguyen, Jörg Rothe
| null | null | 2,020 |
ijcai
|
A Deep Reinforcement Learning Approach to Concurrent Bilateral Negotiation
| null |
We present a novel negotiation model that allows an agent to learn how to negotiate during concurrent bilateral negotiations in unknown and dynamic e-markets. The agent uses an actor-critic architecture with model-free reinforcement learning to learn a strategy expressed as a deep neural network. We pre-train the strategy by supervision from synthetic market data, thereby decreasing the exploration time required for learning during negotiation. As a result, we can build automated agents for concurrent negotiations that can adapt to different e-market settings without the need to be pre-programmed. Our experimental evaluation shows that our deep reinforcement learning based agents outperform two existing well-known negotiation strategies in one-to-many concurrent bilateral negotiations for a range of e-market settings.
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Pallavi Bagga, Nicola Paoletti, Bedour Alrayes, Kostas Stathis
| null | null | 2,020 |
ijcai
|
Tight Approximation for Proportional Approval Voting
| null |
In approval-based multiwinner elections, we are given a set of voters, a set of candidates, and, for each voter, a set of candidates approved by the voter. The goal is to find a committee of size k that maximizes the total utility of the voters. In this paper, we study approximability of Thiele rules, which are known to be NP-hard to solve exactly. We provide a tight polynomial time approximation algorithm for a natural class of geometrically dominant weights that includes such voting rules as Proportional Approval Voting or p-Geometric. The algorithm is relatively simple: first we solve a linear program and then we round a solution by employing a framework called pipage rounding due to Ageev and Sviridenko (2004) and Calinescu et al. (2011). We provide a matching lower bound via a reduction from the Label Cover problem. Moreover, assuming a conjecture called Gap-ETH, we show that better approximation ratio cannot be obtained even in time f(k)*pow(n,o(k)).
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Szymon Dudycz, Pasin Manurangsi, Jan Marcinkowski, Krzysztof Sornat
| null | null | 2,020 |
ijcai
|
Computational Aspects of Conditional Minisum Approval Voting in Elections with Interdependent Issues
| null |
Approval voting provides a simple, practical framework for multi-issue elections, and the most representative example among such election rules is the classic Minisum approval voting rule.
We consider a generalization of Minisum, introduced by the work of Barrot and Lang [2016], referred to as Conditional Minisum, where voters are also allowed to express dependencies between issues.
The price we have to pay when we move to this higher level of expressiveness is that we end up with a computationally hard rule. Motivated by this, we focus on the computational aspects of Conditional Minisum, where progress has been rather scarce so far.
We identify restrictions to every voter's dependencies, under which we provide the first multiplicative approximation algorithms for the problem.
The restrictions involve upper bounds on the number of dependencies an issue can have on the others. At the same time, by additionally requiring certain structural properties for the union of dependencies cast by the whole electorate, we obtain optimal efficient algorithms for well-motivated special cases.
Overall, our work provides a better understanding on the complexity implications introduced by conditional voting.
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Evangelos Markakis, Georgios Papasotiropoulos
| null | null | 2,020 |
ijcai
|
A Multi-Objective Approach to Mitigate Negative Side Effects
| null |
Agents operating in unstructured environments often create negative side effects (NSE) that may not be easy to identify at design time. We examine how various forms of human feedback or autonomous exploration can be used to learn a penalty function associated with NSE during system deployment. We formulate the problem of mitigating the impact of NSE as a multi-objective Markov decision process with lexicographic reward preferences and slack. The slack denotes the maximum deviation from an optimal policy with respect to the agent's primary objective allowed in order to mitigate NSE as a secondary objective. Empirical evaluation of our approach shows that the proposed framework can successfully mitigate NSE and that different feedback mechanisms introduce different biases, which influence the identification of NSE.
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Sandhya Saisubramanian, Ece Kamar, Shlomo Zilberstein
| null | null | 2,020 |
ijcai
|
Combining Direct Trust and Indirect Trust in Multi-Agent Systems
| null |
To assess the trustworthiness of an agent in a multi-agent system, one often combines two types of trust information: direct trust information derived from one's own interactions with that agent, and indirect trust information based on advice from other agents. This paper provides the first systematic study on when it is beneficial to combine these two types of trust as opposed to relying on only one of them. Our large-scale experimental study shows that strong methods for computing indirect trust make direct trust redundant in a surprisingly wide variety of scenarios. Further, a new method for the combination of the two trust types is proposed that, in the remaining scenarios, outperforms the ones known from the literature.
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Elham Parhizkar, Mohammad Hossein Nikravan, Robert C. Holte, Sandra Zilles
| null | null | 2,020 |
ijcai
|
Verifying Fault-Tolerance in Probabilistic Swarm Systems
| null |
We present a method for reasoning about fault-tolerance in unbounded robotic swarms. We introduce a novel semantics that accounts for the probabilistic nature of both the swarm and possible malfunctions, as well as the unbounded nature of swarm systems. We define and interpret a variant of probabilistic linear-time temporal logic on the resulting executions, including those arising from faulty behaviour by some of the agents in the swarm. We specify the decision problem of parameterised fault-tolerance, which concerns determining whether a probabilistic specification holds under possibly faulty behaviour. We outline a verification procedure that we implement and use to study a foraging protocol from swarm robotics, and report the experimental results obtained.
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Alessio Lomuscio, Edoardo Pirovano
| null | null | 2,020 |
ijcai
|
Altruism in Coalition Formation Games
| null |
Nguyen et al. [2016] introduced altruistic hedonic games in which agents’ utilities depend not only on their own preferences but also on those of their friends in the same coalition. We propose to extend their model to coalition formation games in general, considering also the friends in other coalitions. Comparing the two models, we argue that excluding some friends from the altruistic behavior of an agent is a major disadvantage that comes with the restriction to hedonic games. After introducing our model, we additionally study some common stability notions and provide a computational analysis of the associated verification and existence problems.
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Anna Maria Kerkmann, Jörg Rothe
| null | null | 2,020 |
ijcai
|
Model-Free Real-Time Autonomous Energy Management for a Residential Multi-Carrier Energy System: A Deep Reinforcement Learning Approach
| null |
The problem of real-time autonomous energy management is an application area that is receiving unprecedented attention from consumers, governments, academia, and industry. This paper showcases the first application of deep reinforcement learning (DRL) to real-time autonomous energy management for a multi-carrier energy system. The proposed approach is tailored to align with the nature of the energy management problem by posing it in multi-dimensional continuous state and action spaces, in order to coordinate power flows between different energy devices, and to adequately capture the synergistic effect of couplings between different energy carriers. This fundamental contribution is a significant step forward from earlier approaches that only sought to control the power output of a single device and neglected the demand-supply coupling of different energy carriers. Case studies on a real-world scenario demonstrate that the proposed method significantly outperforms existing DRL methods as well as model-based control approaches in achieving the lowest energy cost and yielding a representation of energy management policies that adapt to system uncertainties.
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Yujian Ye, Dawei Qiu, Jonathan Ward, Marcin Abram
| null | null | 2,020 |
ijcai
|
Efficient Algorithms for Learning Revenue-Maximizing Two-Part Tariffs
| null |
A two-part tariff is a pricing scheme that consists of an up-front lump sum fee and a per unit fee. Various products in the real world are sold via a menu, or list, of two-part tariffs---for example gym memberships, cell phone data plans, etc. We study learning high-revenue menus of two-part tariffs from buyer valuation data, in the setting where the mechanism designer has access to samples from the distribution over buyers' values rather than an explicit description thereof. Our algorithms have clear direct uses, and provide the missing piece for the recent generalization theory of two-part tariffs. We present a polynomial time algorithm for optimizing one two-part tariff. We also present an algorithm for optimizing a length-L menu of two-part tariffs with run time exponential in L but polynomial in all other problem parameters. We then generalize the problem to multiple markets. We prove how many samples suffice to guarantee that a two-part tariff scheme that is feasible on the samples is also feasible on a new problem instance with high probability. We then show that computing revenue-maximizing feasible prices is hard even for buyers with additive valuations. Then, for buyers with identical valuation distributions, we present a condition that is sufficient for the two-part tariff scheme from the unsegmented setting to be optimal for the market-segmented setting. Finally, we prove a generalization result that states how many samples suffice so that we can compute the unsegmented solution on the samples and still be guaranteed that we get a near-optimal solution for the market-segmented setting with high probability.
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Maria-Florina Balcan, Siddharth Prasad, Tuomas Sandholm
| null | null | 2,020 |
ijcai
|
Competition Among Contests: a Safety Level Analysis
| null |
We study a competition among two contests, where each contest designer aims to attract as much effort as possible. Such a competition exists in reality, e.g., in crowd-sourcing websites. Our results are phrased in terms of the ``relative prize power'' of a contest, which is the ratio of the total prize offered by this contest designer relative to the sum of total prizes of the two contests. When contestants have a quasi-linear utility function that captures
both a risk-aversion effect and a cost of effort, we show that a simple contest attracts a total effort which approaches the relative prize power of the contest designer assuming a large number of contestants. This holds regardless of the contest policy of the opponent, hence providing a ``safety level'' which is a robust notion similar in spirit to the max-min solution concept.
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Ron Lavi, Omer Shiran-Shvarzbard
| null | null | 2,020 |
ijcai
|
Keeping Your Friends Close: Land Allocation with Friends
| null |
We examine the problem of assigning plots of land to prospective buyers who prefer living next to their friends. In this setting, each agent's utility depends on the plot she receives and the identities of the agents who receive the adjacent plots. We are interested in mechanisms without money that guarantee truthful reporting of both land values and friendships, as well as Pareto optimality and computational efficiency. We explore several modifications of the Random Serial Dictatorship (RSD) mechanism, and identify one that performs well according to these criteria, We also study the expected social welfare of the assignments produced by our mechanisms when agents' values for the land plots are binary; it turns out that we can achieve good approximations to the optimal social welfare, but only if the agents value the friendships highly.
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Edith Elkind, Neel Patel, Alan Tsang, Yair Zick
| null | null | 2,020 |
ijcai
|
When to Follow the Tip: Security Games with Strategic Informants
| null |
Although security games have attracted intensive research attention over the past years, few existing works consider how information from local communities would affect the game. In this paper, we introduce a new player -- a strategic informant, who can observe and report upcoming attacks -- to the defender-attacker security game setting. Characterized by a private type, the informant has his utility structure that leads to his strategic behaviors. We model the game as a 3-player extensive-form game and propose a novel solution concept of Strong Stackelberg-perfect Bayesian equilibrium. To compute the optimal defender strategy, we first show that although the informant can have infinitely many types in general, the optimal defense plan can only include a finite (exponential) number of different patrol strategies. We then prove that there exists a defense plan with only a linear number of patrol strategies that achieve the optimal defender's utility, which significantly reduces the computational burden and allows us to solve the game in polynomial time using linear programming. Finally, we conduct extensive experiments to show the effect of the strategic informant and demonstrate the effectiveness of our algorithm.
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Weiran Shen, Weizhe Chen, Taoan Huang, Rohit Singh, Fei Fang
| null | null | 2,020 |
ijcai
|
PeerNomination: Relaxing Exactness for Increased Accuracy in Peer Selection
| null |
In peer selection agents must choose a subset of themselves for an award or a prize. As agents are self-interested, we want to design algorithms that are impartial, so that an individual agent cannot affect their own chance of being selected. This problem has broad application in resource allocation and mechanism design and has received substantial attention in the artificial intelligence literature. Here, we present a novel algorithm for impartial peer selection, PeerNomination, and provide a theoretical analysis of its accuracy. Our algorithm possesses various desirable features. In particular, it does not require an explicit partitioning of the agents, as previous algorithms in the literature. We show empirically that it achieves higher accuracy than the exiting algorithms over several metrics.
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Nicholas Mattei, Paolo Turrini, Stanislav Zhydkov
| null | null | 2,020 |
ijcai
|
Budgeted Facility Location Games with Strategic Facilities
| null |
This paper studies the facility location games with payments, where facilities are strategic players. In the game, customers and facilities are located at publicly known locations on a line segment. Each selfish facility has an opening-cost as her private information, and she may strategically report it. Upon receiving the reports, the government uses a mechanism to select some facilities to open and pay to them. The cost/utility of each customer depends on the distance to the nearest opened facility. Under a given budget B, which constrains the total payment, we derive upper and lower bounds on the approximation ratios of truthful budget feasible mechanisms for four utilitarian and egalitarian objectives, and study the case when augmented budget is allowed.
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Minming Li, Chenhao Wang, Mengqi Zhang
| null | null | 2,020 |
ijcai
|
Participatory Budgeting with Project Interactions
| null |
Participatory budgeting systems allow city residents to jointly decide on projects they wish to fund using public money, by letting residents vote on such projects. While participatory budgeting is gaining popularity, existing aggregation methods do not take into account the natural possibility of project interactions, such as substitution and complementarity effects. Here we take a step towards fixing this issue: First, we augment the standard model of participatory budgeting by introducing a partition over the projects and model the type and extent of project interactions within each part using certain functions. We study the computational complexity of finding bundles that maximize voter utility, as defined with respect to such functions. Motivated by the desire to incorporate project interactions in real-world participatory budgeting systems, we identify certain cases that admit efficient aggregation in the presence of such project interactions.
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Pallavi Jain, Krzysztof Sornat, Nimrod Talmon
| null | null | 2,020 |
ijcai
|
The Competitive Effects of Variance-based Pricing
| null |
In many markets, like electricity or cloud computing markets, providers incur large costs for keeping sufficient capacity in reserve to accommodate demand fluctuations of a mostly fixed user base. These costs are significantly affected by the unpredictability of the users' demand. Nevertheless, standard mechanisms charge fixed per-unit prices that do not depend on the variability of the users' demand. In this paper, we study a variance-based pricing rule in a two-provider market setting and perform a game-theoretic analysis of the resulting competitive effects. We show that an innovative provider who employs variance-based pricing can choose a pricing strategy that guarantees himself a higher profit than using fixed per-unit prices for any individually rational response of a provider playing a fixed pricing strategy. We then characterize all equilibria for the setting where both providers use variance-based pricing strategies. We show that, in equilibrium, the providers' profits may increase or decrease, depending on their cost functions. However, social welfare always weakly increases.
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Ludwig Dierks, Sven Seuken
| null | null | 2,020 |
ijcai
|
WEFE: The Word Embeddings Fairness Evaluation Framework
| null |
Word embeddings are known to exhibit stereotypical biases towards gender, race, religion, among other criteria. Severa fairness metrics have been proposed in order to automatically quantify these biases. Although all metrics have a similar objective, the relationship between them is by no means clear. Two issues that prevent a clean comparison is that they operate with different inputs, and that their outputs are incompatible with each other. In this paper we propose WEFE, the word embeddings fairness evaluation framework, to encapsulate, evaluate and compare fairness metrics. Our framework needs a list of pre-trained embeddings and a set of fairness criteria, and it is based on checking correlations between fairness rankings induced by these criteria. We conduct a case study showing that rankings produced by existing fairness methods tend to correlate when measuring gender bias. This correlation is considerably less for other biases like race or religion. We also compare the fairness rankings with an embedding benchmark showing that there is no clear correlation between fairness and good performance in downstream tasks.
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Pablo Badilla, Felipe Bravo-Marquez, Jorge Pérez
| null | null | 2,020 |
ijcai
|
Metamorphic Testing and Certified Mitigation of Fairness Violations in NLP Models
| null |
Natural language processing (NLP) models have been increasingly used in sensitive application domains including credit scoring, insurance, and loan assessment. Hence, it is critical to know that the decisions made by NLP models are free of unfair bias toward certain subpopulation groups. In this paper, we propose a novel framework employing metamorphic testing, a well-established software testing scheme, to test NLP models and find discriminatory inputs that provoke fairness violations. Furthermore, inspired by recent breakthroughs in the certified robustness of machine learning, we formulate NLP model fairness in a practical setting as (ε, k)-fairness and accordingly smooth the model predictions to mitigate fairness violations. We demonstrate our technique using popular (commercial) NLP models, and successfully flag thousands of discriminatory inputs that can cause fairness violations. We further enhance the evaluated models by adding certified fairness guarantee at a modest cost.
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Pingchuan Ma, Shuai Wang, Jin Liu
| null | null | 2,020 |
ijcai
|
Individual Fairness Revisited: Transferring Techniques from Adversarial Robustness
| null |
We turn the definition of individual fairness on its head - rather than ascertaining the fairness of a model given a predetermined metric, we find a metric for a given model that satisfies individual fairness. This can facilitate the discussion on the fairness of a model, addressing the issue that it may be difficult to specify a priori a suitable metric. Our contributions are twofold:
First, we introduce the definition of a minimal metric and characterize the behavior of models in terms of minimal metrics. Second, for more complicated models, we apply the mechanism of randomized smoothing from adversarial robustness to make them individually fair under a given weighted Lp metric. Our experiments show that adapting the minimal metrics of linear models to more complicated neural networks can lead to meaningful and interpretable fairness guarantees at little cost to utility.
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Samuel Yeom, Matt Fredrikson
| null | null | 2,020 |
ijcai
|
Modelling Bounded Rationality in Multi-Agent Interactions by Generalized Recursive Reasoning
| null |
Though limited in real-world decision making, most multi-agent reinforcement learning (MARL) models assume perfectly rational agents -- a property hardly met due to individual's cognitive limitation and/or the tractability of the decision problem. In this paper, we introduce generalized recursive reasoning (GR2) as a novel framework to model agents with different \emph{hierarchical} levels of rationality; our framework enables agents to exhibit varying levels of ``thinking'' ability thereby allowing higher-level agents to best respond to various less sophisticated learners. We contribute both theoretically and empirically. On the theory side, we devise the hierarchical framework of GR2 through probabilistic graphical models and prove the existence of a perfect Bayesian equilibrium. Within the GR2, we propose a practical actor-critic solver, and demonstrate its convergent property to a stationary point in two-player games through Lyapunov analysis. On the empirical side, we validate our findings on a variety of MARL benchmarks. Precisely, we first illustrate the hierarchical thinking process on the Keynes Beauty Contest, and then demonstrate significant improvements compared to state-of-the-art opponent modeling baselines on the normal-form games and the cooperative navigation benchmark.
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Ying Wen, Yaodong Yang, Jun Wang
| null | null | 2,020 |
ijcai
|
TextFuseNet: Scene Text Detection with Richer Fused Features
| null |
Arbitrary shape text detection in natural scenes is an extremely challenging task. Unlike existing text detection approaches that only perceive texts based on limited feature representations, we propose a novel framework, namely TextFuseNet, to exploit the use of richer features fused for text detection. More specifically, we propose to perceive texts from three levels of feature representations, i.e., character-, word- and global-level, and then introduce a novel text representation fusion technique to help achieve robust arbitrary text detection. The multi-level feature representation can adequately describe texts by dissecting them into individual characters while still maintaining their general semantics. TextFuseNet then collects and merges the texts’ features from different levels using a multi-path fusion architecture which can effectively align and fuse different representations. In practice, our proposed TextFuseNet can learn a more adequate description of arbitrary shapes texts, suppressing false positives and producing more accurate detection results. Our proposed framework can also be trained with weak supervision for those datasets that lack character-level annotations. Experiments on several datasets show that the proposed TextFuseNet achieves state-of-the-art performance. Specifically, we achieve an F-measure of 94.3% on ICDAR2013, 92.1% on ICDAR2015, 87.1% on Total-Text and 86.6% on CTW-1500, respectively.
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Jian Ye, Zhe Chen, Juhua Liu, Bo Du
| null | null | 2,020 |
ijcai
|
Monte-Carlo Tree Search for Scalable Coalition Formation
| null |
We propose a novel algorithm based on Monte-Carlo tree search for the problem of coalition structure generation (CSG). Specifically, we find the optimal solution by sampling the coalition structure graph and incrementally expanding a search tree, which represents the partial space that has been searched. We prove that our algorithm is complete and converges to the optimal given sufficient number of iterations. Moreover, it is anytime and can scale to large CSG problems with many agents. Experimental results on six common CSG benchmark problems and a disaster response domain confirm the advantages of our approach comparing to the state-of-the-art methods.
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Feng Wu, Sarvapali D. Ramchurn
| null | null | 2,020 |
ijcai
|
Sybil-proof Answer Querying Mechanism
| null |
We study a question answering problem on a social network, where a requester is seeking an answer from the agents on the network. The goal is to design reward mechanisms to incentivize the agents to propagate the requester's query to their neighbours if they don't have the answer. Existing mechanisms are vulnerable to Sybil-attacks, i.e., an agent may get more reward by creating fake identities. Hence, we combat this problem by first proving some impossibility results to resolve Sybil-attacks and then characterizing a class of mechanisms which satisfy Sybil-proofness (prevents Sybil-attacks) as well as other desirable properties. Except for Sybil-proofness, we also consider cost minimization for the requester and agents' collusions.
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Yao Zhang, Xiuzhen Zhang, Dengji Zhao
| null | null | 2,020 |
ijcai
|
Achieving Outcome Fairness in Machine Learning Models for Social Decision Problems
| null |
Effective complements to human judgment, artificial intelligence techniques have started to aid human decisions in complicated social decision problems across the world. Automated machine learning/deep learning(ML/DL) classification models, through quantitative modeling, have the potential to improve upon human decisions in a wide range of decision problems on social resource allocation such as Medicaid and Supplemental Nutrition Assistance Program(SNAP, commonly referred to as Food Stamps). However, given the limitations in ML/DL model design, these algorithms may fail to leverage various factors for decision making, resulting in improper decisions that allocate resources to individuals who may not be in the most need of such resource. In view of such an issue, we propose in this paper the strategy of fairgroups, based on the legal doctrine of disparate impact, to improve fairness in prediction outcomes. Experiments on various datasets demonstrate that our fairgroup construction method effectively boosts the fairness in automated decision making, while maintaining high prediction accuracy.
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Boli Fang, Miao Jiang, Pei-yi Cheng, Jerry Shen, Yi Fang
| null | null | 2,020 |
ijcai
|
Channel-Level Variable Quantization Network for Deep Image Compression
| null |
Deep image compression systems mainly contain four components: encoder, quantizer, entropy model, and decoder. To optimize these four components, a joint rate-distortion framework was proposed, and many deep neural network-based methods achieved great success in image compression. However, almost all convolutional neural network-based methods treat channel-wise feature maps equally, reducing the flexibility in handling different types of information. In this paper, we propose a channel-level variable quantization network to dynamically allocate more bitrates for significant channels and withdraw bitrates for negligible channels. Specifically, we propose a variable quantization controller. It consists of two key components: the channel importance module, which can dynamically learn the importance of channels during training, and the splitting-merging module, which can allocate different bitrates for different channels. We also formulate the quantizer into a Gaussian mixture model manner. Quantitative and qualitative experiments verify the effectiveness of the proposed model and demonstrate that our method achieves superior performance and can produce much better visual reconstructions.
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Zhisheng Zhong, Hiroaki Akutsu, Kiyoharu Aizawa
| null | null | 2,020 |
ijcai
|
SelectScale: Mining More Patterns from Images via Selective and Soft Dropout
| null |
Convolutional neural networks (CNNs) have achieved remarkable success in image recognition. Although the internal patterns of the input images are effectively learned by the CNNs, these patterns only constitute a small proportion of useful patterns contained in the input images. This can be attributed to the fact that the CNNs will stop learning if the learned patterns are enough to make a correct classification. Network regularization methods like dropout and SpatialDropout can ease this problem. During training, they randomly drop the features. These dropout methods, in essence, change the patterns learned by the networks, and in turn, forces the networks to learn other patterns to make the correct classification. However, the above methods have an important drawback. Randomly dropping features is generally inefficient and can introduce unnecessary noise. To tackle this problem, we propose SelectScale. Instead of randomly dropping units, SelectScale selects the important features in networks and adjusts them during training. Using SelectScale, we improve the performance of CNNs on CIFAR and ImageNet.
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Zhengsu Chen, Jianwei Niu, Xuefeng Liu, Shaojie Tang
| null | null | 2,020 |
ijcai
|
A 3D Convolutional Approach to Spectral Object Segmentation in Space and Time
| null |
We formulate object segmentation in video as a spectral graph clustering problem in space and time, in which nodes are pixels and their relations form local neighbourhoods. We claim that the strongest cluster in this pixel-level graph represents the salient object segmentation. We compute the main cluster using a novel and fast 3D filtering technique that finds the spectral clustering solution, namely the principal eigenvector of the graph's adjacency matrix, without building the matrix explicitly - which would be intractable. Our method is based on the power iteration which we prove is equivalent to performing a specific set of 3D convolutions in the space-time feature volume. This allows us to avoid creating the matrix and have a fast parallel implementation on GPU. We show that our method is much faster than classical power iteration applied directly on the adjacency matrix. Different from other works, ours is dedicated to preserving object consistency in space and time at the level of pixels.
In experiments, we obtain consistent improvement over the top state of the art methods on DAVIS-2016 dataset. We also achieve top results on the well-known SegTrackv2 dataset.
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Elena Burceanu, Marius Leordeanu
| null | null | 2,020 |
ijcai
|
Relation-Based Counterfactual Explanations for Bayesian Network Classifiers
| null |
We propose a general method for generating counterfactual explanations (CFXs) for a range of Bayesian Network Classifiers (BCs), e.g. single- or multi-label, binary or multidimensional. We focus on explanations built from relations of (critical and potential) influence between variables, indicating the reasons for classifications, rather than any probabilistic information. We show by means of a theoretical analysis of CFXs’ properties that they serve the purpose of indicating (potentially) pivotal factors in the classification process, whose absence would give rise to different classifications. We then prove empirically for various BCs that CFXs provide useful information in real world settings, e.g. when race plays a part in parole violation prediction, and show that they have inherent advantages over existing explanation methods in the literature.
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Emanuele Albini, Antonio Rago, Pietro Baroni, Francesca Toni
| null | null | 2,020 |
ijcai
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Meta Segmentation Network for Ultra-Resolution Medical Images
| null |
Despite recent great progress on semantic segmentation, there still exist huge challenges in medical ultra-resolution image segmentation. The methods based on multi-branch structure can make a good balance between computational burdens and segmentation accuracy. However, the fusion structure in these methods require to be designed elaborately to achieve desirable result, which leads to model redundancy. In this paper, we propose Meta Segmentation Network (MSN) to solve this challenging problem. With the help of meta-learning, the fusion module of MSN is quite simple but effective. MSN can fast generate the weights of fusion layers through a simple meta-learner, requiring only a few training samples and epochs to converge. In addition, to avoid learning all branches from scratch, we further introduce a particular weight sharing mechanism to realize a fast knowledge adaptation and share the weights among multiple branches, resulting in the performance improvement and significant parameters reduction. The experimental results on two challenging ultra-resolution medical datasets BACH and ISIC show that MSN achieves the best performance compared with the state-of-the-art approaches.
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Tong Wu, Bicheng Dai, Shuxin Chen, Yanyun Qu, Yuan Xie
| null | null | 2,020 |
ijcai
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GSM: Graph Similarity Model for Multi-Object Tracking
| null |
The popular tracking-by-detection paradigm for multi-object tracking (MOT) focuses on solving data association problem, of which a robust similarity model lies in the heart. Most previous works make effort to improve feature representation for individual object while leaving the relations among objects less explored, which may be problematic in some complex scenarios. In this paper, we focus on leveraging the relations among objects to improve robustness of the similarity model. To this end, we propose a novel graph representation that takes both the feature of individual object and the relations among objects into consideration. Besides, a graph matching module is specially designed for the proposed graph representation to alleviate the impact of unreliable relations. With the help of the graph representation and the graph matching module, the proposed graph similarity model, named GSM, is more robust to the occlusion and the targets sharing similar appearance. We conduct extensive experiments on challenging MOT benchmarks and the experimental results demonstrate the effectiveness of the proposed method.
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Qiankun Liu, Qi Chu, Bin Liu, Nenghai Yu
| null | null | 2,020 |
ijcai
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When Pedestrian Detection Meets Nighttime Surveillance: A New Benchmark
| null |
Pedestrian detection at nighttime is a crucial and frontier problem in surveillance, but has not been well explored by the computer vision and artificial intelligence communities. Most of existing methods detect pedestrians under favorable lighting conditions (e.g. daytime) and achieve promising performances. In contrast, they often fail under unstable lighting conditions (e.g. nighttime). Night is a critical time for criminal suspects to act in the field of security. The existing nighttime pedestrian detection dataset is captured by a car camera, specially designed for autonomous driving scenarios. The dataset for nighttime surveillance scenario is still vacant. There are vast differences between autonomous driving and surveillance, including viewpoint and illumination. In this paper, we build a novel pedestrian detection dataset from the nighttime surveillance aspect: NightSurveillance1. As a benchmark dataset for pedestrian detection at nighttime, we compare the performances of state-of-the-art pedestrian detectors and the results reveal that the methods cannot solve all the challenging problems of NightSurveillance. We believe that NightSurveillance can further advance the research of pedestrian detection, especially in the field of surveillance security at nighttime.
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Xiao Wang, Jun Chen, Zheng Wang, Wu Liu, Shin'ichi Satoh, Chao Liang, Chia-Wen Lin
| null | null | 2,020 |
ijcai
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JPEG Artifacts Removal via Compression Quality Ranker-Guided Networks
| null |
Existing deep learning-based image de-blocking methods use only pixel-level loss functions to guide network training. The JPEG compression factor, which reflects the degradation degree, has not been fully utilized. However, due to the non-differentiability, the compression factor cannot be directly utilized to train deep networks. To solve this problem, we propose compression quality ranker-guided networks for this specific JPEG artifacts removal. We first design a quality ranker to measure the compression degree, which is highly correlated with the JPEG quality. Based on this differentiable ranker, we then propose one quality-related loss and one feature matching loss to guide de-blocking and perceptual quality optimization. In addition, we utilize dilated convolutions to extract multi-scale features, which enables our single model to handle multiple compression quality factors. Our method can implicitly use the information contained in the compression factors to produce better results. Experiments demonstrate that our model can achieve comparable or even better performance in both quantitative and qualitative measurements.
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Menglu Wang, Xueyang Fu, Zepei Sun, Zheng-Jun Zha
| null | null | 2,020 |
ijcai
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Multi-graph Fusion for Functional Neuroimaging Biomarker Detection
| null |
Brain functional connectivity analysis on fMRI data could improve the understanding of human brain function. However, due to the influence of the inter-subject variability and the heterogeneity across subjects, previous methods of functional connectivity analysis are often insufficient in capturing disease-related representation so that decreasing disease diagnosis performance.
In this paper, we first propose a new multi-graph fusion framework to fine-tune the original representation derived from Pearson correlation analysis, and then employ L1-SVM on fine-tuned representations to conduct joint brain region selection and disease diagnosis for avoiding the issue of the curse of dimensionality on high-dimensional data. The multi-graph fusion framework automatically learns the connectivity number for every node (i.e., brain region) and integrates all subjects in a unified framework to output homogenous and discriminative representations of all subjects. Experimental results on two real data sets, i.e., fronto-temporal dementia (FTD) and obsessive-compulsive disorder (OCD), verified the effectiveness of our proposed framework, compared to state-of-the-art methods.
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Jiangzhang Gan, Xiaofeng Zhu, Rongyao Hu, Yonghua Zhu, Junbo Ma, Ziwen Peng, Guorong Wu
| null | null | 2,020 |
ijcai
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Collaborative Learning of Depth Estimation, Visual Odometry and Camera Relocalization from Monocular Videos
| null |
Scene perceiving and understanding tasks including depth estimation, visual odometry (VO) and camera relocalization are fundamental for applications such as autonomous driving, robots and drones. Driven by the power of deep learning, significant progress has been achieved on individual tasks but the rich correlations among the three tasks are largely neglected. In previous studies, VO is generally accurate in local scope yet suffers from drift in long distances. By contrast, camera relocalization performs well in the global sense but lacks local precision. We argue that these two tasks should be strategically combined to leverage the complementary advantages, and be further improved by exploiting the 3D geometric information from depth data, which is also beneficial for depth estimation in turn. Therefore, we present a collaborative learning framework, consisting of DepthNet, LocalPoseNet and GlobalPoseNet with a joint optimization loss to estimate depth, VO and camera localization unitedly. Moreover, the Geometric Attention Guidance Model is introduced to exploit the geometric relevance among three branches during learning. Extensive experiments demonstrate that the joint learning scheme is useful for all tasks and our method outperforms current state-of-the-art techniques in depth estimation and camera relocalization with highly competitive performance in VO.
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Haimei Zhao, Wei Bian, Bo Yuan, Dacheng Tao
| null | null | 2,020 |
ijcai
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EViLBERT: Learning Task-Agnostic Multimodal Sense Embeddings
| null |
The problem of grounding language in vision is increasingly attracting scholarly efforts. As of now, however, most of the approaches have been limited to word embeddings, which are not capable of handling polysemous words. This is mainly due to the limited coverage of the available semantically-annotated datasets, hence forcing research to rely on alternative technologies (i.e., image search engines). To address this issue, we introduce EViLBERT, an approach which is able to perform image classification over an open set of concepts, both concrete and non-concrete. Our approach is based on the recently introduced Vision-Language Pretraining (VLP) model, and builds upon a manually-annotated dataset of concept-image pairs. We use our technique to clean up the image-to-concept mapping that is provided within a multilingual knowledge base, resulting in over 258,000 images associated with 42,500 concepts.
We show that our VLP-based model can be used to create multimodal sense embeddings starting from our automatically-created dataset. In turn, we also show that these multimodal embeddings improve the performance of a Word Sense Disambiguation architecture over a strong unimodal baseline. We release code, dataset and embeddings at http://babelpic.org.
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Agostina Calabrese, Michele Bevilacqua, Roberto Navigli
| null | null | 2,020 |
ijcai
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Disentangled Feature Learning Network for Vehicle Re-Identification
| null |
Vehicle Re-Identification (ReID) has attracted lots of research efforts due to its great significance to the public security. In vehicle ReID, we aim to learn features that are powerful in discriminating subtle differences between vehicles which are visually similar, and also robust against different orientations of the same vehicle. However, these two characteristics are hard to be encapsulated into a single feature representation simultaneously with unified supervision. Here we propose a Disentangled Feature Learning Network (DFLNet) to learn orientation specific and common features concurrently, which are discriminative at details and invariant to orientations, respectively. Moreover, to effectively use these two types of features for ReID, we further design a feature metric alignment scheme to ensure the consistency of the metric scales. The experiments show the effectiveness of our method that achieves state-of-the-art performance on three challenging datasets.
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Yan Bai, Yihang Lou, Yongxing Dai, Jun Liu, Ziqian Chen, Ling-Yu Duan
| null | null | 2,020 |
ijcai
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Reference Guided Face Component Editing
| null |
Face portrait editing has achieved great progress in recent years. However, previous methods either 1) operate on pre-defined face attributes, lacking the flexibility of controlling shapes of high-level semantic facial components (e.g., eyes, nose, mouth), or 2) take manually edited mask or sketch as an intermediate representation for observable changes, but such additional input usually requires extra efforts to obtain. To break the limitations (e.g. shape, mask or sketch) of the existing methods, we propose a novel framework termed r FACE (Reference Guided FAce Component Editing) for diverse and controllable face component editing with geometric changes. Specifically, r-FACE takes an image inpainting model as the backbone, utilizing reference images as conditions for controlling the shape of face components. In order to encourage the framework to concentrate on the target face components, an example-guided attention module is designed to fuse attention features and the target face component features extracted from the reference image. Through extensive experimental validation and comparisons, we verify the effectiveness of the proposed framework.
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Qiyao Deng, Jie Cao, Yunfan Liu, Zhenhua Chai, Qi Li, Zhenan Sun
| null | null | 2,020 |
ijcai
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Deep Interleaved Network for Single Image Super-Resolution with Asymmetric Co-Attention
| null |
Recently, Convolutional Neural Networks (CNN) based image super-resolution (SR) have shown significant success in the literature. However, these methods are implemented as single-path stream to enrich feature maps from the input for the final prediction, which fail to fully incorporate former low-level features into later high-level features. In this paper, to tackle this problem, we propose a deep interleaved network (DIN) to learn how information at different states should be combined for image SR where shallow information guides deep representative features prediction. Our DIN follows a multi-branch pattern allowing multiple interconnected branches to interleave and fuse at different states. Besides, the asymmetric co-attention (AsyCA) is proposed and attacked to the interleaved nodes to adaptively emphasize informative features from different states and improve the discriminative ability of networks. Extensive experiments demonstrate the superiority of our proposed DIN in comparison with the state-of-the-art SR methods.
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Feng Li, Runmin Cong, Huihui Bai, Yifan He
| null | null | 2,020 |
ijcai
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Lifelong Zero-Shot Learning
| null |
Zero-Shot Learning (ZSL) handles the problem that some testing classes never appear in training set. Existing ZSL methods are designed for learning from a fixed training set, which do not have the ability to capture and accumulate the knowledge of multiple training sets, causing them infeasible to many real-world applications. In this paper, we propose a new ZSL setting, named as Lifelong Zero-Shot Learning (LZSL), which aims to accumulate the knowledge during the learning from multiple datasets and recognize unseen classes of all trained datasets. Besides, a novel method is conducted to realize LZSL, which effectively alleviates the Catastrophic Forgetting in the continuous training process. Specifically, considering those datasets containing different semantic embeddings, we utilize Variational Auto-Encoder to obtain unified semantic representations. Then, we leverage selective retraining strategy to preserve the
trained weights of previous tasks and avoid negative transfer when fine-tuning the entire model. Finally, knowledge distillation is employed to transfer knowledge from previous training stages to current stage. We also design the LZSL evaluation protocol and the challenging benchmarks. Extensive experiments on these benchmarks indicate that our method tackles LZSL problem effectively, while existing ZSL methods fail.
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Kun Wei, Cheng Deng, Xu Yang
| null | null | 2,020 |
ijcai
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SceneEncoder: Scene-Aware Semantic Segmentation of Point Clouds with A Learnable Scene Descriptor
| null |
Besides local features, global information plays an essential role in semantic segmentation, while recent works usually fail to explicitly extract the meaningful global information and make full use of it. In this paper, we propose a SceneEncoder module to impose a scene-aware guidance to enhance the effect of global information. The module predicts a scene descriptor, which learns to represent the categories of objects existing in the scene and directly guides the point-level semantic segmentation through filtering out categories not belonging to this scene. Additionally, to alleviate segmentation noise in local region, we design a region similarity loss to propagate distinguishing features to their own neighboring points with the same label, leading to the enhancement of the distinguishing ability of point-wise features. We integrate our methods into several prevailing networks and conduct extensive experiments on benchmark datasets ScanNet and ShapeNet. Results show that our methods greatly improve the performance of baselines and achieve state-of-the-art performance.
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Jiachen Xu, Jingyu Gong, Jie Zhou, Xin Tan, Yuan Xie, Lizhuang Ma
| null | null | 2,020 |
ijcai
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Bottom-up and Top-down: Bidirectional Additive Net for Edge Detection
| null |
Image edge detection is considered as a cornerstone task in computer vision. Due to the nature of hierarchical representations learned in CNN, it is intuitive to design side networks utilizing the richer convolutional features to improve the edge detection. However, there is no consensus way to integrate the hierarchical information.
In this paper, we propose an effective and end-to-end framework, named Bidirectional Additive Net (BAN), for image edge detection. In the proposed framework, we focus on two main problems: 1) how to design a universal network for incorporating hierarchical information sufficiently; and 2) how to achieve effective information flow between different stages and gradually improve the edge map stage by stage.
To tackle these problems, we design a consecutive bottom-up and top-down architecture, where a bottom-up branch can gradually remove detailed or sharp boundaries to enable accurate edge detection and a top-down branch offers a chance of error-correcting by revisiting the low-level features that contain rich textual and spatial information.
And attended additive module (AAM) is designed to cumulatively refine edges by selecting pivotal features in each stage.
Experimental results show that our proposed methods can improve the edge detection performance to new records and achieve state-of-the-art results on two public benchmarks: BSDS500 and NYUDv2.
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Lianli Gao, Zhilong Zhou, Heng Tao Shen, Jingkuan Song
| null | null | 2,020 |
ijcai
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Spatiotemporal Super-Resolution with Cross-Task Consistency and Its Semi-supervised Extension
| null |
Spatiotemporal super-resolution (SR) aims to upscale both the spatial and temporal dimensions of input videos, and produces videos with higher frame resolutions and rates. It involves two essential sub-tasks: spatial SR and temporal SR. We design a two-stream network for spatiotemporal SR in this work. One stream contains a temporal SR module followed by a spatial SR module, while the other stream has the same two modules in the reverse order. Based on the interchangeability of performing the two sub-tasks, the two network streams are supposed to produce consistent spatiotemporal SR results. Thus, we present a cross-stream consistency to enforce the similarity between the outputs of the two streams. In this way, the training of the two streams is correlated, which allows the two SR modules to share their supervisory signals and improve each other. In addition, the proposed cross-stream consistency does not consume labeled training data and can guide network training in an unsupervised manner. We leverage this property to carry out semi-supervised spatiotemporal SR. It turns out that our method makes the most of training data, and can derive an effective model with few high-resolution and high-frame-rate videos, achieving the state-of-the-art performance. The source code of this work is available at https://hankweb.github.io/STSRwithCrossTask/.
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Han-Yi Lin, Pi-Cheng Hsiu, Tei-Wei Kuo, Yen-Yu Lin
| null | null | 2,020 |
ijcai
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Real-World Automatic Makeup via Identity Preservation Makeup Net
| null |
This paper focuses on the real-world automatic makeup problem. Given one non-makeup target image and one reference image, the automatic makeup is to generate one face image, which maintains the original identity with the makeup style in the reference image. In the real-world scenario, face makeup task demands a robust system against the environmental variants. The two main challenges in real-world face makeup could be summarized as follow: first, the background in real-world images is complicated. The previous methods are prone to change the style of background as well; second, the foreground faces are also easy to be affected. For instance, the ``heavy'' makeup may lose the discriminative information of the original identity. To address these two challenges, we introduce a new makeup model, called Identity Preservation Makeup Net (IPM-Net), which preserves not only the background but the critical patterns of the original identity. Specifically, we disentangle the face images to two different information codes, i.e., identity content code and makeup style code. When inference, we only need to change the makeup style code to generate various makeup images of the target person. In the experiment, we show the proposed method achieves not only better accuracy in both realism (FID) and diversity (LPIPS) in the test set, but also works well on the real-world images collected from the Internet.
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Zhikun Huang, Zhedong Zheng, Chenggang Yan, Hongtao Xie, Yaoqi Sun, Jianzhong Wang, Jiyong Zhang
| null | null | 2,020 |
ijcai
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Human Consensus-Oriented Image Captioning
| null |
Image captioning aims to describe an image with a concise, accurate, and interesting sentence. To build such an automatic neural captioner, the traditional models align the generated words with a number of human-annotated sentences to mimic human-like captions. However, the crowd-sourced annotations inevitably come with data quality issues such as grammatical errors, wrong identification of visual objects and sub-optimal sentence focus. During the model training, existing methods treat all the annotations equally regardless of the data quality. In this work, we explicitly engage human consensus to measure the quality of ground truth captions in advance, and directly encourage the model to learn high quality captions with high priority. Therefore, the proposed consensus-oriented method can accelerate the training process and achieve superior performance with only supervised objective without time-consuming reinforcement learning. The novel consensus loss can be implemented into most of the existing state-of-the-art methods, boosting the BLEU-4 performance by maximum relative 12.47% comparing to the conventional cross-entropy loss. Extensive experiments are conducted on MS-COCO Image Captioning dataset demonstrating the proposed human consensus-oriented training method can significantly improve the training efficiency and model effectiveness.
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Ziwei Wang, Zi Huang, Yadan Luo
| null | null | 2,020 |
ijcai
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TLPG-Tracker: Joint Learning of Target Localization and Proposal Generation for Visual Tracking
| null |
Target localization and proposal generation are two essential subtasks in generic visual tracking, and it is a challenge to address both the two efficiently. In this paper, we propose an efficient two-stage architecture which makes full use of the complementarity of two subtasks to achieve robust localization and high-quality proposals generation of the target jointly. Specifically, our model performs a novel deformable central correlation operation by an online learning model in both two stages to locate new target centers while generating target proposals in the vicinity of these centers. The proposals are refined in the refinement stage to further improve accuracy and robustness. Moreover, the model benefits from multi-level features aggregation in a neck module and a feature enhancement module. We conduct extensive ablation studies to demonstrate the effectiveness of our proposed methods. Our tracker runs at over 30 FPS and sets a new state-of-the-art on five tracking benchmarks, including LaSOT, VOT2018, TrackingNet, GOT10k, OTB2015.
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Siyuan Li, Zhi Zhang, Ziyu Liu, Anna Wang, Linglong Qiu, Feng Du
| null | null | 2,020 |
ijcai
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Multichannel Color Image Denoising via Weighted Schatten p-norm Minimization
| null |
The R, G and B channels of a color image generally have different noise statistical properties or noise strengths. It is thus problematic to apply grayscale image denoising algorithms to color image denoising. In this paper, based on the non-local self-similarity of an image and the different noise strength across each channel, we propose a MultiChannel Weighted Schatten p-Norm Minimization (MCWSNM) model for RGB color image denoising. More specifically, considering a small local RGB patch in a noisy image, we first find its nonlocal similar cubic patches in a search window with an appropriate size. These similar cubic patches are then vectorized and grouped to construct a noisy low-rank matrix, which can be recovered using the Schatten p-norm minimization framework. Moreover, a weight matrix is introduced to balance each channel’s contribution to the final denoising results. The proposed MCWSNM can be solved via the alternating direction method of multipliers. Convergence property of the proposed method are also theoretically analyzed . Experiments conducted on both synthetic and real noisy color image datasets demonstrate highly competitive denoising performance, outperforming comparison algorithms, including several methods based on neural networks.
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Xinjian Huang, Bo Du, Weiwei Liu
| null | null | 2,020 |
ijcai
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Learning from the Scene and Borrowing from the Rich: Tackling the Long Tail in Scene Graph Generation
| null |
Despite the huge progress in scene graph generation in recent years, its long-tail distribution in object relationships remains a challenging and pestering issue. Existing methods largely rely on either external knowledge or statistical bias information to alleviate this problem. In this paper, we tackle this issue from another two aspects: (1) scene-object interaction aiming at learning specific knowledge from a scene via an additive attention mechanism; and (2) long-tail knowledge transfer which tries to transfer the rich knowledge learned from the head into the tail. Extensive experiments on the benchmark dataset Visual Genome on three tasks demonstrate that our method outperforms current state-of-the-art competitors. Our source code is available at https://github.com/htlsn/issg.
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Tao He, Lianli Gao, Jingkuan Song, Jianfei Cai, Yuan-Fang Li
| null | null | 2,020 |
ijcai
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SimPropNet: Improved Similarity Propagation for Few-shot Image Segmentation
| null |
Few-shot segmentation (FSS) methods perform image segmentation for a particular object class in a target (query) image, using a small set of (support) image-mask pairs. Recent deep neural network based FSS methods leverage high-dimensional feature similarity between the foreground features of the support images and the query image features. In this work, we demonstrate gaps in the utilization of this similarity information in existing methods, and present a framework - SimPropNet, to bridge those gaps. We propose to jointly predict the support and query masks to force the support features to share characteristics with the query features. We also propose to utilize similarities in the background regions of the query and support images using a novel foreground-background attentive fusion mechanism. Our method achieves state-of-the-art results for one-shot and five-shot segmentation on the PASCAL-5i dataset. The paper includes detailed analysis and ablation studies for the proposed improvements and quantitative comparisons with contemporary methods.
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Siddhartha Gairola, Mayur Hemani, Ayush Chopra, Balaji Krishnamurthy
| null | null | 2,020 |
ijcai
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GestureDet: Real-time Student Gesture Analysis with Multi-dimensional Attention-based Detector
| null |
Students’ gestures, hand-raising, stand-up, and sleeping, indicates the engagement of students in classrooms and partially reflects teaching quality. Therefore, fast and automatically recognizing these gestures are of great importance. Due to limited computational resources in primary and secondary schools, we propose a real-time student behavior detector based on light-weight MobileNetV2-SSD to reduce the dependency of GPUs. Firstly, we build a large-scale corpus from real schools to capture various behavior gestures. Based on such a corpus, we transfer the gesture recognition task into object detections. Secondly, we design a multi-dimensional attention-based detector, named GestureDet, for real-time and accurate gesture analysis. The multi-dimensional attention mechanisms simultaneously consider all the dimensions of the training set, aiming to pay more attention to discriminative features and samples that are important for the final performance. Specifically, the spatial attention is constructed with stacked dilated convolution layers to generate a soft and learnable mask for re-weighting foreground and background features; the channel attention introduces the context modeling and squeeze-and-excitation module to focus on discriminative features; the batch attention discriminates important samples with a new designed reweight strategy. Experimental results demonstrate the effectiveness and versatility of GestureDet, which achieves 75.2% mAP on real student behavior dataset, and 74.5% on public PASCAL VOC dataset at 20fps on embedding device Nvidia Jetson TX2. Code will be made publicly available.
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Rui Zheng, Fei Jiang, Ruimin Shen
| null | null | 2,020 |
ijcai
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Channel Pruning via Automatic Structure Search
| null |
Channel pruning is among the predominant approaches to compress deep neural networks. To this end, most existing pruning methods focus on selecting channels (filters) by importance/optimization or regularization based on rule-of-thumb designs, which defects in sub-optimal pruning. In this paper, we propose a new channel pruning method based on artificial bee colony algorithm (ABC), dubbed as ABCPruner, which aims to efficiently find optimal pruned structure, i.e., channel number in each layer, rather than selecting "important" channels as previous works did. To solve the intractably huge combinations of pruned structure for deep networks, we first propose to shrink the combinations where the preserved channels are limited to a specific space, thus the combinations of pruned structure can be significantly reduced. And then, we formulate the search of optimal pruned structure as an optimization problem and integrate the ABC algorithm to solve it in an automatic manner to lessen human interference. ABCPruner has been demonstrated to be more effective, which also enables the fine-tuning to be conducted efficiently in an end-to-end manner. The source codes can be available at https: //github.com/lmbxmu/ABCPruner.
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Mingbao Lin, Rongrong Ji, Yuxin Zhang, Baochang Zhang, Yongjian Wu, Yonghong Tian
| null | null | 2,020 |
ijcai
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Super-Resolution and Inpainting with Degraded and Upgraded Generative Adversarial Networks
| null |
Image super-resolution (SR) and image inpainting are two topical problems in medical image processing. Existing methods for solving the problems are either tailored to recovering a high-resolution version of the low-resolution image or focus on filling missing values, thus inevitably giving rise to poor performance when the acquisitions suffer from multiple degradations. In this paper, we explore the possibility of super-resolving and inpainting images to handle multiple degradations and therefore improve their usability. We construct a unified and scalable framework to overcome the drawbacks of propagated errors caused by independent learning. We additionally provide improvements over previously proposed super-resolution approaches by modeling image degradation directly from data observations rather than bicubic downsampling. To this end, we propose HLH-GAN, which includes a high-to-low (H-L) GAN together with a low-to-high (L-H) GAN in a cyclic pipeline for solving the medical image degradation problem. Our comparative evaluation demonstrates that the effectiveness of the proposed method on different brain MRI datasets. In addition, our method outperforms many existing super-resolution and inpainting approaches.
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Yawen Huang, Feng Zheng, Danyang Wang, Junyu Jiang, Xiaoqian Wang, Ling Shao
| null | null | 2,020 |
ijcai
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Biased Feature Learning for Occlusion Invariant Face Recognition
| null |
To address the challenges posed by unknown occlusions, we propose a Biased Feature Learning (BFL) framework for occlusion-invariant face recognition. We first construct an extended dataset using a multi-scale data augmentation method. For model training, we modify the label loss to adjust the impact of normal and occluded samples. Further, we propose a biased guidance strategy to manipulate the optimization of a network so that the feature embedding space is dominated by non-occluded faces. BFL not only enhances the robustness of a network to unknown occlusions but also maintains or even improves its performance for normal faces. Experimental results demonstrate its superiority as well as the generalization capability with different network architectures and loss functions.
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Changbin Shao, Jing Huo, Lei Qi, Zhen-Hua Feng, Wenbin Li, Chuanqi Dong, Yang Gao
| null | null | 2,020 |
ijcai
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Characterizing Similarity of Visual Stimulus from Associated Neuronal Response
| null |
The problem of characterizing brain functions such as memory, perception, and processing of stimuli has received significant attention in neuroscience literature. These experiments rely on carefully calibrated, albeit complex inputs, to record brain response to signals. A major problem in analyzing brain response to common stimuli such as audio-visual input from videos (e.g., movies) or story narration through audio books, is that observed neuronal responses are due to combinations of ``pure'' factors, many of which may be latent. In this paper, we present a novel methodological framework for deconvolving the brain's response to mixed stimuli into its constituent responses to underlying pure factors. This framework, based on archetypal analysis, is applied to the analysis of imaging data from an adult cohort watching the BBC show, Sherlock. By focusing on visual stimulus, we show strong correlation between our observed deconvolved response and third-party textual video annotations -- demonstrating the significant power of our analyses techniques. Building on these results, we show that our techniques can be used to predict neuronal responses in new subjects (how other individuals react to Sherlock), as well as to new visual content (how individuals react to other videos with known annotations). This paper reports on the first study that relates video features with neuronal responses in a rigorous algorithmic and statistical framework based on deconvolution of observed mixed imaging signals using archetypal analysis.
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Vikram Ravindra, Ananth Grama
| null | null | 2,020 |
ijcai
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E3SN: Efficient End-to-End Siamese Network for Video Object Segmentation
| null |
In the semi-supervised video object segmentation (VOS) field, SiamMask has achieved competitive accuracy and the fastest running speed. However, the two-stage training procedure requires additional manual intervention, and using only single-level features does not maximize the rich hierarchical feature information. This paper proposes an efficient end-to-end Siamese network for VOS. In particular, a supervised sampling strategy is designed to optimize the training procedure. Such an optimization facilitates the training of the entire model in an end-to-end manner. Moreover, a multilevel feature aggregation module is developed to enhance feature representability and improve segmentation accuracy. Experimental results on DAVIS2016 and DAVIS2017 datasets show that the proposed approach outperforms the SiamMask in accuracy with similar FPS. Moreover, this approach also achieves good accuracy-speed trade-off compared with that of other state-of-the-art VOS algorithms.
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Meng Lan, Yipeng Zhang, Qinning Xu, Lefei Zhang
| null | null | 2,020 |
ijcai
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Pay Attention to Devils: A Photometric Stereo Network for Better Details
| null |
We present an attention-weighted loss in a photometric stereo neural network to improve 3D surface recovery accuracy in complex-structured areas, such as edges and crinkles, where existing learning-based methods often failed. Instead of using a uniform penalty for all pixels, our method employs the attention-weighted loss learned in a self-supervise manner for each pixel, avoiding blurry reconstruction result in such difficult regions. The network first estimates a surface normal map and an adaptive attention map, and then the latter is used to calculate a pixel-wise attention-weighted loss that focuses on complex regions. In these regions, the attention-weighted loss applies higher weights of the detail-preserving gradient loss to produce clear surface reconstructions. Experiments on real datasets show that our approach significantly outperforms traditional photometric stereo algorithms and state-of-the-art learning-based methods.
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Yakun Ju, Kin-Man Lam, Yang Chen, Lin Qi, Junyu Dong
| null | null | 2,020 |
ijcai
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DAM: Deliberation, Abandon and Memory Networks for Generating Detailed and Non-repetitive Responses in Visual Dialogue
| null |
Visual Dialogue task requires an agent to be engaged in a conversation with human about an image. The ability of generating detailed and non-repetitive responses is crucial for the agent to achieve human-like conversation. In this paper, we propose a novel generative decoding architecture to generate high-quality responses, which moves away from decoding the whole encoded semantics towards the design that advocates both transparency and flexibility. In this architecture, word generation is decomposed into a series of attention-based information selection steps, performed by the novel recurrent Deliberation, Abandon and Memory (DAM) module. Each DAM module performs an adaptive combination of the response-level semantics captured from the encoder and the word-level semantics specifically selected for generating each word. Therefore, the responses contain more detailed and non-repetitive descriptions while maintaining the semantic accuracy. Furthermore, DAM is flexible to cooperate with existing visual dialogue encoders and adaptive to the encoder structures by constraining the information selection mode in DAM. We apply DAM to three typical encoders and verify the performance on the VisDial v1.0 dataset. Experimental results show that the proposed models achieve new state-of-the-art performance with high-quality responses. The code is available at https://github.com/JXZe/DAM.
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Xiaoze Jiang, Jing Yu, Yajing Sun, Zengchang Qin, Zihao Zhu, Yue Hu, Qi Wu
| null | null | 2,020 |
ijcai
|
G2RL: Geometry-Guided Representation Learning for Facial Action Unit Intensity Estimation
| null |
Facial action unit (AU) intensity estimation aims to measure the intensity of different facial muscle movements. The external knowledge such as AU co-occurrence relationship is typically leveraged to improve performance. However, the AU characteristics may vary among individuals due to different physiological structures of human faces. To this end, we propose a novel geometry-guided representation learning (G2RL) method for facial AU intensity estimation. Specifically, our backbone model is based on a heatmap regression framework, where the produced heatmaps reflect rich information associated with AU intensities and their spatial distributions. Besides, we incorporate the external geometric knowledge into the backbone model to guide the training process via a learned projection matrix. The experimental results on two benchmark datasets demonstrate that our method is comparable with the state-of-the-art approaches, and validate the effectiveness of incorporating external geometric knowledge for facial AU intensity estimation.
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Yingruo Fan, Zhaojiang Lin
| null | null | 2,020 |
ijcai
|
Non-Autoregressive Image Captioning with Counterfactuals-Critical Multi-Agent Learning
| null |
Most image captioning models are autoregressive, i.e. they generate each word by conditioning on previously generated words, which leads to heavy latency during inference. Recently, non-autoregressive decoding has been proposed in machine translation to speed up the inference time by generating all words in parallel. Typically, these models use the word-level cross-entropy loss to optimize each word independently. However, such a learning process fails to consider the sentence-level consistency, thus resulting in inferior generation quality of these non-autoregressive models. In this paper, we propose a Non-Autoregressive Image Captioning (NAIC) model with a novel training paradigm: Counterfactuals-critical Multi-Agent Learning (CMAL). CMAL formulates NAIC as a multi-agent reinforcement learning system where positions in the target sequence are viewed as agents that learn to cooperatively maximize a sentence-level reward. Besides, we propose to utilize massive unlabeled images to boost captioning performance. Extensive experiments on MSCOCO image captioning benchmark show that our NAIC model achieves a performance comparable to state-of-the-art autoregressive models, while brings 13.9x decoding speedup.
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Longteng Guo, Jing Liu, Xinxin Zhu, Xingjian He, Jie Jiang, Hanqing Lu
| null | null | 2,020 |
ijcai
|
Multi-Scale Spatial-Temporal Integration Convolutional Tube for Human Action Recognition
| null |
Applying multi-scale representations leads to consistent performance improvements on a wide range of image recognition tasks. However, with the addition of the temporal dimension in video domain, directly obtaining layer-wise multi-scale spatial-temporal features will add a lot extra computational cost. In this work, we propose a novel and efficient Multi-Scale Spatial-Temporal Integration Convolutional Tube (MSTI) aiming at achieving accurate recognition of actions with lower computational cost. It firstly extracts multi-scale spatial and temporal features through the multi-scale convolution block. Considering the interaction of different-scales representations and the interaction of spatial appearance and temporal motion, we employ the cross-scale attention weighted blocks to perform feature recalibration by integrating multi-scale spatial and temporal features. An end-to-end deep network, MSTI-Net, is also presented based on the proposed MSTI tube for human action recognition. Extensive experimental results show that our MSTI-Net significantly boosts the performance of existing convolution networks and achieves state-of-the-art accuracy on three challenging benchmarks, i.e., UCF-101, HMDB-51 and Kinetics-400, with much fewer parameters and FLOPs.
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Haoze Wu, Jiawei Liu, Xierong Zhu, Meng Wang, Zheng-Jun Zha
| null | null | 2,020 |
ijcai
|
Bi-level Probabilistic Feature Learning for Deformable Image Registration
| null |
We address the challenging issue of deformable registration that robustly and efficiently builds dense correspondences between images. Traditional approaches upon iterative energy optimization typically invoke expensive computational load. Recent learning-based methods are able to efficiently predict deformation maps by incorporating learnable deep networks. Unfortunately, these deep networks are designated to learn deterministic features for classification tasks, which are not necessarily optimal for registration. In this paper, we propose a novel bi-level optimization model that enables jointly learning deformation maps and features for image registration. The bi-level model takes the energy for deformation computation as the upper-level optimization while formulates the maximum \emph{a posterior} (MAP) for features as the lower-level optimization. Further, we design learnable deep networks to simultaneously optimize the cooperative bi-level model, yielding robust and efficient registration. These deep networks derived from our bi-level optimization constitute an unsupervised end-to-end framework for learning both features and deformations. Extensive experiments of image-to-atlas and image-to-image deformable registration on 3D brain MR datasets demonstrate that we achieve state-of-the-art performance in terms of accuracy, efficiency, and robustness.
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Risheng Liu, Zi Li, Yuxi Zhang, Xin Fan, Zhongxuan Luo
| null | null | 2,020 |
ijcai
|
Learning Task-aware Local Representations for Few-shot Learning
| null |
Few-shot learning for visual recognition aims to adapt to novel unseen classes with only a few images. Recent work, especially the work based on low-level information, has achieved great progress. In these work, local representations (LRs) are typically employed, because LRs are more consistent among the seen and unseen classes. However, most of them are limited to an individual image-to-image or image-to-class measure manner, which cannot fully exploit the capabilities of LRs, especially in the context of a certain task. This paper proposes an Adaptive Task-aware Local Representations Network (ATL-Net) to address this limitation by introducing episodic attention, which can adaptively select the important local patches among the entire task, as the process of human recognition. We achieve much superior results on multiple benchmarks. On the miniImagenet, ATL-Net gains 0.93% and 0.88% improvements over the compared methods under the 5-way 1-shot and 5-shot settings. Moreover, ATL-Net can naturally tackle the problem that how to adaptively identify and weight the importance of different key local parts, which is the major concern of fine-grained recognition. Specifically, on the fine-grained dataset Stanford Dogs, ATL-Net outperforms the second best method with 5.39% and 9.69% gains under the 5-way 1-shot and 5-shot settings.
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Chuanqi Dong, Wenbin Li, Jing Huo, Zheng Gu, Yang Gao
| null | null | 2,020 |
ijcai
|
Visual Encoding and Decoding of the Human Brain Based on Shared Features
| null |
Using a convolutional neural network to build visual encoding and decoding models of the human brain is a good starting point for the study on relationship between deep learning and human visual cognitive mechanism. However, related studies have not fully considered their differences. In this paper, we assume that only a portion of neural network features is directly related to human brain signals, which we call shared features. In the encoding process, we extract shared features from the lower and higher layers of the neural network, and then build a non-negative sparse map to predict brain activities. In the decoding process, we use back-propagation to reconstruct visual stimuli, and use dictionary learning and a deep image prior to improve the robustness and accuracy of the algorithm. Experiments on a public fMRI dataset confirm the rationality of the encoding models, and comparing with a recently proposed method, our reconstruction results obtain significantly higher accuracy.
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Chao Li, Baolin Liu, Jianguo Wei
| null | null | 2,020 |
ijcai
|
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