Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence最新文献_第3页

Towards Global Video Scene Segmentation with Context-Aware Transformer 基于上下文感知转换器的全局视频场景分割

Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence Pub Date : 2023-06-26 DOI: 10.1609/aaai.v37i3.25426

Yang Yang, Yurui Huang, Weili Guo, Baohua Xu, Dingyin Xia

{"title":"Towards Global Video Scene Segmentation with Context-Aware Transformer","authors":"Yang Yang, Yurui Huang, Weili Guo, Baohua Xu, Dingyin Xia","doi":"10.1609/aaai.v37i3.25426","DOIUrl":"https://doi.org/10.1609/aaai.v37i3.25426","url":null,"abstract":"Videos such as movies or TV episodes usually need to divide the long storyline into cohesive units, i.e., scenes, to facilitate the understanding of video semantics. The key challenge lies in finding the boundaries of scenes by comprehensively considering the complex temporal structure and semantic information. To this end, we introduce a novel Context-Aware Transformer (CAT) with a self-supervised learning framework to learn high-quality shot representations, for generating well-bounded scenes. More specifically, we design the CAT with local-global self-attentions, which can effectively consider both the long-term and short-term context to improve the shot encoding. For training the CAT, we adopt the self-supervised learning schema. Firstly, we leverage shot-to-scene level pretext tasks to facilitate the pre-training with pseudo boundary, which guides CAT to learn the discriminative shot representations that maximize intra-scene similarity and inter-scene discrimination in an unsupervised manner. Then, we transfer contextual representations for fine-tuning the CAT with supervised data, which encourages CAT to accurately detect the boundary for scene segmentation. As a result, CAT is able to learn the context-aware shot representations and provides global guidance for scene segmentation. Our empirical analyses show that CAT can achieve state-of-the-art performance when conducting the scene segmentation task on the MovieNet dataset, e.g., offering 2.15 improvements on AP.","PeriodicalId":74506,"journal":{"name":"Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence","volume":"23 3 1","pages":"3206-3213"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77373482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Phase-Informed Bayesian Ensemble Models Improve Performance of COVID-19 Forecasts 相位知情贝叶斯集成模型提高COVID-19预测性能

Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence Pub Date : 2023-06-26 DOI: 10.1609/aaai.v37i13.26855

A. Adiga, Gursharn Kaur, Lijing Wang, Benjamin Hurt, P. Porebski, S. Venkatramanan, B. Lewis, M. Marathe

{"title":"Phase-Informed Bayesian Ensemble Models Improve Performance of COVID-19 Forecasts","authors":"A. Adiga, Gursharn Kaur, Lijing Wang, Benjamin Hurt, P. Porebski, S. Venkatramanan, B. Lewis, M. Marathe","doi":"10.1609/aaai.v37i13.26855","DOIUrl":"https://doi.org/10.1609/aaai.v37i13.26855","url":null,"abstract":"Despite hundreds of methods published in the literature, forecasting epidemic dynamics remains challenging yet important. The challenges stem from multiple sources, including: the need for timely data, co-evolution of epidemic dynamics with behavioral and immunological adaptations, and the evolution of new pathogen strains. The ongoing COVID-19 pandemic highlighted these challenges; in an important article, Reich et al. did a comprehensive analysis highlighting many of these challenges.\u0000\u0000In this paper, we take another step in critically evaluating existing epidemic forecasting methods. Our methods are based on a simple yet crucial observation - epidemic dynamics go through a number of phases (waves). Armed with this understanding, we propose a modification to our deployed Bayesian ensembling case time series forecasting framework. We show that ensembling methods employing the phase information and using different weighting schemes for each phase can produce improved forecasts. We evaluate our proposed method with both the currently deployed model and the COVID-19 forecasthub models. The overall performance of the proposed model is consistent across the pandemic but more importantly, it is ranked third and first during two critical rapid growth phases in cases, regimes where the performance of most models from the CDC forecasting hub dropped significantly.","PeriodicalId":74506,"journal":{"name":"Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence","volume":"74 1","pages":"15647-15653"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77380139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Fast and Accurate Binary Neural Networks Based on Depth-Width Reshaping 基于深度-宽度重构的快速精确二值神经网络

Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence Pub Date : 2023-06-26 DOI: 10.1609/aaai.v37i9.26268

Ping Xue, Yang Lu, Jingfei Chang, Xing Wei, Zhenchun Wei

{"title":"Fast and Accurate Binary Neural Networks Based on Depth-Width Reshaping","authors":"Ping Xue, Yang Lu, Jingfei Chang, Xing Wei, Zhenchun Wei","doi":"10.1609/aaai.v37i9.26268","DOIUrl":"https://doi.org/10.1609/aaai.v37i9.26268","url":null,"abstract":"Network binarization (i.e., binary neural networks, BNNs) can efficiently compress deep neural networks and accelerate model inference but cause severe accuracy degradation. Existing BNNs are mainly implemented based on the commonly used full-precision network backbones, and then the accuracy is improved with various techniques. However, there is a question of whether the full-precision network backbone is well adapted to BNNs. We start from the factors of the performance degradation of BNNs and analyze the problems of directly using full-precision network backbones for BNNs: for a given computational budget, the backbone of a BNN may need to be shallower and wider compared to the backbone of a full-precision network. With this in mind, Depth-Width Reshaping (DWR) is proposed to reshape the depth and width of existing full-precision network backbones and further optimize them by incorporating pruning techniques to better fit the BNNs. Extensive experiments demonstrate the analytical result and the effectiveness of the proposed method. Compared with the original backbones, the DWR backbones constructed by the proposed method result in close to O(√s) decrease in activations, while achieving an absolute accuracy increase by up to 1.7% with comparable computational cost. Besides, by using the DWR backbones, existing methods can achieve new state-of-the-art (SOTA) accuracy (e.g., 67.2% on ImageNet with ResNet-18 as the original backbone). We hope this work provides a novel insight into the backbone design of BNNs. The code is available at https://github.com/pingxue-hfut/DWR.","PeriodicalId":74506,"journal":{"name":"Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence","volume":"150 1","pages":"10684-10692"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77477351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-Level Wavelet Mapping Correlation for Statistical Dependence Measurement: Methodology and Performance 统计相关性测量的多级小波映射关联:方法与性能

Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence Pub Date : 2023-06-26 DOI: 10.1609/aaai.v37i5.25799

Yixin Ren, Hao Zhang, Yewei Xia, J. Guan, Shuigeng Zhou

引用次数: 0

Debiasing Intrinsic Bias and Application Bias Jointly via Invariant Risk Minimization (Student Abstract) 基于不变风险最小化的联合消除内在偏差和应用偏差(学生摘要)

Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence Pub Date : 2023-06-26 DOI: 10.1609/aaai.v37i13.27000

Yuzhou Mao, Liu Yu, Yi Yang, Fan Zhou, Ting Zhong

{"title":"Debiasing Intrinsic Bias and Application Bias Jointly via Invariant Risk Minimization (Student Abstract)","authors":"Yuzhou Mao, Liu Yu, Yi Yang, Fan Zhou, Ting Zhong","doi":"10.1609/aaai.v37i13.27000","DOIUrl":"https://doi.org/10.1609/aaai.v37i13.27000","url":null,"abstract":"Demographic biases and social stereotypes are common in pretrained language models (PLMs), while the fine-tuning in downstream applications can also produce new biases or amplify the impact of the original biases. Existing works separate the debiasing from the fine-tuning procedure, which results in a gap between intrinsic bias and application bias. In this work, we propose a debiasing framework CauDebias to eliminate both biases, which directly combines debiasing with fine-tuning and can be applied for any PLMs in downstream tasks. We distinguish the bias-relevant (non-causal factors) and label-relevant (causal factors) parts in sentences from a causal invariant perspective. Specifically, we perform intervention on non-causal factors in different demographic groups, and then devise an invariant risk minimization loss to trade-off performance between bias mitigation and task accuracy. Experimental results on three downstream tasks show that our CauDebias can remarkably reduce biases in PLMs while minimizing the impact on downstream tasks.","PeriodicalId":74506,"journal":{"name":"Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence","volume":"197 1","pages":"16280-16281"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79768635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Isometric Manifold Learning Using Hierarchical Flow 等量流形学习使用分层流

Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence Pub Date : 2023-06-26 DOI: 10.1609/aaai.v37i8.26124

Ziqi Pan, Jianfu Zhang, Li Niu, Liqing Zhang

引用次数: 0

Detecting Exclusive Language during Pair Programming 在结对编程中检测排他语言

Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence Pub Date : 2023-06-26 DOI: 10.1609/aaai.v37i13.26895

S. Ubani, Rodney D. Nielsen, Helen Li

引用次数: 0

Self-Supervised Joint Dynamic Scene Reconstruction and Optical Flow Estimation for Spiking Camera 自监督联合动态场景重建和光流估计

Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence Pub Date : 2023-06-26 DOI: 10.1609/aaai.v37i1.25108

Shiyan Chen, Zhaofei Yu, Tiejun Huang

{"title":"Self-Supervised Joint Dynamic Scene Reconstruction and Optical Flow Estimation for Spiking Camera","authors":"Shiyan Chen, Zhaofei Yu, Tiejun Huang","doi":"10.1609/aaai.v37i1.25108","DOIUrl":"https://doi.org/10.1609/aaai.v37i1.25108","url":null,"abstract":"Spiking camera, a novel retina-inspired vision sensor, has shown its great potential for capturing high-speed dynamic scenes with a sampling rate of 40,000 Hz. The spiking camera abandons the concept of exposure window, with each of its photosensitive units continuously capturing photons and firing spikes asynchronously. However, the special sampling mechanism prevents the frame-based algorithm from being used to spiking camera. It remains to be a challenge to reconstruct dynamic scenes and perform common computer vision tasks for spiking camera. In this paper, we propose a self-supervised joint learning framework for optical flow estimation and reconstruction of spiking camera. The framework reconstructs clean frame-based spiking representations in a self-supervised manner, and then uses them to train the optical flow networks. We also propose an optical flow based inverse rendering process to achieve self-supervision by minimizing the difference with respect to the original spiking temporal aggregation image. The experimental results demonstrate that our method bridges the gap between synthetic and real-world scenes and achieves desired results in real-world scenarios. To the best of our knowledge, this is the first attempt to jointly reconstruct dynamic scenes and estimate optical flow for spiking camera from a self-supervised learning perspective.","PeriodicalId":74506,"journal":{"name":"Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence","volume":"125 1","pages":"350-358"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79254909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Improved Approximation Algorithm for Wage Determination and Online Task Allocation in Crowd-Sourcing 众包中工资确定与在线任务分配的改进逼近算法

Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence Pub Date : 2023-06-26 DOI: 10.1609/aaai.v37i4.25512

Yuya Hikima, Yasunori Akagi, Hideaki Kim, Taichi Asami

{"title":"An Improved Approximation Algorithm for Wage Determination and Online Task Allocation in Crowd-Sourcing","authors":"Yuya Hikima, Yasunori Akagi, Hideaki Kim, Taichi Asami","doi":"10.1609/aaai.v37i4.25512","DOIUrl":"https://doi.org/10.1609/aaai.v37i4.25512","url":null,"abstract":"Crowd-sourcing has attracted much attention due to its growing importance to society, and numerous studies have been conducted on task allocation and wage determination. Recent works have focused on optimizing task allocation and workers' wages, simultaneously. However, existing methods do not provide good solutions for real-world crowd-sourcing platforms due to the low approximation ratio or myopic problem settings. We tackle an optimization problem for wage determination and online task allocation in crowd-sourcing and propose a fast 1-1/(k+3)^(1/2)-approximation algorithm, where k is the minimum of tasks' budgets (numbers of possible assignments). This approximation ratio is greater than or equal to the existing method. The proposed method reduces the tackled problem to a non-convex multi-period continuous optimization problem by approximating the objective function. Then, the method transforms the reduced problem into a minimum convex cost flow problem, which is a well-known combinatorial optimization problem, and solves it by the capacity scaling algorithm. Synthetic experiments and simulation experiments using real crowd-sourcing data show that the proposed method solves the problem faster and outputs higher objective values than existing methods.","PeriodicalId":74506,"journal":{"name":"Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence","volume":"11 1","pages":"3977-3986"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81501815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Trustworthy Residual Vehicle Value Prediction for Auto Finance 汽车金融的可信赖剩余价值预测

Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence Pub Date : 2023-06-26 DOI: 10.1609/aaai.v37i13.26842

Mi-hyung Kim, Jimyung Choi, Jaehyun Kim, Wooyoung Kim, Yeonung Baek, Gisuk Bang, Kwangwoon Son, Yeonman Ryou, Kee-Eung Kim

引用次数: 1