OpenRaiser/Intern-Atlas huggingface dataset:
Intricate Object Detection in Self Driving Environments with Edge-Adaptive Depth Estimation(EADE)
Authors: SuBi Kim, Jieun Kang, Yongik Yoon
Venue: CIKM
Year: 2024

Autonomous vehicles make decisions and controls based on various object recognition results. The driving environment is characterized by the coexistence of a multitude of objects of varying shapes and sizes. Therefore, the ability to accurately recognise fine-grained objects is essential for accurate object recognition in a variety of changing situations. For object detection, the autonomous vehicle performs bounding box and segmentation to provide the detected object information. However, bounding box and segmentation based object detection has difficulties in identifying objects with complex shapes, small or distant objects, and it is hard to distinguish and detect objects with similar colors to the background or similar colors and textures to surrounding objects. This has limitations for reliable object identification in autonomous driving environments containing a variety of objects, which is a challenge for clear criteria-based object avoidance and collision protection. To overcome these limitations, this paper proposes Edge-Adaptive Depth Estimation(EADE). EADE, the combination of edge extraction and depth estimation, enables detailed edge extraction and partial distance estimation of objects even in environments where object shape and size, surrounding objects, and backgrounds make it difficult to recognise distinct objects, which allows for reliable autonomous decision-making and control based on detailed object collision and avoidance criteria. To validate EADE, experiments were conducted with real-world driving environment image data. The results of EADE demonstrate that detailed object recognition is possible with clear edge recognition and estimation of object distance, even for complex shaped objects such as trees with branches in multiple directions, distant objects, and objects that are difficult to distinguish from the background such as curbs.
Dataset Generation for Korean Urban Parks Analysis with Large Language Models
Authors: Honggu Kim, Minwoo Kang, Hyeyoung Choi, Yun-Gyung Cheong
Venue: CIKM
Year: 2024

Understanding how urban parks are utilized and perceived by the public is crucial for effective urban planning and management. This study introduces a novel dataset derived from Instagram, using 42,187 images tagged with #Seoul and #Park hashtags from 2017 to 2023. These images were filtered using InternLM-XComposer2, a Multimodal Large Language Model (MLLM), to confirm they depicted park scenes. GPT-4 then annotated the filtered images, resulting in 29,866 valid image annotations of physical elements, human activities, animals, and emotions. The dataset is publicly available at https://huggingface.co/datasets/RedBall/seoul-urban-park-analysis-by-llm.
Towards Better Utilization of Multiple Views for Bundle Recommendation
Authors: Kyungho Kim, Sunwoo Kim, Geon Lee, Kijung Shin
Venue: CIKM
Year: 2024

Bundle recommender systems aim to recommend suitable collections (i.e., bundles) of items to each user, meeting their diverse needs with all-in-one convenience. Typically, they utilize three distinct types of information: user-bundle purchase interactions (U-B view), user-item purchase interactions (U-I view), and bundle-item affiliations (B-I view). Our focus is on better integrating these three perspectives (i.e., views) to deliver more accurate bundle recommendations. Our examination of different role (main or sub-views) combinations of the views reveals two key observations: (1) the best combination varies across target users (i.e., who receive recommendations), and (2) the U-I view is relatively weak as the main role. Driven by these observations, we propose PET, which synergizes the three views through (1) personalized view weighting, (2) U-I view enhancement, and (3) two-pronged contrastive learning. Our extensive experiments demonstrate that PET significantly outperforms existing methods in all popular benchmark datasets. Our code and datasets are available at https://github.com/K-Kyungho/PET.
Enhancing Anomaly Detection via Generating Diversified and Hard-to-distinguish Synthetic Anomalies
Authors: Hyuntae Kim, Changhee Lee
Venue: CIKM
Year: 2024

Unsupervised anomaly detection is a daunting task, as it relies solely on normality patterns from the training data to identify unseen anomalies during testing. Recent approaches have focused on leveraging domain-specific transformations or perturbations to generate synthetic anomalies from normal samples. The objective here is to acquire insights into normality patterns by learning to differentiate between normal samples and these crafted anomalies. However, these approaches often encounter limitations when domain-specific transformations are not well-specified such as in tabular data, or when it becomes trivial to distinguish between them. To address these issues, we introduce a novel domain-agnostic method that employs a set of conditional perturbators and a discriminator. The perturbators are trained to generate input-dependent perturbations, which are subsequently utilized to construct synthetic anomalies, and the discriminator is trained to distinguish normal samples from them. We ensure that the generated anomalies are both diverse and hard to distinguish through two key strategies: i) directing perturbations to be orthogonal to each other and ii) constraining perturbations to remain in proximity to normal samples. Throughout experiments on real-world datasets, we demonstrate the superiority of our method over state-of-the-art benchmarks, which is evident not only in image data but also in tabular data, where domain-specific transformation is not readily accessible. Additionally, we empirically confirm the adaptability of our method to semi-supervised settings, demonstrating its capacity to incorporate supervised signals to enhance anomaly detection performance even further.
Transformer for Point Anomaly Detection
Authors: Harim Kim, Chang Ha Lee, Charmgil Hong
Venue: CIKM
Year: 2024

In data analysis, unsupervised anomaly detection holds an important position for identifying statistical outliers that signify atypical behavior, erroneous readings, or interesting patterns within data. The Transformer model, known for its ability to capture dependencies within sequences, has revolutionized areas such as text and image data analysis. However, its potential for tabular data, where sequence dependencies are not inherently present, remains underexplored. This paper introduces Transformer for Point Anomaly Detection (TransPAD), a novel Transformer-based AutoEncoder framework specifically designed for point anomaly detection. Our method captures interdependencies across entire datasets, addressing the challenges posed with non-sequential, tabular data. It incorporates unique random and criteria sampling strategies for effective training and anomaly identification, and avoids the common pitfall of trivial generalization that affects many conventional methods. By leveraging an attention weight-based anomaly scoring system, TransPAD offers a more precise approach to detect anomalies. Extensive testing on a range of benchmark tabular datasets shows that TransPAD consistently outperforms existing methods. Our source code is available at https://github.com/nth221/TransPAD.
PolarDSN: An Inductive Approach to Learning the Evolution of Network Polarization in Dynamic Signed Networks
Authors: Min-Jeong Kim, Yeon-Chang Lee, Sang-Wook Kim
Venue: CIKM
Year: 2024

The goal of dynamic signed network embedding (DSNE) is to represent the nodes in a dynamic signed network (DSN) as embeddings that preserve the evolving nature of conflicting relationships between nodes. While existing DSNE methods are useful for understanding polarization between users in diverse domains, they fail to consider the concept of a community boundary that contributes to network-wide polarization and lack inductive ability due to their reliance on homophily bias. To address these limitations, we propose a novel DSNE method, named PolarDSN, which learns the evolution of network POLARization and enhances inductive ability for Dynamic Signed Networks. It leverages node-level community boundaries as well as structural characteristics of nodes such as structural isomorphism and temporal transitivity. Experiments on four real-world DSN datasets demonstrate that PolarDSN consistently and significantly outperforms 12 state-of-the-art methods, achieving up to 31.6% and 21.1% improvement in macro-F1 for transductive and inductive settings, respectively. The code is available at https://github.com/kmj0792/PolarDSN.
Discrepancy-guided Channel Dropout for Domain Generalization
Authors: Seonggyeom Kim, Byeongtae Park, Harim Lee, Dong-Kyu Chae
Venue: CIKM
Year: 2024

Deep Neural Networks (DNNs) tend to perform poorly on unseen domains due to domain shifts. Domain Generalization (DG) aims to improve the performance on such scenarios by minimizing the distribution discrepancy between source domains. Among many studies, dropout-based DG approaches which remove domain-specific features have gained attention. However, they are limited in minimizing the upper bound of generalization risk because they do not explicitly consider the distribution discrepancy when discarding features. In this paper, we propose a novel Discrepancy-guided Channel Dropout (DgCD) for DG that explicitly derives the discrepancy between domains and drops the channels with significant distribution discrepancy. Given a training batch, we perform two ways of standardization: (1) based on the variance/mean of the batch (i.e., sampled from all source domains) and (2) based on the variance/mean of domain-wise samples in the batch. With the two normal distributions, we explicitly derive the discrepancy using KL-divergence and backpropagate it towards each channel. A channel with a higher contribution to the discrepancy is more likely to be dropped. Experimental results show the superiority of DgCD over the state-of-the-art DG baselines, demonstrating the effectiveness of our dropout strategy which is directly coupled to reducing the domain discrepancy. Our code is available at: https://github.com/gyeomo/DgCD
FaDE: A Face Segment Driven Identity Anonymization Framework For Fair Face Recognition
Authors: Ziyi Kou, Yijun Tian, Meng Jiang, Xiangliang Zhang
Venue: CIKM
Year: 2024

Current face recognition (FR) algorithms frequently encounter discrimination issues in terms of various attributes (e.g., gender, age) due to the biased demographic distribution of the training datasets towards specific groups. In this paper, we study an identity protected fair FR problem where the goal is to augment the datasets with external face images while ensuring the anonymity of the corresponding face identities. Our problem is motivated by the limitation of current fairness driven data augmentation approaches that directly utilize the external face images accessed by FR algorithm developers while ignoring the protection on the face identities of the image owners. To address the problem, we develop FaDE, a face segment driven identity anonymization framework that augments biased face image datasets by identifying specific face segments with diversified demographic characteristics from external face images but with least identity disclosure, and then reconstructing the segments to full face images with new identities. As a result, the augmented dataset is under a more balanced demographic distribution and improves the fairness performance of the optimized FR algorithms. We evaluate FaDE on two public face datasets, CelebA and LFW that suffer from various demographic imbalance. The results show that FaDE significantly enhances both fairness and accuracy performance of the optimized FR algorithms, while keeping effective anonymity for the identities of external face images.
Error Detection and Constraint Recovery in Hierarchical Multi-Label Classification without Prior Knowledge
Authors: Joshua Shay Kricheli, Khoa Vo, Aniruddha Datta, Spencer Ozgur, Paulo Shakarian
Venue: CIKM
Year: 2024

Recent advances in Hierarchical Multi-label Classification (HMC), particularly neurosymbolic-based approaches, have demonstrated improved consistency and accuracy by enforcing constraints on a neural model during training. However, such work assumes the existence of such constraints a-priori. In this paper, we relax this strong assumption and present an approach based on Error Detection Rules (EDR) that allow for learning explainable rules about the failure modes of machine learning models. We show that these rules are not only effective in detecting when a machine learning classifier has made an error but also can be leveraged as constraints for HMC, thereby allowing the recovery of explainable constraints even if they are not provided. We show that our approach is effective in detecting machine learning errors and recovering constraints, is noise tolerant, and can function as a source of knowledge for neurosymbolic models on multiple datasets, including a newly introduced military vehicle recognition dataset.
A One-Health Platform for Antimicrobial Resistance Data Analytics
Authors: Benoit Lange, Reza Akbarinia, Florent Masseglia
Venue: CIKM
Year: 2024

Antimicrobial resistance (AMR) poses potentially critical health issues for human and animal populations in the near future. To meet this challenge, we need to adopt a "One Health" strategy, which involves studying and linking information from human and animal populations, as well as from the environment. In this demonstration, we present an early prototype of PROMISE platform, which we are developing for One Health data management and analytics, to enable experts from different fields to gain insights into AMR. It is designed to handle data from 25 academic networks and 42 partners. Our demonstration illustrate the capabilities of our methodology for analyzing these data. The user is freed from considerations related to data heterogeneity, as interoperability issues are managed by the platform. Additionally, each data provider will be able to stay within his/her own vocabulary, whatever the taxonomy used by other data providers.
Federated Deep Equilibrium Learning: Harnessing Compact Global Representations to Enhance Personalization
Authors: Long Tan Le, Tuan Dung Nguyen, Tung-Anh Nguyen, Choong Seon Hong, Suranga Seneviratne, Wei Bao, Nguyen Hoang Tran
Venue: CIKM
Year: 2024

Federated Learning (FL) has emerged as a groundbreaking distributed learning paradigm enabling clients to train a global model collaboratively without exchanging data. Despite enhancing privacy and efficiency in information retrieval and knowledge management contexts, training and deploying FL models confront significant challenges such as communication bottlenecks, data heterogeneity, and memory limitations. To comprehensively address these challenges, we introduce FeDEQ, a novel FL framework that incorporates deep equilibrium learning and consensus optimization to harness compact global data representations for efficient personalization. Specifically, we design a unique model structure featuring an equilibrium layer for global representation extraction, followed by explicit layers tailored for local personalization. We then propose a new FL algorithm rooted in the alternating directions method of multipliers (ADMM), which enables the joint optimization of a shared equilibrium layer and individual personalized layers across distributed datasets. Our theoretical analysis confirms that FeDEQ converges to a stationary point, achieving both compact global representations and optimal personalized parameters for each client. Extensive experiments on various benchmarks demonstrate that FeDEQ matches the performance of state-of-the-art personalized FL methods, while significantly reducing communication size by up to 4 times and memory footprint by 1.5 times during training.
Learning Prompt-Level Quality Variance for Cost-Effective Text-to-Image Generation
Authors: Dongkeun Lee, Wonjun Lee
Venue: CIKM
Year: 2024

Text-to-image generation is a multivariable process in which the resulting quality is determined by both the generative model and the input prompt. While previous efforts rely on a single model either by enhancing its capability or by reformulating prompts, we point out that no single model excels at handling all types of tasks, as there exist inter-model and intra-model quality variance induced by the difference in types of prompts. This paper explores the relationship between the generation quality of text-to-image models and the linguistic features of input prompts by measuring the performance of state-of-the-art models using five different prompt datasets each with its distinctive features. Motivated by our empirical observations, we propose a novel approach that assigns each prompt to its best-performing model based on quality prediction. This enables utilizing a diverse set of models each with its expertise and cost, thereby enhancing cost-effectiveness. Evaluation results show that our approach can reduce the total generation cost by 29.25% with comparable or even higher generation quality than using only the single best model.
DiaKoP: Dialogue-based Knowledge-oriented Programming for Neural-symbolic Knowledge Base Question Answering
Authors: Zhicheng Lee, Zhidian Huang, Zijun Yao, Jinxin Liu, Amy Xin, Lei Hou, Juanzi Li
Venue: CIKM
Year: 2024

We present Dialogue-based Knowledge-oriented Programming system (DiaKoP), a system with a chat interface designed for multi-turn knowledge base question answering (KBQA). DiaKoP enables users to decompose complex questions into multiple simpler follow-up questions and interact with the system to obtain answers. Multi-turn KBQA presents unique challenges because users may switch topics or ask incomplete questions that rely on previous interactions. To address this, we develop a Dialogue History Tracker and Dialogue Policy to manage user conversations effectively. Additionally, we enhance the knowledge from the knowledge graph by integrating parametric knowledge from a large language model (LLM) to provide more comprehensive answers. To mitigate the issue of wrongly parsed questions by semantic parser, we implement a human-in-the-loop mechanism, allowing users to correct errors. We evaluate DiaKoP both qualitatively and quantitatively, with user study indicating that our system better meets users' needs. DiaKoP is open-sourced on https://github.com/THU-KEG/DiaKoP with a guiding demo on https://youtu.be/Tq17k0OxPVg.
Post-Training Embedding Enhancement for Long-Tail Recommendation
Authors: Geon Lee, Kyungho Kim, Kijung Shin
Venue: CIKM
Year: 2024

Item popularity in real-world data follows a long-tail distribution, where a few items attract most of the attention, while the majority receive much less. This disparity results in high-quality embeddings for popular (head) items, but lower-quality embeddings for unpopular (tail) items, leading to less accurate recommendations for the latter. Our observations confirm that embeddings of tail items often exhibit (1) magnitudes (i.e., norms) that are less reflective of actual popularity and (2) directions that are less effective in capturing user preferences, compared to those of head items. To address this issue, we propose EDGE, a post-training embedding enhancement method for long-tail recommendations. EDGE employs two key strategies: (1) refining embedding magnitudes to better reflect item popularity and (2) adjusting embedding directions by leveraging knowledge from head items. Importantly, EDGE is model-agnostic and can be applied to embeddings learned from any trained recommender system. Experimental results show that EDGE significantly improves tail item recommendation performance and overall system performance, achieving up to an improvement of 211.23% in NDCG@20 over the state-of-the-art method. Our code and datasets are available at https://github.com/geon0325/EDGE.
MuLe: Multi-Grained Graph Learning for Multi-Behavior Recommendation
Authors: Seunghan Lee, Geonwoo Ko, Hyun-Je Song, Jinhong Jung
Venue: CIKM
Year: 2024

Multi-behavior recommender systems, rapidly advancing across various domains, utilize plentiful auxiliary interactions on a variety of user behaviors to enhance recommendations for the target behavior, such as purchases. While previous methods have made strides in leveraging such interactions with advanced machine learning methods, they still face challenges in adequately using multi-faceted relationships among behaviors and handling uncertain auxiliary interactions that could potentially lead to purchases or not. In this paper, we propose MuLe (Multi-Grained Graph Learning), a novel graph-based model designed to address these limitations. We design a multi-grained graph learning strategy to capture diverse aspects of behaviors, ranging from unified to specific, and then to target-related behavior interactions. To handle uncertain interactions, we use graph attention, weighting the importance of those interactions related to the target behavior. Afterward, we use an attention mechanism to effectively aggregate diverse behavior embeddings obtained from the multi-grained graph encoders. Extensive experiments show that MuLe significantly outperforms the state-of-the-art methods, achieving improvements of up to 44.6% in HR@10 and 52.9% in NDCG@10, respectively. Our code and datasets are available at https://github.com/geonwooko/MULE.
Vision Language Model is NOT All You Need: Augmentation Strategies for Molecule Language Models
Authors: Namkyeong Lee, Siddhartha Laghuvarapu, Chanyoung Park, Jimeng Sun
Venue: CIKM
Year: 2024

Recently, there has been a growing interest among researchers in understanding molecules and their textual descriptions through molecule language models (MoLM). However, despite some early promising developments, the advancement of MoLM still trails significantly behind that of vision language models (VLM). This is because unique challenges exist apart from VLM in the field of MoLM due to 1) a limited amount of molecule-text paired data and 2) missing expertise that occurred due to the specialized areas of focus among the experts. To this end, we propose AMOLE, which 1) augments molecule-text pairs with structural similarity preserving loss, and 2) transfers the expertise between the molecules. Specifically, AMOLE enriches molecule-text pairs by sharing descriptions among structurally similar molecules with a novel structural similarity preserving loss. Moreover, we propose an expertise reconstruction loss to transfer knowledge from molecules that have extensive expertise to those with less expertise. Extensive experiments on various downstream tasks demonstrate the superiority of AMOLE in comprehending molecules and their descriptions, highlighting its potential for application in real-world drug discovery. The source code for AMOLE is available at https://github.com/Namkyeong/AMOLE.