United States

Robust prediction of citywide traffic flows at different time periods plays a crucial role in intelligent transportation systems. While previous work has made great efforts to model spatio-temporal correlations, existing methods still suffer from two key limitations: i) Most models collectively predict all regions&#39; flows without accounting for spatial heterogeneity, i.e., different regions may have skewed traffic flow distributions. ii) These models fail to capture the temporal heterogeneity induced by time-varying traffic patterns, as they typically model temporal correlations with a shared parameterized space for all time periods. To tackle these challenges, we propose a novel Heterogeneity-aware Spatio-Temporal (HeST) traffic prediction framework which enhances the traffic pattern representations to be reflective of both spatial and temporal heterogeneity, with auxiliary self-supervised learning paradigms. Specifically, our HeST is built over an integrated module with temporal and spatial convolutions for encoding the information across space and time. To achieve the adaptive spatio-temporal self-supervised learning, our HeST first performs the adaptive augmentation over the traffic flow graph data at both attribute- and structure-levels. On top of the augmented traffic graph, two auxiliary tasks are constructed to supplement the main traffic prediction task with spatial and temporal heterogeneity-aware self-supervised signals. Experiments on four benchmark datasets demonstrate that HeST consistently outperforms various state-of-the-art baselines. Since spatio-temporal heterogeneity widely exists in practical datasets, the proposed framework may also cast light on other similar applications. Source codes are available at https://github.com/Echo-Ji/HeST.

AAAI 2023

Spatio-Temporal Self-Supervised Learning for Traffic Flow Prediction

urban computing

spatio-temporal data mining

self-supervised learning

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. 
AAAI is providing you with a conference planner, which you can use to help organize your itinerary of activities. This includes talks to attend in person, talks to attend remotely, breaks with colleagues and your site seeing activities. To access this conference planner, please go to [https://aaai-2023.takemobi.io](https://aaai-2023.takemobi.io).

In order to access this site, you need to register. If you haven't already, please register [here](https://aaai.org/Conferences/AAAI-23/registration/).


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.

Robust prediction of citywide traffic flows at different time periods plays a crucial role in intelligent transportation systems. While previous work has made great efforts to model spatio-temporal correlations, existing methods still suffer from two key limitations: i) Most models collectively predict all regions' flows without accounting for spatial heterogeneity, i.e., different regions may have skewed traffic flow distributions. ii) These models fail to capture the temporal heterogeneity induced by time-varying traffic patterns, as they typically model temporal correlations with a shared parameterized space for all time periods. To tackle these challenges, we propose a novel Heterogeneity-aware Spatio-Temporal (HeST) traffic prediction framework which enhances the traffic pattern representations to be reflective of both spatial and temporal heterogeneity, with auxiliary self-supervised learning paradigms. Specifically, our HeST is built over an integrated module with temporal and spatial convolutions for encoding the information across space and time. To achieve the adaptive spatio-temporal self-supervised learning, our HeST first performs the adaptive augmentation over the traffic flow graph data at both attribute- and structure-levels. On top of the augmented traffic graph, two auxiliary tasks are constructed to supplement the main traffic prediction task with spatial and temporal heterogeneity-aware self-supervised signals. Experiments on four benchmark datasets demonstrate that HeST consistently outperforms various state-of-the-art baselines. Since spatio-temporal heterogeneity widely exists in practical datasets, the proposed framework may also cast light on other similar applications. Source codes are available at https://github.com/Echo-Ji/HeST.

poster

Source free domain adaptation (SFDA) transfers a single-source model to the unlabeled target domain without accessing the source data. With the intelligence development of various fields, a zoo of source models is more commonly available,  arising in a new setting called multi-source-free domain adaptation (MSFDA). We find that the critical inborn challenge of MSFDA is how to estimate the importance (contribution) of each source model. In this paper, we shed new Bayesian light on the fact that the posterior probability of source importance connects to discriminability and transferability. We propose Discriminability And Transferability Estimation (DATE), a universal solution for source importance estimation. Specifically, a proxy discriminability perception module equips with habitat uncertainty and density to evaluate each sample's surrounding environment. A source-similarity transferability perception module quantifies the data distribution similarity and encourages the transferability to be reasonably distributed with a domain diversity loss. Extensive experiments show that DATE can precisely and objectively estimate the source importance and outperform prior arts by non-trivial margins. Moreover, experiments demonstrate that DATE can take the most popular SFDA networks as backbones and make them become advanced MSFDA solutions.

Discriminability and Transferability Estimation: A Bayesian Source Importance Estimation Approach for Multi-Source-Free Domain Adaptation

Recently, the self-supervised pre-training paradigm has shown great potential in leveraging large-scale unlabeled data to improve downstream task performance. However, increasing the scale of unlabeled pre-training data in real-world scenarios requires prohibitive computational costs and faces the challenge of uncurated samples. To address these issues, we build a task-specific self-supervised pre-training framework from a data selection perspective based on a simple hypothesis that pre-training on the unlabeled samples with similar distribution to the target task can bring substantial performance gains. Buttressed by the hypothesis, we propose the first yet novel framework for Scalable and Efficient visual Pre-Training (SEPT) by introducing a retrieval pipeline for data selection. SEPT first leverage a self-supervised pre-trained model to extract the features of the entire unlabeled dataset for retrieval pipeline initialization. Then, for a specific target task, SEPT retrievals the most similar samples from the unlabeled dataset based on feature similarity for each target instance for pre-training. Finally, SEPT pre-trains the target model with the selected unlabeled samples in a self-supervised manner for target data finetuning. By decoupling the scale of pre-training and available upstream data for a target task, SEPT achieves high scalability of the upstream dataset and high efficiency of pre-training, resulting in high model architecture flexibility. Results on various downstream tasks demonstrate that SEPT can achieve competitive or even better performance compared with ImageNet pre-training while reducing the size of training samples by one magnitude without resorting to any extra annotations.

SEPT: Towards Scalable and Efficient Visual Pre-Training

Visible-Infrared Person Re-Identification (VI-ReID) is a challenging retrieval task under complex modality changes. Existing methods usually focus on extracting discriminative visual features while ignoring the reliability and commonality of visual features between different modalities. In this paper, we propose a novel deep learning framework named Progressive Modality-shared Transformer (PMT) for effective VI-ReID. To reduce the negative effect of modality gaps, we first take the gray-scale images as an auxiliary modality and propose a progressive learning strategy. Then, we propose a Modality-Shared Enhancement Loss (MSEL) to guide the model to explore more reliable identity information from modality-shared features. Finally, to cope with the problem of large intra-class differences and small inter-class differences, we propose a Discriminative Center Loss (DCL) combined with the MSEL to further improve the discrimination of reliable features. Extensive experiments on SYSU-MM01 and RegDB datasets show that our proposed framework performs better than most state-of-the-art methods. For model reproduction, we release the source code at https://github.com/hulu88/PMT.

Learning Progressive Modality-shared Transformers for Effective Visible-Infrared Person Re-identification

Self-supervised video representation learning aimed at maximizing similarity between different temporal segments of one video, in order to enforce feature persistence over time. This leads to loss of pertinent information related to temporal relationships, rendering actions such as `enter' and `leave' to be indistinguishable. To mitigate this limitation, we propose Latent Time Navigation (LTN), a time parameterized contrastive learning strategy that is streamlined to capture fine-grained motions. Specifically, we maximize the representation similarity between different video segments from one video, while maintaining their representations time-aware along a subspace of the latent representation code including an orthogonal basis to represent temporal changes. Our extensive experimental analysis suggests that learning video representations by LTN consistently improves performance of action classification in fine-grained and human-oriented tasks (e.g., on Toyota Smarthome dataset). In addition, we demonstrate that our proposed model, when pre-trained on Kinetics-400, generalizes well onto the unseen real world video benchmark datasets UCF101 and HMDB51, achieving state-of-the-art performance in action recognition.

Self-supervised Video Representation Learning via Latent Time Navigation

Few-shot learning models learn representations with limited human annotations, and such a learning paradigm demonstrates practicability in various tasks, e.g., image classification, object detection, etc. However, few-shot object detection methods suffer from an intrinsic defect that the limited training data makes the model cannot sufficiently explore semantic information. To tackle this, we introduce knowledge distillation to the few-shot object detection learning paradigm. We further run a motivating experiment, which demonstrates that in the process of knowledge distillation, the empirical error of the teacher model degenerates the prediction performance of the few-shot object detection model as the student. To understand the reasons behind this phenomenon, we revisit the learning paradigm of knowledge distillation on the few-shot object detection task from the causal theoretic standpoint, and accordingly, develop a Structural Causal Model. Following the theoretical guidance, we propose a backdoor adjustment-based knowledge distillation method for the few-shot object detection task, namely Disentangle and Remerge (D&R), to perform conditional causal intervention toward the corresponding Structural Causal Model. Empirically, the experiments on benchmarks demonstrate that D&R can yield significant performance boosts in few-shot object detection. Code is available at https://github.com/ZYN-1101/DandR.git.

Disentangle and Remerge: Interventional Knowledge Distillation for Few-Shot Object Detection from A Conditional Causal Perspective

 Task Free Continual Learning (TFCL) aims to capture novel concepts from non-stationary data streams without forgetting previously learned knowledge. Mixture models, which add new components when certain conditions are met, have shown promising results in TFCL tasks. However, such approaches do not make use of the knowledge already accumulated for positive knowledge transfer. In this paper, we develop a new model, namely the Online Recursive Variational Autoencoder (ORVAE). ORVAE utilizes the prior knowledge by recursively incorporating the information flow learned by previously trained components with those learned by newly created components into its latent space. To fully explore this information when learning novel samples, we introduce a new attention mechanism to regularize the structural latent space in which the most important information are reused while the information that interferes with novel samples is inactivated. The proposed attention mechanism can maximize the benefit from the forward transfer for learning novel samples without causing forgetting on previously learnt knowledge. We perform several experiments which show that ORVAE achieves state-of-the-art results under TFCL.

Learning Dynamic Latent Spaces for Lifelong Generative Modelling

Audio watermarking is widely used for leaking source tracing. The robustness of the watermark determines the traceability of the algorithm. With the development of digital technology, audio re-recording (AR) has become an efficient and covert means to steal secrets. AR process could drastically destroy the watermark signal while preserving the original information. This puts forward a new requirement for audio watermarking at this stage, that is, to be robust to AR distortions. Unfortunately, none of the existing algorithms can effectively resist AR attacks due to the complexity of the AR process. To address this limitation, this paper proposes DeAR, a deep-learning-based audio re-recording resistant watermarking. Inspired by DNN-based image watermarking, we pioneer a deep learning framework for audio carriers, based on which the watermark signal can be effectively embedded and extracted. Meanwhile, in order to resist the AR attack, we delicately analyze the distortions that occurred in the AR process and design the corresponding distortion layer to cooperate with the proposed watermarking framework. Extensive experiments show that the proposed algorithm can resist not only common electronic channel distortions but also AR distortions. Under the premise of high-quality embedding (SNR=25.86dB), in the case of a common re-recording distance (20cm), the algorithm can effectively achieve an average bit recovery accuracy of 98.55%.

DeAR: A Deep-learning-based Audio Re-recording Resilient Watermarking

Pixel-wise prediction with deep neural network has become an effective paradigm for salient object detection (SOD) and achieved remarkable performance. However, very few SOD models are robust against adversarial attacks which are visually imperceptible for human visual attention. The previous work robust saliency (ROSA) shuffles the pre-segmented superpixels and then refines the coarse saliency map by the densely connected conditional random field (CRF). Different from ROSA that rely on various pre- and post-processings, this paper proposes a light-weight Learnable Noise (LeNo) to defend adversarial attacks for SOD models. LeNo preserves accuracy of SOD models on both adversarial and clean images, as well as inference speed. In general, LeNo consists of a simple shallow noise and noise estimation that embedded in the encoder and decoder of arbitrary SOD networks respectively. Inspired by the center prior of human visual attention mechanism, we initialize the shallow noise with a cross-shaped gaussian distribution for better defense against adversarial attacks. Instead of adding additional network components for post-processing, the proposed noise estimation modifies only one channel of the decoder. With the deeply-supervised noise-decoupled training on state-of-the-art RGB and RGB-D SOD networks, LeNo outperforms previous works not only on adversarial images but also on clean images, which contributes stronger robustness for SOD. Our code is available at https://github.com/ssecv/LeNo.

LeNo: Adversarial Robust Salient Object Detection Networks with Learnable Noise

Cognitive diagnosis is a fundamental yet critical research task in the field of intelligent education, which aims to discover the proficiency level of different students on specific knowledge concepts. Despite the effectiveness of existing efforts, previous methods always considered the mastery level on the whole students, so they still suffer from the Long Tail Effect. A large number of students who have sparse interaction records are usually wrongly diagnosed during inference. To relieve the situation, we proposed a Self-supervised Cognitive Diagnosis (SCD) framework which leverages the self-supervised manner to assist the graph-based cognitive diagnosis, then the performance on those students with sparse data can be improved. Specifically, we came up with a graph confusion method that drops edges under some special rules to generate different sparse views of the graph. By maximizing the cross-view consistency of node representations, our model could pay more attention on long-tailed students. Additionally, we proposed an importance-based view generation rule to improve the influence of long-tailed students. Extensive experiments on real-world datasets show the effectiveness of our approach, especially on the students with much sparser interaction records. Our code is available at https://github.com/zeng-zhen/SCD.

Self-supervised Graph Learning for Long-tailed Cognitive Diagnosis

Skeletal motions have been heavily relied upon for human activity recognition (HAR). Recently, a universal vulnerability of skeleton-based HAR has been identified across a variety of classifiers and data, calling for mitigation. To this end, we propose the first black-box defense method for skeleton-based HAR to our best knowledge. Our method is featured by full Bayesian treatments of the clean data, the adversaries and the classifier, leading to (1) a new Bayesian Energy-based formulation of robust discriminative classifiers, (2) a new adversary sampling scheme based on natural motion manifolds, and (3) a new post-train Bayesian strategy for black-box defense. We name our framework Bayesian Energy-based Adversarial Training or BEAT. BEAT is straightforward but elegant, which turns vulnerable black-box classifiers into robust ones without sacrificing accuracy. It demonstrates surprising and universal effectiveness across a wide range of skeletal HAR classifiers and datasets, under various attacks. Code is available at https://github.com/realcrane/RobustActionRecogniser.

Downloads

Next from AAAI 2023

Discriminability and Transferability Estimation: A Bayesian Source Importance Estimation Approach for Multi-Source-Free Domain Adaptation

Similar lecture

AirFormer: Predicting Nationwide Air Quality in China with Transformers