United States

Zero-shot (ZS) sketch-based three-dimensional (3D) shape retrieval (SBSR) is challenging due to the abstraction of sketches, cross-domain discrepancies between two-dimensional sketches and 3D shapes, and ZS-driven semantic knowledge transference from seen to unseen categories. Extant SBSR datasets suffer from lack of data, and no current SBSR methods consider ZS scenarios. In this paper, we contribute a new Doodle2Object (D2O) dataset consisting of 8,992 3D shapes and over 7M sketches spanning 50 categories. Then, we propose a novel prototype contrastive learning (PCL) method that effectively extracts features from different domains and adapts them to unseen categories. Specifically, our PCL method combines the ideas of contrastive and cluster-based prototype learning, and several randomly selected prototypes of different classes are assigned to each sample. By comparing these prototypes, a given sample can be moved closer to the same semantic class of samples while moving away from negative ones. Extensive experiments on two common SBSR benchmarks and our D2O dataset demonstrate the efficacy of the proposed PCL method for ZS-SBSR. Resource is available at https://github.com/yigohw/doodle2object.

AAAI 2023

Doodle to Object: Practical Zero-Shot Sketch-Based 3D Shape Retrieval

ml: multimodal learning

cv: image and video retrieval

ml: deep neural network algorithms

cv: multi-modal vision

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.

poster

The pre-trained language models, e.g., ELMo and BERT, have recently achieved promising performance improvement in a wide range of NLP tasks, because they can output strong contextualized embedded features of words. Inspired by their great success, in this paper we target at fine-tuning them to effectively handle the text clustering task, i.e., a classic and fundamental challenge in machine learning. Accordingly, we propose a novel BERT-based method, namely Text Clustering with Dual Word-level Augmentation (TCDWA). To be specific, we formulate a self-training objective and enhance it with a dual word-level augmentation technique. First, we suppose that each text contains several most informative words, called anchor words, supporting the full text semantics. We use the embedded features of anchor words as augmented data, which are selected by ranking the norm-based attention weights of words. Second, we formulate an expectation form of word augmentation, which is equivalent to generating infinite augmented features, and further suggest a tractable approximation of Taylor expansion for efficient optimization. To evaluate the effectiveness of TCDWA, we conduct extensive experiments on several benchmark text datasets. The results demonstrate that TCDWA consistently outperforms the state-of-the-art baseline methods.

TC-DWA:Text Clustering with Dual Word-level Augmentation

Crowdsourcing is a favorable computing paradigm for processing computer-hard tasks by harnessing human intelligence. However, generic crowdsourcing systems may lead to privacy-leakage through the sharing of worker data. To tackle this problem, we propose a novel approach, called iFedCrowd (incentive-boosted Federated Crowdsourcing), to manage the privacy and quality of crowdsourcing projects. iFedCrowd allows participants to locally process sensitive data and only upload encrypted training models, and then aggregates the model parameters to build a shared server model to protect data privacy. To motivate workers to build a high-quality global model in an efficacy way, we introduce an incentive mechanism that encourages workers to constantly collect fresh data to train accurate client models and boosts the global model training. We model the incentive-based interaction between the crowdsourcing platform and participating workers as a Stackelberg game, in which each side maximizes its own profit. We derive the Nash Equilibrium of the game to find the optimal solutions for the two sides. Experimental results confirm that iFedCrowd can complete secure crowdsourcing projects with high quality and efficiency.

Incentive-boosted Federated Crowdsourcing

This paper presents a new method for end-to-end Video Question Answering (VideoQA), aside from the current popularity of using large-scale pre-training with huge feature extractors. We achieve this with a pyramidal multimodal transformer (PMT) model, which simply incorporates a learnable word embedding layer, a few convolutional and transformer layers. We use the anisotropic pyramid to fulfill video-language interactions across different spatio-temporal scales. In addition to the canonical pyramid, which includes both bottom-up and top-down pathways with lateral connections, novel strategies are proposed to decompose the visual feature stream into spatial and temporal sub-streams at different scales and implement their interactions with the linguistic semantics while preserving the integrity of local and global semantics. We demonstrate better or on-par performances with high computational efficiency against state-of-the-art methods on five VideoQA benchmarks. Our ablation study shows the scalability of our model that achieves competitive results for text-to-video retrieval by leveraging feature extractors with reusable pre-trained weights, and also the effectiveness of the pyramid.

Efficient End-to-End Video Question Answering with Pyramidal Multimodal Transformer

The concept of Strong Backdoor Sets (SBS) for Constraint Satisfaction Problems is well known as one of the attempts to exploit structural peculiarities in hard instances. However, in practice, finding an SBS for a particular instance is often harder than solving it. Recently, a probabilistic weakened variant of the SBS was introduced: in the SBS, all subproblems must be polynomially solvable, whereas in the probabilistic SBS only a large fraction ρ of them should have this property.
This new variant of backdoors called ρ-backdoors makes it possible to use the Monte Carlo method and metaheuristic optimization to find ρ-backdoors with ρ very close to 1, and relatively fast. Despite the fact that in a ρ-backdoor-based decomposition a portion of hard subproblems remain, in practice the narrowing of the search space often allows solving the problem faster than without it. In this paper, we significantly improve on the concept of ρ-backdoors by extending this concept to backdoor trees, introduce ρ-backdoor trees, show the interconnections between SBS, ρ-backdoors, and the corresponding backdoor trees, and establish some new theoretical properties of backdoor trees. In the experimental part of the paper, we show that moving from the metaheuristic search for ρ-backdoors to that of ρ-backdoor trees allows drastically reducing the time required to construct the required decompositions without compromising their quality.

Probabilistic Generalization of Backdoor Trees with Application to SAT

A Fisher market is an economic model of buyer and seller interactions in which each buyer's utility depends only on the bundle of goods she obtains. Many people's interests, however, are affected by their social interactions with others. In this paper, we introduce a generalization of Fisher markets, namely influence Fisher markets, which captures the impact of social influence on buyers' utilities. We model influence Fisher markets as pseudo-games. Using this framework, we first define and prove the existence of competitive equilibrium in all influence Fisher markets with continuous and concave utility functions. We then construct a second similar pseudo-game, which is monotone and whose solution, a variational equilibrium, and dual together characterize competitive equilibria in influence Fisher markets with continuous, concave, homogeneous (CCH) utility functions. This observation immediately implies that competitive equilibria in these markets can be computed in polynomial time. The dual of this pseudo-game enables us to interpret the competitive equilibria of CCH influence Fisher markets as the Stackelberg equilibria of a single-leader multiple-follower Stackelberg game. Finally, we derive a novel first-order method that solves this Stackelberg game in polynomial time, prove that it is equivalent to computing competitive equilibrium prices via t\^{a}tonnement, and run experiments that confirm our theoretical results.

Fisher Markets with Social Influence

There is a significant need for principled uncertainty reasoning in machine learning systems as they are increasingly deployed in safety-critical domains.
A new approach with uncertainty-aware regression-based neural networks (NNs), based on learning evidential distributions for aleatoric and epistemic uncertainties, shows promise over traditional deterministic methods and typical Bayesian NNs, notably with the capabilities to disentangle aleatoric and epistemic uncertainties.
Despite some empirical success of Deep Evidential Regression (DER), there are important gaps in the mathematical foundation that raise the question of why the proposed technique seemingly works.
We detail the theoretical shortcomings and analyze the performance on synthetic and real-world data sets, showing that Deep Evidential Regression is a heuristic rather than an exact uncertainty quantification.
We go on to discuss corrections and redefinitions of how aleatoric and epistemic uncertainties should be extracted from NNs.

The Unreasonable Effectiveness of Deep Evidential Regression

Sophisticated data augmentations  can greatly boost the efficiency of Graph Contrastive Learning (GCL). However, there are works investigating the feature augmentation for unsupervised contrastive learning, although (class-conditional) feature augmentation has been proven useful in supervised learning. Thus, we present a novel/efficient spectral feature argumentation for contrastive learning on graphs (and images). To this end, from two data views, respectively, we estimate a low-rank approximation per feature map and  subtract that approximation from the map to obtain its complement. This is achieved by the proposed herein  so-called incomplete power iteration, a non-standard power iteration regime which enjoys two valuable byproducts (under the low number of iterations only): (i) relieve the unbalancing spectrum of the feature map, and (ii) injecting the noise into the singular values of the feature map. For two views, we align these complement feature maps as the spectrum-balanced alignment step focuses on less dominant singular values of spectra of both views, whereas the spectral augmentation of singular values does not affect the spectral angle alignment (singular vectors are left intact). We derive the analytical form for the incomplete power iteration to capture its spectrum-balancing effect and the variance of singular values implicitly augmented by the noise. We also show that this augmentation leads to a lower generalization bound. Experiments on graph and image datasets demonstrate the proposed spectral feature augmentation  outperforms baselines, and is complementary with other graph augmentation approaches and compatible with various contrastive losses. 

Spectral Feature Augmentation for Graph Contrastive Learning and Beyond

Cooperative multi-agent reinforcement learning (c-MARL) is widely applied in safety-critical scenarios, thus the analysis of robustness for c-MARL models is profoundly important. However, robustness certification for c-MARLs has not yet been explored in the community. In this paper, we propose a novel certification method, which is the first work to leverage a scalable approach for c-MARLs to determine actions with guaranteed certified bounds. c-MARL certification poses two key challenges compared with single-agent systems: (i) the accumulated uncertainty as the number of agents increases; (ii) the potential lack of impact when changing the action of a single agent into a global team reward. These challenges prevent us from directly using existing algorithms. Hence, we employ the false discovery rate (FDR) controlling procedure considering the importance of each agent to certify per-state robustness and propose a tree-search-based algorithm to find a lower bound of the global reward under the minimal certified perturbation. As our method is general, it can also be applied in single-agent environments. We empirically show that our certification bounds are much tighter than those of the state-of-the-art RL certification solutions. We also run experiments on two popular c-MARL algorithms: QMIX and VDN, in two different environments, with two and four agents. The experimental results show that our method produces a meaningful guaranteed robustness for all models and environments. Our tool CertifyCMARL is available at https://github.com/TrustAI/CertifyCMARL.

Certified Policy Smoothing for Cooperative Multi-Agent Reinforcement Learning

The concept of walkable urban development has gained increased
attention due to its public health, economic, and environmental
sustainability benefits. Unfortunately, land zoning
and historic under-investment have resulted in spatial inequality
in walkability and social inequality among residents.
We tackle the problem of Walkability Optimization through
the lens of combinatorial optimization. The task is to select
locations in which additional amenities (e.g., grocery stores,
schools, restaurants) can be allocated to improve resident access
via walking while taking into account existing amenities
and providing multiple options (e.g., for restaurants).
To this end, we derive Mixed-Integer Linear Programming
(MILP) and Constraint Programming (CP) models. Moreover,
we show that the problem’s objective function is submodular
in special cases, which motivates an efficient greedy
heuristic. We conduct a case study on 31 underserved neighborhoods
in the City of Toronto, Canada. MILP finds the
best solutions in most scenarios but does not scale well with
network size. The greedy algorithm scales well and finds
near-optimal solutions. Our empirical evaluation shows that
neighbourhoods with low walkability have a great potential
for transformation into pedestrian-friendly neighbourhoods
by strategically placing new amenities. Allocating 3 additional
grocery stores, schools, and restaurants can improve the
“WalkScore” by more than 50 points (on a scale of 100) for 4
neighbourhoods and reduce the walking distances to amenities
for 75% of all residential locations to 10 minutes for all
amenity types. Our code and paper appendix are available at
https://github.com/khalil-research/walkability.

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TC-DWA:Text Clustering with Dual Word-level Augmentation

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