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On Discrete Truthful Heterogeneous Two-Facility Location
| null |
We revisit the discrete heterogeneous two-facility location problem, in which there is a set of agents that occupy nodes of a line graph, and have private approval preferences over two facilities. When the facilities are located at some nodes of the line, each agent derives a cost that is equal to her total distance from the facilities she approves. The goal is to decide where to locate the two facilities, so as to (a) incentivize the agents to truthfully report their preferences, and (b) achieve a good approximation of the minimum total (social) cost or the maximum cost among all agents. For both objectives, we design deterministic strategyproof mechanisms with approximation ratios that significantly outperform the state-of-the-art, and complement these results with (almost) tight lower bounds.
|
Panagiotis Kanellopoulos, Alexandros A. Voudouris, Rongsen Zhang
| null | null | 2,022 |
ijcai
|
Light Agents Searching for Hot Information
| null |
Agent-based crawlers are commonly used in network maintenance and information gathering. In order not to disturb the main functionality of the system, whether acting at nodes or being in transit, they need to operate online, perform a single operation fast and use small memory. They should also be preferably deterministic, as crawling agents have limited capabilities of generating a large number of truly random bits. We consider a system in which an agent receives an update, typically an insertion or deletion, of some information upon visiting a node. On request, the agent needs to output hot information, i.e., with the net occurrence above certain frequency threshold. A desired time and memory complexity of such agent should be poly-logarithmic in the number of visited nodes and inversely proportional to the frequency threshold. Ours is the first such agent with rigorous analysis and a complementary almost-matching lower bound.
|
Dariusz R. Kowalski, Dominik Pajak
| null | null | 2,022 |
ijcai
|
Fixing Knockout Tournaments With Seeds
| null |
Knockout tournaments constitute a popular format for organizing sports competitions. While prior results have shown that it is often possible to manipulate a knockout tournament by fixing the bracket, these results ignore the prevalent aspect of player seeds, which can significantly constrain the chosen bracket. We show that certain structural conditions that guarantee that a player can win a knockout tournament without seeds are no longer sufficient in light of seed constraints. On the other hand, we prove that when the pairwise match outcomes are generated randomly, all players are still likely to be knockout winners under the same probability threshold with seeds as without seeds. In addition, we investigate the complexity of deciding whether a manipulation is possible when seeds are present.
|
Pasin Manurangsi, Warut Suksompong
| null | null | 2,022 |
ijcai
|
Automated Synthesis of Mechanisms
| null |
Mechanism Design aims to design a game so that a desirable outcome is reached regardless of agents' self-interests. In this paper, we show how this problem can be rephrased as a synthesis problem, where mechanisms are automatically synthesized from a partial or complete specification in a high-level logical language. We show that Quantitative Strategy Logic is a perfect candidate for specifying mechanisms as it can express complex strategic and quantitative properties. We solve automated mechanism design in two cases: when the number of actions is bounded, and when agents play in turn.
|
Munyque Mittelmann, Bastien Maubert, Aniello Murano, Laurent Perrussel
| null | null | 2,022 |
ijcai
|
Proportional Budget Allocations: Towards a Systematization
| null |
We contribute to the programme of lifting proportionality axioms from the multi-winner voting setting to participatory budgeting. We define novel proportionality axioms for participatory budgeting and test them on known proportionality-driven rules such as Phragmén and Rule X. We investigate logical implications among old and new axioms and provide a systematic overview of proportionality criteria in participatory budgeting.
|
Maaike Los, Zoé Christoff, Davide Grossi
| null | null | 2,022 |
ijcai
|
I Will Have Order! Optimizing Orders for Fair Reviewer Assignment
| null |
We study mechanisms that allocate reviewers to papers in a fair and efficient manner. We model reviewer assignment as an instance of a fair allocation problem, presenting an extension of the classic round-robin mechanism, called Reviewer Round Robin (RRR). Round-robin mechanisms are a standard tool to ensure envy-free up to one item (EF1) allocations. However, fairness often comes at the cost of decreased efficiency. To overcome this challenge, we carefully select an approximately optimal round-robin order. Applying a relaxation of submodularity, γ-weak submodularity, we show that greedily inserting papers into an order yields a (1+γ²)-approximation to the maximum welfare attainable by our round-robin mechanism under any order. Our Greedy Reviewer Round Robin (GRRR) approach outputs highly efficient EF1 allocations for three real conference datasets, offering comparable performance to state-of-the-art paper assignment methods in fairness, efficiency, and runtime, while providing the only EF1 guarantee.
|
Justin Payan, Yair Zick
| null | null | 2,022 |
ijcai
|
Parameterized Algorithms for Kidney Exchange
| null |
In kidney exchange programs, multiple patient-donor pairs each of whom are otherwise incompatible, exchange their donors to receive compatible kidneys. The Kidney Exchange problem is typically modelled as a directed graph where every vertex is either an altruistic donor or a pair of patient and donor; directed edges are added from a donor to its compatible patients. The computational task is to find if there exists a collection of disjoint cycles and paths starting from altruistic donor vertices of length at most l_c and l_p respectively that covers at least some specific number t of non-altruistic vertices (patients). We study parameterized algorithms for the kidney exchange problem in this paper. Specifically, we design FPT algorithms parameterized by each of the following parameters: (1) the number of patients who receive kidney, (2) treewidth of the input graph + max{l_p, l_c}, and (3) the number of vertex types in the input graph when l_p <= l_c. We also present interesting algorithmic and hardness results on the kernelization complexity of the problem. Finally, we present an approximation algorithm for an important special case of Kidney Exchange.
|
Arnab Maiti, Palash Dey
| null | null | 2,022 |
ijcai
|
Robust Solutions for Multi-Defender Stackelberg Security Games
| null |
Multi-defender Stackelberg Security Games (MSSG) have recently gained increasing attention in the literature.
However, the solutions offered to date are highly sensitive, wherein even small perturbations in the attacker's utility or slight uncertainties thereof can dramatically change the defenders' resulting payoffs and alter the equilibrium.
In this paper, we introduce a robust model for MSSGs, which admits solutions that are resistant to small perturbations or uncertainties in the game's parameters.
First, we formally define the notion of robustness, as well as the robust MSSG model. Then, for the non-cooperative setting, we prove the existence of a robust approximate equilibrium in any such game, and provide an efficient construction thereof. For the cooperative setting, we show that any such game admits a robust approximate (alpha) core, and provide an efficient construction thereof.
Lastly, we show that stronger types of the core may be empty.
Interestingly, the robust solutions can substantially increase the defenders' utilities over those of the non-robust ones.
|
Dolev Mutzari, Yonatan Aumann, Sarit Kraus
| null | null | 2,022 |
ijcai
|
Modelling the Dynamics of Multi-Agent Q-learning: The Stochastic Effects of Local Interaction and Incomplete Information
| null |
The theoretical underpinnings of multiagent reinforcement learning has recently attracted much attention.
In this work, we focus on the generalized social learning (GSL) protocol --- an agent interaction protocol that is widely adopted in the literature, and aim to develop an accurate theoretical model for the Q-learning dynamics under this protocol.
Noting that previous models fail to characterize the effects of local interactions and incomplete information that arise from GSL, we model the Q-values dynamics of each individual agent as a system of stochastic differential equations (SDE).
Based on the SDE, we express the time evolution of the probability density function of Q-values in the population with a Fokker-Planck equation.
We validate the correctness of our model through extensive comparisons with agent-based simulation results across different types of symmetric games.
In addition, we show that as the interactions between agents are more limited and information is less complete, the population can converge to a outcome that is qualitatively different than that with global interactions and complete information.
|
Chin-wing Leung, Shuyue Hu, Ho-fung Leung
| null | null | 2,022 |
ijcai
|
Exploring the Benefits of Teams in Multiagent Learning
| null |
For problems requiring cooperation, many multiagent systems implement solutions among either individual agents or across an entire population towards a common goal. Multiagent teams are primarily studied when in conflict; however, organizational psychology (OP) highlights the benefits of teams among human populations for learning how to coordinate and cooperate. In this paper, we propose a new model of multiagent teams for reinforcement learning (RL) agents inspired by OP and early work on teams in artificial intelligence. We validate our model using complex social dilemmas that are popular in recent multiagent RL and find that agents divided into teams develop cooperative pro-social policies despite incentives to not cooperate. Furthermore, agents are better able to coordinate and learn emergent roles within their teams and achieve higher rewards compared to when the interests of all agents are aligned.
|
David Radke, Kate Larson, Tim Brecht
| null | null | 2,022 |
ijcai
|
Transfer Learning Based Adaptive Automated Negotiating Agent Framework
| null |
With the availability of domain specific historical negotiation data, the practical applications of machine learning techniques can prove to be increasingly effective in the field of automated negotiation. Yet a large portion of the literature focuses on domain independent negotiation and thus passes the possibility of leveraging any domain specific insights from historical data. Moreover, during sequential negotiation, utility functions may alter due to various reasons including market demand, partner agreements, weather conditions, etc. This poses a unique set of challenges and one can easily infer that one strategy that fits all is rather impossible in such scenarios. In this work, we present a simple yet effective method of learning an end-to-end negotiation strategy from historical negotiation data. Next, we show that transfer learning based solutions are effective in designing adaptive strategies when underlying utility functions of agents change. Additionally, we also propose an online method of detecting and measuring such changes in the utility functions. Combining all three contributions we propose an adaptive automated negotiating agent framework that enables the automatic creation of transfer learning based negotiating agents capable of adapting to changes in utility functions. Finally, we present the results of an agent generated using our framework in different ANAC domains with 100 different utility functions each and show that our agent outperforms the benchmark score by domain independent agents by 6%.
|
Ayan Sengupta, Shinji Nakadai, Yasser Mohammad
| null | null | 2,022 |
ijcai
|
Multiwinner Elections under Minimax Chamberlin-Courant Rule in Euclidean Space
| null |
We consider multiwinner elections in Euclidean space using the minimax Chamberlin-Courant rule.
In this setting, voters and candidates are embedded in a d-dimensional Euclidean space,
and the goal is to choose a committee of k candidates so that the rank of any voter's
most preferred candidate in the committee is minimized. (The problem is also equivalent to the
ordinal version of the classical k-center problem.)
We show that the problem is NP-hard in any dimension d >= 2, and also provably hard to approximate.
Our main results are three polynomial-time approximation schemes, each of which finds a committee
with provably good minimax score. In all cases, we show that our approximation bounds are tight or close to tight.
We mainly focus on the 1-Borda rule but some of our results also hold for the more general r-Borda.
|
Chinmay Sonar, Subhash Suri, Jie Xue
| null | null | 2,022 |
ijcai
|
Fair, Individually Rational and Cheap Adjustment
| null |
Consider the practical goal of making a desired action profile played,
when the planner can only change the payoffs, bound by
stringent constraints.
Applications include motivating people
to choose the closest school, the closest subway station, or to coordinate
on a communication protocol or an investment strategy.
Employing subsidies and tolls, we adjust the game so that choosing this predefined action profile
becomes strictly dominant.
Inspired mainly by the work of Monderer and Tennenholtz,
where the promised subsidies do not materialise in the not played
profiles, we provide a fair and individually rational game
adjustment, such that the total outside investments sum up
to zero at any profile, thereby facilitating easy and frequent
usage of our adjustment without bearing costs, even if some
players behave unexpectedly. The resultant action profile itself needs no
adjustment. Importantly, we also prove that our adjustment minimises
the general transfer among all such adjustments, counting the total subsidising and taxation.
|
Gleb Polevoy, Marcin Dziubiński
| null | null | 2,022 |
ijcai
|
Group Wisdom at a Price: Jury Theorems with Costly Information
| null |
We study epistemic voting on binary issues where voters are characterized by their competence, i.e., the probability of voting for the correct alternative, and can choose between two actions: voting or abstaining. In our setting voting involves the expenditure of some effort, which is required to achieve the appropriate level of competence, whereas abstention carries no effort. We model this scenario as a game and characterize its equilibria under several variations. Our results show that when agents are aware of everyone's incentives, then the addition of effort may lead to Nash equilibria where wisdom of the crowds is lost. We further show that if agents' awareness of each other is constrained by a social network, the topology of the network may actually mitigate this effect.
|
Matteo Michelini, Adrian Haret, Davide Grossi
| null | null | 2,022 |
ijcai
|
The Power of Media Agencies in Ad Auctions: Improving Utility through Coordinated Bidding
| null |
The increasing competition in digital advertising induced a proliferation of media agencies playing the role of intermediaries between advertisers and platforms selling ad slots. When a group of competing advertisers is managed by a common agency, many forms of collusion, such as bid rigging, can be implemented by coordinating bidding strategies, dramatically increasing advertisers' value. We study the problem of finding bids and monetary transfers maximizing the utility of a group of colluders, under GSP and VCG mechanisms. First, we introduce an abstract bid optimization problem---called weighted utility problem (WUP)---, which is useful in proving our results. We show that the utilities of bidding strategies are related to the length of paths in a directed acyclic weighted graph, whose structure and weights depend on the mechanism under study. This allows us to solve WUP in polynomial time by finding a shortest path of the graph. Next, we switch to our original problem, focusing on two settings that differ for the incentives they allow for. Incentive constraints ensure that colluders do not leave the agency, and they can be enforced by implementing monetary transfers between the agency and the advertisers. In particular, we study the arbitrary transfers setting, where any kind of monetary transfer to and from the advertisers is allowed, and the more realistic limited liability setting, in which no advertiser can be paid by the agency.
In the former, we cast the problem as a WUP instance and solve it by our graph-based algorithm, while, in the latter, we formulate it as a linear program with exponentially-many variables efficiently solvable by applying the ellipsoid algorithm to its dual. This requires to solve a suitable separation problem in polynomial time, which can be done by reducing it to the weighted utility problem a WUP instance.
|
Giulia Romano, Matteo Castiglioni, Alberto Marchesi, Nicola Gatti
| null | null | 2,022 |
ijcai
|
How to Sample Approval Elections?
| null |
We extend the map-of-elections framework to the case of approval elections. While doing so, we study a number of statistical cultures, including some new ones, and we analyze their properties. We find that approval elections can be understood in terms of the average number of approvals in the votes, and the extent to which the votes are chaotic.
|
Stanisław Szufa, Piotr Faliszewski, Łukasz Janeczko, Martin Lackner, Arkadii Slinko, Krzysztof Sornat, Nimrod Talmon
| null | null | 2,022 |
ijcai
|
Maxmin Participatory Budgeting
| null |
Participatory Budgeting (PB) is a popular voting method by which a limited budget is divided among a set of projects, based on the preferences of voters over the projects. PB is broadly categorised as divisible PB (if the projects are fractionally implementable) and indivisible PB (if the projects are atomic). Egalitarianism, an important objective in PB, has not received much attention in the context of indivisible PB. This paper addresses this gap through a detailed study of a natural egalitarian rule, Maxmin Participatory Budgeting (MPB), in the context of indivisible PB. Our study is in two parts: (1) computational (2) axiomatic. In the first part, we prove that MPB is computationally hard and give pseudo-polynomial time and polynomial-time algorithms when parameterized by certain well-motivated parameters. We propose an algorithm that achieves for MPB, additive approximation guarantees for restricted spaces of instances and empirically show that our algorithm in fact gives exact optimal solutions on real-world PB datasets. We also establish an upper bound on the approximation ratio achievable for MPB by the family of exhaustive strategy-proof PB algorithms. In the second part, we undertake an axiomatic study of the MPB rule by generalizing known axioms in the literature. Our study leads to the proposal of a new axiom, maximal coverage, which captures fairness aspects. We prove that MPB satisfies maximal coverage.
|
Gogulapati Sreedurga, Mayank Ratan Bhardwaj, Yadati Narahari
| null | null | 2,022 |
ijcai
|
Search-Based Testing of Reinforcement Learning
| null |
Evaluation of deep reinforcement learning (RL) is inherently challenging. Especially the opaqueness of learned policies and the stochastic nature of both agents and environments make testing the behavior of deep RL agents difficult. We present a search-based testing framework that enables a wide range of novel analysis capabilities for evaluating the safety and performance of deep RL agents. For safety testing, our framework utilizes a search algorithm that searches for a reference trace that solves the RL task. The backtracking states of the search, called boundary states, pose safety-critical situations. We create safety test-suites that evaluate how well the RL agent escapes safety-critical situations near these boundary states. For robust performance testing, we create a diverse set of traces via fuzz testing. These fuzz traces are used to bring the agent into a wide variety of potentially unknown states from which the average performance of the agent is compared to the average performance of the fuzz traces. We apply our search-based testing approach on RL for Nintendo's Super Mario Bros.
|
Martin Tappler, Filip Cano Córdoba, Bernhard K. Aichernig, Bettina Könighofer
| null | null | 2,022 |
ijcai
|
Near-Tight Algorithms for the Chamberlin-Courant and Thiele Voting Rules
| null |
We present an almost optimal algorithm for the classic Chamberlin-Courant multiwinner voting rule (CC) on single-peaked preference profiles. Given n voters and m candidates, it runs in almost linear time in the input size improving the previous best O(nm^2) time algorithm. We also study multiwinner voting rules on nearly single-peaked preference profiles in terms of the candidate-deletion operation. We show a polynomial-time algorithm for CC where a given candidate-deletion set D has logarithmic size. Actually, our algorithm runs in 2^|D| * poly(n,m) time and the base of the power cannot be improved under the Strong Exponential Time Hypothesis. We also adapt these results to all non-constant Thiele rules which generalize CC with approval ballots.
|
Krzysztof Sornat, Virginia Vassilevska Williams, Yinzhan Xu
| null | null | 2,022 |
ijcai
|
Strategy Proof Mechanisms for Facility Location with Capacity Limits
| null |
An important feature of many real world facility location problems are capacity limits on the number of agents served by each facility. We provide a comprehensive picture of strategy proof mechanisms for facility location problems with capacity constraints that are anonymous and Pareto optimal. First, we prove a strong characterization theorem. For locating two identical facilities with capacity limits and no spare capacity, the INNERPOINT mechanism is the unique strategy proof mechanism that is both anonymous and Pareto optimal. Second, when there is spare capacity, we identify a more general class of strategy proof mechanisms that interpolates smoothly between INNERPOINT and ENDPOINT which are anonymous and Pareto optimal. Third, with two facilities of different capacities, we prove a strong impossibility theorem that no mechanism can be both anonymous and Pareto optimal except when the capacities differ by just a single agent. Fourth, with three or more facilities we prove a second impossibility theorem that no mechanism can be both anonymous and Pareto optimal even when facilities have equal capacity. Our characterization and impossibility results are all minimal as multiple mechanisms exist if we drop one property.
|
Toby Walsh
| null | null | 2,022 |
ijcai
|
Fourier Analysis-based Iterative Combinatorial Auctions
| null |
Recent advances in Fourier analysis have brought new tools to efficiently represent and learn set functions. In this paper, we bring the power of Fourier analysis to the design of combinatorial auctions (CAs). The key idea is to approximate bidders' value functions using Fourier-sparse set functions, which can be computed using a relatively small number of queries. Since this number is still too large for practical CAs, we propose a new hybrid design: we first use neural networks (NNs) to learn bidders’ values and then apply Fourier analysis to the learned representations. On a technical level, we formulate a Fourier transform-based winner determination problem and derive its mixed integer program formulation. Based on this, we devise an iterative CA that asks Fourier-based queries. We experimentally show that our hybrid ICA achieves higher efficiency than prior auction designs, leads to a fairer distribution of social welfare, and significantly reduces runtime. With this paper, we are the first to leverage Fourier analysis in CA design and lay the foundation for future work in this area. Our code is available on GitHub: https://github.com/marketdesignresearch/FA-based-ICAs.
|
Jakob Weissteiner, Chris Wendler, Sven Seuken, Ben Lubin, Markus Püschel
| null | null | 2,022 |
ijcai
|
Modelling the Dynamics of Regret Minimization in Large Agent Populations: a Master Equation Approach
| null |
Understanding the learning dynamics in multiagent systems is an important and challenging task. Past research on multi-agent learning mostly focuses on two-agent settings. In this paper, we consider the scenario in which a population of infinitely many agents apply regret minimization in repeated symmetric games. We propose a new formal model based on the master equation approach in statistical physics to describe the evolutionary dynamics in the agent population. Our model takes the form of a partial differential equation, which describes how the probability distribution of regret evolves over time. Through experiments, we show that our theoretical results are consistent with the agent-based simulation results.
|
Zhen Wang, Chunjiang Mu, Shuyue Hu, Chen Chu, Xuelong Li
| null | null | 2,022 |
ijcai
|
Real-Time BDI Agents: A Model and Its Implementation
| null |
The BDI model proved to be effective for the developing of applications requiring high-levels of autonomy and to deal with the complexity and unpredictability of real-world scenarios. The model, however, has significant limitations in reacting and handling contingencies within the given real-time constraints. Without an explicit representation of time, existing real-time BDI implementations overlook the temporal implications during the agent’s decision process that may result in delays or unresponsiveness of the system when it gets overloaded. In this paper, we redefine the BDI agent control loop inspired by traditional and well establish algorithms for real-time systems to ensure a proper reaction of agents and their effective application in typical real-time domains. Our model proposes an effective real-time management of goals, plans, and actions with respect to time constraints and resources availability. We propose an implementation of the model for a resource-collection video-game and we validate the approach against a set of significant scenarios.
|
Andrea Traldi, Francesco Bruschetti, Marco Robol, Marco Roveri, Paolo Giorgini
| null | null | 2,022 |
ijcai
|
Max-Sum with Quadtrees for Decentralized Coordination in Continuous Domains
| null |
In this paper we put forward a novel extension of the classic Max-Sum algorithm to the framework of Continuous Distributed Constrained Optimization Problems (Continuous DCOPs), by utilizing a popular geometric algorithm, namely Quadtrees. In its standard form, Max-Sum can only solve Continuous DCOPs with an a priori discretization procedure. Existing Max-Sum extensions to continuous multiagent coordination domains require additional assumptions regarding the form of the factors, such as access to the gradient, or the ability to model them as continuous piecewise linear functions. Our proposed approach has no such requirements: we model the exchanged messages with Quadtrees, and, as such, the discretization procedure is dynamic and embedded in the internal Max-Sum operations (addition and marginal maximization). We apply Max-Sum with Quadtrees to lane-free autonomous driving. Our experimental evaluation showcases the effectiveness of our approach in this challenging coordination domain.
|
Dimitrios Troullinos, Georgios Chalkiadakis, Vasilis Samoladas, Markos Papageorgiou
| null | null | 2,022 |
ijcai
|
Monotone-Value Neural Networks: Exploiting Preference Monotonicity in Combinatorial Assignment
| null |
Many important resource allocation problems involve the combinatorial assignment of items, e.g., auctions or course allocation. Because the bundle space grows exponentially in the number of items, preference elicitation is a key challenge in these domains. Recently, researchers have proposed ML-based mechanisms that outperform traditional mechanisms while reducing preference elicitation costs for agents. However, one major shortcoming of the ML algorithms that were used is their disregard of important prior knowledge about agents' preferences. To address this, we introduce monotone-value neural networks (MVNNs), which are designed to capture combinatorial valuations, while enforcing monotonicity and normality. On a technical level, we prove that our MVNNs are universal in the class of monotone and normalized value functions, and we provide a mixed-integer linear program (MILP) formulation to make solving MVNN-based winner determination problems (WDPs) practically feasible. We evaluate our MVNNs experimentally in spectrum auction domains. Our results show that MVNNs improve the prediction performance, they yield state-of-the-art allocative efficiency in the auction, and they also reduce the run-time of the WDPs. Our code is available on GitHub: https://github.com/marketdesignresearch/MVNN.
|
Jakob Weissteiner, Jakob Heiss, Julien Siems, Sven Seuken
| null | null | 2,022 |
ijcai
|
Learn to Reverse DNNs from AI Programs Automatically
| null |
With the privatization deployment of DNNs on edge devices, the security of on-device DNNs has raised significant concern. To quantify the model leakage risk of on-device DNNs automatically, we propose NNReverse, the first learning-based method which can reverse DNNs from AI programs without domain knowledge. NNReverse trains a representation model to represent the semantics of binary code for DNN layers. By searching the most similar function in our database, NNReverse infers the layer type of a given function’s binary code. To represent assembly instructions semantics precisely, NNReverse proposes a more fine-grained embedding model to represent the textual and structural-semantic of assembly functions.
|
Simin Chen, Hamed Khanpour, Cong Liu, Wei Yang
| null | null | 2,022 |
ijcai
|
Environment Design for Biased Decision Makers
| null |
We study the environment design problem for biased decision makers. In an environment design problem, an informed principal aims to update the decision making environment to influence the decisions made by the agent. This problem is ubiquitous in various domains, e.g., a social networking platform might want to update its website to encourage more user engagement. In this work, we focus on the scenario in which the agent might exhibit biases in decision making. We relax the common assumption that the agent is rational and aim to incorporate models of biased agents in environment design. We formulate the environment design problem under the Markov decision process (MDP) and incorporate common models of biased agents through introducing general time-discounting functions. We then formalize the environment design problem as constrained optimization problems and propose corresponding algorithms. We conduct both simulations and real human-subject experiments with workers recruited from Amazon Mechanical Turk to evaluate our proposed algorithms.
|
Guanghui Yu, Chien-Ju Ho
| null | null | 2,022 |
ijcai
|
On Preferred Abductive Explanations for Decision Trees and Random Forests
| null |
Abductive explanations take a central place in eXplainable Artificial Intelligence (XAI) by clarifying with few features
the way data instances are classified. However, instances may have exponentially many minimum-size abductive explanations, and
this source of complexity holds even for ``intelligible'' classifiers, such as decision trees. When the number of such abductive explanations is huge,
computing one of them, only, is often not informative enough. Especially, better explanations than the one
that is derived may exist. As a way to circumvent this issue, we propose to leverage
a model of the explainee, making precise her / his preferences about explanations, and to compute only
preferred explanations. In this paper, several models are pointed out and discussed. For each model, we present and
evaluate an algorithm for computing preferred majoritary reasons, where majoritary reasons are specific abductive
explanations suited to random forests. We show that in practice the preferred majoritary reasons for an instance
can be far less numerous than its majoritary reasons.
|
Gilles Audemard, Steve Bellart, Louenas Bounia, Frederic Koriche, Jean-Marie Lagniez, Pierre Marquis
| null | null | 2,022 |
ijcai
|
Evolutionary Approach to Security Games with Signaling
| null |
Green Security Games have become a popular way to model scenarios involving the protection of natural resources, such as wildlife. Sensors (e.g. drones equipped with cameras) have also begun to play a role in these scenarios by providing real-time information.
Incorporating both human and sensor defender resources strategically is the subject of recent work on Security Games with Signaling (SGS). However, current methods to solve SGS do not scale well in terms of time or memory.
We therefore propose a novel approach to SGS, which, for the first time in this domain, employs an Evolutionary Computation paradigm: EASGS. EASGS effectively searches the huge SGS solution space via suitable solution encoding in a chromosome and a specially-designed set of operators. The operators include three types of mutations, each focusing on a particular aspect of the SGS solution, optimized crossover and a local coverage improvement scheme (a memetic aspect of EASGS). We also introduce a new set of benchmark games, based on dense or locally-dense graphs that reflect real-world SGS settings.
In the majority of 342 test game instances, EASGS outperforms state-of-the-art methods, including a reinforcement learning method, in terms of time scalability, nearly constant memory utilization, and quality of the returned defender's strategies (expected payoffs).
|
Adam Żychowski, Jacek Mańdziuk, Elizabeth Bondi, Aravind Venugopal, Milind Tambe, Balaraman Ravindran
| null | null | 2,022 |
ijcai
|
Axiomatic Foundations of Explainability
| null |
Improving trust in decisions made by classification models is becoming crucial for the acceptance of automated systems, and an important way of doing that is by providing explanations for the behaviour of the models. Different explainers have been proposed in the recent literature for that purpose, however their formal properties are under-studied.
This paper investigates theoretically explainers that provide reasons behind decisions independently of instances. Its contributions are fourfold. The first is to lay the foundations of such explainers by proposing key axioms, i.e.,
desirable properties they would satisfy. Two axioms are incompatible leading to two subsets. The second contribution consists of demonstrating that the first subset of axioms characterizes a family of explainers that return sufficient reasons while the second characterizes a family that provides necessary reasons. This sheds light on the axioms which distinguish the two types of reasons. As a third contribution, the paper introduces various explainers of both families, and fully characterizes some of them. Those explainers make use of the whole feature space. The fourth contribution is a family of explainers that generate explanations from finite datasets (subsets of the feature space). This family, seen as an abstraction of Anchors and LIME, violates some axioms including one which prevents incorrect explanations.
|
Leila Amgoud, Jonathan Ben-Naim
| null | null | 2,022 |
ijcai
|
How Does Frequency Bias Affect the Robustness of Neural Image Classifiers against Common Corruption and Adversarial Perturbations?
| null |
Model robustness is vital for the reliable deployment of machine learning models in real-world applications. Recent studies have shown that data augmentation can result in model over-relying on features in the low-frequency domain, sacrificing performance against low-frequency corruptions, highlighting a connection between frequency and robustness. Here, we take one step further to more directly study the frequency bias of a model through the lens of its Jacobians and its implication to model robustness. To achieve this, we propose Jacobian frequency regularization for models' Jacobians to have a larger ratio of low-frequency components. Through experiments on four image datasets, we show that biasing classifiers towards low (high)-frequency components can bring performance gain against high (low)-frequency corruption and adversarial perturbation, albeit with a tradeoff in performance for low (high)-frequency corruption. Our approach elucidates a more direct connection between the frequency bias and robustness of deep learning models.
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Alvin Chan, Yew Soon Ong, Clement Tan
| null | null | 2,022 |
ijcai
|
Strategyproof Mechanisms for Group-Fair Facility Location Problems
| null |
We study the facility location problems where agents are located on a real line and divided into groups based on criteria such as ethnicity or age. Our aim is to design mechanisms to locate a facility to approximately minimize the costs of groups of agents to the facility fairly while eliciting the agents' locations truthfully. We first explore various well-motivated group fairness cost objectives for the problems and show that many natural objectives have an unbounded approximation ratio. We then consider minimizing the maximum total group cost and minimizing the average group cost objectives. For these objectives, we show that existing classical mechanisms (e.g., median) and new group-based mechanisms provide bounded approximation ratios, where the group-based mechanisms can achieve better ratios. We also provide lower bounds for both objectives. To measure fairness between groups and within each group, we study a new notion of intergroup and intragroup fairness (IIF) . We consider two IIF objectives and provide mechanisms with tight approximation ratios.
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Houyu Zhou, Minming Li, Hau Chan
| null | null | 2,022 |
ijcai
|
Fast and Fine-grained Autoscaler for Streaming Jobs with Reinforcement Learning
| null |
On computing clusters, the autoscaler is responsible for allocating resources for jobs or fine-grained tasks to ensure their Quality of Service. Due to a more precise resource management, fine-grained autoscaling can generally achieve better performance. However, the fine-grained autoscaling for streaming jobs needs intensive computation to model the complicated running states of tasks, and has not been adequately studied previously. In this paper, we propose a novel fine-grained autoscaler for streaming jobs based on reinforcement learning. We first organize the running states of streaming jobs as spatio-temporal graphs. To efficiently make autoscaling decisions, we propose a Neural Variational Subgraph Sampler to sample spatio-temporal subgraphs. Furthermore, we propose a mutual-information-based objective function to explicitly guide the sampler to extract more representative subgraphs. After that, the autoscaler makes decisions based on the learned subgraph representations. Experiments conducted on real-world datasets demonstrate the superiority of our method over six competitive baselines.
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Mingzhe Xing, Hangyu Mao, Zhen Xiao
| null | null | 2,022 |
ijcai
|
Detecting Out-Of-Context Objects Using Graph Contextual Reasoning Network
| null |
This paper presents an approach for detecting out-of-context (OOC) objects in images. Given an image with a set of objects, our goal is to determine if an object is inconsistent with the contextual relations and detect the OOC object with a bounding box. In this work, we consider common contextual relations such as co-occurrence relations, the relative size of an object with respect to other objects, and the position of the object in the scene. We posit that contextual cues are useful to determine object labels for in-context objects and inconsistent context cues are detrimental to determining object labels for out-of-context objects. To realize this hypothesis, we propose a graph contextual reasoning network (GCRN) to detect OOC objects. GCRN consists of two separate graphs to predict object labels based on the contextual cues in the image: 1) a representation graph to learn object features based on the neighboring objects and 2) a context graph to explicitly capture contextual cues from the neighboring objects. GCRN explicitly captures the contextual cues to improve the detection of in-context objects and identify objects that violate contextual relations.
In order to evaluate our approach, we create a large-scale dataset by adding OOC object instances to the COCO images. We also evaluate on recent OCD benchmark. Our results show that GCRN outperforms competitive baselines in detecting OOC objects and correctly detecting in-context objects. Code and data: https://nusci.csl.sri.com/project/trinity-ooc
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Manoj Acharya, Anirban Roy, Kaushik Koneripalli, Susmit Jha, Christopher Kanan, Ajay Divakaran
| null | null | 2,022 |
ijcai
|
Correlation-Based Algorithm for Team-Maxmin Equilibrium in Multiplayer Extensive-Form Games
| null |
Efficient algorithms computing a Nash equilibrium have been successfully applied to large zero- sum two-player extensive-form games (e.g., poker). However, in multiplayer games, computing a Nash equilibrium is generally hard, and the equilibria are not exchangeable, which makes players face the problem of selecting one of many different Nash equilibria. In this paper, we focus on an alternative solution concept in zero-sum multiplayer extensive-form games called Team-Maxmin Equilibrium (TME). It is a Nash equilibrium that maximizes each team member’s utility. As TME is unique in general, it avoids the equilibrium selection problem. However, it is still difficult (FNP- hard) to find a TME. Computing it can be formulated as a non-convex program, but existing algorithms are capable of solving this program for only very small games. In this paper, we first refine the complexity result for computing a TME by using a correlation plan to show that a TME can be found in polynomial time in a specific class of games according to our boundary for complexity. Second, we propose an efficient correlation-based algorithm to solve the non-convex program for TME in games not belonging to this class. The algorithm combines two special correlation plans based on McCormick envelopes for convex relaxation and von Stengel-Forges polytope for correlated equilibria. We show that restricting the feasible solution space to von Stengel-Forges polytope will strictly reduce the feasible solution space after convex re- laxation of nonlinear terms. Finally, experiments show that our algorithm is about four orders of magnitude faster than the prior state of the art and can solve many previously unsolvable games.
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Youzhi Zhang, Bo An, V. S. Subrahmanian
| null | null | 2,022 |
ijcai
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Mechanism Design with Predictions
| null |
Improving algorithms via predictions is a very active research topic in recent years. This paper initiates the systematic study of mechanism design in this model. In a number of well-studied mechanism design settings, we make use of imperfect predictions to design mechanisms that perform much better than traditional mechanisms if the predictions are accurate (consistency), while always retaining worst-case guarantees even with very imprecise predictions (robustness). Furthermore, we refer to the largest prediction error sufficient to give a good performance as the error tolerance of a mechanism, and observe that an intrinsic tradeoff among consistency, robustness and error tolerance is common for mechanism design with predictions.
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Chenyang Xu, Pinyan Lu
| null | null | 2,022 |
ijcai
|
Manipulating Elections by Changing Voter Perceptions
| null |
The integrity of elections is central to democratic systems. However, a myriad of malicious actors aspire to influence election outcomes for financial or political benefit. A common means to such ends is by manipulating perceptions of the voting public about select candidates, for example, through misinformation. We present a formal model of the impact of perception manipulation on election outcomes in the framework of spatial voting theory, in which the preferences of voters over candidates are generated based on their relative distance in the space of issues. We show that controlling elections in this model is, in general, NP-hard, whether issues are binary or real-valued. However, we demonstrate that critical to intractability is the diversity of opinions on issues exhibited by the voting public. When voter views lack diversity, and we can instead group them into a small number of categories---for example, as a result of political polarization---the election control problem can be solved in polynomial time in the number of issues and candidates for arbitrary scoring rules.
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Junlin Wu, Andrew Estornell, Lecheng Kong, Yevgeniy Vorobeychik
| null | null | 2,022 |
ijcai
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Efficient Multi-Agent Communication via Shapley Message Value
| null |
Utilizing messages from teammates is crucial in cooperative multi-agent tasks due to the partially observable nature of the environment. Naively asking messages from all teammates without pruning may confuse individual agents, hindering the learning process and impairing the whole system's performance. Most previous work either utilizes a gate or employs an attention mechanism to extract relatively important messages. However, they do not explicitly evaluate each message's value, failing to learn an efficient communication protocol in more complex scenarios. To tackle this issue, we model the teammates of an agent as a message coalition and calculate the Shapley Message Value (SMV) of each agent within it. SMV reflects the contribution of each message to an agent and redundant messages can be spotted in this way effectively. On top of that, we design a novel framework named Shapley Message Selector (SMS), which learns to predict the SMVs of teammates for an agent solely based on local information so that the agent can only query those teammates with positive SMVs. Empirically, we demonstrate that our method can prune redundant messages and achieve comparable or better performance in various multi-agent cooperative scenarios than full communication settings and existing strong baselines.
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Di Xue, Lei Yuan, Zongzhang Zhang, Yang Yu
| null | null | 2,022 |
ijcai
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On the Complexity of Calculating Approval-Based Winners in Candidates-Embedded Metrics
| null |
We study approval-based multiwinner voting where candidates are in a metric space and committees are valuated in terms of their distances to the given votes. In particular, we consider three different distance functions, and for each of them we study both the utilitarian rules and the egalitarian rules, resulting in six variants of winners determination problems. We focus on the (parameterized) complexity of these problems for both the general metric and several special metrics. For hardness results, we also discuss their approximability.
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Yongjie Yang
| null | null | 2,022 |
ijcai
|
PPT: Backdoor Attacks on Pre-trained Models via Poisoned Prompt Tuning
| null |
Recently, prompt tuning has shown remarkable performance as a new learning paradigm, which freezes pre-trained language models (PLMs) and only tunes some soft prompts. A fixed PLM only needs to be loaded with different prompts to adapt different downstream tasks. However, the prompts associated with PLMs may be added with some malicious behaviors, such as backdoors. The victim model will be implanted with a backdoor by using the poisoned prompt. In this paper, we propose to obtain the poisoned prompt for PLMs and corresponding downstream tasks by prompt tuning. We name this Poisoned Prompt Tuning method "PPT". The poisoned prompt can lead a shortcut between the specific trigger word and the target label word to be created for the PLM. So the attacker can simply manipulate the prediction of the entire model by just a small prompt. Our experiments on various text classification tasks show that PPT can achieve a 99% attack success rate with almost no accuracy sacrificed on original task. We hope this work can raise the awareness of the possible security threats hidden in the prompt.
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Wei Du, Yichun Zhao, Boqun Li, Gongshen Liu, Shilin Wang
| null | null | 2,022 |
ijcai
|
Multi-Agent Concentrative Coordination with Decentralized Task Representation
| null |
Value-based multi-agent reinforcement learning (MARL) methods hold the promise of promoting coordination in cooperative settings. Popular MARL methods mainly focus on the scalability or the representational capacity of value functions. Such a learning paradigm can reduce agents' uncertainties and promote coordination. However, they fail to leverage the task structure decomposability, which generally exists in real-world multi-agent systems (MASs), leading to a significant amount of time exploring the optimal policy in complex scenarios. To address this limitation, we propose a novel framework Multi-Agent Concentrative Coordination (MACC) based on task decomposition, with which an agent can implicitly form local groups to reduce the learning space to facilitate coordination. In MACC, agents first learn representations for subtasks from their local information and then implement an attention mechanism to concentrate on the most relevant ones. Thus, agents can pay targeted attention to specific subtasks and improve coordination. Extensive experiments on various complex multi-agent benchmarks demonstrate that MACC achieves remarkable performance compared to existing methods.
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Lei Yuan, Chenghe Wang, Jianhao Wang, Fuxiang Zhang, Feng Chen, Cong Guan, Zongzhang Zhang, Chongjie Zhang, Yang Yu
| null | null | 2,022 |
ijcai
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SoFaiR: Single Shot Fair Representation Learning
| null |
To avoid discriminatory uses of their data, organizations can learn to map them into a representation that filters out information related to sensitive attributes. However, all existing methods in fair representation learning generate a fairness-information trade-off. To achieve different points on the fairness-information plane, one must train different models. In this paper, we first demonstrate that fairness-information trade-offs are fully characterized by rate-distortion trade-offs. Then, we use this key result and propose SoFaiR, a single shot fair representation learning method that generates with one trained model many points on the fairness-information plane. Besides its computational saving, our single-shot approach is, to the extent of our knowledge, the first fair representation learning method that explains what information is affected by changes in the fairness / distortion properties of the representation. Empirically, we find on three datasets that SoFaiR achieves similar fairness information trade-offs as its multi-shot counterparts.
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Xavier Gitiaux, Huzefa Rangwala
| null | null | 2,022 |
ijcai
|
Individual Fairness Guarantees for Neural Networks
| null |
We consider the problem of certifying the individual fairness (IF) of feed-forward neural networks (NNs).
In particular, we work with the epsilon-delta-IF formulation, which, given a NN and a similarity metric learnt from data, requires that the output difference between any pair of epsilon-similar individuals is bounded by a maximum decision tolerance delta >= 0.
Working with a range of metrics, including the Mahalanobis distance, we propose a method to overapproximate the resulting optimisation problem using piecewise-linear functions to lower and upper bound the NN's non-linearities globally over the input space.
We encode this computation as the solution of a Mixed-Integer Linear Programming problem and demonstrate that it can be used to compute IF guarantees on four datasets widely used for fairness benchmarking.
We show how this formulation can be used to encourage models' fairness at training time by modifying the NN loss, and empirically confirm our approach yields NNs that are orders of magnitude fairer than state-of-the-art methods.
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Elias Benussi, Andrea Patane', Matthew Wicker, Luca Laurenti, Marta Kwiatkowska
| null | null | 2,022 |
ijcai
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Investigating and Explaining the Frequency Bias in Image Classification
| null |
CNNs exhibit many behaviors different from humans, one of which is the capability of employing high-frequency components. This paper discusses the frequency bias phenomenon in image classification tasks: the high-frequency components are actually much less exploited than the low- and mid- frequency components. We first investigate the frequency bias phenomenon by presenting two observations on feature discrimination and learning priority. Furthermore, we hypothesize that (1) the spectral density, (2) class consistency directly affect the frequency bias. Specifically, our investigations verify that the spectral density of datasets mainly affects the learning priority, while the class consistency mainly affects the feature discrimination.
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Zhiyu Lin, Yifei Gao, Jitao Sang
| null | null | 2,022 |
ijcai
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Counterfactual Interpolation Augmentation (CIA): A Unified Approach to Enhance Fairness and Explainability of DNN
| null |
Bias in the training data can jeopardize fairness and explainability of deep neural network prediction on test data. We propose a novel bias-tailored data augmentation approach, Counterfactual Interpolation Augmentation (CIA), attempting to debias the training data by d-separating the spurious correlation between the target variable and the sensitive attribute. CIA generates counterfactual interpolations along a path simulating the distribution transitions between the input and its counterfactual example. CIA as a pre-processing approach enjoys two advantages: First, it couples with either plain training or debiasing training to markedly increase fairness over the sensitive attribute. Second, it enhances the explainability of deep neural networks by generating attribution maps via integrating counterfactual gradients. We demonstrate the superior performance of the CIA-trained deep neural network models using qualitative and quantitative experimental results. Our code is available at: https://github.com/qiangyao1988/CIA
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Yao Qiang, Chengyin Li, Marco Brocanelli, Dongxiao Zhu
| null | null | 2,022 |
ijcai
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AttExplainer: Explain Transformer via Attention by Reinforcement Learning
| null |
Transformer and its variants, built based on attention mechanisms, have recently achieved remarkable performance in many NLP tasks. Most existing works on Transformer explanation tend to reveal and utilize the attention matrix with human subjective intuitions in a qualitative manner. However, the huge size of dimensions directly challenges these methods to quantitatively analyze the attention matrix. Therefore, in this paper, we propose a novel reinforcement learning (RL) based framework for Transformer explanation via attention matrix, namely AttExplainer. The RL agent learns to perform step-by-step masking operations by observing the change in attention matrices. We have adapted our method to two scenarios, perturbation-based model explanation and text adversarial attack. Experiments on three widely used text classification benchmarks validate the effectiveness of the proposed method compared to state-of-the-art baselines. Additional studies show that our method is highly transferable and consistent with human intuition. The code of this paper is available at https://github.com/niuzaisheng/AttExplainer .
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Runliang Niu, Zhepei Wei, Yan Wang, Qi Wang
| null | null | 2,022 |
ijcai
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BayCon: Model-agnostic Bayesian Counterfactual Generator
| null |
Generating counterfactuals to discover hypothetical predictive scenarios is the de facto standard for explaining machine learning models and their predictions. However, building a counterfactual explainer that is time-efficient, scalable, and model-agnostic, in addition to being compatible with continuous and categorical attributes, remains an open challenge. To complicate matters even more, ensuring that the contrastive instances are optimised for feature sparsity, remain close to the explained instance, and are not drawn from outside of the data manifold, is far from trivial. To address this gap we propose BayCon: a novel counterfactual generator based on probabilistic feature sampling and Bayesian optimisation. Such an approach can combine multiple objectives by employing a surrogate model to guide the counterfactual search. We demonstrate the advantages of our method through a collection of experiments based on six real-life datasets representing three regression tasks and three classification tasks.
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Piotr Romashov, Martin Gjoreski, Kacper Sokol, Maria Vanina Martinez, Marc Langheinrich
| null | null | 2,022 |
ijcai
|
Fairness without the Sensitive Attribute via Causal Variational Autoencoder
| null |
In recent years, most fairness strategies in machine learning have focused on mitigating unwanted biases by assuming that the sensitive information is available. However, in practice this is not always the case: due to privacy purposes and regulations such as RGPD in EU, many personal sensitive attributes are frequently not collected. Yet, only a few prior works address the issue of mitigating bias in such a difficult setting, in particular to meet classical fairness objectives such as Demographic Parity and Equalized Odds. By leveraging recent developments for approximate inference, we propose in this paper an approach to fill this gap. To infer a sensitive information proxy, we introduce a new variational auto-encoding-based framework named SRCVAE that relies on knowledge of the underlying causal graph. The bias mitigation is then done in an adversarial fairness approach. Our proposed method empirically achieves significant improvements over existing works in the field. We observe that the generated proxy’s latent space correctly recovers sensitive information and that our approach achieves a higher accuracy while obtaining the same level of fairness on two real datasets.
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Vincent Grari, Sylvain Lamprier, Marcin Detyniecki
| null | null | 2,022 |
ijcai
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What Does My GNN Really Capture? On Exploring Internal GNN Representations
| null |
Graph Neural Networks (GNNs) are very efficient at classifying graphs but their internal functioning is opaque which limits their field of application. Existing methods to explain GNN focus on disclosing the relationships between input graphs and model decision. In this article, we propose a method that goes further and isolates the internal features, hidden in the network layers, that are automatically identified by the GNN and used in the decision process. We show that this method makes possible to know the parts of the input graphs used by GNN with much less bias that SOTA methods and thus to bring confidence in the decision process.
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Luca Veyrin-Forrer, Ataollah Kamal, Stefan Duffner, Marc Plantevit, Céline Robardet
| null | null | 2,022 |
ijcai
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Cluster Attack: Query-based Adversarial Attacks on Graph with Graph-Dependent Priors
| null |
While deep neural networks have achieved great success in graph analysis, recent work has shown that they are vulnerable to adversarial attacks. Compared with adversarial attacks on image classification, performing adversarial attacks on graphs is more challenging because of the discrete and non-differential nature of the adjacent matrix for a graph. In this work, we propose Cluster Attack --- a Graph Injection Attack (GIA) on node classification, which injects fake nodes into the original graph to degenerate the performance of graph neural networks (GNNs) on certain victim nodes while affecting the other nodes as little as possible. We demonstrate that a GIA problem can be equivalently formulated as a graph clustering problem; thus, the discrete optimization problem of the adjacency matrix can be solved in the context of graph clustering. In particular, we propose to measure the similarity between victim nodes by a metric of Adversarial Vulnerability, which is related to how the victim nodes will be affected by the injected fake node, and to cluster the victim nodes accordingly. Our attack is performed in a practical and unnoticeable query-based black-box manner with only a few nodes on the graphs that can be accessed. Theoretical analysis and extensive experiments demonstrate the effectiveness of our method by fooling the node classifiers with only a small number of queries.
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Zhengyi Wang, Zhongkai Hao, Ziqiao Wang, Hang Su, Jun Zhu
| null | null | 2,022 |
ijcai
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Taking Situation-Based Privacy Decisions: Privacy Assistants Working with Humans
| null |
Privacy on the Web is typically managed by giving consent to individual Websites for various aspects of data usage. This paradigm requires too much human effort and thus is impractical for Internet of Things (IoT) applications where humans interact with many new devices on a daily basis. Ideally, software privacy assistants can help by making privacy decisions in different situations on behalf of the users. To realize this, we propose an agent-based model for a privacy assistant. The model identifies the contexts that a situation implies and computes the trustworthiness of these contexts. Contrary to traditional trust models that capture trust in an entity by observing large number of interactions, our proposed model can assess the trustworthiness even if the user has not interacted with the particular device before. Moreover, our model can decide which situations are inherently ambiguous and thus can request the human to make the decision. We evaluate various aspects of the model using a real-life data set and report adjustments that are needed to serve different types of users well.
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Nadin Kökciyan, Pinar Yolum
| null | null | 2,022 |
ijcai
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Shielding Federated Learning: Robust Aggregation with Adaptive Client Selection
| null |
Federated learning (FL) enables multiple clients to collaboratively train an accurate global model while protecting clients' data privacy. However, FL is susceptible to Byzantine attacks from malicious participants. Although the problem has gained significant attention, existing defenses have several flaws: the server irrationally chooses malicious clients for aggregation even after they have been detected in previous rounds; the defenses perform ineffectively against sybil attacks or in the heterogeneous data setting.
To overcome these issues, we propose MAB-RFL, a new method for robust aggregation in FL. By modelling the client selection as an extended multi-armed bandit (MAB) problem, we propose an adaptive client selection strategy to choose honest clients that are more likely to contribute high-quality updates. We then propose two approaches to identify malicious updates from sybil and non-sybil attacks, based on which rewards for each client selection decision can be accurately evaluated to discourage malicious behaviors. MAB-RFL achieves a satisfying balance between exploration and exploitation on the potential benign clients. Extensive experimental results show that MAB-RFL outperforms existing defenses in three attack scenarios under different percentages of attackers.
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Wei Wan, Shengshan Hu, jianrong Lu, Leo Yu Zhang, Hai Jin, Yuanyuan He
| null | null | 2,022 |
ijcai
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Anti-Forgery: Towards a Stealthy and Robust DeepFake Disruption Attack via Adversarial Perceptual-aware Perturbations
| null |
DeepFake is becoming a real risk to society and brings potential threats to both individual privacy and political security due to the DeepFaked multimedia are realistic and convincing. However, the popular DeepFake passive detection is an ex-post forensics countermeasure and failed in blocking the disinformation spreading in advance. To address this limitation, researchers study the proactive defense techniques by adding adversarial noises into the source data to disrupt the DeepFake manipulation. However, the existing studies on proactive DeepFake defense via injecting adversarial noises are not robust, which could be easily bypassed by employing simple image reconstruction revealed in a recent study MagDR.
In this paper, we investigate the vulnerability of the existing forgery techniques and propose a novel anti-forgery technique that helps users protect the shared facial images from attackers who are capable of applying the popular forgery techniques. Our proposed method generates perceptual-aware perturbations in an incessant manner which is vastly different from the prior studies by adding adversarial noises that is sparse. Experimental results reveal that our perceptual-aware perturbations are robust to diverse image transformations, especially the competitive evasion technique, MagDR via image reconstruction. Our findings potentially open up a new research direction towards thorough understanding and investigation of perceptual-aware adversarial attack for protecting facial images against DeepFakes in a proactive and robust manner. Code is available at https://github.com/AbstractTeen/AntiForgery.
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Run Wang, Ziheng Huang, Zhikai Chen, Li Liu, Jing Chen, Lina Wang
| null | null | 2,022 |
ijcai
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Approximately EFX Allocations for Indivisible Chores
| null |
In this paper we study how to fairly allocate a set of m indivisible chores to a group of n agents, each of which has a general additive cost function on the items. Since envy-free (EF) allocation is not guaranteed to exist, we consider the notion of envy-freeness up to any item (EFX). In contrast to the fruitful results regarding the (approximation of) EFX allocations for goods, very little is known for the allocation of chores. Prior to our work, for the allocation of chores, it is known that EFX allocations always exist for two agents, or general number of agents with identical ordering cost functions. For general instances, no non-trivial approximation result regarding EFX allocation is known. In this paper we make some progress in this direction by showing that for three agents we can always compute a 5-approximation of EFX allocation in polynomial time. For n>=4 agents, our algorithm always computes an allocation that achieves an approximation ratio of 3n^2 regarding EFX. We also study the bi-valued instances, in which agents have at most two cost values on the chores, and provide polynomial time algorithms for the computation of EFX allocation when n=3, and (n-1)-approximation of EFX allocation when n>=4.
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Shengwei Zhou, Xiaowei Wu
| null | null | 2,022 |
ijcai
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MetaFinger: Fingerprinting the Deep Neural Networks with Meta-training
| null |
As deep neural networks (DNNs) play a critical role in various fields, the models themselves hence are becoming an important asset that needs to be protected. To achieve this, various neural network fingerprint methods have been proposed. However, existing fingerprint methods fingerprint the decision boundary by adversarial examples, which is not robust to model modification and adversarial defenses. To fill this gap, we propose a robust fingerprint method MetaFinger, which fingerprints the inner decision area of the model by meta-training, rather than the decision boundary. Specifically, we first generate many shadow models with DNN augmentation as meta-data. Then we optimize some images by meta-training to ensure that only models derived from the protected model can recognize them. To demonstrate the robustness of our fingerprint approach, we evaluate our method against two types of attacks including input modification and model modification. Experiments show that our method achieves 99.34% and 97.69% query accuracy on average, surpassing existing methods over 30%, 25% on CIFAR-10 and Tiny-ImageNet, respectively. Our code is available at https://github.com/kangyangWHU/MetaFinger.
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Kang Yang, Run Wang, Lina Wang
| null | null | 2,022 |
ijcai
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Event-driven Video Deblurring via Spatio-Temporal Relation-Aware Network
| null |
Video deblurring with event information has attracted considerable attention. To help deblur each frame, existing methods usually compress a specific event sequence into a feature tensor with the same size as the corresponding video. However, this strategy neither considers the pixel-level spatial brightness changes nor the temporal correlation between events at each time step, resulting in insufficient use of spatio-temporal information. To address this issue, we propose a new Spatio-Temporal Relation-Attention network (STRA), for the specific event-based video deblurring. Concretely, to utilize spatial consistency between the frame and event, we model the brightness changes as an extra prior to aware blurring contexts in each frame; to record temporal relationship among different events, we develop a temporal memory block to restore long-range dependencies of event sequences continuously. In this way, the complementary information contained in the events and frames, as well as the correlation of neighboring events, can be fully utilized to recover spatial texture from events constantly. Experiments show that our STRA significantly outperforms several competing methods, e.g., on the HQF dataset, our network achieves up to 1.3 dB in terms of PSNR over the most advanced method. The code is available at https://github.com/Chengzhi-Cao/STRA.
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Chengzhi Cao, Xueyang Fu, Yurui Zhu, Gege Shi, Zheng-Jun Zha
| null | null | 2,022 |
ijcai
|
Zero-Shot Logit Adjustment
| null |
Semantic-descriptor-based Generalized Zero-Shot Learning (GZSL) poses challenges in recognizing novel classes in the test phase. The development of generative models enables current GZSL techniques to probe further into the semantic-visual link, culminating in a two-stage form that includes a generator and a classifier. However, existing generation-based methods focus on enhancing the generator's effect while neglecting the improvement of the classifier. In this paper, we first analyze of two properties of the generated pseudo unseen samples: bias and homogeneity. Then, we perform variational Bayesian inference to back-derive the evaluation metrics, which reflects the balance of the seen and unseen classes. As a consequence of our derivation, the aforementioned two properties are incorporated into the classifier training as seen-unseen priors via logit adjustment. The Zero-Shot Logit Adjustment further puts semantic-based classifiers into effect in generation-based GZSL. Our experiments demonstrate that the proposed technique achieves state-of-the-art when combined with the basic generator, and it can improve various generative Zero-Shot Learning frameworks. Our codes are available on https://github.com/cdb342/IJCAI-2022-ZLA.
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Dubing Chen, Yuming Shen, Haofeng Zhang, Philip H.S. Torr
| null | null | 2,022 |
ijcai
|
AutoAlign: Pixel-Instance Feature Aggregation for Multi-Modal 3D Object Detection
| null |
Object detection through either RGB images or the LiDAR point clouds has been extensively explored in autonomous driving. However, it remains challenging to make these two data sources complementary and beneficial to each other. In this paper, we propose AutoAlign, an automatic feature fusion strategy for 3D object detection. Instead of establishing deterministic correspondence with camera projection matrix, we model the mapping relationship between the image and point clouds with a learnable alignment map. This map enables our model to automate the alignment of non-homogenous features in a dynamic and data-driven manner. Specifically, a cross-attention feature alignment module is devised to adaptively aggregate pixel-level image features for each voxel. To enhance the semantic consistency during feature alignment, we also design a self-supervised cross-modal feature interaction module, through which the model can learn feature aggregation with instance-level feature guidance. Extensive experimental results show that our approach can lead to 2.3 mAP and 7.0 mAP improvements on the KITTI and nuScenes datasets respectively. Notably, our best model reaches 70.9 NDS on the nuScenes testing leaderboard, achieving competitive performance among various state-of-the-arts.
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Zehui Chen, Zhenyu Li, Shiquan Zhang, Liangji Fang, Qinhong Jiang, Feng Zhao, Bolei Zhou, Hang Zhao
| null | null | 2,022 |
ijcai
|
SpanConv: A New Convolution via Spanning Kernel Space for Lightweight Pansharpening
| null |
Standard convolution operations can effectively perform feature extraction and representation but result in high computational cost, largely due to the generation of the original convolution kernel corresponding to the channel dimension of the feature map, which will cause unnecessary redundancy. In this paper, we focus on kernel generation and present an interpretable span strategy, named SpanConv, for the effective construction of kernel space. Specifically, we first learn two navigated kernels with single channel as bases, then extend the two kernels by learnable coefficients, and finally span the two sets of kernels by their linear combination to construct the so-called SpanKernel. The proposed SpanConv is realized by replacing plain convolution kernel by SpanKernel. To verify the effectiveness of SpanConv, we design a simple network with SpanConv. Experiments demonstrate the proposed network significantly reduces parameters comparing with benchmark networks for remote sensing pansharpening, while achieving competitive performance and excellent generalization. Code is available at https://github.com/zhi-xuan-chen/IJCAI-2022 SpanConv.
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Zhi-Xuan Chen, Cheng Jin, Tian-Jing Zhang, Xiao Wu, Liang-Jian Deng
| null | null | 2,022 |
ijcai
|
Model Stealing Defense against Exploiting Information Leak through the Interpretation of Deep Neural Nets
| null |
Model stealing techniques allow adversaries to create attack models that mimic the functionality of black-box machine learning models, querying only class membership or probability outcomes. Recently, interpretable AI is getting increasing attention, to enhance our understanding of AI models, provide additional information for diagnoses, or satisfy legal requirements. However, it has been recently reported that providing such additional information can make AI models more vulnerable to model stealing attacks. In this paper, we propose DeepDefense, the first defense mechanism that protects an AI model against model stealing attackers exploiting both class probabilities and interpretations. DeepDefense uses a misdirection model to hide the critical information of the original model against model stealing attacks, with minimal degradation on both the class probability and the interpretability of prediction output. DeepDefense is highly applicable for any model stealing scenario since it makes minimal assumptions about the model stealing adversary. In our experiments, DeepDefense shows significantly higher defense performance than the existing state-of-the-art defenses on various datasets and interpreters.
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Jeonghyun Lee, Sungmin Han, Sangkyun Lee
| null | null | 2,022 |
ijcai
|
Robust Single Image Dehazing Based on Consistent and Contrast-Assisted Reconstruction
| null |
Single image dehazing as a fundamental low-level vision task, is essential for the development of robust intelligent surveillance system. In this paper, we make an early effort to consider dehazing robustness under variational haze density, which is a realistic while under-studied problem in the research filed of singe image dehazing. To properly address this problem, we propose a novel density-variational learning framework to improve the robustness of the image dehzing model assisted by a variety of negative hazy images, to better deal with various complex hazy scenarios. Specifically, the dehazing network is optimized under the consistency-regularized framework with the proposed Contrast-Assisted Reconstruction Loss (CARL). The CARL can fully exploit the negative information to facilitate the traditional positive-orient dehazing objective function, by squeezing the dehazed image to its clean target from different directions. Meanwhile, the consistency regularization keeps consistent outputs given multi-level hazy images, thus improving the model robustness. Extensive experimental results on two synthetic and three real-world datasets demonstrate that our method significantly surpasses the state-of-the-art approaches.
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De Cheng, Yan Li, Dingwen Zhang, Nannan Wang, Xinbo Gao, Jiande Sun
| null | null | 2,022 |
ijcai
|
I²R-Net: Intra- and Inter-Human Relation Network for Multi-Person Pose Estimation
| null |
In this paper, we present the Intra- and Inter-Human Relation Networks I²R-Net for Multi-Person Pose Estimation. It involves two basic modules. First, the Intra-Human Relation Module operates on a single person and aims to capture Intra-Human dependencies. Second, the Inter-Human Relation Module considers the relation between multiple instances and focuses on capturing Inter-Human interactions. The Inter-Human Relation Module can be designed very lightweight by reducing the resolution of feature map, yet learn useful relation information to significantly boost the performance of the Intra-Human Relation Module. Even without bells and whistles, our method can compete or outperform current competition winners. We conduct extensive experiments on COCO, CrowdPose, and OCHuman datasets. The results demonstrate that the proposed model surpasses all the state-of-the-art methods. Concretely, the proposed method achieves 77.4% AP on CrowPose dataset and 67.8% AP on OCHuman dataset respectively, outperforming existing methods by a large margin. Additionally, the ablation study and visualization analysis also prove the effectiveness of our model.
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Yiwei Ding, Wenjin Deng, Yinglin Zheng, Pengfei Liu, Meihong Wang, Xuan Cheng, Jianmin Bao, Dong Chen, Ming Zeng
| null | null | 2,022 |
ijcai
|
Unsupervised Multi-Modal Medical Image Registration via Discriminator-Free Image-to-Image Translation
| null |
In clinical practice, well-aligned multi-modal images, such as Magnetic Resonance (MR) and Computed Tomography (CT), together can provide complementary information for image-guided therapies. Multi-modal image registration is essential for the accurate alignment of these multi-modal images. However, it remains a very challenging task due to complicated and unknown spatial correspondence between different modalities. In this paper, we propose a novel translation-based unsupervised deformable image registration approach to convert the multi-modal registration problem to a mono-modal one. Specifically, our approach incorporates a discriminator-free translation network to facilitate the training of the registration network and a patchwise contrastive loss to encourage the translation network to preserve object shapes. Furthermore, we propose to replace an adversarial loss, that is widely used in previous multi-modal image registration methods, with a pixel loss in order to integrate the output of translation into the target modality. This leads to an unsupervised method requiring no ground-truth deformation or pairs of aligned images for training. We evaluate four variants of our approach on the public Learn2Reg 2021 datasets. The experimental results demonstrate that the proposed architecture achieves state-of-the-art performance. Our code is available at https://github.com/heyblackC/DFMIR.
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Zekang Chen, Jia Wei, Rui Li
| null | null | 2,022 |
ijcai
|
MotionMixer: MLP-based 3D Human Body Pose Forecasting
| null |
In this work, we present MotionMixer, an efficient 3D human body pose forecasting model based solely on multi-layer perceptrons (MLPs). MotionMixer learns the spatial-temporal 3D body pose dependencies by sequentially mixing both modalities. Given a stacked sequence of 3D body poses, a spatial-MLP extracts fine-grained spatial dependencies of the body joints. The interaction of the body joints over time is then modelled by a temporal MLP. The spatial-temporal mixed features are finally aggregated and decoded to obtain the future motion. To calibrate the influence of each time step in the pose sequence, we make use of squeeze-and-excitation (SE) blocks. We evaluate our approach on Human3.6M, AMASS, and 3DPW datasets using the standard evaluation protocols. For all evaluations, we demonstrate state-of-the-art performance, while having a model with a smaller number of parameters. Our code is available at: https://github.com/MotionMLP/MotionMixer.
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Arij Bouazizi, Adrian Holzbock, Ulrich Kressel, Klaus Dietmayer, Vasileios Belagiannis
| null | null | 2,022 |
ijcai
|
MNet: Rethinking 2D/3D Networks for Anisotropic Medical Image Segmentation
| null |
The nature of thick-slice scanning causes severe inter-slice discontinuities of 3D medical images, and the vanilla 2D/3D convolutional neural networks (CNNs) fail to represent sparse inter-slice information and dense intra-slice information in a balanced way, leading to severe underfitting to inter-slice features (for vanilla 2D CNNs) and overfitting to noise from long-range slices (for vanilla 3D CNNs). In this work, a novel mesh network (MNet) is proposed to balance the spatial representation inter axes via learning. 1) Our MNet latently fuses plenty of representation processes by embedding multi-dimensional convolutions deeply into basic modules, making the selections of representation processes flexible, thus balancing representation for sparse inter-slice information and dense intra-slice information adaptively. 2) Our MNet latently fuses multi-dimensional features inside each basic module, simultaneously taking the advantages of 2D (high segmentation accuracy of the easily recognized regions in 2D view) and 3D (high smoothness of 3D organ contour) representations, thus obtaining more accurate modeling for target regions. Comprehensive experiments are performed on four public datasets (CT\&MR), the results consistently demonstrate the proposed MNet outperforms the other methods. The code and datasets are available at: https://github.com/zfdong-code/MNet
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Zhangfu Dong, Yuting He, Xiaoming Qi, Yang Chen, Huazhong Shu, Jean-Louis Coatrieux, Guanyu Yang, Shuo Li
| null | null | 2,022 |
ijcai
|
KPN-MFI: A Kernel Prediction Network with Multi-frame Interaction for Video Inverse Tone Mapping
| null |
Up to now, the image-based inverse tone mapping (iTM) models have been widely investigated, while there is little research on video-based iTM methods. It would be interesting to make use of these existing image-based models in the video iTM task. However, directly transferring the imagebased iTM models to video data without modeling spatial-temporal information remains nontrivial and challenging. Considering both the intra-frame quality and the inter-frame consistency of a video, this article presents a new video iTM method based on a kernel prediction network (KPN), which takes advantage of multi-frame interaction (MFI) module to capture temporal-spatial information for video data. Specifically, a basic encoder-decoder KPN, essentially designed for image iTM, is trained to guarantee the mapping quality within each frame. More importantly, the MFI module is incorporated to capture temporal-spatial context information and preserve the inter-frame consistency by exploiting the correction between adjacent frames. Notably, we can readily extend any existing image iTM models to video iTM ones by involving the proposed MFI module. Furthermore, we propose an inter-frame brightness consistency loss function based on the Gaussian pyramid to reduce the video temporal inconsistency. Extensive experiments demonstrate that our model outperforms state-ofthe-art image and video-based methods. The code
is available at https://github.com/caogaofeng/KPNMFI.
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Gaofeng Cao, Fei Zhou, Han Yan, Anjie Wang, Leidong Fan
| null | null | 2,022 |
ijcai
|
Uncertainty-Aware Representation Learning for Action Segmentation
| null |
In this paper, we propose an uncertainty-aware representation Learning (UARL) method for action segmentation. Most existing action segmentation methods exploit continuity information of the action period to predict frame-level labels, which ignores the temporal ambiguity of the transition region between two actions. Moreover, similar periods of different actions, e.g., the beginning of some actions, will confuse the network if they are annotated with different labels, which causes spatial ambiguity. To address this, we design the UARL to exploit the transitional expression between two action periods by uncertainty learning. Specially, we model every frame of actions with an active distribution that represents the probabilities of different actions, which captures the uncertainty of the action and exploits the tendency during the action. We evaluate our method on three popular action prediction datasets: Breakfast, Georgia Tech Egocentric Activities (GTEA), and 50Salads. The experimental results demonstrate that our method achieves the performance with state-of-the-art.
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Lei Chen, Muheng Li, Yueqi Duan, Jie Zhou, Jiwen Lu
| null | null | 2,022 |
ijcai
|
Region-Aware Metric Learning for Open World Semantic Segmentation via Meta-Channel Aggregation
| null |
As one of the most challenging and practical segmentation tasks, open-world semantic segmentation requires the model to segment the anomaly regions in the images and incrementally learn to segment out-of-distribution (OOD) objects, especially under a few-shot condition. The current state-of-the-art (SOTA) method, Deep Metric Learning Network (DMLNet), relies on pixel-level metric learning, with which the identification of similar regions having different semantics is difficult. Therefore, we propose a method called region-aware metric learning (RAML), which first separates the regions of the images and generates region-aware features for further metric learning. RAML improves the integrity of the segmented anomaly regions. Moreover, we propose a novel meta-channel aggregation (MCA) module to further separate anomaly regions, forming high-quality sub-region candidates and thereby improving the model performance for OOD objects. To evaluate the proposed RAML, we have conducted extensive experiments and ablation studies on Lost And Found and Road Anomaly datasets for anomaly segmentation and the CityScapes dataset for incremental few-shot learning. The results show that the proposed RAML achieves SOTA performance in both stages of open world segmentation. Our code and appendix are available at https://github.com/czifan/RAML.
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Hexin Dong, Zifan Chen, Mingze Yuan, Yutong Xie, Jie Zhao, Fei Yu, Bin Dong, Li Zhang
| null | null | 2,022 |
ijcai
|
Exploring Fourier Prior for Single Image Rain Removal
| null |
Deep convolutional neural networks (CNNs) have become dominant in the task of single image rain removal. Most of current CNN methods, however, suffer from the problem of overfitting on one single synthetic dataset as they neglect the intrinsic prior of the physical properties of rain streaks. To address this issue, we propose a simple but effective prior - Fourier prior to improve the generalization ability of an image rain removal model. The Fourier prior is a kind of property of rainy images. It is based on a key observation of us - replacing the Fourier amplitude of rainy images with that of clean images greatly suppresses the synthetic and real-world rain streaks. This means the amplitude contains most of the rain streak information and the phase keeps the similar structures of the background. So it is natural for single image rain removal to process the amplitude and phase information of the rainy images separately. In this paper, we develop a two-stage model where the first stage restores the amplitude of rainy images to clean rain streaks, and the second stage restores the phase information to refine fine-grained background structures. Extensive experiments on synthetic rainy data demonstrate the power of Fourier prior. Moreover, when trained on synthetic data, a robust generalization ability to real-world images can also be obtained. The code will be publicly available at https://github.com/willinglucky/ExploringFourier-Prior-for-Single-Image-Rain-Removal.
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Xin Guo, Xueyang Fu, Man Zhou, Zhen Huang, Jialun Peng, Zheng-Jun Zha
| null | null | 2,022 |
ijcai
|
ICGNet: Integration Context-based Reverse-Contour Guidance Network for Polyp Segmentation
| null |
Precise segmentation of polyps from colonoscopic images is extremely significant for the early diagnosis and treatment of colorectal cancer. However, it is still a challenging task due to: (1)the boundary between the polyp and the background is blurred makes delineation difficult; (2)the various size and shapes causes feature representation of polyps difficult. In this paper, we propose an integration context-based reverse-contour guidance network (ICGNet) to solve these challenges. The ICGNet firstly utilizes a reverse-contour guidance module to aggregate low-level edge detail information and meanwhile constraint reverse region. Then, the newly designed adaptive context module is used to adaptively extract local-global information of the current layer and complementary information of the previous layer to get larger and denser features. Lastly, an innovative hybrid pyramid pooling fusion module fuses the multi-level features generated from the decoder in the case of considering salient features and less background. Our proposed approach is evaluated on the EndoScene, Kvasir-SEG and CVC-ColonDB datasets with eight evaluation metrics, and gives competitive results compared with other state-of-the-art methods in both learning ability and generalization capability.
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Xiuquan Du, Xuebin Xu, Kunpeng Ma
| null | null | 2,022 |
ijcai
|
Learning Coated Adversarial Camouflages for Object Detectors
| null |
An adversary can fool deep neural network object detectors by generating adversarial noises. Most of the existing works focus on learning local visible noises in an adversarial "patch" fashion. However, the 2D patch attached to a 3D object tends to suffer from an inevitable reduction in attack performance as the viewpoint changes. To remedy this issue, this work proposes the Coated Adversarial Camouflage (CAC) to attack the detectors in arbitrary viewpoints. Unlike the patch trained in the 2D space, our camouflage generated by a conceptually different training framework consists of 3D rendering and dense proposals attack. Specifically, we make the camouflage perform 3D spatial transformations according to the pose changes of the object. Based on the multi-view rendering results, the top-n proposals of the region proposal network are fixed, and all the classifications in the fixed dense proposals are attacked simultaneously to output errors. In addition, we build a virtual 3D scene to fairly and reproducibly evaluate different attacks. Extensive experiments demonstrate the superiority of CAC over the existing attacks, and it shows impressive performance both in the virtual scene and the real world. This poses a potential threat to the security-critical computer vision systems.
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Yexin Duan, Jialin Chen, Xingyu Zhou, Junhua Zou, Zhengyun He, Jin Zhang, Wu Zhang, Zhisong Pan
| null | null | 2,022 |
ijcai
|
Region-Aware Temporal Inconsistency Learning for DeepFake Video Detection
| null |
The rapid development of face forgery techniques has drawn growing attention due to security concerns. Existing deepfake video detection methods always attempt to capture the discriminative features by directly exploiting static temporal convolution to mine temporal inconsistency, without explicit exploration on the diverse temporal dynamics of different forged regions. To effectively and comprehensively capture the various inconsistency, in this paper, we propose a novel Region-Aware Temporal Filter (RATF) module which automatically generates corresponding temporal filters for different spatial regions. Specifically, we decouple the dynamic temporal kernel into a set of region-agnostic basic filters and region-sensitive aggregation weights. And different weights guide the corresponding regions to adaptively learn temporal inconsistency, which greatly enhances the overall representational ability. Moreover, to cover the long-term temporal dynamics, we divide the video into multiple snippets and propose a Cross-Snippet Attention (CSA) to promote the cross-snippet information interaction. Extensive experiments and visualizations on several benchmarks demonstrate the effectiveness of our method against state-of-the-art competitors.
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Zhihao Gu, Taiping Yao, Yang Chen, Ran Yi, Shouhong Ding, Lizhuang Ma
| null | null | 2,022 |
ijcai
|
D-DPCC: Deep Dynamic Point Cloud Compression via 3D Motion Prediction
| null |
The non-uniformly distributed nature of the 3D Dynamic Point Cloud (DPC) brings significant challenges to its high-efficient inter-frame compression. This paper proposes a novel 3D sparse convolution-based Deep Dynamic Point Cloud Compression (D-DPCC) network to compensate and compress the DPC geometry with 3D motion estimation and motion compensation in the feature space. In the proposed D-DPCC network, we design a Multi-scale Motion Fusion (MMF) module to accurately estimate the 3D optical flow between the feature representations of adjacent point cloud frames. Specifically, we utilize a 3D sparse convolution-based encoder to obtain the latent representation for motion estimation in the feature space and introduce the proposed MMF module for fused 3D motion embedding. Besides, for motion compensation, we propose a 3D Adaptively Weighted Interpolation (3DAWI) algorithm with a penalty coefficient to adaptively decrease the impact of distant neighbours. We compress the motion embedding and the residual with a lossy autoencoder-based network. To our knowledge, this paper is the first work proposing an end-to-end deep dynamic point cloud compression framework. The experimental result shows that the proposed D-DPCC framework achieves an average 76% BD-Rate (Bjontegaard Delta Rate) gains against state-of-the-art Video-based Point Cloud Compression (V-PCC) v13 in inter mode.
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Tingyu Fan, Linyao Gao, Yiling Xu, Zhu Li, Dong Wang
| null | null | 2,022 |
ijcai
|
Lightweight Bimodal Network for Single-Image Super-Resolution via Symmetric CNN and Recursive Transformer
| null |
Single-image super-resolution (SISR) has achieved significant breakthroughs with the development of deep learning. However, these methods are difficult to be applied in real-world scenarios since they are inevitably accompanied by the problems of computational and memory costs caused by the complex operations. To solve this issue, we propose a Lightweight Bimodal Network (LBNet) for SISR. Specifically, an effective Symmetric CNN is designed for local feature extraction and coarse image reconstruction. Meanwhile, we propose a Recursive Transformer to fully learn the long-term dependence of images thus the global information can be fully used to further refine texture details. Studies show that the hybrid of CNN and Transformer can build a more efficient model. Extensive experiments have proved that our LBNet achieves more prominent performance than other state-of-the-art methods with a relatively low computational cost and memory consumption. The code is available at https://github.com/IVIPLab/LBNet.
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Guangwei Gao, Zhengxue Wang, Juncheng Li, Wenjie Li, Yi Yu, Tieyong Zeng
| null | null | 2,022 |
ijcai
|
SparseTT: Visual Tracking with Sparse Transformers
| null |
Transformers have been successfully applied to the visual tracking task and significantly promote tracking performance. The self-attention mechanism designed to model long-range dependencies is the key to the success of Transformers. However, self-attention lacks focusing on the most relevant information in the search regions, making it easy to be distracted by background. In this paper, we relieve this issue with a sparse attention mechanism by focusing the most relevant information in the search regions, which enables a much accurate tracking. Furthermore, we introduce a double-head predictor to boost the accuracy of foreground-background classification and regression of target bounding boxes, which further improve the tracking performance. Extensive experiments show that, without bells and whistles, our method significantly outperforms the state-of-the-art approaches on LaSOT, GOT-10k, TrackingNet, and UAV123, while running at 40 FPS. Notably, the training time of our method is reduced by 75% compared to that of TransT. The source code and models are available at https://github.com/fzh0917/SparseTT.
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Zhihong Fu, Zehua Fu, Qingjie Liu, Wenrui Cai, Yunhong Wang
| null | null | 2,022 |
ijcai
|
Self-supervised Semantic Segmentation Grounded in Visual Concepts
| null |
Unsupervised semantic segmentation requires assigning a label to every pixel without any human annotations. Despite recent advances in self-supervised representation learning for individual images, unsupervised semantic segmentation with pixel-level representations is still a challenging task and remains underexplored. In this work, we propose a self-supervised pixel representation learning method for semantic segmentation by using visual concepts (i.e., groups of pixels with semantic meanings, such as parts, objects, and scenes) extracted from images. To guide self-supervised learning, we leverage three types of relationships between pixels and concepts, including the relationships between pixels and local concepts, local and global concepts, as well as the co-occurrence of concepts. We evaluate the learned pixel embeddings and visual concepts on three datasets, including PASCAL VOC 2012, COCO 2017, and DAVIS 2017. Our results show that the proposed method gains consistent and substantial improvements over recent unsupervised semantic segmentation approaches, and also demonstrate that visual concepts can reveal insights into image datasets.
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Wenbin He, William Surmeier, Arvind Kumar Shekar, Liang Gou, Liu Ren
| null | null | 2,022 |
ijcai
|
Rethinking Image Aesthetics Assessment: Models, Datasets and Benchmarks
| null |
Challenges in image aesthetics assessment (IAA) arise from that images of different themes correspond to different evaluation criteria, and learning aesthetics directly from images while ignoring the impact of theme variations on human visual perception inhibits the further development of IAA; however, existing IAA datasets and models overlook this problem. To address this issue, we show that a theme-oriented dataset and model design are effective for IAA. Specifically, 1) we elaborately build a novel dataset, called TAD66K, that contains 66K images covering 47 popular themes, and each image is densely annotated by more than 1200 people with dedicated theme evaluation criteria. 2) We develop a baseline model, TANet, which can effectively extract theme information and adaptively establish perception rules to evaluate images with different themes. 3) We develop a large-scale benchmark (the most comprehensive thus far) by comparing 17 methods with TANet on three representative datasets: AVA, FLICKR-AES and the proposed TAD66K, TANet achieves state-of-the-art performance on all three datasets. Our work offers the community an opportunity to explore more challenging directions; the code, dataset and supplementary material are available at https://github.com/woshidandan/TANet.
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Shuai He, Yongchang Zhang, Rui Xie, Dongxiang Jiang, Anlong Ming
| null | null | 2,022 |
ijcai
|
DANet: Image Deraining via Dynamic Association Learning
| null |
Rain streaks and background components in a rainy input are highly correlated, making the deraining task a composition of the rain streak removal and background restoration. However, the correlation of these two components is barely considered, leading to unsatisfied deraining results. To this end, we propose a dynamic associated network (DANet) to achieve the association learning between rain streak removal and background recovery. There are two key aspects to fulfill the association learning: 1) DANet unveils the latent association knowledge between rain streak prediction and background texture recovery, and leverages it as an extra prior via an associated learning module (ALM) to promote the texture recovery. 2) DANet introduces the parametric association constraint for enhancing the compatibility of deraining model with background reconstruction, enabling it to be automatically learned from the training data. Moreover, we observe that the sampled rainy image enjoys the similar distribution to the original one. We thus propose to learn the rain distribution at the sampling space, and exploit super-resolution to reconstruct high-frequency background details for computation and memory reduction. Our proposed DANet achieves the approximate deraining performance to the state-of-the-art MPRNet but only requires 52.6\% and 23\% inference time and computational cost, respectively.
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Kui Jiang, Zhongyuan Wang, Zheng Wang, Peng Yi, Junjun Jiang, Jinsheng Xiao, Chia-Wen Lin
| null | null | 2,022 |
ijcai
|
SVTR: Scene Text Recognition with a Single Visual Model
| null |
Dominant scene text recognition models commonly contain two building blocks, a visual model for feature extraction and a sequence model for text transcription. This hybrid architecture, although accurate, is complex and less efficient. In this study, we propose a Single Visual model for Scene Text recognition within the patch-wise image tokenization framework, which dispenses with the sequential modeling entirely. The method, termed SVTR, firstly decomposes an image text into small patches named character components. Afterward, hierarchical stages are recurrently carried out by component-level mixing, merging and/or combining. Global and local mixing blocks are devised to perceive the inter-character and intra-character patterns, leading to a multi-grained character component perception. Thus, characters are recognized by a simple linear prediction. Experimental results on both English and Chinese scene text recognition tasks demonstrate the effectiveness of SVTR. SVTR-L (Large) achieves highly competitive accuracy in English and outperforms existing methods by a large margin in Chinese, while running faster. In addition, SVTR-T (Tiny) is an effective and much smaller model, which shows appealing speed at inference. The code is publicly available at https://github.com/PaddlePaddle/PaddleOCR.
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Yongkun Du, Zhineng Chen, Caiyan Jia, Xiaoting Yin, Tianlun Zheng, Chenxia Li, Yuning Du, Yu-Gang Jiang
| null | null | 2,022 |
ijcai
|
Learning Target-aware Representation for Visual Tracking via Informative Interactions
| null |
We introduce a novel backbone architecture to improve target-perception ability of feature representation for tracking. Having observed de facto frameworks perform feature matching simply using the backbone outputs for target localization, there is no direct feedback from the matching module to the backbone network, especially the shallow layers. Concretely, only the matching module can directly access the target information, while the representation learning of candidate frame is blind to the reference target. Therefore, the accumulated target-irrelevant interference in shallow stages may degrade the feature quality of deeper layers. In this paper, we approach the problem by conducting multiple branch-wise interactions inside the Siamese-like backbone networks (InBN). The core of InBN is a general interaction modeler (GIM) that injects the target information to different stages of the backbone network, leading to better target-perception of candidate feature representation with negligible computation cost. The proposed GIM module and InBN mechanism are general and applicable to different backbone types including CNN and Transformer for improvements, as evidenced on multiple benchmarks. In particular, the CNN version improves the baseline with 3.2/6.9 absolute gains of SUC on LaSOT/TNL2K. The Transformer version obtains SUC of 65.7/52.0 on LaSOT/TNL2K, which are on par with recent SOTAs.
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Mingzhe Guo, Zhipeng Zhang, Heng Fan, Liping Jing, Yilin Lyu, Bing Li, Weiming Hu
| null | null | 2,022 |
ijcai
|
AQT: Adversarial Query Transformers for Domain Adaptive Object Detection
| null |
Adversarial feature alignment is widely used in domain adaptive object detection. Despite the effectiveness on CNN-based detectors, its applicability to transformer-based detectors is less studied. In this paper, we present AQT (adversarial query transformers) to integrate adversarial feature alignment into detection transformers. The generator is a detection transformer which yields a sequence of feature tokens, and the discriminator consists of a novel adversarial token and a stack of cross-attention layers. The cross-attention layers take the adversarial token as the query and the feature tokens from the generator as the key-value pairs. Through adversarial learning, the adversarial token in the discriminator attends to the domain-specific feature tokens, while the generator produces domain-invariant features, especially on the attended tokens, hence realizing adversarial feature alignment on transformers. Thorough experiments over several domain adaptive object detection benchmarks demonstrate that our approach performs favorably against the state-of-the-art methods. Source code is available at https://github.com/weii41392/AQT.
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Wei-Jie Huang, Yu-Lin Lu, Shih-Yao Lin, Yusheng Xie, Yen-Yu Lin
| null | null | 2,022 |
ijcai
|
Online Hybrid Lightweight Representations Learning: Its Application to Visual Tracking
| null |
This paper presents a novel hybrid representation learning framework for streaming data, where an image frame in a video is modeled by an ensemble
of two distinct deep neural networks; one is a low-bit quantized network and the other is a lightweight full-precision network. The former learns coarse
primary information with low cost while the latter conveys residual information for high fidelity to original representations. The proposed parallel architecture is effective to maintain complementary information since fixed-point arithmetic can be utilized in the quantized network and the lightweight
model provides precise representations given by a compact channel-pruned network. We incorporate the hybrid representation technique into an online
visual tracking task, where deep neural networks need to handle temporal variations of target appearances in real-time. Compared to the state-of-the-art
real-time trackers based on conventional deep neural networks, our tracking algorithm demonstrates competitive accuracy on the standard benchmarks
with a small fraction of computational cost and memory footprint.
|
Ilchae Jung, Minji Kim, Eunhyeok Park, Bohyung Han
| null | null | 2,022 |
ijcai
|
ScaleFormer: Revisiting the Transformer-based Backbones from a Scale-wise
Perspective for Medical Image Segmentation
| null |
Recently, a variety of vision transformers have been developed as their capability of modeling long-range dependency. In current transformer-based backbones for medical image segmentation, convolutional layers were replaced with pure transformers, or transformers were added to the deepest encoder to learn global context. However, there are mainly two challenges in a scale-wise perspective: (1) intra-scale problem: the existing methods lacked in extracting local-global cues in each scale, which may impact the signal propagation of small objects; (2) inter-scale problem: the existing methods failed to explore distinctive information from multiple scales, which may hinder the representation learning from objects with widely variable size, shape and location. To address these limitations, we propose a novel backbone, namely ScaleFormer, with two appealing designs: (1) A scale-wise intra-scale transformer is designed to couple the CNN-based local features with the transformer-based global cues in each scale, where the row-wise and column-wise global dependencies can be extracted by a lightweight Dual-Axis MSA. (2) A simple and effective spatial-aware inter-scale transformer is designed to interact among consensual regions in multiple scales, which can highlight the cross-scale dependency and resolve the complex scale variations. Experimental results on different benchmarks demonstrate that our Scale-Former outperforms the current state-of-the-art methods. The code is publicly available at: https://github.com/ZJUGiveLab/ScaleFormer.
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Huimin Huang, Shiao Xie, Lanfen Lin, Yutaro Iwamoto, Xian-Hua Han, Yen-Wei Chen, Ruofeng Tong
| null | null | 2,022 |
ijcai
|
Semantic Compression Embedding for Generative Zero-Shot Learning
| null |
Generative methods have been successfully applied in zero-shot learning (ZSL) by learning an implicit mapping to alleviate the visual-semantic domain gaps and synthesizing unseen samples to handle the data imbalance between seen and unseen classes. However, existing generative methods simply use visual features extracted by the pre-trained CNN backbone. These visual features lack attribute-level semantic information. Consequently, seen classes are indistinguishable, and the knowledge transfer from seen to unseen classes is limited. To tackle this issue, we propose a novel Semantic Compression Embedding Guided Generation (SC-EGG) model, which cascades a semantic compression embedding network (SCEN) and an embedding guided generative network (EGGN). The SCEN extracts a group of attribute-level local features for each sample and further compresses them into the new low-dimension visual feature. Thus, a dense-semantic visual space is obtained. The EGGN learns a mapping from the class-level semantic space to the dense-semantic visual space, thus improving the discriminability of the synthesized dense-semantic unseen visual features. Extensive experiments on three benchmark datasets, i.e., CUB, SUN and AWA2, demonstrate the significant performance gains of SC-EGG over current state-of-the-art methods and its baselines.
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Ziming Hong, Shiming Chen, Guo-Sen Xie, Wenhan Yang, Jian Zhao, Yuanjie Shao, Qinmu Peng, Xinge You
| null | null | 2,022 |
ijcai
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SatFormer: Saliency-Guided Abnormality-Aware Transformer for Retinal Disease Classification in Fundus Image
| null |
Automatic and accurate retinal disease diagnosis is critical to guide proper therapy and prevent potential vision loss. Previous works simply exploit the most discriminative features while ignoring the pathological visual clues of scattered subtle lesions. Therefore, without a comprehensive understanding of features from different lesion regions, they are vulnerable to noise from complex backgrounds and suffer from misclassification failures. In this paper, we address these limitations with a novel saliency-guided abnormality-aware transformer which explicitly captures the correlation between different lesion features from a global perspective with enhanced pathological semantics. The model has several merits. First, we propose a saliency enhancement module (SEM) which adaptively integrates disease related semantics and highlights potentially salient lesion regions. Second, to the best of our knowledge, this is the first work to explore comprehensive lesion feature dependencies via a tailored efficient self-attention. Third, with the saliency enhancement module and abnormality-aware attention, we propose a new variant of Vision Transformer models, called SatFormer, which outperforms the state-of-the-art methods on two public retinal disease classification benchmarks. Ablation study shows that the proposed components can be easily embedded into any Vision Transformers via a plug-and-play manner and effectively boost the performance.
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Yankai Jiang, Ke Xu, Xinyue Wang, Yuan Li, Hongguang Cui, Yubo Tao, Hai Lin
| null | null | 2,022 |
ijcai
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Attention-guided Contrastive Hashing for Long-tailed Image Retrieval
| null |
Image hashing is to represent an image using a binary code for efficient storage and accurate retrieval. Recently, deep hashing methods have shown great improvements on ideally balanced datasets, however, long-tailed data is more common due to rare samples or data collection costs in the real world. Toward that end, this paper introduces a simple yet effective model named Attention-guided Contrastive Hashing Network (ACHNet) for long-tailed hashing. Specifically, a cross attention feature enhancement module is proposed to predict the importance of features for hashing, alleviating the loss of information originated from data dimension reduction. Moreover, unlike recently sota contrastive methods that focus on instance-level discrimination, we optimize an innovative category-centered contrastive hashing to obtain discriminative results, which is more suitable for long-tailed scenarios. Experiments on two popular benchmarks verify the superiority of the proposed method. Our code is available at: https://github.com/KUXN98/ACHNet.
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Xuan Kou, Chenghao Xu, Xu Yang, Cheng Deng
| null | null | 2,022 |
ijcai
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Domain Generalization through the Lens of Angular Invariance
| null |
Domain generalization (DG) aims at generalizing a classifier trained on multiple source domains to an unseen target domain with domain shift. A common pervasive theme in existing DG literature is domain-invariant representation learning with various invariance assumptions. However, prior works restrict themselves to an impractical assumption for real-world challenges: If a mapping induced by a deep neural network (DNN) could align the source domains well, then such a mapping aligns a target domain as well. In this paper, we simply take DNNs as feature extractors to relax the requirement of distribution alignment. Specifically, we put forward a novel angular invariance and the accompanied norm shift assumption. Based on the proposed term of invariance, we propose a novel deep DG method dubbed Angular Invariance Domain Generalization Network (AIDGN). The optimization objective of AIDGN is developed with a von-Mises Fisher (vMF) mixture model. Extensive experiments on multiple DG benchmark datasets validate the effectiveness of the proposed AIDGN method.
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Yujie Jin, Xu Chu, Yasha Wang, Wenwu Zhu
| null | null | 2,022 |
ijcai
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What is Right for Me is Not Yet Right for You: A Dataset for Grounding Relative Directions via Multi-Task Learning
| null |
Understanding spatial relations is essential for intelligent agents to act and communicate in the physical world. Relative directions are spatial relations that describe the relative positions of target objects with regard to the intrinsic orientation of reference objects. Grounding relative directions is more difficult than grounding absolute directions because it not only requires a model to detect objects in the image and to identify spatial relation based on this information, but it also needs to recognize the orientation of objects and integrate this information into the reasoning process. We investigate the challenging problem of grounding relative directions with end-to-end neural networks. To this end, we provide GRiD-3D, a novel dataset that features relative directions and complements existing visual question answering (VQA) datasets, such as CLEVR, that involve only absolute directions. We also provide baselines for the dataset with two established end-to-end VQA models. Experimental evaluations show that answering questions on relative directions is feasible when questions in the dataset simulate the necessary subtasks for grounding relative directions. We discover that those subtasks are learned in an order that reflects the steps of an intuitive pipeline for processing relative directions.
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Jae Hee Lee, Matthias Kerzel, Kyra Ahrens, Cornelius Weber, Stefan Wermter
| null | null | 2,022 |
ijcai
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Robustifying Vision Transformer without Retraining from Scratch by Test-Time Class-Conditional Feature Alignment
| null |
Vision Transformer (ViT) is becoming more popular in image processing. Specifically, we investigate the effectiveness of test-time adaptation (TTA) on ViT, a technique that has emerged to correct its prediction during test-time by itself. First, we benchmark various test-time adaptation approaches on ViT-B16 and ViT-L16. It is shown that the TTA is effective on ViT and the prior-convention (sensibly selecting modulation parameters) is not necessary when using proper loss function. Based on the observation, we propose a new test-time adaptation method called class-conditional feature alignment (CFA), which minimizes both the class-conditional distribution differences and the whole distribution differences of the hidden representation between the source and target in an online manner. Experiments of image classification tasks on common corruption (CIFAR-10-C, CIFAR-100-C, and ImageNet-C) and domain adaptation (digits datasets and ImageNet-Sketch) show that CFA stably outperforms the existing baselines on various datasets. We also verify that CFA is model agnostic by experimenting on ResNet, MLP-Mixer, and several ViT variants (ViT-AugReg, DeiT, and BeiT). Using BeiT backbone, CFA achieves 19.8% top-1 error rate on ImageNet-C, outperforming the existing test-time adaptation baseline 44.0%. This is a state-of-the-art result among TTA methods that do not need to alter training phase.
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Takeshi Kojima, Yutaka Matsuo, Yusuke Iwasawa
| null | null | 2,022 |
ijcai
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Learning to Assemble Geometric Shapes
| null |
Assembling parts into an object is a combinatorial problem that arises in a variety of contexts in the real world and involves numerous applications in science and engineering. Previous related work tackles limited cases with identical unit parts or jigsaw-style parts of textured shapes, which greatly mitigate combinatorial challenges of the problem. In this work, we introduce the more challenging problem of shape assembly, which involves textureless fragments of arbitrary shapes with indistinctive junctions, and then propose a learning-based approach to solving it. We demonstrate the effectiveness on shape assembly tasks with various scenarios, including the ones with abnormal fragments (e.g., missing and distorted), the different number of fragments, and different rotation discretization.
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Jinhwi Lee, Jungtaek Kim, Hyunsoo Chung, Jaesik Park, Minsu Cho
| null | null | 2,022 |
ijcai
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Iterative Geometry-Aware Cross Guidance Network for Stereo Image Inpainting
| null |
Currently, single image inpainting has achieved promising results based on deep convolutional neural networks. However, inpainting on stereo images with missing regions has not been explored thoroughly, which is also a significant but different problem. One crucial requirement for stereo image inpainting is stereo consistency. To achieve it, we propose an Iterative Geometry-Aware Cross Guidance Network (IGGNet). The IGGNet contains two key ingredients, i.e., a Geometry-Aware Attention(GAA) module and an Iterative Cross Guidance(ICG) strategy. The GAA module relies on the epipolar geometry cues and learns the geometry-aware guidance from one view to another, which is beneficial to make the corresponding regions in two views consistent. However, learning guidance from co-existing missing regions is challenging. To address this issue, the ICG strategy is proposed, which can alternately narrow down the missing regions of the two views in an iterative manner. Experimental results demonstrate that our proposed network outperforms the latest stereo image inpainting model and state-of-the-art single image inpainting models.
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Ang Li, Shanshan Zhao, Zhang Qingjie, Qiuhong Ke
| null | null | 2,022 |
ijcai
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Beyond the Prototype: Divide-and-conquer Proxies for Few-shot Segmentation
| null |
Few-shot segmentation, which aims to segment unseen-class objects given only a handful of densely labeled samples, has received widespread attention from the community. Existing approaches typically follow the prototype learning paradigm to perform meta-inference, which fails to fully exploit the underlying information from support image-mask pairs, resulting in various segmentation failures, e.g., incomplete objects, ambiguous boundaries, and distractor activation. To this end, we propose a simple yet versatile framework in the spirit of divide-and-conquer. Specifically, a novel self-reasoning scheme is first implemented on the annotated support image, and then the coarse segmentation mask is divided into multiple regions with different properties. Leveraging effective masked average pooling operations, a series of support-induced proxies are thus derived, each playing a specific role in conquering the above challenges. Moreover, we devise a unique parallel decoder structure that integrates proxies with similar attributes to boost the discrimination power. Our proposed approach, named divide-and-conquer proxies (DCP), allows for the development of appropriate and reliable information as a guide at the “episode” level, not just about the object cues themselves. Extensive experiments on PASCAL-5i and COCO-20i demonstrate the superiority of DCP over conventional prototype-based approaches (up to 5~10% on average), which also establishes a new state-of-the-art. Code is available at github.com/chunbolang/DCP.
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Chunbo Lang, Binfei Tu, Gong Cheng, Junwei Han
| null | null | 2,022 |
ijcai
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MMNet: Muscle Motion-Guided Network for Micro-Expression Recognition
| null |
Facial micro-expressions (MEs) are involuntary facial motions revealing people’s real feelings and play an important role in the early intervention of mental illness, the national security, and many human-computer interaction systems. However, existing micro-expression datasets are limited and usually pose some challenges for training good classifiers. To model the subtle facial muscle motions, we propose a robust micro-expression recognition (MER) framework, namely muscle motion-guided network (MMNet). Specifically, a continuous attention (CA) block is introduced to focus on modeling local subtle muscle motion patterns with little identity information, which is different from most previous methods that directly extract features from complete video frames with much identity information. Besides, we design a position calibration (PC) module based on the vision transformer. By adding the position embeddings of the face generated by the PC module at the end of the two branches, the PC module can help to add position information to facial muscle motion-pattern features for the MER. Extensive experiments on three public micro-expression datasets demonstrate that our approach outperforms state-of-the-art methods by a large margin. Code is available at https://github.com/muse1998/MMNet.
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Hanting Li, Mingzhe Sui, Zhaoqing Zhu, Feng Zhao
| null | null | 2,022 |
ijcai
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PlaceNet: Neural Spatial Representation Learning with Multimodal Attention
| null |
Spatial representation capable of learning a myriad of environmental features is a significant challenge for natural spatial understanding of mobile AI agents.
Deep generative models have the potential of discovering rich representations of observed 3D scenes.
However, previous approaches have been mainly evaluated on simple environments, or focused only on high-resolution rendering of small-scale scenes, hampering generalization of the representations to various spatial variability.
To address this, we present PlaceNet, a neural representation that learns through random observations in a self-supervised manner, and represents observed scenes with triplet attention using visual, topographic, and semantic cues.
We evaluate the proposed method on a large-scale multimodal scene dataset consisting of 120 million indoor scenes, and show that PlaceNet successfully generalizes to various environments with lower training loss, higher image quality and structural similarity of predicted scenes, compared to a competitive baseline model.
Additionally, analyses of the representations demonstrate that PlaceNet activates more specialized and larger numbers of kernels in the spatial representation, capturing multimodal spatial properties in complex environments.
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Chung-Yeon Lee, Youngjae Yoo, Byoung-Tak Zhang
| null | null | 2,022 |
ijcai
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Self-Guided Hard Negative Generation for Unsupervised Person Re-Identification
| null |
Recent unsupervised person re-identification (reID) methods mostly apply pseudo labels from clustering algorithms as supervision signals. Despite great success, this fashion is very likely to aggregate different identities with similar appearances into the same cluster. In result, the hard negative samples, playing important role in training reID models, are significantly reduced. To alleviate this problem, we propose a self-guided hard negative generation method for unsupervised person re-ID. Specifically, a joint framework is developed which incorporates a hard negative generation network (HNGN) and a re-ID network. To continuously generate harder negative samples to provide effective supervisions in the contrastive learning, the two networks are alternately trained in an adversarial manner to improve each other, where the reID network guides HNGN to generate challenging data and HNGN enforces the re-ID network to enhance discrimination ability. During inference, the performance of re-ID network is improved without introducing any extra parameters. Extensive experiments demonstrate that the proposed method significantly outperforms a strong baseline and also achieves better results than state-of-the-art methods.
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Dongdong Li, Zhigang Wang, Jian Wang, Xinyu Zhang, Errui Ding, Jingdong Wang, Zhaoxiang Zhang
| null | null | 2,022 |
ijcai
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Learning Graph-based Residual Aggregation Network for Group Activity Recognition
| null |
Group activity recognition aims to understand the overall behavior performed by a group of people. Recently, some graph-based methods have made progress by learning the relation graphs among multiple persons. However, the differences between an individual and others play an important role in identifying confusable group activities, which have not been elaborately explored by previous methods. In this paper, a novel Graph-based Residual AggregatIon Network (GRAIN) is proposed to model the differences among all persons of the whole group, which is end-to-end trainable. Specifically, a new local residual relation module is explicitly proposed to capture the local spatiotemporal differences of relevant persons, which is further combined with the multi-graph relation networks. Moreover, a weighted aggregation strategy is devised to adaptively select multi-level spatiotemporal features from the appearance-level information to high level relations. Finally, our model is capable of extracting a comprehensive representation and inferring the group activity in an end-to-end manner. The experimental results on two popular benchmarks for group activity recognition clearly demonstrate the superior performance of our method in comparison with the state-of-the-art methods.
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Wei Li, Tianzhao Yang, Xiao Wu, Zhaoquan Yuan
| null | null | 2,022 |
ijcai
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Representation Learning for Compressed Video Action Recognition via Attentive Cross-modal Interaction with Motion Enhancement
| null |
Compressed video action recognition has recently drawn growing attention, since it remarkably reduces the storage and computational cost via replacing raw videos by sparsely sampled RGB frames and compressed motion cues (e.g., motion vectors and residuals). However, this task severely suffers from the coarse and noisy dynamics and the insufficient fusion of the heterogeneous RGB and motion modalities. To address the two issues above, this paper proposes a novel framework, namely Attentive Cross-modal Interaction Network with Motion Enhancement (MEACI-Net). It follows the two-stream architecture, i.e. one for the RGB modality and the other for the motion modality. Particularly, the motion stream employs a multi-scale block embedded with a denoising module to enhance representation learning. The interaction between the two streams is then strengthened by introducing the Selective Motion Complement (SMC) and Cross-Modality Augment (CMA) modules, where SMC complements the RGB modality with spatio-temporally attentive local motion features and CMA further combines the two modalities with selective feature augmentation. Extensive experiments on the UCF-101, HMDB-51 and Kinetics-400 benchmarks demonstrate the effectiveness and efficiency of MEACI-Net.
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Bing Li, Jiaxin Chen, Dongming Zhang, Xiuguo Bao, Di Huang
| null | null | 2,022 |
ijcai
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RePFormer: Refinement Pyramid Transformer for Robust Facial Landmark Detection
| null |
This paper presents a Refinement Pyramid Transformer (RePFormer) for robust facial landmark detection. Most facial landmark detectors focus on learning representative image features. However, these CNN-based feature representations are not robust enough to handle complex real-world scenarios due to ignoring the internal structure of landmarks, as well as the relations between landmarks and context. In this work, we formulate the facial landmark detection task as refining landmark queries along pyramid memories. Specifically, a pyramid transformer head (PTH) is introduced to build both homologous relations among landmarks and heterologous relations between landmarks and cross-scale contexts. Besides, a dynamic landmark refinement (DLR) module is designed to decompose the landmark regression into an end-to-end refinement procedure, where the dynamically aggregated queries are transformed to residual coordinates predictions. Extensive experimental results on four facial landmark detection benchmarks and their various subsets demonstrate the superior performance and high robustness of our framework.
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Jinpeng Li, Haibo Jin, Shengcai Liao, Ling Shao, Pheng-Ann Heng
| null | null | 2,022 |
ijcai
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ER-SAN: Enhanced-Adaptive Relation Self-Attention Network for Image Captioning
| null |
Image captioning (IC), bringing vision to language, has drawn extensive attention. Precisely describing visual relations between image objects is a key challenge in IC. We argue that the visual relations, that is geometric positions (i.e., distance and size) and semantic interactions (i.e., actions and possessives), indicate the mutual correlations between objects. Existing Transformer-based methods typically resort to geometric positions to enhance the representation of visual relations, yet only using the shallow geometric is unable to precisely cover the complex and actional correlations. In this paper, we propose to enhance the correlations between objects from a comprehensive view that jointly considers explicit semantic and geometric relations, generating plausible captions with accurate relationship predictions. Specifically, we propose a novel Enhanced-Adaptive Relation Self-Attention Network (ER-SAN). We design the direction-sensitive semantic-enhanced attention, which considers content objects to semantic relations and semantic relations to content objects attention to learn explicit semantic-aware relations. Further, we devise an adaptive re-weight relation module that determines how much semantic and geometric attention should be activated to each relation feature. Extensive experiments on MS-COCO dataset demonstrate the effectiveness of our ER-SAN, with improvements of CIDEr from 128.6% to 135.3%, achieving state-of-the-art performance. Codes will be released \url{https://github.com/CrossmodalGroup/ER-SAN}.
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Jingyu Li, Zhendong Mao, Shancheng Fang, Hao Li
| null | null | 2,022 |
ijcai
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Dite-HRNet: Dynamic Lightweight High-Resolution Network for Human Pose Estimation
| null |
A high-resolution network exhibits remarkable capability in extracting multi-scale features for human pose estimation, but fails to capture long-range interactions between joints and has high computational complexity. To address these problems, we present a Dynamic lightweight High-Resolution Network (Dite-HRNet), which can efficiently extract multi-scale contextual information and model long-range spatial dependency for human pose estimation. Specifically, we propose two methods, dynamic split convolution and adaptive context modeling, and embed them into two novel lightweight blocks, which are named dynamic multi-scale context block and dynamic global context block. These two blocks, as the basic component units of our Dite-HRNet, are specially designed for the high-resolution networks to make full use of the parallel multi-resolution architecture. Experimental results show that the proposed network achieves superior performance on both COCO and MPII human pose estimation datasets, surpassing the state-of-the-art lightweight networks. Code is available at: https://github.com/ZiyiZhang27/Dite-HRNet.
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Qun Li, Ziyi Zhang, Fu Xiao, Feng Zhang, Bir Bhanu
| null | null | 2,022 |
ijcai
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