AAAI
Collection
Accepted papers for AAAI (AAAI Conference on Artificial Intelligence), one dataset per year. • 10 items • Updated
paper_id string | title string | paper_url string | pdf_url string | authors list | abstract large_string | track string | primary_area string | doi string | volume string | issue string | pages string | abstract_source string | arxiv_id string | arxiv_id_source string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
10.1609/aaai.v39i8.32922 | Relaxed Rotational Equivariance via G-Biases in Vision | https://ojs.aaai.org/index.php/AAAI/article/view/32922 | https://ojs.aaai.org/index.php/AAAI/article/download/32922/35077 | [
"Zhiqiang Wu",
"Yingjie Liu",
"Licheng Sun",
"Jian Yang",
"Hanlin Dong",
"Shing-Ho J. Lin",
"Xuan Tang",
"Jinpeng Mi",
"Bo Jin",
"Xian Wei"
] | Group Equivariant Convolution (GConv) can capture rotational equivariance from original data. It assumes uniform and strict rotational equivariance across all features as the transformations under the specific group. However, the presentation or distribution of real-world data rarely conforms to strict rotational equiv... | main | null | 10.1609/aaai.v39i8.32922 | 39 | 8 | 8541-8549 | official | 2408.12454 | title_snapshot |
10.1609/aaai.v39i1.32024 | Enhancing the Adversarial Robustness via Manifold Projection | https://ojs.aaai.org/index.php/AAAI/article/view/32024 | https://ojs.aaai.org/index.php/AAAI/article/download/32024/34179 | [
"Zhiting Li",
"Shibai Yin",
"Tai-Xiang Jiang",
"Yexun Hu",
"Jia-Mian Wu",
"Guowei Yang",
"Guisong Liu"
] | Deep learning has been widely applied to various aspects of computer vision, but the emergence of adversarial attacks raises concerns about its reliability. Adversarial training (AT) is one of the most effective defense methods, which incorporates adversarial examples into the training data. However, AT is typically em... | main | null | 10.1609/aaai.v39i1.32024 | 39 | 1 | 451-459 | official | null | null |
10.1609/aaai.v39i19.34265 | Functional Connectomes of Neural Networks | https://ojs.aaai.org/index.php/AAAI/article/view/34265 | https://ojs.aaai.org/index.php/AAAI/article/download/34265/36420 | [
"Tananun Songdechakraiwut",
"Yutong Wu"
] | The human brain is a complex system, and understanding its mechanisms has been a long-standing challenge in neuroscience. The study of the functional connectome, which maps the functional connections between different brain regions, has provided valuable insights through various advanced analysis techniques developed o... | main | null | 10.1609/aaai.v39i19.34265 | 39 | 19 | 20558-20566 | official | 2412.15279 | title_snapshot |
10.1609/aaai.v39i15.33683 | Improving Deep Learning Speed and Performance Through Synaptic Neural Balance | https://ojs.aaai.org/index.php/AAAI/article/view/33683 | https://ojs.aaai.org/index.php/AAAI/article/download/33683/35838 | [
"Antonios Alexos",
"Ian Domingo",
"Pierre Baldi"
] | We present theory of synaptic neural balance and we show experimentally that synaptic neural balance can improve deep learning speed, and accuracy, even in data-scarce environments. Given an additive cost function (regularizer) of the synaptic weights, a neuron is said to be in balance if the total cost of its incoming... | main | null | 10.1609/aaai.v39i15.33683 | 39 | 15 | 15339-15346 | official | null | null |
10.1609/aaai.v39i15.33734 | Cross-View Graph Consistency Learning for Invariant Graph Representations | https://ojs.aaai.org/index.php/AAAI/article/view/33734 | https://ojs.aaai.org/index.php/AAAI/article/download/33734/35889 | [
"Jie Chen",
"Hua Mao",
"Wai Lok Woo",
"Chuanbin Liu",
"Xi Peng"
] | Graph representation learning is fundamental for analyzing graph-structured data. Exploring invariant graph representations remains a challenge for most existing graph representation learning methods. In this paper, we propose a cross-view graph consistency learning (CGCL) method that learns invariant graph representat... | main | null | 10.1609/aaai.v39i15.33734 | 39 | 15 | 15795-15802 | official | 2311.11821 | title_snapshot |
10.1609/aaai.v39i23.34695 | DOMBA: Double Model Balancing for Access-Controlled Language Models via Minimum-Bounded Aggregation | https://ojs.aaai.org/index.php/AAAI/article/view/34695 | https://ojs.aaai.org/index.php/AAAI/article/download/34695/36850 | [
"Tom Segal",
"Asaf Shabtai",
"Yuval Elovici"
] | The utility of large language models (LLMs) depends heavily on the quality and quantity of their training data. Many organizations possess large data corpora that could be leveraged to train or fine-tune LLMs tailored to their specific needs. However, these datasets often come with access restrictions that are based on... | main | null | 10.1609/aaai.v39i23.34695 | 39 | 23 | 25101-25109 | official | 2408.11121 | title_snapshot |
10.1609/aaai.v39i24.34722 | Thought-Path Contrastive Learning via Premise-Oriented Data Augmentation for Logical Reading Comprehension | https://ojs.aaai.org/index.php/AAAI/article/view/34722 | https://ojs.aaai.org/index.php/AAAI/article/download/34722/36877 | [
"Chenxu Wang",
"Ping Jian",
"Zhen Yang"
] | Logical reading comprehension is a challenging task that entails grasping the underlying semantics of text and applying reasoning to deduce the correct answer. Prior researches have primarily focused on enhancing logical reasoning capabilities through Chain-of-Thought (CoT) or data augmentation. However, previous work ... | main | null | 10.1609/aaai.v39i24.34722 | 39 | 24 | 25345-25352 | official | 2409.14495 | title_snapshot |
10.1609/aaai.v39i24.34791 | Multi-Attribute Multi-Grained Adaptation of Pre-Trained Language Models for Text Understanding from Bayesian Perspective | https://ojs.aaai.org/index.php/AAAI/article/view/34791 | https://ojs.aaai.org/index.php/AAAI/article/download/34791/36946 | [
"You Zhang",
"Jin Wang",
"Liang-Chih Yu",
"Dan Xu",
"Xuejie Zhang"
] | Current neural networks often employ multi-domain-learning or attribute-injecting mechanisms to incorporate non-independent and identically distributed (non-IID) information for text understanding tasks by capturing individual characteristics and the relationships among samples. However, the extent of the impact of non... | main | null | 10.1609/aaai.v39i24.34791 | 39 | 24 | 25967-25975 | official | 2503.06085 | title_snapshot |
10.1609/aaai.v39i25.34910 | Searching for and Avoiding Hidden Sets Using Queries with Local Feedback | https://ojs.aaai.org/index.php/AAAI/article/view/34910 | https://ojs.aaai.org/index.php/AAAI/article/download/34910/37065 | [
"Tomasz Jurdzinski",
"Dariusz R. Kowalski"
] | Discovering elements of a hidden set, also known as Group Testing (GT), is a well-established area in which one party tries to discover elements hidden by the other party by asking queries and analyzing feedback. The feedback is a function of the intersection of the query with the hidden set - in our case, it is a clas... | main | null | 10.1609/aaai.v39i25.34910 | 39 | 25 | 27036-27044 | official | null | null |
10.1609/aaai.v39i11.33237 | Solving Higher-Order Quantified Boolean Satisfiability via Higher-Order Model Checking | https://ojs.aaai.org/index.php/AAAI/article/view/33237 | https://ojs.aaai.org/index.php/AAAI/article/download/33237/35392 | [
"Hiroshi Unno",
"Takeshi Tsukada",
"Jie-Hong Roland Jiang"
] | The satisfiability (SAT) problem of higher-order quantified Boolean formula (HOQBF) emerged as a natural generalization of SAT, quantified SAT, and second-order quantified SAT. It allows succinct encoding of k-EXPTIME problems beyond the reach of prior Boolean satisfiability formulations, but its application was hamper... | main | null | 10.1609/aaai.v39i11.33237 | 39 | 11 | 11372-11380 | official | null | null |
10.1609/aaai.v39i13.33541 | Improved Regret Bounds for Online Fair Division with Bandit Learning | https://ojs.aaai.org/index.php/AAAI/article/view/33541 | https://ojs.aaai.org/index.php/AAAI/article/download/33541/35696 | [
"Benjamin Schiffer",
"Shirley Zhang"
] | We study online fair division when there are a finite number of item types and the player values for the items are drawn randomly from distributions with unknown means. In this setting, a sequence of indivisible items arrives according to a random online process, and each item must be allocated to a single player. The ... | main | null | 10.1609/aaai.v39i13.33541 | 39 | 13 | 14079-14086 | official | 2501.07022 | title_snapshot |
10.1609/aaai.v39i13.33546 | Uncommon Belief in Rationality | https://ojs.aaai.org/index.php/AAAI/article/view/33546 | https://ojs.aaai.org/index.php/AAAI/article/download/33546/35701 | [
"Qi Shi",
"Pavel Naumov"
] | Common knowledge/belief in rationality is the traditional standard assumption in analysing interaction among agents. This paper proposes a graph-based language for capturing significantly more complicated structures of higher-order beliefs that agents might have about the rationality of the other agents. The two main c... | main | null | 10.1609/aaai.v39i13.33546 | 39 | 13 | 14120-14128 | official | 2412.09407 | title_snapshot |
10.1609/aaai.v39i17.33943 | Learning Complexity of Gradient Descent and Conjugate Gradient Algorithms | https://ojs.aaai.org/index.php/AAAI/article/view/33943 | https://ojs.aaai.org/index.php/AAAI/article/download/33943/36098 | [
"Xianqi Jiao",
"Jia Liu",
"Zhiping Chen"
] | Gradient Descent (GD) and Conjugate Gradient (CG) methods are among the most effective iterative algorithms for solving unconstrained optimization problems, particularly in machine learning and statistical modeling, where they are employed to minimize cost functions. In these algorithms, tunable parameters, such as ste... | main | null | 10.1609/aaai.v39i17.33943 | 39 | 17 | 17671-17679 | official | 2412.13473 | title_snapshot |
10.1609/aaai.v39i18.34132 | The Gradient of Algebraic Model Counting | https://ojs.aaai.org/index.php/AAAI/article/view/34132 | https://ojs.aaai.org/index.php/AAAI/article/download/34132/36287 | [
"Jaron Maene",
"Luc De Raedt"
] | Algebraic model counting unifies many inference tasks on logic formulas by exploiting semirings. Rather than focusing on inference, we consider learning, especially in statistical-relational and neurosymbolic AI, which combine logical, probabilistic and neural representations. Concretely, we show that the very same sem... | main | null | 10.1609/aaai.v39i18.34132 | 39 | 18 | 19367-19377 | official | 2502.18406 | title_snapshot |
10.1609/aaai.v39i19.34250 | Protecting Model Adaptation from Trojans in the Unlabeled Data | https://ojs.aaai.org/index.php/AAAI/article/view/34250 | https://ojs.aaai.org/index.php/AAAI/article/download/34250/36405 | [
"Lijun Sheng",
"Jian Liang",
"Ran He",
"Zilei Wang",
"Tieniu Tan"
] | Model adaptation tackles the distribution shift problem with a pre-trained model instead of raw data, which has become a popular paradigm due to its great privacy protection. Existing methods always assume adapting to a clean target domain, overlooking the security risks of unlabeled samples. This paper for the first t... | main | null | 10.1609/aaai.v39i19.34250 | 39 | 19 | 20427-20435 | official | 2401.06030 | title_snapshot |
10.1609/aaai.v39i21.34452 | Batch Selection for Multi-Label Classification Guided by Uncertainty and Dynamic Label Correlations | https://ojs.aaai.org/index.php/AAAI/article/view/34452 | https://ojs.aaai.org/index.php/AAAI/article/download/34452/36607 | [
"Ao Zhou",
"Bin Liu",
"Jin Wang",
"Grigorios Tsoumakas"
] | The accuracy of deep neural networks is significantly influenced by the effectiveness of mini-batch construction during training. In single-label scenarios, such as binary and multi-class classification tasks, it has been demonstrated that batch selection algorithms preferring samples with higher uncertainty achieve be... | main | null | 10.1609/aaai.v39i21.34452 | 39 | 21 | 22902-22909 | official | 2412.16521 | title_snapshot |
10.1609/aaai.v39i20.35385 | ICE-T: Interactions-aware Cross-column Contrastive Embedding for Heterogeneous Tabular Datasets | https://ojs.aaai.org/index.php/AAAI/article/view/35385 | https://ojs.aaai.org/index.php/AAAI/article/download/35385/37540 | [
"Tomas Tokar",
"Scott Sanner"
] | Finding high-quality representations of heterogeneous tabular datasets is crucial for their effective use in downstream machine learning tasks. Contrastive representation learning (CRL) methods have been previously shown to provide a straightforward way to learn such representations across various data domains. Current... | main | null | 10.1609/aaai.v39i20.35385 | 39 | 20 | 20904-20911 | official | null | null |
10.1609/aaai.v39i21.34458 | On Probabilistic Truncation in Privacy-preserving Machine Learning | https://ojs.aaai.org/index.php/AAAI/article/view/34458 | https://ojs.aaai.org/index.php/AAAI/article/download/34458/36613 | [
"Lijing Zhou",
"Bingsheng Zhang",
"Ziyu Wang",
"Tianpei Lu",
"Qingrui Song",
"Su Zhang",
"Hongrui Cui",
"Yu Yu"
] | Probabilistic truncation has been widely used in a broad range of privacy-preserving machine learning (PPML) platforms, such as EdaBits (Crypto 20), ABY 2.0 (Usenix 21), Crypten (NIPS 21), Piranha-Falcon (Usenix 22), and Bicoptor (S&P 23), etc. In this work, we examine the problems of common probabilistic truncation pr... | main | null | 10.1609/aaai.v39i21.34458 | 39 | 21 | 22955-22964 | official | 2309.04909 | title_judge |
10.1609/aaai.v39i3.32279 | Dis²Booth: Learning Image Distribution with Disentangled Features for Text-to-Image Diffusion Models | https://ojs.aaai.org/index.php/AAAI/article/view/32279 | https://ojs.aaai.org/index.php/AAAI/article/download/32279/34434 | [
"Guanqi Ding",
"Chengyu Yang",
"Shuhui Wang",
"Xincheng Li",
"Jinzhe Zhang",
"Xin Jin",
"Qingming Huang"
] | Personalized image generation enables customized content creation based on the text-to-image diffusion models.However, existing personalization methods focus on fine-tuning generative models to learn to generate specific single individuals or concepts, such as an image of a specific Corgi, but are unable to generate da... | main | null | 10.1609/aaai.v39i3.32279 | 39 | 3 | 2744-2752 | official | null | null |
10.1609/aaai.v39i5.32589 | Path-Adaptive Matting for Efficient Inference Under Various Computational Cost Constraints | https://ojs.aaai.org/index.php/AAAI/article/view/32589 | https://ojs.aaai.org/index.php/AAAI/article/download/32589/34744 | [
"Qinglin Liu",
"Zonglin Li",
"Xiaoqian Lv",
"Xin Sun",
"Ru Li",
"Shengping Zhang"
] | In this paper, we explore a novel image matting task aimed at achieving efficient inference under various computational cost constraints, specifically FLOP limitations, using a single matting network. Existing matting methods which have not explored scalable architectures or path-learning strategies, fail to tackle thi... | main | null | 10.1609/aaai.v39i5.32589 | 39 | 5 | 5532-5540 | official | 2503.03228 | title_snapshot |
10.1609/aaai.v39i6.32686 | SeeDiff: Off-the-Shelf Seeded Mask Generation from Diffusion Models | https://ojs.aaai.org/index.php/AAAI/article/view/32686 | https://ojs.aaai.org/index.php/AAAI/article/download/32686/34841 | [
"Joon Hyun Park",
"Kumju Jo",
"Sungyong Baik"
] | Entrusted with the goal of pixel-level object classification, the semantic segmentation networks entails the laborious preparation of pixel-level annotation masks. To obtain pixel-level annotation masks for a given class without human efforts, recent few works have proposed to generate pairs of images and annotation ma... | main | null | 10.1609/aaai.v39i6.32686 | 39 | 6 | 6406-6415 | official | 2507.19808 | title_snapshot |
10.1609/aaai.v39i1.32083 | Disentangled Table-Graph Representation for Interpretable Transmission Line Fault Location | https://ojs.aaai.org/index.php/AAAI/article/view/32083 | https://ojs.aaai.org/index.php/AAAI/article/download/32083/34238 | [
"Na Yu",
"Yutong Deng",
"Shunyu Liu",
"Kaixuan Chen",
"Tongya Zheng",
"Mingli Song"
] | The fault location task in power grids is crucial for maintaining social order and ensuring public safety. However, existing methods that rely on tabular state records often neglect the intrinsic topological influences of transmission lines, resulting in a segmented approach to fault location that consists of multiple ... | main | null | 10.1609/aaai.v39i1.32083 | 39 | 1 | 977-985 | official | null | null |
10.1609/aaai.v39i6.32694 | 3D-aware Select, Expand, and Squeeze Token for Aerial Action Recognition | https://ojs.aaai.org/index.php/AAAI/article/view/32694 | https://ojs.aaai.org/index.php/AAAI/article/download/32694/34849 | [
"Luying Peng",
"Xiangbo Shu",
"Yazhou Yao",
"Guo-Sen Xie"
] | Aerial Action Recognition (AAR) in videos captured by Unmanned Aerial Vehicles (UAVs) plays a vital role in numerous applications. However, current methods related to traditional action recognition primarily cater to fixed or near cameras, and rarely consider the movement disturbance of UAVs, including their varying at... | main | null | 10.1609/aaai.v39i6.32694 | 39 | 6 | 6479-6487 | official | null | null |
10.1609/aaai.v39i7.32733 | Fast Omni-Directional Image Super-Resolution: Adapting the Implicit Image Function with Pixel and Semantic-Wise Spherical Geometric Priors | https://ojs.aaai.org/index.php/AAAI/article/view/32733 | https://ojs.aaai.org/index.php/AAAI/article/download/32733/34888 | [
"Xuelin Shen",
"Yitong Wang",
"Silin Zheng",
"Kang Xiao",
"Wenhan Yang",
"Xu Wang"
] | In the context of Omni-Directional Image (ODI) Super-Resolution (SR), the unique challenge arises from the non-uniform oversampling characteristics caused by EquiRectangular Projection (ERP). Considerable efforts in designing complex spherical convolutions or polyhedron reprojection offer significant performance improv... | main | null | 10.1609/aaai.v39i7.32733 | 39 | 7 | 6833-6841 | official | 2502.05902 | title_snapshot |
10.1609/aaai.v39i7.32788 | From Representation Space to Prognostic Insights: Whole Slide Image Generation with Hierarchical Diffusion Model for Survival Prediction | https://ojs.aaai.org/index.php/AAAI/article/view/32788 | https://ojs.aaai.org/index.php/AAAI/article/download/32788/34943 | [
"Zhihao Tang",
"Xi Zhang",
"Chaozhuo Li"
] | Deep learning has significantly enhanced survival prediction using whole slide images (WSIs) by adopting a two-stage learning paradigm: WSI preparation and patient-level prediction. While existing research generally concentrates on developing advanced patient-level prediction modules, the critical importance of WSI pre... | main | null | 10.1609/aaai.v39i7.32788 | 39 | 7 | 7329-7337 | official | null | null |
10.1609/aaai.v39i10.33097 | Pragmatist: Multiview Conditional Diffusion Models for High-Fidelity 3D Reconstruction from Unposed Sparse Views | https://ojs.aaai.org/index.php/AAAI/article/view/33097 | https://ojs.aaai.org/index.php/AAAI/article/download/33097/35252 | [
"Songchun Zhang",
"Chunhui Zhao"
] | Inferring 3D structures from sparse, unposed observations is challenging due to its unconstrained nature. Recent methods propose to predict implicit representations directly from unposed inputs in a data-driven manner, achieving promising results. However, these methods do not utilize geometric priors and cannot halluc... | main | null | 10.1609/aaai.v39i10.33097 | 39 | 10 | 10112-10120 | official | 2412.08412 | title_snapshot |
10.1609/aaai.v39i10.33190 | TAMER: Tree-Aware Transformer for Handwritten Mathematical Expression Recognition | https://ojs.aaai.org/index.php/AAAI/article/view/33190 | https://ojs.aaai.org/index.php/AAAI/article/download/33190/35345 | [
"Jianhua Zhu",
"Wenqi Zhao",
"Yu Li",
"Xingjian Hu",
"Liangcai Gao"
] | Handwritten Mathematical Expression Recognition (HMER) has extensive applications in automated grading and office automation. However, existing sequence-based decoding methods, which directly predict LaTeX sequences, struggle to understand and model the inherent tree structure of LaTeX and often fail to ensure syntacti... | main | null | 10.1609/aaai.v39i10.33190 | 39 | 10 | 10950-10958 | official | 2408.08578 | title_snapshot |
10.1609/aaai.v39i12.33344 | AD4CD: Causal-Guided Anomaly Detection for Enhancing Cognitive Diagnosis | https://ojs.aaai.org/index.php/AAAI/article/view/33344 | https://ojs.aaai.org/index.php/AAAI/article/download/33344/35499 | [
"Haiping Ma",
"Yue Yao",
"Changqian Wang",
"Siyu Song",
"Yong Yang"
] | Cognitive diagnosis is a key task in computer-aided education, aimed at assessing a students' proficiency in specific knowledge concepts based on their responses to exercises. However, existing cognitive diagnosis models often overlook anomalies in students and exercises. For instance, some students might incorrectly r... | main | null | 10.1609/aaai.v39i12.33344 | 39 | 12 | 12337-12345 | official | null | null |
10.1609/aaai.v39i1.32009 | MOL-Mamba: Enhancing Molecular Representation with Structural & Electronic Insights | https://ojs.aaai.org/index.php/AAAI/article/view/32009 | https://ojs.aaai.org/index.php/AAAI/article/download/32009/34164 | [
"Jingjing Hu",
"Dan Guo",
"Zhan Si",
"Deguang Liu",
"Yunfeng Diao",
"Jing Zhang",
"Jinxing Zhou",
"Meng Wang"
] | Molecular representation learning plays a crucial role in various downstream tasks, such as molecular property prediction and drug design. To accurately represent molecules, Graph Neural Networks (GNNs) and Graph Transformers (GTs) have shown potential in the realm of self-supervised pretraining. However, existing appr... | main | null | 10.1609/aaai.v39i1.32009 | 39 | 1 | 317-325 | official | 2412.16483 | title_snapshot |
10.1609/aaai.v39i2.32114 | Dynamic Interactive Bimodal Hypergraph Networks for Emotion Recognition in Conversations | https://ojs.aaai.org/index.php/AAAI/article/view/32114 | https://ojs.aaai.org/index.php/AAAI/article/download/32114/34269 | [
"Xuping Chen",
"Wuzhen Shi"
] | The advancement in multimodal research has increased focus on Emotion Recognition in Conversations (ERC), targeting accurately identifying emotional changes. Methods based on graph convolution can better capture the dynamic changes of emotions and improve the accuracy and robustness of emotion recognition. However, exi... | main | null | 10.1609/aaai.v39i2.32114 | 39 | 2 | 1256-1264 | official | null | null |
10.1609/aaai.v39i3.32321 | ParseCaps: An Interpretable Parsing Capsule Network for Medical Image Diagnosis | https://ojs.aaai.org/index.php/AAAI/article/view/32321 | https://ojs.aaai.org/index.php/AAAI/article/download/32321/34476 | [
"Xinyu Geng",
"Jiaming Wang",
"Xiaolin Huang",
"Fanglin Chen",
"Jun Xu"
] | Deep learning has excelled in medical image classification, but its clinical application is limited by poor interpretability. Capsule networks, known for encoding hierarchical relationships and spatial features, show potential in addressing this issue. Nevertheless, traditional capsule networks often underperform due t... | main | null | 10.1609/aaai.v39i3.32321 | 39 | 3 | 3122-3130 | official | 2411.01564 | title_snapshot |
10.1609/aaai.v39i12.33469 | Addressing Cold-Start Problem in Click-Through Rate Prediction via Supervised Diffusion Modeling | https://ojs.aaai.org/index.php/AAAI/article/view/33469 | https://ojs.aaai.org/index.php/AAAI/article/download/33469/35624 | [
"Wenqiao Zhu",
"Lulu Wang",
"Jun Wu"
] | Predicting Click-Through Rates is a crucial function within recommendation and advertising platforms, as the output of CTR prediction determines the order of items shown to users. The Embedding and MLP paradigm has become a standard approach for industrial recommendation systems and has been widely deployed. However, t... | main | null | 10.1609/aaai.v39i12.33469 | 39 | 12 | 13455-13463 | official | 2504.06270 | title_snapshot |
10.1609/aaai.v39i16.33837 | Bootstrapping Heterogeneous Graph Representation Learning via Large Language Models: A Generalized Approach | https://ojs.aaai.org/index.php/AAAI/article/view/33837 | https://ojs.aaai.org/index.php/AAAI/article/download/33837/35992 | [
"Hang Gao",
"Chenhao Zhang",
"Fengge Wu",
"Changwen Zheng",
"Junsuo Zhao",
"Huaping Liu"
] | Graph representation learning methods are highly effective in handling complex non-Euclidean data by capturing intricate relationships and features within graph structures. However, traditional methods face challenges when dealing with heterogeneous graphs that contain various types of nodes and edges due to the divers... | main | null | 10.1609/aaai.v39i16.33837 | 39 | 16 | 16717-16726 | official | 2412.08038 | title_snapshot |
10.1609/aaai.v39i5.32483 | FNIN: A Fourier Neural Operator-based Numerical Integration Network for Surface-from-gradients | https://ojs.aaai.org/index.php/AAAI/article/view/32483 | https://ojs.aaai.org/index.php/AAAI/article/download/32483/34638 | [
"Jiaqi Leng",
"Yakun Ju",
"Yuanxu Duan",
"Jiangnan Zhang",
"Qingxuan Lv",
"Zuxuan Wu",
"Hao Fan"
] | Surface-from-gradients (SfG) aims to recover a three-dimensional (3D) surface from its gradients. Traditional methods encounter significant challenges in achieving high accuracy and handling high-resolution inputs, particularly facing the complex nature of discontinuities and the inefficiencies associated with large-sc... | main | null | 10.1609/aaai.v39i5.32483 | 39 | 5 | 4580-4588 | official | 2501.11876 | title_judge |
10.1609/aaai.v39i17.34001 | Real-Time Recurrent Reinforcement Learning | https://ojs.aaai.org/index.php/AAAI/article/view/34001 | https://ojs.aaai.org/index.php/AAAI/article/download/34001/36156 | [
"Julian Lemmel",
"Radu Grosu"
] | We introduce a biologically plausible RL framework for solving tasks in partially observable Markov decision processes (POMDPs). The proposed algorithm combines three integral parts: (1) A Meta-RL architecture, resembling the mammalian basal ganglia; (2) A biologically plausible reinforcement learning algorithm, exploi... | main | null | 10.1609/aaai.v39i17.34001 | 39 | 17 | 18189-18197 | official | 2311.04830 | title_snapshot |
10.1609/aaai.v39i8.32962 | CLIP-driven View-aware Prompt Learning for Unsupervised Vehicle Re-identification | https://ojs.aaai.org/index.php/AAAI/article/view/32962 | https://ojs.aaai.org/index.php/AAAI/article/download/32962/35117 | [
"Jiyang Xu",
"Qi Wang",
"Xin Xiong",
"Di Gai",
"Ruihua Zhou",
"Dong Wang"
] | With the emergence of vision-language pre-trained models, such as CLIP, some textual prompts have been gradually introduced recently into re-identification (Re-ID) tasks to obtain considerably robust multimodal information. However, most textual descriptions based on vehicle Re-ID tasks only contain identity index word... | main | null | 10.1609/aaai.v39i8.32962 | 39 | 8 | 8896-8904 | official | null | null |
10.1609/aaai.v39i21.34363 | Dynamic Expansion Diffusion Learning for Lifelong Generative Modelling | https://ojs.aaai.org/index.php/AAAI/article/view/34363 | https://ojs.aaai.org/index.php/AAAI/article/download/34363/36518 | [
"Fei Ye",
"Adrian G. Bors",
"Kun Zhang"
] | The diffusion model has lately been shown to achieve remarkable performances through its ability of generating high quality images. However, current diffusion model studies consider only learning from a single data distribution, resulting in catastrophic forgetting when attempting to learn new data. In this paper, we e... | main | null | 10.1609/aaai.v39i21.34363 | 39 | 21 | 22101-22109 | official | null | null |
10.1609/aaai.v39i12.33442 | Learned Image Transmission with Hierarchical Variational Autoencoder | https://ojs.aaai.org/index.php/AAAI/article/view/33442 | https://ojs.aaai.org/index.php/AAAI/article/download/33442/35597 | [
"Guangyi Zhang",
"Hanlei Li",
"Yunlong Cai",
"Qiyu Hu",
"Guanding Yu",
"Runmin Zhang"
] | In this paper, we introduce an innovative hierarchical joint source-channel coding (HJSCC) framework for image transmission, utilizing a hierarchical variational autoencoder (VAE). Our approach leverages a combination of bottom-up and top-down paths at the transmitter to autoregressively generate multiple hierarchical ... | main | null | 10.1609/aaai.v39i12.33442 | 39 | 12 | 13215-13223 | official | 2408.16340 | title_snapshot |
10.1609/aaai.v39i18.34095 | Noisy Node Classification by Bi-level Optimization Based Multi-Teacher Distillation | https://ojs.aaai.org/index.php/AAAI/article/view/34095 | https://ojs.aaai.org/index.php/AAAI/article/download/34095/36250 | [
"Yujing Liu",
"Zongqian Wu",
"Zhengyu Lu",
"Ci Nie",
"Guoqiu Wen",
"Yonghua Zhu",
"Xiaofeng Zhu"
] | Previous graph neural networks (GNNs) usually assume that the graph data is with clean labels for representation learning, but it is not true in real applications. In this paper, we propose a new multi-teacher distillation method based on bi-level optimization (namely BO-NNC), to conduct noisy node classification on th... | main | null | 10.1609/aaai.v39i18.34095 | 39 | 18 | 19033-19040 | official | 2404.17875 | title_snapshot |
10.1609/aaai.v39i23.34688 | Divide-Solve-Combine: An Interpretable and Accurate Prompting Framework for Zero-shot Multi-Intent Detection | https://ojs.aaai.org/index.php/AAAI/article/view/34688 | https://ojs.aaai.org/index.php/AAAI/article/download/34688/36843 | [
"Libo Qin",
"Qiguang Chen",
"Jingxuan Zhou",
"Jin Wang",
"Hao Fei",
"Wanxiang Che",
"Min Li"
] | Zero-shot multi-intent detection is capable of capturing multiple intents within a single utterance without any training data, which gains increasing attention. Building on the success of large language models (LLM), dominant approaches in the literature explore prompting techniques to enable zero-shot multi-intent det... | main | null | 10.1609/aaai.v39i23.34688 | 39 | 23 | 25038-25046 | official | null | null |
10.1609/aaai.v39i23.34629 | Utterance-level Emotion Recognition in Conversation with Conversation-level Supervision | https://ojs.aaai.org/index.php/AAAI/article/view/34629 | https://ojs.aaai.org/index.php/AAAI/article/download/34629/36784 | [
"Ximing Li",
"Yuanchao Dai",
"Zhiyao Yang",
"Jinjin Chi",
"Wanfu Gao",
"Lin Yuanbo Wu"
] | Emotion Recognition in Conversations (ERC) involves automatically identifying the emotion of each utterance in conversations. The emotion of an utterance is contingent to the conversation context, and thus, annotating each utterance in ERC entails repetitive screening the whole conversation from annotators. Such a requ... | main | null | 10.1609/aaai.v39i23.34629 | 39 | 23 | 24503-24511 | official | null | null |
10.1609/aaai.v39i21.34466 | Fully Test-time Adaptation for Tabular Data | https://ojs.aaai.org/index.php/AAAI/article/view/34466 | https://ojs.aaai.org/index.php/AAAI/article/download/34466/36621 | [
"Zhi Zhou",
"Kun-Yang Yu",
"Lan-Zhe Guo",
"Yu-Feng Li"
] | Tabular data plays a vital role in various real-world scenarios and finds extensive applications. Although recent deep tabular models have shown remarkable success, they still struggle to handle data distribution shifts, leading to performance degradation when testing distributions change. To remedy this, a robust tabu... | main | null | 10.1609/aaai.v39i21.34466 | 39 | 21 | 23027-23035 | official | 2412.10871 | title_snapshot |
10.1609/aaai.v39i23.34602 | Importance Weighting Can Help Large Language Models Self-Improve | https://ojs.aaai.org/index.php/AAAI/article/view/34602 | https://ojs.aaai.org/index.php/AAAI/article/download/34602/36757 | [
"Chunyang Jiang",
"Chi-Min Chan",
"Wei Xue",
"Qifeng Liu",
"Yike Guo"
] | Large language models (LLMs) have shown remarkable capability in numerous tasks and applications. However, fine-tuning LLMs using high-quality datasets under external supervision remains prohibitively expensive. In response, LLM self-improvement approaches have been vibrantly developed recently. The typical paradigm of... | main | null | 10.1609/aaai.v39i23.34602 | 39 | 23 | 24257-24265 | official | 2408.09849 | title_snapshot |
10.1609/aaai.v39i23.34594 | Enhancing Non-English Capabilities of English-Centric Large Language Models Through Deep Supervision Fine-Tuning | https://ojs.aaai.org/index.php/AAAI/article/view/34594 | https://ojs.aaai.org/index.php/AAAI/article/download/34594/36749 | [
"Wenshuai Huo",
"Xiaocheng Feng",
"Yichong Huang",
"Chengpeng Fu",
"Baohang Li",
"Yangfan Ye",
"Zhirui Zhang",
"Dandan Tu",
"Duyu Tang",
"Yunfei Lu",
"Hui Wang",
"Bing Qin"
] | Large language models (LLMs) have demonstrated significant progress in multilingual language understanding and generation. However, due to the imbalance in training data, their capabilities in non-English languages are limited. Recent studies revealed the English-pivot multilingual mechanism of LLMs, where LLMs implici... | main | null | 10.1609/aaai.v39i23.34594 | 39 | 23 | 24185-24193 | official | 2503.01275 | title_snapshot |
10.1609/aaai.v39i25.34845 | Revelations: A Decidable Class of POMDPs with Omega-Regular Objectives | https://ojs.aaai.org/index.php/AAAI/article/view/34845 | https://ojs.aaai.org/index.php/AAAI/article/download/34845/37000 | [
"Marius Belly",
"Nathanaël Fijalkow",
"Hugo Gimbert",
"Florian Horn",
"Guillermo A. Pérez",
"Pierre Vandenhove"
] | Partially observable Markov decision processes (POMDPs) form a prominent model for uncertainty in sequential decision making. We are interested in constructing algorithms with theoretical guarantees to determine whether the agent has a strategy ensuring a given specification with probability 1. This well-studied proble... | main | null | 10.1609/aaai.v39i25.34845 | 39 | 25 | 26454-26462 | official | 2412.12063 | title_snapshot |
10.1609/aaai.v39i2.32228 | AoP-SAM: Automation of Prompts for Efficient Segmentation | https://ojs.aaai.org/index.php/AAAI/article/view/32228 | https://ojs.aaai.org/index.php/AAAI/article/download/32228/34383 | [
"Yi Chen",
"Muyoung Son",
"Chuanbo Hua",
"Joo-Young Kim"
] | The Segment Anything Model (SAM) is a powerful foundation model for image segmentation, showing robust zero-shot generalization through prompt engineering. However, relying on manual prompts is impractical for real-world applications, particularly in scenarios where rapid prompt provision and resource efficiency are cr... | main | null | 10.1609/aaai.v39i2.32228 | 39 | 2 | 2284-2292 | official | 2505.11980 | title_snapshot |
10.1609/aaai.v39i17.34033 | Hierarchical Mixture of Experts: Generalizable Learning for High-Level Synthesis | https://ojs.aaai.org/index.php/AAAI/article/view/34033 | https://ojs.aaai.org/index.php/AAAI/article/download/34033/36188 | [
"Weikai Li",
"Ding Wang",
"Zijian Ding",
"Atefeh Sohrabizadeh",
"Zongyue Qin",
"Jason Cong",
"Yizhou Sun"
] | High-level synthesis (HLS) is a widely used tool in designing Field Programmable Gate Array (FPGA). HLS enables FPGA design with software programming languages by compiling the source code into an FPGA circuit. The source code includes a program (called ``kernel'') and several pragmas that instruct hardware synthesis, ... | main | null | 10.1609/aaai.v39i17.34033 | 39 | 17 | 18476-18484 | official | 2410.19225 | title_snapshot |
10.1609/aaai.v39i4.32372 | Exploiting Multimodal Spatial-temporal Patterns for Video Object Tracking | https://ojs.aaai.org/index.php/AAAI/article/view/32372 | https://ojs.aaai.org/index.php/AAAI/article/download/32372/34527 | [
"Xiantao Hu",
"Ying Tai",
"Xu Zhao",
"Chen Zhao",
"Zhenyu Zhang",
"Jun Li",
"Bineng Zhong",
"Jian Yang"
] | Multimodal tracking has garnered widespread attention as a result of its ability to effectively address the inherent limitations of traditional RGB tracking. However, existing multimodal trackers mainly focus on the fusion and enhancement of spatial features or merely leverage the sparse temporal relationships between ... | main | null | 10.1609/aaai.v39i4.32372 | 39 | 4 | 3581-3589 | official | 2412.15691 | title_snapshot |
10.1609/aaai.v39i22.34524 | XCOT: Cross-lingual Instruction Tuning for Cross-lingual Chain-of-Thought Reasoning | https://ojs.aaai.org/index.php/AAAI/article/view/34524 | https://ojs.aaai.org/index.php/AAAI/article/download/34524/36679 | [
"Linzheng Chai",
"Jian Yang",
"Tao Sun",
"Hongcheng Guo",
"Jiaheng Liu",
"Bing Wang",
"Xinnian Liang",
"Jiaqi Bai",
"Tongliang Li",
"Qiyao Peng",
"Zhoujun Li"
] | Chain-of-thought (CoT) has emerged as a powerful technique to elicit reasoning in large language models and improve a variety of downstream tasks. CoT mainly demonstrates excellent performance in English, but its usage in low-resource languages is constrained due to poor language generalization. To bridge the gap among... | main | null | 10.1609/aaai.v39i22.34524 | 39 | 22 | 23550-23558 | official | 2401.07037 | title_snapshot |
10.1609/aaai.v39i8.32924 | RETRACTED: GEONet: Global Enhancement and Optimization Network for Lane Detection | https://ojs.aaai.org/index.php/AAAI/article/view/32924 | https://ojs.aaai.org/index.php/AAAI/article/download/32924/35079 | [
"Suyang Xi",
"Yunhao Liu",
"Hong Ding",
"Mingshuo Wang",
"Zhenghan Chen",
"Xiaoxuan Liang"
] | Lane detection plays a crucial role in autonomous driving systems, enabling vehicles to navigate safely and efficiently in complex environment. Despite significant advancements in recent years, accurate lane detection remains a challenging task, particularly in scenarios with occlusions, ambiguous lane markings, and di... | main | null | 10.1609/aaai.v39i8.32924 | 39 | 8 | 8559-8566 | official | null | null |
10.1609/aaai.v39i18.34083 | AeroGTO: An Efficient Graph-Transformer Operator for Learning Large-Scale Aerodynamics of 3D Vehicle Geometries | https://ojs.aaai.org/index.php/AAAI/article/view/34083 | https://ojs.aaai.org/index.php/AAAI/article/download/34083/36238 | [
"Pengwei Liu",
"Pengkai Wang",
"Xingyu Ren",
"Hangjie Yuan",
"Zhongkai Hao",
"Chao Xu",
"Shengze Cai",
"Dong Ni"
] | Obtaining high-precision aerodynamics in the automotive industry relies on large-scale simulations with computational fluid dynamics, which are generally time-consuming and computationally expensive. Recent advances in operator learning for partial differential equations offer promising improvements in terms of efficie... | main | null | 10.1609/aaai.v39i18.34083 | 39 | 18 | 18924-18932 | official | null | null |
10.1609/aaai.v39i5.32556 | Decoupling Appearance Variations with 3D Consistent Features in Gaussian Splatting | https://ojs.aaai.org/index.php/AAAI/article/view/32556 | https://ojs.aaai.org/index.php/AAAI/article/download/32556/34711 | [
"Jiaqi Lin",
"Zhihao Li",
"Binxiao Huang",
"Xiao Tang",
"Jianzhuang Liu",
"Shiyong Liu",
"Xiaofei Wu",
"Fenglong Song",
"Wenming Yang"
] | Gaussian Splatting has emerged as a prominent 3D representation in novel view synthesis, but it still suffers from appearance variations, which are caused by various factors, such as modern camera ISPs, different time of day, weather conditions, and local light changes. These variations can lead to floaters and color d... | main | null | 10.1609/aaai.v39i5.32556 | 39 | 5 | 5236-5244 | official | 2501.10788 | title_snapshot |
10.1609/aaai.v39i6.32669 | EOV-Seg: Efficient Open-Vocabulary Panoptic Segmentation | https://ojs.aaai.org/index.php/AAAI/article/view/32669 | https://ojs.aaai.org/index.php/AAAI/article/download/32669/34824 | [
"Hongwei Niu",
"Jie Hu",
"Jianghang Lin",
"Guannan Jiang",
"Shengchuan Zhang"
] | Open-vocabulary panoptic segmentation aims to segment and classify everything in diverse scenes across an unbounded vocabulary. Existing methods typically employ two-stage or single-stage framework. The two-stage framework involves cropping the image multiple times using masks generated by a mask generator, followed by... | main | null | 10.1609/aaai.v39i6.32669 | 39 | 6 | 6254-6262 | official | 2412.08628 | title_snapshot |
10.1609/aaai.v39i12.33387 | UniPCGC: Towards Practical Point Cloud Geometry Compression via an Efficient Unified Approach | https://ojs.aaai.org/index.php/AAAI/article/view/33387 | https://ojs.aaai.org/index.php/AAAI/article/download/33387/35542 | [
"Kangli Wang",
"Wei Gao"
] | Learning-based point cloud compression methods have made significant progress in terms of performance. However, these methods still encounter challenges including high complexity, limited compression modes, and a lack of support for variable rate, which restrict the practical application of these methods. In order to p... | main | null | 10.1609/aaai.v39i12.33387 | 39 | 12 | 12721-12729 | official | 2503.18541 | title_snapshot |
10.1609/aaai.v39i11.33285 | Beyond Graph Convolution: Multimodal Recommendation with Topology-aware MLPs | https://ojs.aaai.org/index.php/AAAI/article/view/33285 | https://ojs.aaai.org/index.php/AAAI/article/download/33285/35440 | [
"Junjie Huang",
"Jiarui Qin",
"Yong Yu",
"Weinan Zhang"
] | Given the large volume of side information from different modalities, multimodal recommender systems have become increasingly vital, as they exploit richer semantic information beyond user-item interactions. Recent works highlight that leveraging Graph Convolutional Networks (GCNs) to explicitly model multimodal item-i... | main | null | 10.1609/aaai.v39i11.33285 | 39 | 11 | 11808-11816 | official | 2412.11747 | title_snapshot |
10.1609/aaai.v39i19.34257 | PatentLMM: Large Multimodal Model for Generating Descriptions for Patent Figures | https://ojs.aaai.org/index.php/AAAI/article/view/34257 | https://ojs.aaai.org/index.php/AAAI/article/download/34257/36412 | [
"Shreya Shukla",
"Nakul Sharma",
"Manish Gupta",
"Anand Mishra"
] | Writing comprehensive and accurate descriptions of technical drawings in patent documents is crucial to effective knowledge sharing and enabling the replication and protection of intellectual property. However, automation of this task has been largely overlooked by the research community. To this end, we introduce Pate... | main | null | 10.1609/aaai.v39i19.34257 | 39 | 19 | 20488-20496 | official | 2501.15074 | title_snapshot |
10.1609/aaai.v39i12.33466 | Tokenphormer: Structure-aware Multi-token Graph Transformer for Node Classification | https://ojs.aaai.org/index.php/AAAI/article/view/33466 | https://ojs.aaai.org/index.php/AAAI/article/download/33466/35621 | [
"Zijie Zhou",
"Zhaoqi Lu",
"Xuekai Wei",
"Rongqin Chen",
"Shenghui Zhang",
"Pak Lon Ip",
"Leong Hou U"
] | Graph Neural Networks (GNNs) are widely used in graph data mining tasks. Traditional GNNs follow a message passing scheme that can effectively utilize local and structural information. However, the phenomena of over-smoothing and over-squashing limit the receptive field in message passing processes. Graph Transformers ... | main | null | 10.1609/aaai.v39i12.33466 | 39 | 12 | 13428-13436 | official | 2412.15302 | title_snapshot |
10.1609/aaai.v39i10.33183 | Expanding the Scope of Negatives: Boosting Image-Text Matching with Negatives Distribution Guided Learning | https://ojs.aaai.org/index.php/AAAI/article/view/33183 | https://ojs.aaai.org/index.php/AAAI/article/download/33183/35338 | [
"Zhao Zhou",
"Weizhong Zhang",
"Xiangcheng Du",
"Yingbin Zheng",
"Cheng Jin"
] | Image-text matching is a crucial task that bridges visual and linguistic modalities. Recent research typically formulates it into the problem of maximizing the margin with the truly hardest negatives to enhance the learning efficiency and avoid the poor local optima. We argue that such formulation can lead to a serious... | main | null | 10.1609/aaai.v39i10.33183 | 39 | 10 | 10887-10895 | official | null | null |
10.1609/aaai.v39i9.33019 | MM-Tracker: Motion Mamba for UAV-platform Multiple Object Tracking | https://ojs.aaai.org/index.php/AAAI/article/view/33019 | https://ojs.aaai.org/index.php/AAAI/article/download/33019/35174 | [
"Mufeng Yao",
"Jinlong Peng",
"Qingdong He",
"Bo Peng",
"Hao Chen",
"Mingmin Chi",
"Chao Liu",
"Jon Atli Benediktsson"
] | Multiple object tracking (MOT) from unmanned aerial vehicle (UAV) platforms requires efficient motion modeling. This is because UAV-MOT faces both local object motion and global camera motion. Motion blur also increases the difficulty of detecting large moving objects. Previous UAV motion modeling approaches either foc... | main | null | 10.1609/aaai.v39i9.33019 | 39 | 9 | 9409-9417 | official | 2407.10485 | title_judge |
10.1609/aaai.v39i3.32290 | Vision-guided Text Mining for Unsupervised Cross-modal Hashing with Community Similarity Quantization | https://ojs.aaai.org/index.php/AAAI/article/view/32290 | https://ojs.aaai.org/index.php/AAAI/article/download/32290/34445 | [
"Haozhi Fan",
"Yuan Cao"
] | Cross-modal retrieval, as an emerging field within multimedia research, has gained significant attention in recent years. Unsupervised cross-modal hashing methods are attractive due to their ability to capture latent relationships within the data without label supervision and to produce compact hash codes for high sear... | main | null | 10.1609/aaai.v39i3.32290 | 39 | 3 | 2843-2851 | official | null | null |
10.1609/aaai.v39i19.34282 | Single-View Graph Contrastive Learning with Soft Neighborhood Awareness | https://ojs.aaai.org/index.php/AAAI/article/view/34282 | https://ojs.aaai.org/index.php/AAAI/article/download/34282/36437 | [
"Qingqiang Sun",
"Chaoqi Chen",
"Ziyue Qiao",
"Xubin Zheng",
"Kai Wang"
] | Most graph contrastive learning (GCL) methods heavily rely on cross-view contrast, thus facing several concomitant challenges, such as the complexity of designing effective augmentations, the potential for information loss between views, and increased computational costs. To mitigate reliance on cross-view contrasts, w... | main | null | 10.1609/aaai.v39i19.34282 | 39 | 19 | 20708-20716 | official | 2412.09261 | title_snapshot |
10.1609/aaai.v39i16.33838 | Auto-Regressive Moving Diffusion Models for Time Series Forecasting | https://ojs.aaai.org/index.php/AAAI/article/view/33838 | https://ojs.aaai.org/index.php/AAAI/article/download/33838/35993 | [
"Jiaxin Gao",
"Qinglong Cao",
"Yuntian Chen"
] | Time series forecasting (TSF) is essential in various domains, and recent advancements in diffusion-based TSF models have shown considerable promise. However, these models typically adopt traditional diffusion patterns, treating TSF as a noise-based conditional generation task. This approach neglects the inherent conti... | main | null | 10.1609/aaai.v39i16.33838 | 39 | 16 | 16727-16735 | official | 2412.09328 | title_snapshot |
10.1609/aaai.v39i20.35451 | SkillTree: Explainable Skill-Based Deep Reinforcement Learning for Long-Horizon Control Tasks | https://ojs.aaai.org/index.php/AAAI/article/view/35451 | https://ojs.aaai.org/index.php/AAAI/article/download/35451/37606 | [
"Yongyan Wen",
"Siyuan Li",
"Rongchang Zuo",
"Lei Yuan",
"Hangyu Mao",
"Peng Liu"
] | Deep reinforcement learning (DRL) has achieved remarkable success in various domains, yet its reliance on neural networks results in a lack of transparency, which limits its practical applications in safety-critical and human-agent interaction domains. Decision trees, known for their notable explainability, have emerge... | main | null | 10.1609/aaai.v39i20.35451 | 39 | 20 | 21491-21500 | official | 2411.12173 | title_snapshot |
10.1609/aaai.v39i16.33922 | Adaptive Prompt-Based Semantic Embedding with Inspire Potential of Implicit Knowledge for Cross-Modal Retrieval | https://ojs.aaai.org/index.php/AAAI/article/view/33922 | https://ojs.aaai.org/index.php/AAAI/article/download/33922/36077 | [
"Xin Huang",
"Shilong Wang",
"Tong Jia",
"Zhihang Gou",
"Jingjing Li"
] | In the era of big data, cross-modal retrieval is increasingly important in research and application. Given the latent complexity and non-intuitive nature of cross-modal relationships, leveraging external knowledge such as large models has become a popular approach to facilitate modality alignment. Existing methods typi... | main | null | 10.1609/aaai.v39i16.33922 | 39 | 16 | 17485-17493 | official | null | null |
10.1609/aaai.v39i15.33686 | Unleashing the Potential of Model Bias for Generalized Category Discovery | https://ojs.aaai.org/index.php/AAAI/article/view/33686 | https://ojs.aaai.org/index.php/AAAI/article/download/33686/35841 | [
"Wenbin An",
"Haonan Lin",
"Jiahao Nie",
"Feng Tian",
"Wenkai Shi",
"Yaqiang Wu",
"Qianying Wang",
"Ping Chen"
] | Generalized Category Discovery is a significant and complex task that aims to identify both known and undefined novel categories from a set of unlabeled data, leveraging another labeled dataset containing only known categories. The primary challenges stem from model bias induced by pre-training on only known categories... | main | null | 10.1609/aaai.v39i15.33686 | 39 | 15 | 15365-15373 | official | 2412.12501 | title_snapshot |
10.1609/aaai.v39i1.32050 | Dual-Channel Interactive Graph Transformer for Traffic Classification with Message-Aware Flow Representation | https://ojs.aaai.org/index.php/AAAI/article/view/32050 | https://ojs.aaai.org/index.php/AAAI/article/download/32050/34205 | [
"Xing Qiu",
"Guang Cheng",
"Weizhou Zhu",
"Dandan Niu",
"Nan Fu"
] | Traffic classification is crucial for network management and security. Recently, deep learning-based methods have demonstrated good performance in traffic classification. However, they primarily capture features from raw packet bytes, overlooking the significance of inter-packet correlations within flows from a global ... | main | null | 10.1609/aaai.v39i1.32050 | 39 | 1 | 685-693 | official | null | null |
10.1609/aaai.v39i11.33280 | Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning | https://ojs.aaai.org/index.php/AAAI/article/view/33280 | https://ojs.aaai.org/index.php/AAAI/article/download/33280/35435 | [
"Chengkai Han",
"Jingyuan Wang",
"Yongyao Wang",
"Xie Yu",
"Hao Lin",
"Chao Li",
"Junjie Wu"
] | Effective urban traffic management is vital for sustainable city development, relying on intelligent systems with machine learning tasks such as traffic flow prediction and travel time estimation. Traditional approaches usually focus on static road network and trajectory representation learning, and overlook the dynami... | main | null | 10.1609/aaai.v39i11.33280 | 39 | 11 | 11763-11771 | official | 2502.06870 | title_snapshot |
10.1609/aaai.v39i24.34706 | Structured Packing in LLM Training Improves Long Context Utilization | https://ojs.aaai.org/index.php/AAAI/article/view/34706 | https://ojs.aaai.org/index.php/AAAI/article/download/34706/36861 | [
"Konrad Staniszewski",
"Szymon Tworkowski",
"Sebastian Jaszczur",
"Yu Zhao",
"Henryk Michalewski",
"Łukasz Kuciński",
"Piotr Miłoś"
] | Recent advancements in long-context language modeling have attracted significant attention, yet their practical applications often suffer from suboptimal context utilization. To efficiently address this issue, we introduce the Structured Packing for Long Context, SPLiCe, a method that uses retrieval to collate mutually... | main | null | 10.1609/aaai.v39i24.34706 | 39 | 24 | 25201-25209 | official | 2312.17296 | title_snapshot |
10.1609/aaai.v39i11.33267 | Amplifier: Bringing Attention to Neglected Low-Energy Components in Time Series Forecasting | https://ojs.aaai.org/index.php/AAAI/article/view/33267 | https://ojs.aaai.org/index.php/AAAI/article/download/33267/35422 | [
"Jingru Fei",
"Kun Yi",
"Wei Fan",
"Qi Zhang",
"Zhendong Niu"
] | We propose an energy amplification technique to address the issue that existing models easily overlook low-energy components in time series forecasting. This technique comprises an energy amplification block and an energy restoration block. The energy amplification block enhances the energy of low-energy components to ... | main | null | 10.1609/aaai.v39i11.33267 | 39 | 11 | 11645-11653 | official | 2501.17216 | title_snapshot |
10.1609/aaai.v39i25.34829 | Mjölnir: Breaking the Shield of Perturbation-Protected Gradients via Adaptive Diffusion | https://ojs.aaai.org/index.php/AAAI/article/view/34829 | https://ojs.aaai.org/index.php/AAAI/article/download/34829/36984 | [
"Xuan Liu",
"Siqi Cai",
"Qihua Zhou",
"Song Guo",
"Ruibin Li",
"Kaiwei Lin"
] | Perturbation-based mechanisms, such as differential privacy, mitigate gradient leakage attacks by introducing noise into the gradients, thereby preventing attackers from reconstructing clients' private data from the leaked gradients. However, can gradient perturbation protection mechanisms truly defend against all grad... | main | null | 10.1609/aaai.v39i25.34829 | 39 | 25 | 26308-26316 | official | 2407.05285 | title_snapshot |
10.1609/aaai.v39i12.33417 | Federated Graph Condensation with Information Bottleneck Principles | https://ojs.aaai.org/index.php/AAAI/article/view/33417 | https://ojs.aaai.org/index.php/AAAI/article/download/33417/35572 | [
"Bo Yan",
"Sihao He",
"Cheng Yang",
"Shang Liu",
"Yang Cao",
"Chuan Shi"
] | Graph condensation (GC), which reduces the size of a large-scale graph by synthesizing a small-scale condensed graph as its substitution, has benefited various graph learning tasks. However, existing GC methods rely on centralized data storage, which is unfeasible for real-world decentralized data distribution, and ove... | main | null | 10.1609/aaai.v39i12.33417 | 39 | 12 | 12990-12998 | official | 2405.03911 | title_snapshot |
10.1609/aaai.v39i11.33263 | Active Large Language Model-Based Knowledge Distillation for Session-Based Recommendation | https://ojs.aaai.org/index.php/AAAI/article/view/33263 | https://ojs.aaai.org/index.php/AAAI/article/download/33263/35418 | [
"Yingpeng Du",
"Zhu Sun",
"Ziyan Wang",
"Haoyan Chua",
"Jie Zhang",
"Yew-Soon Ong"
] | Large language models (LLMs) provide a promising way for accurate session-based recommendation (SBR), but they demand substantial computational time and memory. Knowledge distillation (KD)-based methods can alleviate these issues by transferring the knowledge to a small student, which trains a student based on the pred... | main | null | 10.1609/aaai.v39i11.33263 | 39 | 11 | 11607-11615 | official | 2502.15685 | title_snapshot |
10.1609/aaai.v39i1.32061 | dyAb: Flow Matching for Flexible Antibody Design with AlphaFold-driven Pre-binding Antigen | https://ojs.aaai.org/index.php/AAAI/article/view/32061 | https://ojs.aaai.org/index.php/AAAI/article/download/32061/34216 | [
"Cheng Tan",
"Yijie Zhang",
"Zhangyang Gao",
"Yufei Huang",
"Haitao Lin",
"Lirong Wu",
"Fandi Wu",
"Mathieu Blanchette",
"Stan Z. Li"
] | The development of therapeutic antibodies heavily relies on accurate predictions of how antigens will interact with antibodies. Existing computational methods in antibody design often overlook crucial conformational changes that antigens undergo during the binding process, significantly impacting the reliability of the... | main | null | 10.1609/aaai.v39i1.32061 | 39 | 1 | 782-790 | official | 2503.01910 | title_snapshot |
10.1609/aaai.v39i6.32605 | Asymmetric Visual Semantic Embedding Framework for Efficient Vision-Language Alignment | https://ojs.aaai.org/index.php/AAAI/article/view/32605 | https://ojs.aaai.org/index.php/AAAI/article/download/32605/34760 | [
"Yang Liu",
"Mengyuan Liu",
"Shudong Huang",
"Jiancheng Lv"
] | Learning visual semantic similarity is a critical challenge in bridging the gap between images and texts. However, there exist inherent variations between vision and language data, such as information density, i.e., images can contain textual information from multiple different views, which makes it difficult to comput... | main | null | 10.1609/aaai.v39i6.32605 | 39 | 6 | 5676-5684 | official | 2503.06974 | title_snapshot |
10.1609/aaai.v39i24.34752 | UniMuMo: Unified Text, Music, and Motion Generation | https://ojs.aaai.org/index.php/AAAI/article/view/34752 | https://ojs.aaai.org/index.php/AAAI/article/download/34752/36907 | [
"Han Yang",
"Kun Su",
"Yutong Zhang",
"Jiaben Chen",
"Kaizhi Qian",
"Gaowen Liu",
"Chuang Gan"
] | We introduce UniMuMo, a unified multimodal model capable of taking arbitrary text, music, and motion data as input conditions to generate outputs across all three modalities. To address the lack of time-synchronized data, we align unpaired music and motion data based on rhythmic patterns to leverage existing large-scal... | main | null | 10.1609/aaai.v39i24.34752 | 39 | 24 | 25615-25623 | official | 2410.04534 | title_snapshot |
10.1609/aaai.v39i16.33841 | Asymmetric Reinforcing Against Multi-Modal Representation Bias | https://ojs.aaai.org/index.php/AAAI/article/view/33841 | https://ojs.aaai.org/index.php/AAAI/article/download/33841/35996 | [
"Xiyuan Gao",
"Bing Cao",
"Pengfei Zhu",
"Nannan Wang",
"Qinghua Hu"
] | The strength of multimodal learning lies in its ability to integrate information from various sources, providing rich and comprehensive insights. However, in real-world scenarios, multi-modal systems often face the challenge of dynamic modality contributions, the dominance of different modalities may change with the en... | main | null | 10.1609/aaai.v39i16.33841 | 39 | 16 | 16754-16762 | official | 2501.01240 | title_snapshot |
10.1609/aaai.v39i8.32857 | HomoMatcher: Achieving Dense Feature Matching with Semi-Dense Efficiency by Homography Estimation | https://ojs.aaai.org/index.php/AAAI/article/view/32857 | https://ojs.aaai.org/index.php/AAAI/article/download/32857/35012 | [
"Xiaolong Wang",
"Lei Yu",
"Yingying Zhang",
"Jiangwei Lao",
"Lixiang Ru",
"Liheng Zhong",
"Jingdong Chen",
"Yu Zhang",
"Ming Yang"
] | Feature matching between image pairs is a fundamental problem in computer vision that drives many applications, such as SLAM. Recently, semi-dense matching approaches have achieved substantial performance enhancements and established a widely-accepted coarse-to-fine paradigm. However, the majority of existing methods f... | main | null | 10.1609/aaai.v39i8.32857 | 39 | 8 | 7952-7960 | official | 2411.06700 | title_judge |
10.1609/aaai.v39i20.35497 | FedVCK: Non-IID Robust and Communication-Efficient Federated Learning via Valuable Condensed Knowledge for Medical Image Analysis | https://ojs.aaai.org/index.php/AAAI/article/view/35497 | https://ojs.aaai.org/index.php/AAAI/article/download/35497/37652 | [
"Guochen Yan",
"Luyuan Xie",
"Xinyi Gao",
"Wentao Zhang",
"Qingni Shen",
"Yuejian Fang",
"Zhonghai Wu"
] | Federated learning has become a promising solution for collaboration among medical institutions. However, data owned by each institution would be highly heterogeneous and the distribution is always non-independent and identical distribution (non-IID), resulting in client drift and unsatisfactory performance. Despite ex... | main | null | 10.1609/aaai.v39i20.35497 | 39 | 20 | 21904-21912 | official | 2412.18557 | title_snapshot |
10.1609/aaai.v39i12.33445 | Lightweight Yet Fine-Grained: A Graph Capsule Convolutional Network with Subspace Alignment for Shared-Account Sequential Recommendation | https://ojs.aaai.org/index.php/AAAI/article/view/33445 | https://ojs.aaai.org/index.php/AAAI/article/download/33445/35600 | [
"Jinyu Zhang",
"Zhongying Zhao",
"Chao Li",
"Yanwei Yu"
] | Shared-account Sequential Recommendation (SSR) aims to provide personalized recommendations for accounts shared by multiple users with varying sequential preferences. Previous studies on SSR struggle to capture the fine-grained associations between interactions and different latent users within the shared account's hyb... | main | null | 10.1609/aaai.v39i12.33445 | 39 | 12 | 13242-13250 | official | 2412.13408 | title_snapshot |
10.1609/aaai.v39i10.33149 | Anti-Diffusion: Preventing Abuse of Modifications of Diffusion-Based Models | https://ojs.aaai.org/index.php/AAAI/article/view/33149 | https://ojs.aaai.org/index.php/AAAI/article/download/33149/35304 | [
"Li Zheng",
"Liangbin Xie",
"Jiantao Zhou",
"Xintao Wang",
"Haiwei Wu",
"Jinyu Tian"
] | Although diffusion-based techniques have shown remarkable success in image generation and editing tasks, their abuse can lead to severe negative social impacts. Recently, some works have been proposed to provide defense against the abuse of diffusion-based methods. However, their protection may be limited in specific s... | main | null | 10.1609/aaai.v39i10.33149 | 39 | 10 | 10582-10590 | official | 2503.05595 | title_snapshot |
10.1609/aaai.v39i8.32903 | Structural Pruning via Spatial-aware Information Redundancy for Semantic Segmentation | https://ojs.aaai.org/index.php/AAAI/article/view/32903 | https://ojs.aaai.org/index.php/AAAI/article/download/32903/35058 | [
"Dongyue Wu",
"Zilin Guo",
"Li Yu",
"Nong Sang",
"Changxin Gao"
] | In recent years, semantic segmentation has flourished in various applications. However, the high computational cost remains a significant challenge that hinders its further adoption. The filter pruning method for structured network slimming offers a direct and effective solution for the reduction of segmentation networ... | main | null | 10.1609/aaai.v39i8.32903 | 39 | 8 | 8368-8376 | official | 2412.12672 | title_snapshot |
10.1609/aaai.v39i8.32842 | Bright-NeRF: Brightening Neural Radiance Field with Color Restoration from Low-Light RAW Images | https://ojs.aaai.org/index.php/AAAI/article/view/32842 | https://ojs.aaai.org/index.php/AAAI/article/download/32842/34997 | [
"Min Wang",
"Xin Huang",
"Guoqing Zhou",
"Qifeng Guo",
"Qing Wang"
] | Neural Radiance Fields (NeRF) have demonstrated prominent performance in novel view synthesis tasks. However, their input heavily relies on image acquisition under normal light conditions, making it challenging to learn accurate scene contents in low-light environments where images typically exhibit significant noise a... | main | null | 10.1609/aaai.v39i8.32842 | 39 | 8 | 7817-7825 | official | 2412.14547 | title_snapshot |
10.1609/aaai.v39i5.32592 | DeRainGS: Gaussian Splatting for Enhanced Scene Reconstruction in Rainy Environments | https://ojs.aaai.org/index.php/AAAI/article/view/32592 | https://ojs.aaai.org/index.php/AAAI/article/download/32592/34747 | [
"Shuhong Liu",
"Xiang Chen",
"Hongming Chen",
"Quanfeng Xu",
"Mingrui Li"
] | Reconstruction under adverse rainy conditions poses significant challenges due to reduced visibility and the distortion of visual perception. These conditions can severely impair the quality of geometric maps, which is essential for applications ranging from autonomous planning to environmental monitoring. In response ... | main | null | 10.1609/aaai.v39i5.32592 | 39 | 5 | 5558-5566 | official | 2408.11540 | title_snapshot |
10.1609/aaai.v39i7.32721 | ISPDiffuser: Learning RAW-to-sRGB Mappings with Texture-Aware Diffusion Models and Histogram-Guided Color Consistency | https://ojs.aaai.org/index.php/AAAI/article/view/32721 | https://ojs.aaai.org/index.php/AAAI/article/download/32721/34876 | [
"Yang Ren",
"Hai Jiang",
"Menglong Yang",
"Wei Li",
"Shuaicheng Liu"
] | RAW-to-sRGB mapping, or the simulation of the traditional camera image signal processor (ISP), aims to generate DSLR-quality sRGB images from raw data captured by smartphone sensors. Despite achieving comparable results to sophisticated handcrafted camera ISP solutions, existing learning-based methods still struggle wi... | main | null | 10.1609/aaai.v39i7.32721 | 39 | 7 | 6722-6730 | official | 2503.19283 | title_snapshot |
10.1609/aaai.v39i22.34496 | REVECA: Adaptive Planning and Trajectory-Based Validation in Cooperative Language Agents Using Information Relevance and Relative Proximity | https://ojs.aaai.org/index.php/AAAI/article/view/34496 | https://ojs.aaai.org/index.php/AAAI/article/download/34496/36651 | [
"SeungWon Seo",
"SeongRae Noh",
"Junhyeok Lee",
"SooBin Lim",
"Won Hee Lee",
"HyeongYeop Kang"
] | We address the challenge of multi-agent cooperation, where agents achieve a common goal by cooperating with decentralized agents under complex partial observations. Existing cooperative agent systems often struggle with efficiently processing continuously accumulating information, managing globally suboptimal planning ... | main | null | 10.1609/aaai.v39i22.34496 | 39 | 22 | 23295-23303 | official | 2405.16751 | title_snapshot |
10.1609/aaai.v39i19.34293 | Hybrid Data-Free Knowledge Distillation | https://ojs.aaai.org/index.php/AAAI/article/view/34293 | https://ojs.aaai.org/index.php/AAAI/article/download/34293/36448 | [
"Jialiang Tang",
"Shuo Chen",
"Chen Gong"
] | Data-free knowledge distillation aims to learn a compact student network from a pre-trained large teacher network without using the original training data of the teacher network. Existing collection-based and generation-based methods train student networks by collecting massive real examples and generating synthetic ex... | main | null | 10.1609/aaai.v39i19.34293 | 39 | 19 | 20805-20813 | official | 2412.13525 | title_snapshot |
10.1609/aaai.v39i25.34822 | Learning to Rewind via Iterative Prediction of Past Weights for Practical Unlearning | https://ojs.aaai.org/index.php/AAAI/article/view/34822 | https://ojs.aaai.org/index.php/AAAI/article/download/34822/36977 | [
"Jinhyeok Jang",
"Jaehong Kim",
"Chan-Hyun Youn"
] | In artificial intelligence (AI), many legal conflicts have arisen, especially concerning privacy and copyright associated with training data. When an AI model's training data incurs privacy concerns, it becomes imperative to develop a new model devoid of influences from such contentious data. However, retraining from s... | main | null | 10.1609/aaai.v39i25.34822 | 39 | 25 | 26248-26255 | official | null | null |
10.1609/aaai.v39i15.33772 | Creating Coherence in Federated Non-Negative Matrix Factorization | https://ojs.aaai.org/index.php/AAAI/article/view/33772 | https://ojs.aaai.org/index.php/AAAI/article/download/33772/35927 | [
"Sebastian Dalleiger",
"Aristides Gionis"
] | In many real-world applications, data is inherently decentralized, necessitating data analysis methods that prioritize privacy while delivering interpretable results. Federated Non-Negative Matrix Factorization (FedNMF) meets this requirement by factorizing latent components from distributed data that cannot be freely ... | main | null | 10.1609/aaai.v39i15.33772 | 39 | 15 | 16135-16143 | official | null | null |
10.1609/aaai.v39i15.33758 | WatE: A Wasserstein t-distributed Embedding Method for Information-enriched Graph Visualization | https://ojs.aaai.org/index.php/AAAI/article/view/33758 | https://ojs.aaai.org/index.php/AAAI/article/download/33758/35913 | [
"Minjie Cheng",
"Dixin Luo",
"Hongteng Xu"
] | As a fundamental problem of graph analysis, graph visualization aims to embed a set of graphs in a low-dimensional (e.g., 2D) space and provide insights into their distribution and clustering structure. Focusing on this problem, we propose a novel Wasserstein t-distributed embedding (WatE) method, leading to an informa... | main | null | 10.1609/aaai.v39i15.33758 | 39 | 15 | 16010-16018 | official | null | null |
10.1609/aaai.v39i19.34278 | Beyond Skip Connection: Pooling and Unpooling Design for Elimination Singularities | https://ojs.aaai.org/index.php/AAAI/article/view/34278 | https://ojs.aaai.org/index.php/AAAI/article/download/34278/36433 | [
"Chengkun Sun",
"Jinqian Pan",
"Zhuoli Jin",
"Russell Stevens Terry",
"Jiang Bian",
"Jie Xu"
] | Training deep Convolutional Neural Networks (CNNs) presents unique challenges, including the pervasive issue of elimination singularities—consistent deactivation of nodes leading to degenerate manifolds within the loss landscape. These singularities impede efficient learning by disrupting feature propagation. To mitiga... | main | null | 10.1609/aaai.v39i19.34278 | 39 | 19 | 20672-20680 | official | 2409.13154 | title_snapshot |
10.1609/aaai.v39i22.34489 | Unsupervised Translation of Emergent Communication | https://ojs.aaai.org/index.php/AAAI/article/view/34489 | https://ojs.aaai.org/index.php/AAAI/article/download/34489/36644 | [
"Ido Levy",
"Orr Paradise",
"Boaz Carmeli",
"Ron Meir",
"Shafi Goldwasser",
"Yonatan Belinkov"
] | Emergent Communication (EC) provides a unique window into the language systems that emerge autonomously when agents are trained to jointly achieve shared goals. However, it is difficult to interpret EC and evaluate its relationship with natural languages (NL). This study employs unsupervised neural machine translation ... | main | null | 10.1609/aaai.v39i22.34489 | 39 | 22 | 23231-23239 | official | 2502.07552 | title_snapshot |
10.1609/aaai.v39i2.32162 | Progressive Self-Learning for Domain Adaptation on Symbolic Regression of Integer Sequences | https://ojs.aaai.org/index.php/AAAI/article/view/32162 | https://ojs.aaai.org/index.php/AAAI/article/download/32162/34317 | [
"Yaohui Zhu",
"Kaiming Sun",
"Zhengdong Luo",
"Lingfeng Wang"
] | Symbolic Regression of Integer Sequences (SRIS) aims to discover precise mathematical formulas from integer sequences. The neural machine translation-based method of SRIS trains the model using randomly generated data, and directly utilizes the trained model for inference on target sequences. However, the method often ... | main | null | 10.1609/aaai.v39i2.32162 | 39 | 2 | 1692-1699 | official | null | null |
10.1609/aaai.v39i20.35495 | Explanation Bottleneck Models | https://ojs.aaai.org/index.php/AAAI/article/view/35495 | https://ojs.aaai.org/index.php/AAAI/article/download/35495/37650 | [
"Shin'ya Yamaguchi",
"Kosuke Nishida"
] | Recent concept-based interpretable models have succeeded in providing meaningful explanations by pre-defined concept sets. However, the dependency on the pre-defined concepts restricts the application because of the limited number of concepts for explanations. This paper proposes a novel interpretable deep neural netwo... | main | null | 10.1609/aaai.v39i20.35495 | 39 | 20 | 21886-21894 | official | 2409.17663 | title_snapshot |
10.1609/aaai.v39i4.32367 | BloomScene: Lightweight Structured 3D Gaussian Splatting for Crossmodal Scene Generation | https://ojs.aaai.org/index.php/AAAI/article/view/32367 | https://ojs.aaai.org/index.php/AAAI/article/download/32367/34522 | [
"Xiaolu Hou",
"Mingcheng Li",
"Dingkang Yang",
"Jiawei Chen",
"Ziyun Qian",
"Xiao Zhao",
"Yue Jiang",
"Jinjie Wei",
"Qingyao Xu",
"Lihua Zhang"
] | With the widespread use of virtual reality applications, 3D scene generation has become a new challenging research frontier. 3D scenes have highly complex structures and need to ensure that the output is dense, coherent, and contains all necessary structures. Many current 3D scene generation methods rely on pre-trained... | main | null | 10.1609/aaai.v39i4.32367 | 39 | 4 | 3536-3544 | official | 2501.10462 | title_snapshot |
10.1609/aaai.v39i5.32545 | Semantic-guided Masked Mutual Learning for Multi-modal Brain Tumor Segmentation with Arbitrary Missing Modalities | https://ojs.aaai.org/index.php/AAAI/article/view/32545 | https://ojs.aaai.org/index.php/AAAI/article/download/32545/34700 | [
"Guoyan Liang",
"Qin Zhou",
"Zhe Wang",
"Jingyuan Chen",
"Lin Gu",
"Chang Yao",
"Sai Wu",
"Bingcang Huang",
"Kai Chen"
] | Malignant brain tumors have become an aggressive and dangerous disease that leads to death worldwide. Multi-modal MRI data is crucial for accurate brain tumor segmentation, but missing modalities common in clinical practice can severely degrade the segmentation performance. While incomplete multi-modal learning methods... | main | null | 10.1609/aaai.v39i5.32545 | 39 | 5 | 5137-5145 | official | 2507.07592 | title_snapshot |
10.1609/aaai.v39i11.33324 | Exploring the Relationship Between Samples and Masks for Robust Defect Localization | https://ojs.aaai.org/index.php/AAAI/article/view/33324 | https://ojs.aaai.org/index.php/AAAI/article/download/33324/35479 | [
"Jiang Lin",
"Hui Xue",
"Fanxiu Sun",
"Yaping Yan"
] | Defect detection aims to detect and localize regions out of the normal distribution. The previous approaches often explicitly incorporate the defect detection concept, such as by utilizing self-supervised ground truth or manually defined feature comparison. The aforementioned processes involve modeling the distribution... | main | null | 10.1609/aaai.v39i11.33324 | 39 | 11 | 12156-12164 | official | 2306.10720 | title_snapshot |
10.1609/aaai.v39i12.33355 | Tab-Shapley: Identifying Top-k Tabular Data Quality Insights | https://ojs.aaai.org/index.php/AAAI/article/view/33355 | https://ojs.aaai.org/index.php/AAAI/article/download/33355/35510 | [
"Manisha Padala",
"Lokesh Nagalapatti",
"Atharv Tyagi",
"Ramasuri Narayanam",
"Shiv Kumar Saini"
] | We present an unsupervised method for aggregating anomalies in tabular datasets by identifying the top-k tabular data quality insights. Each insight consists of a set of anomalous attributes and the corresponding subsets of records that serve as evidence to the user. The process of identifying these insight blocks is c... | main | null | 10.1609/aaai.v39i12.33355 | 39 | 12 | 12435-12442 | official | 2501.06685 | title_snapshot |
10.1609/aaai.v39i13.33490 | Nearly Tight Bounds on Approximate Equilibria in Spatial Competition on the Line | https://ojs.aaai.org/index.php/AAAI/article/view/33490 | https://ojs.aaai.org/index.php/AAAI/article/download/33490/35645 | [
"Umang Bhaskar",
"Soumyajit Pyne"
] | In Hotelling's model of spatial competition, a unit mass of voters is distributed in the interval [0,1] (with their location corresponding to their political persuasion), and each of m candidates selects as a strategy their distinct position in this interval. Each voter votes for the nearest candidate, and candidates c... | main | null | 10.1609/aaai.v39i13.33490 | 39 | 13 | 13641-13648 | official | 2405.04696 | title_snapshot |
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