pwc-archive/papers-with-abstracts huggingface dataset:
Watchog: A Light-weight Contrastive Learning based Framework for Column Annotation
Authors: Zhengjie Miao, Jin Wang
Date: 2023-12-12 00:00:00
Proceeding: Proceedings of the ACM on Management of Data 2023 12
Tasks: Benchmarking, Columns Property Annotation, Column Type Annotation, Contrastive Learning

Relational Web tables provide valuable resources for numerous downstream applications, making table understanding, especially column annotation that identifies semantic types and relations of columns, a hot topic in the field of data management. Despite recent efforts to improve different tasks in table understanding by using the power of large pre-trained language models, existing methods heavily rely on large-scale and high-quality labeled instances, while they still suffer from the data sparsity problem due to the imbalanced data distribution among different classes. In this paper, we propose the Watchog framework, which employs contrastive learning techniques to learn robust representations for tables by leveraging a large-scale unlabeled table corpus with minimal overhead. Our approach enables the learned table representations to enhance fine tuning with much fewer additional labeled instances than in prior studies for downstream column annotation tasks. Besides, we further proposed optimization techniques for semi-supervised settings. Experimental results on popular benchmarking datasets illustrate the superiority of our proposed techniques in two column annotation tasks under different settings. In particular, our Watchog framework effectively alleviates the class imbalance issue caused by a long-tailed label distribution. In the semi-supervised setting, Watchog outperforms the best-known method by up to 26% and 41% in Micro and Macro F1 scores, respectively, on the task of semantic type detection.
Integration and Performance Analysis of Artificial Intelligence and Computer Vision Based on Deep Learning Algorithms
Authors: Bo Liu, Liqiang Yu, Chang Che, Qunwei Lin, Hao Hu, Xinyu Zhao
Date: 2023-12-20 00:00:00
Tasks: Deep Learning, image-classification, Image Classification, object-detection, Object Detection

This paper focuses on the analysis of the application effectiveness of the integration of deep learning and computer vision technologies. Deep learning achieves a historic breakthrough by constructing hierarchical neural networks, enabling end-to-end feature learning and semantic understanding of images. The successful experiences in the field of computer vision provide strong support for training deep learning algorithms. The tight integration of these two fields has given rise to a new generation of advanced computer vision systems, significantly surpassing traditional methods in tasks such as machine vision image classification and object detection. In this paper, typical image classification cases are combined to analyze the superior performance of deep neural network models while also pointing out their limitations in generalization and interpretability, proposing directions for future improvements. Overall, the efficient integration and development trend of deep learning with massive visual data will continue to drive technological breakthroughs and application expansion in the field of computer vision, making it possible to build truly intelligent machine vision systems. This deepening fusion paradigm will powerfully promote unprecedented tasks and functions in computer vision, providing stronger development momentum for related disciplines and industries.
NodeMixup: Tackling Under-Reaching for Graph Neural Networks
Authors: Weigang Lu, Ziyu Guan, Wei Zhao, Yaming Yang, Long Jin
Date: 2023-12-20 00:00:00
Tasks: Node Classification

Graph Neural Networks (GNNs) have become mainstream methods for solving the semi-supervised node classification problem. However, due to the uneven location distribution of labeled nodes in the graph, labeled nodes are only accessible to a small portion of unlabeled nodes, leading to the \emph{under-reaching} issue. In this study, we firstly reveal under-reaching by conducting an empirical investigation on various well-known graphs. Then, we demonstrate that under-reaching results in unsatisfactory distribution alignment between labeled and unlabeled nodes through systematic experimental analysis, significantly degrading GNNs' performance. To tackle under-reaching for GNNs, we propose an architecture-agnostic method dubbed NodeMixup. The fundamental idea is to (1) increase the reachability of labeled nodes by labeled-unlabeled pairs mixup, (2) leverage graph structures via fusing the neighbor connections of intra-class node pairs to improve performance gains of mixup, and (3) use neighbor label distribution similarity incorporating node degrees to determine sampling weights for node mixup. Extensive experiments demonstrate the efficacy of NodeMixup in assisting GNNs in handling under-reaching. The source code is available at \url{https://github.com/WeigangLu/NodeMixup}.
Enhancing Multi-View Pedestrian Detection Through Generalized 3D Feature Pulling
Authors: Sithu Aung, Haesol Park, Hyungjoo Jung, Junghyun Cho
Date: 2023-12-20 00:00:00
Proceeding: IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2023 12
Tasks: multi-view detection, Multiview Detection, Pedestrian Detection, valid

The main challenge in multi-view pedestrian detection is integrating view-specific features into a unified space for comprehensive end-to-end perception. Prior multi-view detection methods have focused on projecting perspective-view features onto the ground plane, creating a "bird's eye view" (BEV) representation of the scene. This paper proposes a simple but effective architecture that utilizes a non-parametric 3D feature-pulling strategy. This strategy directly extracts the corresponding 2D features for each valid voxel within the 3D feature volume, addressing the feature loss that may arise in previous methods. The proposed framework introduces three novel modules, each crafted to bolster the generalization capabilities of multi-view detection systems. Through extensive experiments, the efficacy of the proposed model is demonstrated. The results show a new state-of-the-art accuracy, both in conventional scenarios and particularly in the context of scene generalization benchmarks.
Can Transformers Learn Sequential Function Classes In Context?
Authors: Ryan Campbell, Emma Guo, Evan Hu, Reya Vir, Ethan Hsiao
Date: 2023-12-19 00:00:00
Tasks: In-Context Learning

In-context learning (ICL) has revolutionized the capabilities of transformer models in NLP. In our project, we extend the understanding of the mechanisms underpinning ICL by exploring whether transformers can learn from sequential, non-textual function class data distributions. We introduce a novel sliding window sequential function class and employ toy-sized transformers with a GPT-2 architecture to conduct our experiments. Our analysis indicates that these models can indeed leverage ICL when trained on non-textual sequential function classes. Additionally, our experiments with randomized y-label sequences highlights that transformers retain some ICL capabilities even when the label associations are obfuscated. We provide evidence that transformers can reason with and understand sequentiality encoded within function classes, as reflected by the effective learning of our proposed tasks. Our results also show that the performance deteriorated with increasing randomness in the labels, though not to the extent one might expect, implying a potential robustness of learned sequentiality against label noise. Future research may want to look into how previous explanations of transformers, such as induction heads and task vectors, relate to sequentiality in ICL in these toy examples. Our investigation lays the groundwork for further research into how transformers process and perceive sequential data.
dIR -- Discrete Information Retrieval: Conversational Search over Unstructured (and Structured) Data with Large Language Models
Authors: Pablo M. Rodriguez Bertorello, Jean Rodmond Junior Laguerre
Date: 2023-12-20 00:00:00
Tasks: Conversational Search, Information Retrieval, Language Modeling, Language Modelling, Large Language Model, Retrieval, Text to SQL, Text-To-SQL

Data is stored in both structured and unstructured form. Querying both, to power natural language conversations, is a challenge. This paper introduces dIR, Discrete Information Retrieval, providing a unified interface to query both free text and structured knowledge. Specifically, a Large Language Model (LLM) transforms text into expressive representation. After the text is extracted into columnar form, it can then be queried via a text-to-SQL Semantic Parser, with an LLM converting natural language into SQL. Where desired, such conversation may be effected by a multi-step reasoning conversational agent. We validate our approach via a proprietary question/answer data set, concluding that dIR makes a whole new class of queries on free text possible when compared to traditionally fine-tuned dense-embedding-model-based Information Retrieval (IR) and SQL-based Knowledge Bases (KB). For sufficiently complex queries, dIR can succeed where no other method stands a chance.
Conditional Image Generation with Pretrained Generative Model
Authors: Rajesh Shrestha, Bowen Xie
Date: 2023-12-20 00:00:00
Tasks: Conditional Image Generation, Image Generation, model

In recent years, diffusion models have gained popularity for their ability to generate higher-quality images in comparison to GAN models. However, like any other large generative models, these models require a huge amount of data, computational resources, and meticulous tuning for successful training. This poses a significant challenge, rendering it infeasible for most individuals. As a result, the research community has devised methods to leverage pre-trained unconditional diffusion models with additional guidance for the purpose of conditional image generative. These methods enable conditional image generations on diverse inputs and, most importantly, circumvent the need for training the diffusion model. In this paper, our objective is to reduce the time-required and computational overhead introduced by the addition of guidance in diffusion models -- while maintaining comparable image quality. We propose a set of methods based on our empirical analysis, demonstrating a reduction in computation time by approximately threefold.
SISMIK for brain MRI: Deep-learning-based motion estimation and model-based motion correction in k-space
Authors: Oscar Dabrowski, Jean-Luc Falcone, Antoine Klauser, Julien Songeon, Michel Kocher, Bastien Chopard, François Lazeyras, Sébastien Courvoisier
Date: 2023-12-20 00:00:00
Tasks: Image Reconstruction, Motion Estimation

MRI, a widespread non-invasive medical imaging modality, is highly sensitive to patient motion. Despite many attempts over the years, motion correction remains a difficult problem and there is no general method applicable to all situations. We propose a retrospective method for motion estimation and correction to tackle the problem of in-plane rigid-body motion, apt for classical 2D Spin-Echo scans of the brain, which are regularly used in clinical practice. Due to the sequential acquisition of k-space, motion artifacts are well localized. The method leverages the power of deep neural networks to estimate motion parameters in k-space and uses a model-based approach to restore degraded images to avoid ''hallucinations''. Notable advantages are its ability to estimate motion occurring in high spatial frequencies without the need of a motion-free reference. The proposed method operates on the whole k-space dynamic range and is moderately affected by the lower SNR of higher harmonics. As a proof of concept, we provide models trained using supervised learning on 600k motion simulations based on motion-free scans of 43 different subjects. Generalization performance was tested with simulations as well as in-vivo. Qualitative and quantitative evaluations are presented for motion parameter estimations and image reconstruction. Experimental results show that our approach is able to obtain good generalization performance on simulated data and in-vivo acquisitions. We provide a Python implementation at https://gitlab.unige.ch/Oscar.Dabrowski/sismik_mri/.
FiFAR: A Fraud Detection Dataset for Learning to Defer
Authors: Jean V. Alves, Diogo Leitão, Sérgio Jesus, Marco O. P. Sampaio, Pedro Saleiro, Mário A. T. Figueiredo, Pedro Bizarro
Date: 2023-12-20 00:00:00
Tasks: Benchmarking, Decision Making, Fraud Detection

Public dataset limitations have significantly hindered the development and benchmarking of learning to defer (L2D) algorithms, which aim to optimally combine human and AI capabilities in hybrid decision-making systems. In such systems, human availability and domain-specific concerns introduce difficulties, while obtaining human predictions for training and evaluation is costly. Financial fraud detection is a high-stakes setting where algorithms and human experts often work in tandem; however, there are no publicly available datasets for L2D concerning this important application of human-AI teaming. To fill this gap in L2D research, we introduce the Financial Fraud Alert Review Dataset (FiFAR), a synthetic bank account fraud detection dataset, containing the predictions of a team of 50 highly complex and varied synthetic fraud analysts, with varied bias and feature dependence. We also provide a realistic definition of human work capacity constraints, an aspect of L2D systems that is often overlooked, allowing for extensive testing of assignment systems under real-world conditions. We use our dataset to develop a capacity-aware L2D method and rejection learning approach under realistic data availability conditions, and benchmark these baselines under an array of 300 distinct testing scenarios. We believe that this dataset will serve as a pivotal instrument in facilitating a systematic, rigorous, reproducible, and transparent evaluation and comparison of L2D methods, thereby fostering the development of more synergistic human-AI collaboration in decision-making systems. The public dataset and detailed synthetic expert information are available at: https://github.com/feedzai/fifar-dataset
A 3D super-resolution of wind fields via physics-informed pixel-wise self-attention generative adversarial network
Authors: Takuya Kurihana, Kyongmin Yeo, Daniela Szwarcman, Bruce Elmegreen, Karthik Mukkavilli, Johannes Schmude, Levente Klein
Date: 2023-12-20 00:00:00
Tasks: Generative Adversarial Network, Super-Resolution

To mitigate global warming, greenhouse gas sources need to be resolved at a high spatial resolution and monitored in time to ensure the reduction and ultimately elimination of the pollution source. However, the complexity of computation in resolving high-resolution wind fields left the simulations impractical to test different time lengths and model configurations. This study presents a preliminary development of a physics-informed super-resolution (SR) generative adversarial network (GAN) that super-resolves the three-dimensional (3D) low-resolution wind fields by upscaling x9 times. We develop a pixel-wise self-attention (PWA) module that learns 3D weather dynamics via a self-attention computation followed by a 2D convolution. We also employ a loss term that regularizes the self-attention map during pretraining, capturing the vertical convection process from input wind data. The new PWA SR-GAN shows the high-fidelity super-resolved 3D wind data, learns a wind structure at the high-frequency domain, and reduces the computational cost of a high-resolution wind simulation by x89.7 times.
HCDIR: End-to-end Hate Context Detection, and Intensity Reduction model for online comments
Authors: Neeraj Kumar Singh, Koyel Ghosh, Joy Mahapatra, Utpal Garain, Apurbalal Senapati
Date: 2023-12-20 00:00:00
Tasks: Hate Speech Detection, Language Modeling, Language Modelling, Masked Language Modeling

Warning: This paper contains examples of the language that some people may find offensive. Detecting and reducing hateful, abusive, offensive comments is a critical and challenging task on social media. Moreover, few studies aim to mitigate the intensity of hate speech. While studies have shown that context-level semantics are crucial for detecting hateful comments, most of this research focuses on English due to the ample datasets available. In contrast, low-resource languages, like Indian languages, remain under-researched because of limited datasets. Contrary to hate speech detection, hate intensity reduction remains unexplored in high-resource and low-resource languages. In this paper, we propose a novel end-to-end model, HCDIR, for Hate Context Detection, and Hate Intensity Reduction in social media posts. First, we fine-tuned several pre-trained language models to detect hateful comments to ascertain the best-performing hateful comments detection model. Then, we identified the contextual hateful words. Identification of such hateful words is justified through the state-of-the-art explainable learning model, i.e., Integrated Gradient (IG). Lastly, the Masked Language Modeling (MLM) model has been employed to capture domain-specific nuances to reduce hate intensity. We masked the 50\% hateful words of the comments identified as hateful and predicted the alternative words for these masked terms to generate convincing sentences. An optimal replacement for the original hate comments from the feasible sentences is preferred. Extensive experiments have been conducted on several recent datasets using automatic metric-based evaluation (BERTScore) and thorough human evaluation. To enhance the faithfulness in human evaluation, we arranged a group of three human annotators with varied expertise.
AccidentGPT: Accident Analysis and Prevention from V2X Environmental Perception with Multi-modal Large Model
Authors: Lening Wang, Yilong Ren, Han Jiang, Pinlong Cai, Daocheng Fu, Tianqi Wang, Zhiyong Cui, Haiyang Yu, Xuesong Wang, Hanchu Zhou, Helai Huang, Yinhai Wang
Date: 2023-12-20 00:00:00
Tasks: Autonomous Driving, Scene Understanding

Traffic accidents, being a significant contributor to both human casualties and property damage, have long been a focal point of research for many scholars in the field of traffic safety. However, previous studies, whether focusing on static environmental assessments or dynamic driving analyses, as well as pre-accident predictions or post-accident rule analyses, have typically been conducted in isolation. There has been a lack of an effective framework for developing a comprehensive understanding and application of traffic safety. To address this gap, this paper introduces AccidentGPT, a comprehensive accident analysis and prevention multi-modal large model. AccidentGPT establishes a multi-modal information interaction framework grounded in multi-sensor perception, thereby enabling a holistic approach to accident analysis and prevention in the field of traffic safety. Specifically, our capabilities can be categorized as follows: for autonomous driving vehicles, we provide comprehensive environmental perception and understanding to control the vehicle and avoid collisions. For human-driven vehicles, we offer proactive long-range safety warnings and blind-spot alerts while also providing safety driving recommendations and behavioral norms through human-machine dialogue and interaction. Additionally, for traffic police and management agencies, our framework supports intelligent and real-time analysis of traffic safety, encompassing pedestrian, vehicles, roads, and the environment through collaborative perception from multiple vehicles and road testing devices. The system is also capable of providing a thorough analysis of accident causes and liability after vehicle collisions. Our framework stands as the first large model to integrate comprehensive scene understanding into traffic safety studies. Project page: https://accidentgpt.github.io
Scaling Compute Is Not All You Need for Adversarial Robustness
Authors: Edoardo Debenedetti, Zishen Wan, Maksym Andriushchenko, Vikash Sehwag, Kshitij Bhardwaj, Bhavya Kailkhura
Date: 2023-12-20 00:00:00
Tasks: Adversarial Robustness, All, Benchmarking

The last six years have witnessed significant progress in adversarially robust deep learning. As evidenced by the CIFAR-10 dataset category in RobustBench benchmark, the accuracy under $\ell_\infty$ adversarial perturbations improved from 44\% in \citet{Madry2018Towards} to 71\% in \citet{peng2023robust}. Although impressive, existing state-of-the-art is still far from satisfactory. It is further observed that best-performing models are often very large models adversarially trained by industrial labs with significant computational budgets. In this paper, we aim to understand: ``how much longer can computing power drive adversarial robustness advances?" To answer this question, we derive \emph{scaling laws for adversarial robustness} which can be extrapolated in the future to provide an estimate of how much cost we would need to pay to reach a desired level of robustness. We show that increasing the FLOPs needed for adversarial training does not bring as much advantage as it does for standard training in terms of performance improvements. Moreover, we find that some of the top-performing techniques are difficult to exactly reproduce, suggesting that they are not robust enough for minor changes in the training setup. Our analysis also uncovers potentially worthwhile directions to pursue in future research. Finally, we make our benchmarking framework (built on top of \texttt{timm}~\citep{rw2019timm}) publicly available to facilitate future analysis in efficient robust deep learning.
VSR-Net: Vessel-like Structure Rehabilitation Network with Graph Clustering
Authors: Haili Ye, Xiaoqing Zhang, Yan Hu, Huazhu Fu, Jiang Liu
Date: 2023-12-20 00:00:00
Tasks: Clustering, Graph Clustering, Segmentation

The morphologies of vessel-like structures, such as blood vessels and nerve fibres, play significant roles in disease diagnosis, e.g., Parkinson's disease. Deep network-based refinement segmentation methods have recently achieved promising vessel-like structure segmentation results. There are still two challenges: (1) existing methods have limitations in rehabilitating subsection ruptures in segmented vessel-like structures; (2) they are often overconfident in predicted segmentation results. To tackle these two challenges, this paper attempts to leverage the potential of spatial interconnection relationships among subsection ruptures from the structure rehabilitation perspective. Based on this, we propose a novel Vessel-like Structure Rehabilitation Network (VSR-Net) to rehabilitate subsection ruptures and improve the model calibration based on coarse vessel-like structure segmentation results. VSR-Net first constructs subsection rupture clusters with Curvilinear Clustering Module (CCM). Then, the well-designed Curvilinear Merging Module (CMM) is applied to rehabilitate the subsection ruptures to obtain the refined vessel-like structures. Extensive experiments on five 2D/3D medical image datasets show that VSR-Net significantly outperforms state-of-the-art (SOTA) refinement segmentation methods with lower calibration error. Additionally, we provide quantitative analysis to explain the morphological difference between the rehabilitation results of VSR-Net and ground truth (GT), which is smaller than SOTA methods and GT, demonstrating that our method better rehabilitates vessel-like structures by restoring subsection ruptures.