pwc-archive/papers-with-abstracts huggingface dataset:
Domain Adaptation in Agricultural Image Analysis: A Comprehensive Review from Shallow Models to Deep Learning
Authors: Xing Hu, Siyuan Chen, Dawei Zhang
Date: 2025-06-06 00:00:00
Tasks: Domain Adaptation

With the increasing use of computer vision in agriculture, image analysis has become crucial for tasks like crop health monitoring and pest detection. However, significant domain shifts between source and target domains-due to environmental differences, crop types, and data acquisition methods-pose challenges. These domain gaps limit the ability of models to generalize across regions, seasons, and complex agricultural environments. This paper explores how Domain Adaptation (DA) techniques can address these challenges, focusing on their role in enhancing the cross-domain transferability of agricultural image analysis. DA has gained attention in agricultural vision tasks due to its potential to mitigate domain heterogeneity. The paper systematically reviews recent advances in DA for agricultural imagery, particularly its practical applications in complex agricultural environments. We examine the key drivers for adopting DA in agriculture, such as limited labeled data, weak model transferability, and dynamic environmental conditions. We also discuss its use in crop health monitoring, pest detection, and fruit recognition, highlighting improvements in performance across regions and seasons. The paper categorizes DA methods into shallow and deep learning models, with further divisions into supervised, semi-supervised, and unsupervised approaches. A special focus is given to adversarial learning-based DA methods, which have shown great promise in challenging agricultural scenarios. Finally, we review key public datasets in agricultural imagery, analyzing their value and limitations in DA research. This review provides a comprehensive framework for researchers, offering insights into current research gaps and supporting the advancement of DA methods in agricultural image analysis.
AI-powered Contextual 3D Environment Generation: A Systematic Review
Authors: Miguel Silva, Alexandre Valle de Carvalho
Date: 2025-06-05 00:00:00
Tasks: Scene Generation, Text to 3D

The generation of high-quality 3D environments is crucial for industries such as gaming, virtual reality, and cinema, yet remains resource-intensive due to the reliance on manual processes. This study performs a systematic review of existing generative AI techniques for 3D scene generation, analyzing their characteristics, strengths, limitations, and potential for improvement. By examining state-of-the-art approaches, it presents key challenges such as scene authenticity and the influence of textual inputs. Special attention is given to how AI can blend different stylistic domains while maintaining coherence, the impact of training data on output quality, and the limitations of current models. In addition, this review surveys existing evaluation metrics for assessing realism and explores how industry professionals incorporate AI into their workflows. The findings of this study aim to provide a comprehensive understanding of the current landscape and serve as a foundation for future research on AI-driven 3D content generation. Key findings include that advanced generative architectures enable high-quality 3D content creation at a high computational cost, effective multi-modal integration techniques like cross-attention and latent space alignment facilitate text-to-3D tasks, and the quality and diversity of training data combined with comprehensive evaluation metrics are critical to achieving scalable, robust 3D scene generation.
A Compendium of Autonomous Navigation using Object Detection and Tracking in Unmanned Aerial Vehicles
Authors: Mohit Arora, Pratyush Shukla, Shivali Chopra
Date: 2025-05-31 00:00:00
Tasks: Autonomous Navigation, object-detection, Object Detection

Unmanned Aerial Vehicles (UAVs) are one of the most revolutionary inventions of 21st century. At the core of a UAV lies the central processing system that uses wireless signals to control their movement. The most popular UAVs are quadcopters that use a set of four motors, arranged as two on either side with opposite spin. An autonomous UAV is called a drone. Drones have been in service in the US army since the 90's for covert missions critical to national security. It would not be wrong to claim that drones make up an integral part of the national security and provide the most valuable service during surveillance operations. While UAVs are controlled using wireless signals, there reside some challenges that disrupt the operation of such vehicles such as signal quality and range, real time processing, human expertise, robust hardware and data security. These challenges can be solved by programming UAVs to be autonomous, using object detection and tracking, through Computer Vision algorithms. Computer Vision is an interdisciplinary field that seeks the use of deep learning to gain a high-level understanding of digital images and videos for the purpose of automating the task of human visual system. Using computer vision, algorithms for detecting and tracking various objects can be developed suitable to the hardware so as to allow real time processing for immediate judgement. This paper attempts to review the various approaches several authors have proposed for the purpose of autonomous navigation of UAVs by through various algorithms of object detection and tracking in real time, for the purpose of applications in various fields such as disaster management, dense area exploration, traffic vehicle surveillance etc.
Generative Models at the Frontier of Compression: A Survey on Generative Face Video Coding
Authors: Bolin Chen, Shanzhi Yin, Goluck Konuko, Giuseppe Valenzise, Zihan Zhang, Shiqi Wang, Yan Ye
Date: 2025-06-09 00:00:00
Tasks: Benchmarking, Video Compression

The rise of deep generative models has greatly advanced video compression, reshaping the paradigm of face video coding through their powerful capability for semantic-aware representation and lifelike synthesis. Generative Face Video Coding (GFVC) stands at the forefront of this revolution, which could characterize complex facial dynamics into compact latent codes for bitstream compactness at the encoder side and leverages powerful deep generative models to reconstruct high-fidelity face signal from the compressed latent codes at the decoder side. As such, this well-designed GFVC paradigm could enable high-fidelity face video communication at ultra-low bitrate ranges, far surpassing the capabilities of the latest Versatile Video Coding (VVC) standard. To pioneer foundational research and accelerate the evolution of GFVC, this paper presents the first comprehensive survey of GFVC technologies, systematically bridging critical gaps between theoretical innovation and industrial standardization. In particular, we first review a broad range of existing GFVC methods with different feature representations and optimization strategies, and conduct a thorough benchmarking analysis. In addition, we construct a large-scale GFVC-compressed face video database with subjective Mean Opinion Scores (MOSs) based on human perception, aiming to identify the most appropriate quality metrics tailored to GFVC. Moreover, we summarize the GFVC standardization potentials with a unified high-level syntax and develop a low-complexity GFVC system which are both expected to push forward future practical deployments and applications. Finally, we envision the potential of GFVC in industrial applications and deliberate on the current challenges and future opportunities.
Attention-based transformer models for image captioning across languages: An in-depth survey and evaluation
Authors: Israa A. Albadarneh, Bassam H. Hammo, Omar S. Al-Kadi
Date: 2025-06-03 00:00:00
Tasks: Caption Generation, Image Captioning, Scene Understanding, Survey

Image captioning involves generating textual descriptions from input images, bridging the gap between computer vision and natural language processing. Recent advancements in transformer-based models have significantly improved caption generation by leveraging attention mechanisms for better scene understanding. While various surveys have explored deep learning-based approaches for image captioning, few have comprehensively analyzed attention-based transformer models across multiple languages. This survey reviews attention-based image captioning models, categorizing them into transformer-based, deep learning-based, and hybrid approaches. It explores benchmark datasets, discusses evaluation metrics such as BLEU, METEOR, CIDEr, and ROUGE, and highlights challenges in multilingual captioning. Additionally, this paper identifies key limitations in current models, including semantic inconsistencies, data scarcity in non-English languages, and limitations in reasoning ability. Finally, we outline future research directions, such as multimodal learning, real-time applications in AI-powered assistants, healthcare, and forensic analysis. This survey serves as a comprehensive reference for researchers aiming to advance the field of attention-based image captioning.
AUTOMATIC PRONUNCIATION MISTAKE DETECTOR PROJECT REPORT
Authors: Kamal Acharya
Date: 2025-06-25 00:00:00
Proceeding: ResearchGate 2025 6
Tasks: Mistake Detection, speech-recognition, Speech Recognition

Given the drawbacks of traditional English pronunciation correction systems, such as failure to provide timely feedback and correct learners' pronunciation errors, slow improvement of learners' English proficiency, and even misleading learners, it is critical to developing a scientific and efficient automatic correction system for English pronunciation errors. Our Automatic Pronunciation Mistake Detection project is an efficient automatic correction system for English pronunciation errors. It is designed to enable students/user to improve their pronunciation skills. By using Speech recognition, pyaudio and pyttsx3, the project aims to efficiently diminish the error rate and enhance the accuracy of error detection.
Edge-Enabled Collaborative Object Detection for Real-Time Multi-Vehicle Perception
Authors: Everett Richards, Bipul Thapa, Lena Mashayekhy
Date: 2025-06-06 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles, Edge-computing, Object, object-detection, Object Detection

Accurate and reliable object detection is critical for ensuring the safety and efficiency of Connected Autonomous Vehicles (CAVs). Traditional on-board perception systems have limited accuracy due to occlusions and blind spots, while cloud-based solutions introduce significant latency, making them unsuitable for real-time processing demands required for autonomous driving in dynamic environments. To address these challenges, we introduce an innovative framework, Edge-Enabled Collaborative Object Detection (ECOD) for CAVs, that leverages edge computing and multi-CAV collaboration for real-time, multi-perspective object detection. Our ECOD framework integrates two key algorithms: Perceptive Aggregation and Collaborative Estimation (PACE) and Variable Object Tally and Evaluation (VOTE). PACE aggregates detection data from multiple CAVs on an edge server to enhance perception in scenarios where individual CAVs have limited visibility. VOTE utilizes a consensus-based voting mechanism to improve the accuracy of object classification by integrating data from multiple CAVs. Both algorithms are designed at the edge to operate in real-time, ensuring low-latency and reliable decision-making for CAVs. We develop a hardware-based controlled testbed consisting of camera-equipped robotic CAVs and an edge server to evaluate the efficacy of our framework. Our experimental results demonstrate the significant benefits of ECOD in terms of improved object classification accuracy, outperforming traditional single-perspective onboard approaches by up to 75%, while ensuring low-latency, edge-driven real-time processing. This research highlights the potential of edge computing to enhance collaborative perception for latency-sensitive autonomous systems.
NeUQI: Near-Optimal Uniform Quantization Parameter Initialization
Authors: Li Lin, Xinyu Hu, Xiaojun Wan
Date: 2025-05-23 00:00:00
Tasks: Quantization

Large language models (LLMs) achieve impressive performance across domains but face significant challenges when deployed on consumer-grade GPUs or personal devices such as laptops, due to high memory consumption and inference costs. Post-training quantization (PTQ) of LLMs offers a promising solution that reduces their memory footprint and decoding latency. In practice, PTQ with uniform quantization representation is favored for its efficiency and ease of deployment since uniform quantization is widely supported by mainstream hardware and software libraries. Recent studies on $\geq 2$-bit uniform quantization have led to noticeable improvements in post-quantization model performance; however, they primarily focus on quantization methodologies, while the initialization of quantization parameters is underexplored and still relies on the suboptimal Min-Max strategies. In this work, we propose NeUQI, a method devoted to efficiently determining near-optimal initial parameters for uniform quantization. NeUQI is orthogonal to prior quantization methodologies and can seamlessly integrate with them. The experiments with the LLaMA and Qwen families on various tasks demonstrate that our NeUQI consistently outperforms existing methods. Furthermore, when combined with a lightweight distillation strategy, NeUQI can achieve superior performance to PV-tuning, a much more resource-intensive approach.
SpikeSMOKE: Spiking Neural Networks for Monocular 3D Object Detection with Cross-Scale Gated Coding
Authors: Xuemei Chen, Huamin Wang, Hangchi Shen, Shukai Duan, Shiping Wen, TingWen Huang
Date: 2025-06-09 00:00:00
Tasks: 3D Object Detection, Autonomous Driving, Monocular 3D Object Detection, object-detection, Object Detection

Low energy consumption for 3D object detection is an important research area because of the increasing energy consumption with their wide application in fields such as autonomous driving. The spiking neural networks (SNNs) with low-power consumption characteristics can provide a novel solution for this research. Therefore, we apply SNNs to monocular 3D object detection and propose the SpikeSMOKE architecture in this paper, which is a new attempt for low-power monocular 3D object detection. As we all know, discrete signals of SNNs will generate information loss and limit their feature expression ability compared with the artificial neural networks (ANNs).In order to address this issue, inspired by the filtering mechanism of biological neuronal synapses, we propose a cross-scale gated coding mechanism(CSGC), which can enhance feature representation by combining cross-scale fusion of attentional methods and gated filtering mechanisms.In addition, to reduce the computation and increase the speed of training, we present a novel light-weight residual block that can maintain spiking computing paradigm and the highest possible detection performance. Compared to the baseline SpikeSMOKE under the 3D Object Detection, the proposed SpikeSMOKE with CSGC can achieve 11.78 (+2.82, Easy), 10.69 (+3.2, Moderate), and 10.48 (+3.17, Hard) on the KITTI autonomous driving dataset by AP|R11 at 0.7 IoU threshold, respectively. It is important to note that the results of SpikeSMOKE can significantly reduce energy consumption compared to the results on SMOKE. For example,the energy consumption can be reduced by 72.2% on the hard category, while the detection performance is reduced by only 4%. SpikeSMOKE-L (lightweight) can further reduce the amount of parameters by 3 times and computation by 10 times compared to SMOKE.
CONELPABO: composite networks learning via parallel Bayesian optimization to predict remaining useful life in predictive maintenance
Authors: David Solís-Martín, Juan Galán-Páez, Joaquín Borrego-Díaz
Date: 2025-01-29 00:00:00
Proceeding: Neural Computing and Applications 2025 1
Tasks: Bayesian Optimization, Time Series

Maintaining equipment and machinery in industries is imperative for maximizing operational efficiency and prolonging their lifespan. The adoption of predictive maintenance enhances resource allocation, productivity, and product quality by proactively identifying and addressing potential equipment anomalies through rigorous data analysis before they escalate into critical issues. Consequently, these measures strengthen market competitiveness and generate favorable economic outcomes. In many applications, sensors operate at high frequencies or capture data over extended periods. This work introduces CONELPABO (Composite Networks Learning via Parallel Bayesian Optimization), a framework for analyzing long time series data, particularly for predicting the remaining useful life of a system or component. It uses a divide-and-conquer strategy to manage the exponential growth in the hyperparameter search space during Bayesian Optimization and to accelerate model training by 50%. Additionally, this strategy enables the training of deeper networks with limited resources. The usefulness of the framework is demonstrated through two case studies, in which it achieves state-of-the-art results, showing that CNN-CNN and RNN-RNN architectures are highly effective for long time-series data. These architectures outperform many existing approaches and challenge the common academic focus on CNN-RNN hybrids.
DiffLIME: Enhancing Explainability with a Diffusion-Based LIME Algorithm for Fault Diagnosis
Authors: David Solis-Martin, Juan Galan-Paez, Joaquin Borrego-Diaz
Date: 2025-05-30 00:00:00
Proceeding: International Journal of Prognostics and Health Management 2025 5
Tasks: Fault Diagnosis

The aim of predictive maintenance within the field of Prognostics and Health Management (PHM) is to identify and anticipate potential issues in equipment before they become serious. Deep learning models, such as deep convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and transformers, have been widely adopted for this task, achieving significant success. However, these models are often considered “black boxes” due to their opaque decision-making processes, making it challenging to explain their outputs to stakeholders, such as industrial equipment experts. The complexity and large number of parameters in these models further complicate the understanding of their predictions. This paper presents a novel explainable AI algorithm that extends the well-known Local Interpretable Model-agnostic Explanations (LIME). Our approach utilizes a conditioned probabilistic diffusion model to generate altered samples in the neighborhood of the source sample. We validate our method using various rotating machinery diagnosis datasets. Additionally, we compare our method against LIME, employing a set of metrics to quantify desirable properties of any explainable AI approach. The results highlight that DiffLIME consistently outperforms LIME in terms of coherence and stability while maintaining comparable performance in the selectivity metric. Moreover, the ability of DiffLIME to incorporate domain-specific meta-attributes, such as frequency components and signal envelopes, significantly enhances its explainability in the context of fault diagnosis. This approach provides more precise and meaningful insights into the predictions made by the model.
Comparison of lidar semantic segmentation performance on the structured SemanticKITTI and off-road RELLIS-3D datasets
Authors: Mason McVicker, Lauren Ervin, Yongzhi Yang, Kenneth G. Ricks
Date: 2024-08-20 00:00:00
Proceeding: Springer International Journal of Intelligent Robotics and Applications 2024 8
Tasks: 3D Semantic Segmentation, Autonomous Vehicles, LIDAR Semantic Segmentation, Semantic Segmentation

Existing lidar-based semantic segmentation algorithms and datasets focus on autonomous vehicles operating in urban environments. This has greatly improved the safety and reliability of these autonomous vehicles in predictable scenery. A new dataset provides lidar data focusing on off-road environments as seen by autonomous ground vehicles, ushering in a new era of off-road exploration capabilities. To the best of our knowledge, no new algorithms have been developed specifically for this unstructured environment. To gain an understanding of how existing algorithms perform in an off-road environment, we assess the baseline performance of four algorithms, KPConv, SalsaNext, Cylinder3D, and SphereFormer, on a commonly used on-road dataset, SemanticKITTI. We then compare the results with an off-road dataset, RELLIS-3D. We discuss the degradation of each algorithm on the off-road dataset and investigate potential causes such as class imbalance, inconsistencies in the labeled data, and the inherent difficulty of segmenting off-road environments. We present the strengths and weaknesses of each algorithm’s segmentation abilities and provide a comparison of the runtime of each algorithm for real-time capabilities. This is crucial for identifying what network architecture features are potentially the most beneficial for unstructured scenes. A robust, open-source software implementation via docker containers and bash scripts provides simple, repeatable execution of all algorithm training and evaluations. All code is publicly available at https://github.com/UA-Lidar-Segmentation-Research.
Discounting and Drug Seeking in Biological Hierarchical Reinforcement Learning
Authors: Vardhan Palod, Pranav Mahajan, Veeky Baths, Boris S. Gutkin
Date: 2025-06-05 00:00:00
Tasks: Hierarchical Reinforcement Learning, reinforcement-learning, Reinforcement Learning

Despite a strong desire to quit, individuals with long-term substance use disorder (SUD) often struggle to resist drug use, even when aware of its harmful consequences. This disconnect between knowledge and compulsive behavior reflects a fundamental cognitive-behavioral conflict in addiction. Neurobiologically, differential cue-induced activity within striatal subregions, along with dopamine-mediated connectivity from the ventral to the dorsal striatum, contributes to compulsive drug-seeking. However, the functional mechanism linking these findings to behavioral conflict remains unclear. Another hallmark of addiction is temporal discounting: individuals with drug dependence exhibit steeper discount rates than non-users. Assuming the ventral-dorsal striatal organization reflects a gradient from cognitive to motor representations, addiction can be modeled within a hierarchical reinforcement learning (HRL) framework. However, integrating discounting into biologically grounded HRL remains an open challenge. In this work, we build on a model showing how action choices reinforced with drug rewards become insensitive to the negative consequences that follow. We address the integration of discounting by ensuring natural reward values converge across all levels in the HRL hierarchy, while drug rewards diverge due to their dopaminergic effects. Our results show that high discounting amplifies drug-seeking across the hierarchy, linking faster discounting with increased addiction severity and impulsivity. We demonstrate alignment with empirical findings on temporal discounting and propose testable predictions, establishing addiction as a disorder of hierarchical decision-making.