pwc-archive/papers-with-abstracts huggingface dataset:
Optimizing Sales Forecasts through Automated Integration of Market Indicators
Authors: Lina Döring, Felix Grumbach, Pascal Reusch
Date: 2024-05-15 00:00:00
Tasks: feature selection, Time Series

Recognizing that traditional forecasting models often rely solely on historical demand, this work investigates the potential of data-driven techniques to automatically select and integrate market indicators for improving customer demand predictions. By adopting an exploratory methodology, we integrate macroeconomic time series, such as national GDP growth, from the \textit{Eurostat} database into \textit{Neural Prophet} and \textit{SARIMAX} forecasting models. Suitable time series are automatically identified through different state-of-the-art feature selection methods and applied to sales data from our industrial partner. It could be shown that forecasts can be significantly enhanced by incorporating external information. Notably, the potential of feature selection methods stands out, especially due to their capability for automation without expert knowledge and manual selection effort. In particular, the Forward Feature Selection technique consistently yielded superior forecasting accuracy for both SARIMAX and Neural Prophet across different company sales datasets. In the comparative analysis of the errors of the selected forecasting models, namely Neural Prophet and SARIMAX, it is observed that neither model demonstrates a significant superiority over the other.
Towards objective and interpretable speech disorder assessment: a comparative analysis of CNN and transformer-based models
Authors: Malo Maisonneuve, Corinne Fredouille, Muriel Lalain, Alain Ghio, Virginie Woisard
Date: 2024-06-07 00:00:00

Head and Neck Cancers (HNC) significantly impact patients' ability to speak, affecting their quality of life. Commonly used metrics for assessing pathological speech are subjective, prompting the need for automated and unbiased evaluation methods. This study proposes a self-supervised Wav2Vec2-based model for phone classification with HNC patients, to enhance accuracy and improve the discrimination of phonetic features for subsequent interpretability purpose. The impact of pre-training datasets, model size, and fine-tuning datasets and parameters are explored. Evaluation on diverse corpora reveals the effectiveness of the Wav2Vec2 architecture, outperforming a CNN-based approach, used in previous work. Correlation with perceptual measures also affirms the model relevance for impaired speech analysis. This work paves the way for better understanding of pathological speech with interpretable approaches for clinicians, by leveraging complex self-learnt speech representations.
Bts-e: Audio deepfake detection using breathing-talking-silence encoder
Authors: Thien-Phuc Doan, Long Nguyen-Vu, Souhwan Jung, Kihun Hong
Date: 2023-05-05 00:00:00
Proceeding: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2023 5
Tasks: Audio Deepfake Detection, DeepFake Detection, Face Swapping, Speaker Verification, Speech Synthesis, text-to-speech, Text to Speech

Voice phishing (vishing) is increasingly popular due to the development of speech synthesis technology. In particular, the use of deep learning to generate an arbitrary-content audio clip simulating the victim’s voice makes it difficult not only for humans but also for automatic speaker verification (ASV) systems to distinguish. Countermeasure (CM) systems have been developed recently to help ASV combat synthetic speech. In this work, we propose BTS-E, a framework to evaluate the correlation between Breathing, Talking (speech), and Silence sounds in an audio clip, then use this information for deepfake detection tasks. We argue that natural human sounds, such as breathing, are hard to synthesize by Text-to-speech (TTS) system. We conducted a large-scale evaluation using ASVspoof 2019 and 2021 evaluation set to validate our hypothesis. The experiment results show the applicability of the breathing sound feature in detecting deepfake voices. In general, the proposed system significantly increases the performance of the classifier by up to 46%.
Unsupervised Grammatical Error Correction Rivaling Supervised Methods
Authors: Hannan Cao, Liping Yuan, Yuchen Zhang, Hwee Tou Ng
Date: 2023-12-06 00:00:00
Proceeding: EMNLP 2023 2023 12
Tasks: Grammatical Error Correction, Sentence

State-of-the-art grammatical error correction (GEC) systems rely on parallel training data (ungrammatical sentences and their manually corrected counterparts), which are expensive to construct. In this paper, we employ the Break-It-Fix-It (BIFI) method to build an unsupervised GEC system. The BIFI framework generates parallel data from unlabeled text using a fixer to transform ungrammatical sentences into grammatical ones, and a critic to predict sentence grammaticality. We present an unsupervised approach to build the fixer and the critic, and an algorithm that allows them to iteratively improve each other. We evaluate our unsupervised GEC system on English and Chinese GEC. Empirical results show that our GEC system outperforms previous unsupervised GEC systems, and achieves performance comparable to supervised GEC systems without ensemble. Furthermore, when combined with labeled training data, our system achieves new state-of-the-art results on the CoNLL-2014 and NLPCC-2018 test sets.
Are Graph Embeddings the Panacea? An Empirical Survey from the Data Fitness Perspective
Authors: Qiang Sun, Du Q. Huynh, Mark Reynolds, Wei Liu
Date: 2024-04-25 00:00:00
Proceeding: Pacific-Asia Conference on Knowledge Discovery and Data Mining 2024 4
Tasks: Graph Classification, Graph Embedding, Graph Learning, Graph Neural Network, Graph Representation Learning, Network Embedding, Node Classification, Representation Learning

Graph representation learning has emerged as a machine learning go-to technique, outperforming traditional tabular view of data across many domains. Current surveys on graph representation learning predominantly have an algorithmic focus with the primary goal of explaining foundational principles and comparing performances, yet the natural and practical question “Are graph embeddings the panacea?” has been so far neglected. In this paper, we propose to examine graph embedding algorithms from a data fitness perspective by offering a methodical analysis that aligns network characteristics of data with appropriate embedding algorithms. The overarching objective is to provide researchers and practitioners with comprehensive and methodical investigations, enabling them to confidently answer pivotal questions confronting node classification problems: 1) Is there a potential benefit of applying graph representation learning? 2) Is structural information alone sufficient? 3) Which embedding technique would best suit my dataset? Through 1400 experiments across 35 datasets, we have evaluated four network embedding algorithms – three popular GNN-based algorithms (GraphSage, GCN, GAE) and node2vec – over traditional classification methods, namely SVM, KNN, and Random Forest (RF). Our results indicate that the cohesiveness of the network, the representation of relation information, and the number of classes in a classification problem play significant roles in algorithm selection.
RVT-2: Learning Precise Manipulation from Few Demonstrations
Authors: Ankit Goyal, Valts Blukis, Jie Xu, Yijie Guo, Yu-Wei Chao, Dieter Fox
Date: 2024-06-12 00:00:00
Tasks: Robot Manipulation, Robot Manipulation Generalization

In this work, we study how to build a robotic system that can solve multiple 3D manipulation tasks given language instructions. To be useful in industrial and household domains, such a system should be capable of learning new tasks with few demonstrations and solving them precisely. Prior works, like PerAct and RVT, have studied this problem, however, they often struggle with tasks requiring high precision. We study how to make them more effective, precise, and fast. Using a combination of architectural and system-level improvements, we propose RVT-2, a multitask 3D manipulation model that is 6X faster in training and 2X faster in inference than its predecessor RVT. RVT-2 achieves a new state-of-the-art on RLBench, improving the success rate from 65% to 82%. RVT-2 is also effective in the real world, where it can learn tasks requiring high precision, like picking up and inserting plugs, with just 10 demonstrations. Visual results, code, and trained model are provided at: https://robotic-view-transformer-2.github.io/.
A novel method for identifying rice seed purity based on hybrid machine learning algorithms
Authors: Phan Thi-Thu-Hong, Vo Quoc-Trinh, Nguyen Huu-Du
Date: 2024-06-09 00:00:00
Tasks: Hybrid Machine Learning

In the grain industry, the identification of seed purity is a crucial task as it is an important factor in evaluating the quality of seeds. For rice seeds, this property allows for the reduction of unexpected influences of other varieties on rice yield, nutrient composition, and price. However, in practice, they are often mixed with seeds from others. This study proposes a novel method for automatically identifying the rice seed purity of a certain rice variety based on hybrid machine learning algorithms. The main idea is to use deep learning architectures for extracting important features from the raw data and then use machine learning algorithms for classification. Several experiments are conducted following a practical implementation to evaluate the performance of the proposed model. The obtained results show that the novel method improves significantly the performance of existing methods. Thus, it can be applied to design effective identification systems for rice seed purity.
SR-CACO-2: A Dataset for Confocal Fluorescence Microscopy Image Super-Resolution
Authors: Soufiane Belharbi, Mara KM Whitford, Phuong Hoang, Shakeeb Murtaza, Luke McCaffrey, Eric Granger
Date: 2024-06-13 00:00:00
Tasks: Benchmarking, Image Super-Resolution, Super-Resolution

Confocal fluorescence microscopy is one of the most accessible and widely used imaging techniques for the study of biological processes at the cellular and subcellular levels. Scanning confocal microscopy allows the capture of high-quality images from thick three-dimensional (3D) samples, yet suffers from well-known limitations such as photobleaching and phototoxicity of specimens caused by intense light exposure, limiting its applications. Cellular damage can be alleviated by changing imaging parameters to reduce light exposure, often at the expense of image quality. Machine/deep learning methods for single-image super-resolution (SISR) can be applied to restore image quality by upscaling lower-resolution (LR) images to yield high-resolution images (HR). These SISR methods have been successfully applied to photo-realistic images due partly to the abundance of publicly available data. In contrast, the lack of publicly available data partly limits their application and success in scanning confocal microscopy. In this paper, we introduce a large scanning confocal microscopy dataset named SR-CACO-2 that is comprised of low- and high-resolution image pairs marked for three different fluorescent markers. It allows the evaluation of performance of SISR methods on three different upscaling levels (X2, X4, X8). SR-CACO-2 contains the human epithelial cell line Caco-2 (ATCC HTB-37), and it is composed of 2,200 unique images, captured with four resolutions and three markers, forming 9,937 image patches for SISR methods. We provide benchmarking results for 16 state-of-the-art methods of the main SISR families. Results show that these methods have limited success in producing high-resolution textures. The dataset is freely accessible under a Creative Commons license (CC BY-NC-SA 4.0). Our dataset, code and pretrained weights for SISR methods are available: https://github.com/sbelharbi/sr-caco-2.
Explore the Limits of Omni-modal Pretraining at Scale
Authors: Yiyuan Zhang, Handong Li, Jing Liu, Xiangyu Yue
Date: 2024-06-13 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, Multimodal Large Language Model, Question Answering, Retrieval, Video Retrieval, Visual Question Answering

We propose to build omni-modal intelligence, which is capable of understanding any modality and learning universal representations. In specific, we propose a scalable pretraining paradigm, named Multimodal Context (MiCo), which can scale up the numbers of modalities and amount of data, together with the model parameters, in the pretraining process. With MiCo, the pretrained models show significant emergent abilities in multimodal learning, which are evaluated on the following tasks: i) single-modality perception benchmarks of 10 different modalities, ii) 25 cross-modality understanding tasks of retrieval, question-answering, captioning, and iii) 18 multimodal large language model benchmarks. Our models establish 37 new records for state-of-the-art performance. We hope that our research could contribute to the development of omni-modal intelligence. Code and Models are at https://github.com/invictus717/MiCo
Intelligent Multi-View Test Time Augmentation
Authors: Efe Ozturk, Mohit Prabhushankar, Ghassan AlRegib
Date: 2024-06-12 00:00:00
Tasks: Classification, image-classification, Image Classification

In this study, we introduce an intelligent Test Time Augmentation (TTA) algorithm designed to enhance the robustness and accuracy of image classification models against viewpoint variations. Unlike traditional TTA methods that indiscriminately apply augmentations, our approach intelligently selects optimal augmentations based on predictive uncertainty metrics. This selection is achieved via a two-stage process: the first stage identifies the optimal augmentation for each class by evaluating uncertainty levels, while the second stage implements an uncertainty threshold to determine when applying TTA would be advantageous. This methodological advancement ensures that augmentations contribute to classification more effectively than a uniform application across the dataset. Experimental validation across several datasets and neural network architectures validates our approach, yielding an average accuracy improvement of 1.73% over methods that use single-view images. This research underscores the potential of adaptive, uncertainty-aware TTA in improving the robustness of image classification in the presence of viewpoint variations, paving the way for further exploration into intelligent augmentation strategies.
Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification
Authors: Yuankai Luo, Lei Shi, Xiao-Ming Wu
Date: 2024-06-13 00:00:00
Tasks: Node Classification, Node Property Prediction

Graph Transformers (GTs) have recently emerged as popular alternatives to traditional message-passing Graph Neural Networks (GNNs), due to their theoretically superior expressiveness and impressive performance reported on standard node classification benchmarks, often significantly outperforming GNNs. In this paper, we conduct a thorough empirical analysis to reevaluate the performance of three classic GNN models (GCN, GAT, and GraphSAGE) against GTs. Our findings suggest that the previously reported superiority of GTs may have been overstated due to suboptimal hyperparameter configurations in GNNs. Remarkably, with slight hyperparameter tuning, these classic GNN models achieve state-of-the-art performance, matching or even exceeding that of recent GTs across 17 out of the 18 diverse datasets examined. Additionally, we conduct detailed ablation studies to investigate the influence of various GNN configurations, such as normalization, dropout, residual connections, and network depth, on node classification performance. Our study aims to promote a higher standard of empirical rigor in the field of graph machine learning, encouraging more accurate comparisons and evaluations of model capabilities.
Vertical LoRA: Dense Expectation-Maximization Interpretation of Transformers
Authors: Zhuolin Fu
Date: 2024-06-13 00:00:00

In this paper, we show how Transformers can be interpreted as dense Expectation-Maximization algorithms performed on Bayesian Nets. Based on the above interpretation, we propose a new model design paradigm, namely Vertical LoRA (VLoRA), which reduces the parameter count dramatically while preserving performance. In VLoRA, a model consists of layers, each of which recursively learns an increment based on the previous layer. We then apply LoRA decomposition to the increments. VLoRA works on the base model, which is orthogonal to LoRA, meaning they can be used together. We do experiments on various tasks and models. The results show that 1) with VLoRA, the Transformer model parameter count can be reduced dramatically and 2) the performance of the original model is preserved. The source code is available at \url{https://github.com/neverUseThisName/vlora}
BEVSpread: Spread Voxel Pooling for Bird's-Eye-View Representation in Vision-based Roadside 3D Object Detection
Authors: Wenjie Wang, Yehao Lu, Guangcong Zheng, Shuigen Zhan, Xiaoqing Ye, Zichang Tan, Jingdong Wang, Gaoang Wang, Xi Li
Date: 2024-06-13 00:00:00
Proceeding: CVPR 2024 1
Tasks: 3D Object Detection, Autonomous Driving, object-detection, Object Detection

Vision-based roadside 3D object detection has attracted rising attention in autonomous driving domain, since it encompasses inherent advantages in reducing blind spots and expanding perception range. While previous work mainly focuses on accurately estimating depth or height for 2D-to-3D mapping, ignoring the position approximation error in the voxel pooling process. Inspired by this insight, we propose a novel voxel pooling strategy to reduce such error, dubbed BEVSpread. Specifically, instead of bringing the image features contained in a frustum point to a single BEV grid, BEVSpread considers each frustum point as a source and spreads the image features to the surrounding BEV grids with adaptive weights. To achieve superior propagation performance, a specific weight function is designed to dynamically control the decay speed of the weights according to distance and depth. Aided by customized CUDA parallel acceleration, BEVSpread achieves comparable inference time as the original voxel pooling. Extensive experiments on two large-scale roadside benchmarks demonstrate that, as a plug-in, BEVSpread can significantly improve the performance of existing frustum-based BEV methods by a large margin of (1.12, 5.26, 3.01) AP in vehicle, pedestrian and cyclist.
MuirBench: A Comprehensive Benchmark for Robust Multi-image Understanding
Authors: Fei Wang, Xingyu Fu, James Y. Huang, Zekun Li, Qin Liu, Xiaogeng Liu, Mingyu Derek Ma, Nan Xu, Wenxuan Zhou, Kai Zhang, Tianyi Lorena Yan, Wenjie Jacky Mo, Hsiang-Hui Liu, Pan Lu, Chunyuan Li, Chaowei Xiao, Kai-Wei Chang, Dan Roth, Sheng Zhang, Hoifung Poon, Muhao Chen
Date: 2024-06-13 00:00:00
Tasks: Multiple-choice, Scene Understanding

We introduce MuirBench, a comprehensive benchmark that focuses on robust multi-image understanding capabilities of multimodal LLMs. MuirBench consists of 12 diverse multi-image tasks (e.g., scene understanding, ordering) that involve 10 categories of multi-image relations (e.g., multiview, temporal relations). Comprising 11,264 images and 2,600 multiple-choice questions, MuirBench is created in a pairwise manner, where each standard instance is paired with an unanswerable variant that has minimal semantic differences, in order for a reliable assessment. Evaluated upon 20 recent multi-modal LLMs, our results reveal that even the best-performing models like GPT-4o and Gemini Pro find it challenging to solve MuirBench, achieving 68.0% and 49.3% in accuracy. Open-source multimodal LLMs trained on single images can hardly generalize to multi-image questions, hovering below 33.3% in accuracy. These results highlight the importance of MuirBench in encouraging the community to develop multimodal LLMs that can look beyond a single image, suggesting potential pathways for future improvements.
STAR: A First-Ever Dataset and A Large-Scale Benchmark for Scene Graph Generation in Large-Size Satellite Imagery
Authors: Yansheng Li, LinLin Wang, Tingzhu Wang, Xue Yang, Junwei Luo, Qi Wang, Youming Deng, Wenbin Wang, Xian Sun, Haifeng Li, Bo Dang, Yongjun Zhang, Yi Yu, Junchi Yan
Date: 2024-06-13 00:00:00
Tasks: Graph Generation, Object, object-detection, Object Detection, Scene Graph Generation

Scene graph generation (SGG) in satellite imagery (SAI) benefits promoting understanding of geospatial scenarios from perception to cognition. In SAI, objects exhibit great variations in scales and aspect ratios, and there exist rich relationships between objects (even between spatially disjoint objects), which makes it attractive to holistically conduct SGG in large-size very-high-resolution (VHR) SAI. However, there lack such SGG datasets. Due to the complexity of large-size SAI, mining triplets <subject, relationship, object> heavily relies on long-range contextual reasoning. Consequently, SGG models designed for small-size natural imagery are not directly applicable to large-size SAI. This paper constructs a large-scale dataset for SGG in large-size VHR SAI with image sizes ranging from 512 x 768 to 27,860 x 31,096 pixels, named STAR (Scene graph generaTion in lArge-size satellite imageRy), encompassing over 210K objects and over 400K triplets. To realize SGG in large-size SAI, we propose a context-aware cascade cognition (CAC) framework to understand SAI regarding object detection (OBD), pair pruning and relationship prediction for SGG. We also release a SAI-oriented SGG toolkit with about 30 OBD and 10 SGG methods which need further adaptation by our devised modules on our challenging STAR dataset. The dataset and toolkit are available at: https://linlin-dev.github.io/project/STAR.
4M-21: An Any-to-Any Vision Model for Tens of Tasks and Modalities
Authors: Roman Bachmann, Oğuzhan Fatih Kar, David Mizrahi, Ali Garjani, Mingfei Gao, David Griffiths, Jiaming Hu, Afshin Dehghan, Amir Zamir
Date: 2024-06-13 00:00:00
Tasks: Instance Segmentation, multimodal generation, Semantic Segmentation

Current multimodal and multitask foundation models like 4M or UnifiedIO show promising results, but in practice their out-of-the-box abilities to accept diverse inputs and perform diverse tasks are limited by the (usually rather small) number of modalities and tasks they are trained on. In this paper, we expand upon the capabilities of them by training a single model on tens of highly diverse modalities and by performing co-training on large-scale multimodal datasets and text corpora. This includes training on several semantic and geometric modalities, feature maps from recent state of the art models like DINOv2 and ImageBind, pseudo labels of specialist models like SAM and 4DHumans, and a range of new modalities that allow for novel ways to interact with the model and steer the generation, for example image metadata or color palettes. A crucial step in this process is performing discrete tokenization on various modalities, whether they are image-like, neural network feature maps, vectors, structured data like instance segmentation or human poses, or data that can be represented as text. Through this, we expand on the out-of-the-box capabilities of multimodal models and specifically show the possibility of training one model to solve at least 3x more tasks/modalities than existing ones and doing so without a loss in performance. This enables more fine-grained and controllable multimodal generation capabilities and allows us to study the distillation of models trained on diverse data and objectives into a unified model. We successfully scale the training to a three billion parameter model using tens of modalities and different datasets. The resulting models and training code are open sourced at 4m.epfl.ch.