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As a sub-task of visual grounding, linking people across text and images aims to localize target people in images with corresponding sentences. Existing approaches tend to capture superficial features of people (e.g., dress and location) that suffer from the incompleteness information across text and images. We observe that humans are adept at exploring social relations to assist identifying people. Therefore, we propose a Social Relation Reasoning (SRR) approach to address
the aforementioned issues. Firstly, we design a Social Relation Extraction (SRE) module to extract social relations between people in the input sentences. Specially, SRE module is based on zero-shot learning that is able to extract social relations even though they are not defined in the existing datasets. Reasoning based Cross-modal Matching (RCM) module is further used to generate matching matrices by reasoning on the social relations and visual features. Experimental results show that the accuracy of our proposed SRR approach outperforms the state-of-the-art models on the challenging datasets Who’s Waldo and FL: MSRE, by more than 5% and 7%, respectively. Our code will be available.

AAAI 2023

Linking People Across Text and Images Based on Social Relation Reasoning

linking people across text and images based on social relation reasoning

visual grounding

zero-shot learning

The purpose of the AAAI conference is to promote research in artificial intelligence (AI) and scientific exchange among AI researchers, practitioners, scientists, and engineers in affiliated disciplines. 

AAAI 2021

poster

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
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The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

Offline reinforcement learning (RL) aims to learn policy from the passively collected offline dataset. Applying existing RL methods on the static dataset straightforwardly will raise distribution shift, causing these unconstrained RL methods to fail. To cope with the distribution shift problem, a common practice in offline RL is to constrain the policy explicitly or implicitly close to behavioral policy. However, the available dataset usually contains sub-optimal or inferior actions, constraining the policy near all these actions will make the policy inevitably learn inferior behaviors, limiting the performance of the algorithm. Based on this observation, we propose a weighted policy constraints (wPC) method that only constrains the learned policy to desirable behaviors, making room for policy improvement on other parts. Our algorithm outperforms existing state-of-the-art offline RL algorithms on the D4RL offline gym datasets. Moreover, the proposed algorithm is simple to implement with few hyper-parameters, making the proposed wPC algorithm a robust offline RL method with low computational complexity.

Weighted Policy Constraints for Offline Reinforcement Learning

Multi-label classification (MLC), which assigns multiple labels to each instance, is crucial to domains from computer vision to text mining. Conventional methods for MLC require huge amounts of labeled data to capture complex dependencies between labels. However, such labeled datasets are expensive, or even impossible, to acquire. Worse yet, these pre-trained MLC models can only be used for the particular label set covered in the training data. Despite this severe limitation, few methods exist for expanding the set of labels predicted by pre-trained models. Instead, we acquire vast amounts of new labeled data and retrain a new model from scratch. Here, we propose combining the knowledge from multiple pre-trained models (teachers) to train a new student model that covers the union of the labels predicted by this set of teachers. This student supports a broader label set than any one of its teachers without using labeled data. We call this new problem knowledge amalgamation for multi-label classification. Our new method, Adaptive KNowledge Transfer (ANT), trains a student by learning from each teacher’s partial knowledge of label dependencies to infer the global dependencies between all labels across the teachers. We show that ANT succeeds in unifying label dependencies among teachers, outperforming five state-of-the-art methods on eight real-world datasets.

Knowledge Amalgamation for Multi-Label Classification via Label Dependency Transfer

Active Object Tracking (AOT) aims at keeping a specific relation between tracker and object(s) by autonomously controlling the motion system of a tracker given observations. It is an important research area widely used in various applications, such as mobile robots, intelligent city, autonomous driving, etc. However, it remains challenging to build a generalizable active tracker to robustly work across different scenarios, especially in those unstructured environments with cluttered obstacles and diverse layouts. To realize this, we argue that the key is to construct a state representation that can model the geometry structure of the surroundings and the dynamics of the target. To this end, we propose a framework to form a structure-aware motion representation by Reconstructing the Surroundings of the tracker and Predicting the Trajectory of the target, called RSPT. Moreover, we further enhance the generalization of the policy network by training in the asymmetric dueling mechanism. Empirical results in virtual environments show that the tracker with RSPT significantly outperforms the existing methods among unseen environments, especially in environments with cluttered obstacles and diverse layouts. We further deploy the RSPT in a real-world scenario, showing good generalization in sim-to-real transfer.

Towards Generalizable Active Object Tracking with Reconstructing Surroundings and Predicting Trajectory

Vector Quantization (VQ) is a method for discretizing latent representations and has become a major part of the deep learning toolkit. It has been theoretically and empirically shown that discretization of representations leads to improved generalization, including in reinforcement learning where discretization can be used to bottleneck multi-agent communication to promote agent specialization and robustness. The discretization tightness of most VQ-based methods is defined by the number of discrete codes in the representation vector and the codebook size, which are fixed as hyperparameters. In this work, we propose learning to dynamically select discretization tightness conditioned on inputs, based on the hypothesis that data naturally contains variations in complexity that call for different levels of representational coarseness which is observed in many heterogeneous data sets. We show that dynamically varying tightness in communication bottlenecks can improve model performance on visual reasoning and reinforcement learning tasks with heterogeneity in representations.

Adaptive Discrete Communication Bottlenecks with Dynamic Vector Quantization for Heterogeneous Representational Coarseness

Predictor-based Neural Architecture Search (NAS) employs an architecture performance predictor to improve the sample efficiency. However, predictor-based NAS suffers from the severe ``cold-start'' problem, since a large amount of architecture-performance data is required to get a working predictor. In this paper, we focus on exploiting information in cheaper-to-obtain performance estimations (i.e., low-fidelity information) to mitigate the large data requirements of predictor training. Despite the intuitiveness of this idea, we observe that using inappropriate low-fidelity information even damages the prediction ability and different search spaces have different preferences for low-fidelity information types. To solve the problem and better fuse beneficial information provided by different types of low-fidelity information, we propose a novel dynamic ensemble predictor framework that comprises two steps. In the first step, we train different sub-predictors on different types of available low-fidelity information to extract beneficial knowledge as low-fidelity experts. In the second step, we learn a gating network to dynamically output a set of weighting coefficients conditioned on each input neural architecture, which will be used to combine the predictions of different low-fidelity experts in a weighted sum. The overall predictor is optimized on a small set of actual architecture-performance data to fuse the knowledge from different low-fidelity experts to make the final prediction. We conduct extensive experiments across five search spaces with different architecture encoders under various experimental settings. For example, our methods can improve the Kendall's Tau correlation coefficient between actual performance and predicted scores from 0.2549 to 0.7064 with only 25 actual architecture-performance data on NDS-ResNet. Our method can easily be incorporated into existing predictor-based NAS frameworks to discover better architectures. Our method will be implemented in Mindspore, and the example code is published at https://github.com/A-LinCui/DELE.

Dynamic Ensemble of Low-fidelity Experts: Mitigating NAS "Cold-Start"

MapReduce (MR) algorithms for maximizing monotone, submodular functions subject to a cardinality constraint (SMCC) are currently restricted to the use of the linear-adaptive (non-parallelizable) algorithm GREEDY. Low-adaptive algorithms do not satisfy the requirements of these distributed MR frameworks, thereby limiting their performance. We study the SMCC problem in a distributed setting and propose the first MR algorithms with sublinear adaptive complexity. Our algorithms, R-DASH, T-DASH and G-DASH provide $0.316-\varepsilon$, $3/8 -\varepsilon$, and $1 - 1/e -\varepsilon$   approximation ratios, respectively, with nearly optimal adaptive complexity and nearly linear time complexity. Additionally, we provide a framework to increase, under some mild assumptions, the maximum permissible cardinality constraint from  $O( n / \ell^2)$ of prior MR algorithms to $O( n / \ell )$, where n is the data size and $\ell$ is the number of machines; under a stronger condition on the objective function, we increase the maximum constraint value to n. Finally, we provide empirical evidence to demonstrate that our sublinear-adaptive, distributed algorithms provide orders of magnitude faster runtime compared to current state-of-the-art distributed algorithms.

DASH: A Distributed and Parallelizable Algorithm for Size-Constrained Submodular Maximization

Collaborative Communication platforms (e.g., Slack) support multi-party conversations which contain a large number of messages on shared channels. Multiple conversations intermingle within these messages. The task of conversation disentanglement is to cluster these intermingled messages into conversations. Existing approaches are trained using loss functions that optimize only local decisions, i.e. predicting reply-to links for each message and thereby creating clusters of conversations. 
In this work, we propose an end-to-end reinforcement learning (RL) approach that directly optimizes a global metric. We observe that using existing global metrics such as variation of information and adjusted rand index as a reward for the RL agent deteriorates its performance. This behaviour is because these metrics completely ignore the reply-to links between messages (local decisions) during reward computation. Therefore, we propose a novel thread-level reward function that captures the global metric without ignoring the local decisions.
Through experiments on the Ubuntu IRC dataset, we demonstrate that the proposed RL model improves the performance on both link-level and conversation-level metrics. We have attached the code as supplementary material.

End-to-End Deep Reinforcement learning for Conversation Disentanglement

Pre-trained programming language (PL) models (such as CodeT5, CodeBERT, GraphCodeBERT, etc.,) have the potential to automate software engineering tasks involving code understanding and code generation. However, these models operate in the natural channel of code, i.e., primarily concerned with the human understanding of code. They are not robust to changes in the input and thus, are potentially susceptible to adversarial attacks in the natural channel. We propose, Code Attack, a simple yet effective black-box attack model that uses code structure to generate effective, efficient, and imperceptible adversarial code samples and demonstrates the vulnerabilities of the state-of-the-art PL models to code-specific adversarial attacks. We evaluate the transferability of CodeAttack on several code-code (translation and repair) and code-NL (summarization) tasks across different programming languages. Code Attack outperforms state-of-the-art adversarial NLP attack models to achieve the best overall drop in performance while being more efficient, imperceptible, consistent, and fluent.

Code Attack: Code-based Adversarial Attacks for Pre-Trained Programming Language Models

Meeting today’s major societal challenges requires understanding dynamics of cooperation and conflict in complex adaptive systems. Artificial Intelligence is intimately connected with these challenges, both as an application domain and as a source of new computational techniques: On the one hand, AI suggests new algorithmic recommendations and interaction paradigms, offering novel possibilities to engineer cooperation and alleviate conflict in (hybrid) populations; on the other hand, new learning algorithms provide improved techniques to simulate agents and multiagent systems. My research lies at the interface between complex systems and AI: I develop computational methods to understand cooperation and conflict among learning agents. I focus on mapping interaction rules and incentives onto emerging macroscopic patterns and long-term dynamics. Examples of this research agenda, that I will survey in this talk, include modelling 1) the connection between reputation systems and cooperation dynamics; 2) the impact of link recommendation algorithms on potential sources of conflict (e.g., radicalization and polarization); 3) the strategic adaptation process of populations to classification algorithms, through a combination of utility maximization and behavioral imitation of peers.

Dynamics of Cooperation and Conflict in Multiagent Systems

Despite their great success in many artificial intelligence tasks, deep neural networks (DNNs) still suffer from a few limitations, such as poor generalization behavior for out-of-distribution (OOD) data and the "black-box" nature. Information theory offers fresh insights to solve these challenges.

In this talk, we first present a novel matrix-based Renyi's entropy functional estimator, which measures information-theoretic quantities (e.g., mutual information) in terms of the eigenspectrum of the Hermitian matrix of the projected data in the reproducing kernel Hilbert space (RKHS), without explicit evaluation of the underlying data distributions. Second, we give two recent applications of this estimator. In particular, we show how to analyze and understand the inner mechanism (e.g., the flow of information, the Information Bottleneck hypothesis) of DNNs and how to improve the generalization performance of a DNN from an information-theoretic perspective. We finally present the applications of information-theoretic learning principles (e.g., the Information Bottleneck and the Principle of Relevant Information) to develop interpretable and generalizable graph neural networks for brain network-based psychiatric diagnosis.

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