pwc-archive/papers-with-abstracts huggingface dataset:
Multiple Object Tracking as ID Prediction
Authors: Ruopeng Gao, Yijun Zhang, LiMin Wang
Date: 2024-03-25 00:00:00
Proceeding: CVPR 2025 1
Tasks: Multi-Object Tracking, Multiple Object Tracking, Object, object-detection, Object Detection, Object Tracking, Prediction

In Multiple Object Tracking (MOT), tracking-by-detection methods have stood the test for a long time, which split the process into two parts according to the definition: object detection and association. They leverage robust single-frame detectors and treat object association as a post-processing step through hand-crafted heuristic algorithms and surrogate tasks. However, the nature of heuristic techniques prevents end-to-end exploitation of training data, leading to increasingly cumbersome and challenging manual modification while facing complicated or novel scenarios. In this paper, we regard this object association task as an End-to-End in-context ID prediction problem and propose a streamlined baseline called MOTIP. Specifically, we form the target embeddings into historical trajectory information while considering the corresponding IDs as in-context prompts, then directly predict the ID labels for the objects in the current frame. Thanks to this end-to-end process, MOTIP can learn tracking capabilities straight from training data, freeing itself from burdensome hand-crafted algorithms. Without bells and whistles, our method achieves impressive state-of-the-art performance in complex scenarios like DanceTrack and SportsMOT, and it performs competitively with other transformer-based methods on MOT17. We believe that MOTIP demonstrates remarkable potential and can serve as a starting point for future research. The code is available at https://github.com/MCG-NJU/MOTIP.
WangchanLion and WangchanX MRC Eval
Authors: Wannaphong Phatthiyaphaibun, Surapon Nonesung, Patomporn Payoungkhamdee, Peerat Limkonchotiwat, Can Udomcharoenchaikit, Jitkapat Sawatphol, Chompakorn Chaksangchaichot, Ekapol Chuangsuwanich, Sarana Nutanong
Date: 2024-03-24 00:00:00
Tasks: Instruction Following, Machine Reading Comprehension, Reading Comprehension

This technical report describes the development of WangchanLion, an instruction fine-tuned model focusing on Machine Reading Comprehension (MRC) in the Thai language. Our model is based on SEA-LION and a collection of instruction following datasets. To promote open research and reproducibility, we publicly release all training data, code, and the final model weights under the Apache-2 license. To assess the contextual understanding capability, we conducted extensive experimental studies using two Thai MRC datasets, XQuAD and Iapp_wiki_qa_squad. Experimental results demonstrate the model's ability to comprehend the context and produce an answer faithful to the reference one in 0-shot and 1-shot settings. In addition, our evaluation goes beyond the traditional MRC. We propose a new evaluation scheme assessing the answer's correctness, helpfulness, conciseness, and contextuality. Our code is available publicly at https://github.com/vistec-AI/WangchanLion.
Detection of Opioid Users from Reddit Posts via an Attention-based Bidirectional Recurrent Neural Network
Authors: Yuchen Wang, Zhengyu Fang, Wei Du, Shuai Xu, Rong Xu, Jing Li
Date: 2024-02-09 00:00:00

The opioid epidemic, referring to the growing hospitalizations and deaths because of overdose of opioid usage and addiction, has become a severe health problem in the United States. Many strategies have been developed by the federal and local governments and health communities to combat this crisis. Among them, improving our understanding of the epidemic through better health surveillance is one of the top priorities. In addition to direct testing, machine learning approaches may also allow us to detect opioid users by analyzing data from social media because many opioid users may choose not to do the tests but may share their experiences on social media anonymously. In this paper, we take advantage of recent advances in machine learning, collect and analyze user posts from a popular social network Reddit with the goal to identify opioid users. Posts from more than 1,000 users who have posted on three sub-reddits over a period of one month have been collected. In addition to the ones that contain keywords such as opioid, opiate, or heroin, we have also collected posts that contain slang words of opioid such as black or chocolate. We apply an attention-based bidirectional long short memory model to identify opioid users. Experimental results show that the approaches significantly outperform competitive algorithms in terms of F1-score. Furthermore, the model allows us to extract most informative words, such as opiate, opioid, and black, from posts via the attention layer, which provides more insights on how the machine learning algorithm works in distinguishing drug users from non-drug users.
CloudS2Mask: A novel deep learning approach for improved cloud and cloud shadow masking in Sentinel-2 imagery
Authors: Nicholas Wright, John M.A. Duncan, J. Nik Callow, Sally E. Thompson, Richard J. George
Date: 2024-05-15 00:00:00
Proceeding: Remote Sensing of Environment 2024 5
Tasks: Active Learning, Shadow Detection, Transfer Learning

The Sentinel-2 satellite constellation produces high-resolution multispectral data, covering the entire Earth's land surface every five days. However, the use of this data is significantly impacted by the presence of pixels affected by cloud and shadow. Precise and efficient cloud and cloud shadow masking methods are required for the automated use of this data. Here, we present CloudS2Mask, a new and open-source Python deep-learning library to detect cloud and cloud shadows in Sentinel-2 data accurately and efficiently. CloudS2Mask is trained on multiple large global datasets including CloudSEN12, KappaSet, Sentinel-2 Cloud Mask Catalogue, Sentinel-2 reference cloud masks generated by an active learning method and a custom CloudS2Mask dataset. Using techniques such as progressive resizing, hard fine-tuning, and transfer learning, CloudS2Mask outperforms published models when evaluated on both the CloudSEN12 and the PixBox datasets in terms of classification accuracy. Compared to the next best-published results, CloudS2Mask improves Balanced Overall Accuracy metrics on the CloudSEN12 dataset by 1.2% for cloud, 2.8% for cloud shadow and 1.1% for clear classification. CloudS2Mask provides a range of customisable options combined with novel pre-processing and post-processing approaches to meet task-specific needs, enabling mean scene processing speeds up to 44 times faster than current state-of-the-art methods. The application of novel techniques in CloudS2Mask, combined with its accuracy and processing speed, underscores its potential to streamline the automated cloud and cloud shadow detection of Sentinel-2 data.
Enabling Uncertainty Estimation in Iterative Neural Networks
Authors: Nikita Durasov, Doruk Oner, Jonathan Donier, Hieu Le, Pascal Fua
Date: 2024-03-25 00:00:00
Tasks: Bayesian Optimization, Out-of-Distribution Detection, Uncertainty Quantification

Turning pass-through network architectures into iterative ones, which use their own output as input, is a well-known approach for boosting performance. In this paper, we argue that such architectures offer an additional benefit: The convergence rate of their successive outputs is highly correlated with the accuracy of the value to which they converge. Thus, we can use the convergence rate as a useful proxy for uncertainty. This results in an approach to uncertainty estimation that provides state-of-the-art estimates at a much lower computational cost than techniques like Ensembles, and without requiring any modifications to the original iterative model. We demonstrate its practical value by embedding it in two application domains: road detection in aerial images and the estimation of aerodynamic properties of 2D and 3D shapes.
Improving Diffusion Models's Data-Corruption Resistance using Scheduled Pseudo-Huber Loss
Authors: Artem Khrapov, Vadim Popov, Tasnima Sadekova, Assel Yermekova, Mikhail Kudinov
Date: 2024-03-25 00:00:00

Diffusion models are known to be vulnerable to outliers in training data. In this paper we study an alternative diffusion loss function, which can preserve the high quality of generated data like the original squared $L_{2}$ loss while at the same time being robust to outliers. We propose to use pseudo-Huber loss function with a time-dependent parameter to allow for the trade-off between robustness on the most vulnerable early reverse-diffusion steps and fine details restoration on the final steps. We show that pseudo-Huber loss with the time-dependent parameter exhibits better performance on corrupted datasets in both image and audio domains. In addition, the loss function we propose can potentially help diffusion models to resist dataset corruption while not requiring data filtering or purification compared to conventional training algorithms.
Towards a Formalisation of Value-based Actions and Consequentialist Ethics
Authors: Adam Wyner, Tomasz Zurek, DOrota Stachura-Zurek
Date: 2024-03-25 00:00:00
Tasks: Ethics

Agents act to bring about a state of the world that is more compatible with their personal or institutional values. To formalise this intuition, the paper proposes an action framework based on the STRIPS formalisation. Technically, the contribution expresses actions in terms of Value-based Formal Reasoning (VFR), which provides a set of propositions derived from an Agent's value profile and the Agent's assessment of propositions with respect to the profile. Conceptually, the contribution provides a computational framework for a form of consequentialist ethics which is satisficing, luralistic, act-based, and preferential.
One-Shot Domain Incremental Learning
Authors: Yasushi Esaki, Satoshi Koide, Takuro Kutsuna
Date: 2024-03-25 00:00:00
Tasks: Continual Learning, Incremental Learning

Domain incremental learning (DIL) has been discussed in previous studies on deep neural network models for classification. In DIL, we assume that samples on new domains are observed over time. The models must classify inputs on all domains. In practice, however, we may encounter a situation where we need to perform DIL under the constraint that the samples on the new domain are observed only infrequently. Therefore, in this study, we consider the extreme case where we have only one sample from the new domain, which we call one-shot DIL. We first empirically show that existing DIL methods do not work well in one-shot DIL. We have analyzed the reason for this failure through various investigations. According to our analysis, we clarify that the difficulty of one-shot DIL is caused by the statistics in the batch normalization layers. Therefore, we propose a technique regarding these statistics and demonstrate the effectiveness of our technique through experiments on open datasets.
Investigation of the effectiveness of applying ChatGPT in Dialogic Teaching Using Electroencephalography
Authors: Jiayue Zhang, Yiheng Liu, WenQi Cai, Lanlan Wu, Yali Peng, Jingjing Yu, Senqing Qi, Taotao Long, Bao Ge
Date: 2024-03-25 00:00:00
Tasks: EEG

In recent years, the rapid development of artificial intelligence technology, especially the emergence of large language models (LLMs) such as ChatGPT, has presented significant prospects for application in the field of education. LLMs possess the capability to interpret knowledge, answer questions, and consider context, thus providing support for dialogic teaching to students. Therefore, an examination of the capacity of LLMs to effectively fulfill instructional roles, thereby facilitating student learning akin to human educators within dialogic teaching scenarios, is an exceptionally valuable research topic. This research recruited 34 undergraduate students as participants, who were randomly divided into two groups. The experimental group engaged in dialogic teaching using ChatGPT, while the control group interacted with human teachers. Both groups learned the histogram equalization unit in the information-related course "Digital Image Processing". The research findings show comparable scores between the two groups on the retention test. However, students who engaged in dialogue with ChatGPT exhibited lower performance on the transfer test. Electroencephalography data revealed that students who interacted with ChatGPT exhibited higher levels of cognitive activity, suggesting that ChatGPT could help students establish a knowledge foundation and stimulate cognitive activity. However, its strengths on promoting students. knowledge application and creativity were insignificant. Based upon the research findings, it is evident that ChatGPT cannot fully excel in fulfilling teaching tasks in the dialogue teaching in information related courses. Combining ChatGPT with traditional human teachers might be a more ideal approach. The synergistic use of both can provide students with more comprehensive learning support, thus contributing to enhancing the quality of teaching.
Understanding the Functional Roles of Modelling Components in Spiking Neural Networks
Authors: Huifeng Yin, Hanle Zheng, Jiayi Mao, Siyuan Ding, Xing Liu, Mingkun Xu, Yifan Hu, Jing Pei, Lei Deng
Date: 2024-03-25 00:00:00
Tasks: Computational Efficiency

Spiking neural networks (SNNs), inspired by the neural circuits of the brain, are promising in achieving high computational efficiency with biological fidelity. Nevertheless, it is quite difficult to optimize SNNs because the functional roles of their modelling components remain unclear. By designing and evaluating several variants of the classic model, we systematically investigate the functional roles of key modelling components, leakage, reset, and recurrence, in leaky integrate-and-fire (LIF) based SNNs. Through extensive experiments, we demonstrate how these components influence the accuracy, generalization, and robustness of SNNs. Specifically, we find that the leakage plays a crucial role in balancing memory retention and robustness, the reset mechanism is essential for uninterrupted temporal processing and computational efficiency, and the recurrence enriches the capability to model complex dynamics at a cost of robustness degradation. With these interesting observations, we provide optimization suggestions for enhancing the performance of SNNs in different scenarios. This work deepens the understanding of how SNNs work, which offers valuable guidance for the development of more effective and robust neuromorphic models.
Deep Reinforcement Learning and Mean-Variance Strategies for Responsible Portfolio Optimization
Authors: Fernando Acero, Parisa Zehtabi, Nicolas Marchesotti, Michael Cashmore, Daniele Magazzeni, Manuela Veloso
Date: 2024-03-25 00:00:00
Tasks: Deep Reinforcement Learning, Portfolio Optimization, reinforcement-learning, Reinforcement Learning

Portfolio optimization involves determining the optimal allocation of portfolio assets in order to maximize a given investment objective. Traditionally, some form of mean-variance optimization is used with the aim of maximizing returns while minimizing risk, however, more recently, deep reinforcement learning formulations have been explored. Increasingly, investors have demonstrated an interest in incorporating ESG objectives when making investment decisions, and modifications to the classical mean-variance optimization framework have been developed. In this work, we study the use of deep reinforcement learning for responsible portfolio optimization, by incorporating ESG states and objectives, and provide comparisons against modified mean-variance approaches. Our results show that deep reinforcement learning policies can provide competitive performance against mean-variance approaches for responsible portfolio allocation across additive and multiplicative utility functions of financial and ESG responsibility objectives.
Revisiting the Sleeping Beauty problem
Authors: Paulo S. Piva, Gabriel Ruffolo
Date: 2024-03-25 00:00:00

The Sleeping Beauty problem is a probability riddle with no definite solution for more than two decades and its solution is of great interest in many fields of knowledge. There are two main competing solutions to the problem: the halfer approach, and the thirder approach. The main reason for disagreement in the literature is connected to the use of different probability spaces to represent the same probabilistic riddle. In this work, we analyse the problem from a mathematical perspective, identifying probability distributions induced directly from the thought experiment's rules. The precise choices of probability spaces provide both halfer and thirder solutions to the problem. To try and decide on which approach to follow, a criterion involving the information available to Sleeping Beauty is proposed.
CLHA: A Simple yet Effective Contrastive Learning Framework for Human Alignment
Authors: Feiteng Fang, Liang Zhu, Min Yang, Xi Feng, Jinchang Hou, Qixuan Zhao, Chengming Li, Xiping Hu, Ruifeng Xu
Date: 2024-03-25 00:00:00
Tasks: Contrastive Learning, reinforcement-learning, Reinforcement Learning

Reinforcement learning from human feedback (RLHF) is a crucial technique in aligning large language models (LLMs) with human preferences, ensuring these LLMs behave in beneficial and comprehensible ways to users. However, a longstanding challenge in human alignment techniques based on reinforcement learning lies in their inherent complexity and difficulty in training. To address this challenge, we present a simple yet effective Contrastive Learning Framework for Human Alignment (CLHA) to align LLMs with human preferences directly. CLHA employs a novel rescoring strategy to evaluate the noise within the data by considering its inherent quality and dynamically adjusting the training process. Simultaneously, CLHA utilizes pairwise contrastive loss and adaptive supervised fine-tuning loss to adaptively modify the likelihood of generating responses, ensuring enhanced alignment with human preferences. Using advanced methods, CLHA surpasses other algorithms, showcasing superior performance in terms of reward model scores, automatic evaluations, and human assessments on the widely used ``Helpful and Harmless'' dataset.
Bridging Privacy and Robustness for Trustworthy Machine Learning
Authors: Xiaojin Zhang, Wei Chen
Date: 2024-03-25 00:00:00
Tasks: Bayesian Inference, Privacy Preserving

The advent of machine learning has led to transformative changes across various domains, but the sensitive nature of data raises concerns about privacy and security. While Local Differential Privacy (LDP) has been a cornerstone in addressing these concerns, recent research has proposed privacy concepts aligned with the Bayesian inference perspective of an adversary, such as Average Bayesian Privacy (ABP) and Maximum Bayesian Privacy (MBP). This paper explores the intricate relationships between LDP, ABP, and MBP, and their implications for algorithmic robustness. We establish theoretical connections between these privacy notions, proving that LDP implies MBP and vice versa under certain conditions, and deriving bounds connecting MBP and ABP. We also investigate the relationship between PAC robust learning and privacy preservation, demonstrating how to derive PAC robustness from privacy-preserving algorithms and construct privacy-preserving algorithms from PAC robust ones. Our findings provide valuable insights for constructing privacy-preserving and robust machine learning algorithms.
In the Search for Optimal Multi-view Learning Models for Crop Classification with Global Remote Sensing Data
Authors: Francisco Mena, Diego Arenas, Andreas Dengel
Date: 2024-03-25 00:00:00
Tasks: Crop Classification, Multimodal Deep Learning, MULTI-VIEW LEARNING, Sensor Fusion, Time Series

Studying and analyzing cropland is a difficult task due to its dynamic and heterogeneous growth behavior. Usually, diverse data sources can be collected for its estimation. Although deep learning models have proven to excel in the crop classification task, they face substantial challenges when dealing with multiple inputs, named Multi-View Learning (MVL). The methods used in the MVL scenario can be structured based on the encoder architecture, the fusion strategy, and the optimization technique. The literature has primarily focused on using specific encoder architectures for local regions, lacking a deeper exploration of other components in the MVL methodology. In contrast, we investigate the simultaneous selection of the fusion strategy and encoder architecture, assessing global-scale cropland and crop-type classifications. We use a range of five fusion strategies (Input, Feature, Decision, Ensemble, Hybrid) and five temporal encoders (LSTM, GRU, TempCNN, TAE, L-TAE) as possible configurations in the MVL method. We use the CropHarvest dataset for validation, which provides optical, radar, weather time series, and topographic information as input data. We found that in scenarios with a limited number of labeled samples, a unique configuration is insufficient for all the cases. Instead, a specialized combination should be meticulously sought, including an encoder and fusion strategy. To streamline this search process, we suggest identifying the optimal encoder architecture tailored for a particular fusion strategy, and then determining the most suitable fusion strategy for the classification task. We provide a methodological framework for researchers exploring crop classification through an MVL methodology.
SegICL: A Multimodal In-context Learning Framework for Enhanced Segmentation in Medical Imaging
Authors: Lingdong Shen, Fangxin Shang, Xiaoshuang Huang, Yehui Yang, Haifeng Huang, Shiming Xiang
Date: 2024-03-25 00:00:00
Tasks: Few-Shot Learning, Image Segmentation, In-Context Learning, Medical Image Segmentation, Segmentation, Semantic Segmentation, Universal Segmentation

In the field of medical image segmentation, tackling Out-of-Distribution (OOD) segmentation tasks in a cost-effective manner remains a significant challenge. Universal segmentation models is a solution, which aim to generalize across the diverse modality of medical images, yet their effectiveness often diminishes when applied to OOD data modalities and tasks, requiring intricate fine-tuning of model for optimal performance. Few-shot learning segmentation methods are typically designed for specific modalities of data and cannot be directly transferred for use with another modality. Therefore, we introduce SegICL, a novel approach leveraging In-Context Learning (ICL) for image segmentation. Unlike existing methods, SegICL has the capability to employ text-guided segmentation and conduct in-context learning with a small set of image-mask pairs, eliminating the need for training the model from scratch or fine-tuning for OOD tasks (including OOD modality and dataset). Extensive experimental demonstrates a positive correlation between the number of shots and segmentation performance on OOD tasks. The performance of segmentation when provided thre-shots is approximately 1.5 times better than the performance in a zero-shot setting. This indicates that SegICL effectively address new segmentation tasks based on contextual information. Additionally, SegICL also exhibits comparable performance to mainstream models on OOD and in-distribution tasks. Our code will be released after paper review.