pwc-archive/papers-with-abstracts huggingface dataset:
Attention-Guided Integration of CLIP and SAM for Precise Object Masking in Robotic Manipulation
Authors: Muhammad A. Muttaqien, Tomohiro Motoda, Ryo Hanai, Domae Yukiyasu
Date: 2025-02-26 00:00:00

This paper introduces a novel pipeline to enhance the precision of object masking for robotic manipulation within the specific domain of masking products in convenience stores. The approach integrates two advanced AI models, CLIP and SAM, focusing on their synergistic combination and the effective use of multimodal data (image and text). Emphasis is placed on utilizing gradient-based attention mechanisms and customized datasets to fine-tune performance. While CLIP, SAM, and Grad- CAM are established components, their integration within this structured pipeline represents a significant contribution to the field. The resulting segmented masks, generated through this combined approach, can be effectively utilized as inputs for robotic systems, enabling more precise and adaptive object manipulation in the context of convenience store products.
CryptoPulse: Short-Term Cryptocurrency Forecasting with Dual-Prediction and Cross-Correlated Market Indicators
Authors: Amit Kumar, Taoran Ji
Date: 2025-02-26 00:00:00
Tasks: Decision Making

Cryptocurrencies fluctuate in markets with high price volatility, posing significant challenges for investors. To aid in informed decision-making, systems predicting cryptocurrency market movements have been developed, typically focusing on historical patterns. However, these methods often overlook three critical factors influencing market dynamics: 1) the macro investing environment, reflected in major cryptocurrency fluctuations affecting collaborative investor behaviors; 2) overall market sentiment, heavily influenced by news impacting investor strategies; and 3) technical indicators, offering insights into overbought or oversold conditions, momentum, and market trends, which are crucial for short-term price movements. This paper proposes a dual prediction mechanism that forecasts the next day's closing price by incorporating macroeconomic fluctuations, technical indicators, and individual cryptocurrency price changes. Additionally, a novel refinement mechanism enhances predictions through market sentiment-based rescaling and fusion. Experiments demonstrate that the proposed model achieves state-of-the-art performance, consistently outperforming ten comparison methods.
Application of Attention Mechanism with Bidirectional Long Short-Term Memory (BiLSTM) and CNN for Human Conflict Detection using Computer Vision
Authors: Erick da Silva Farias, Eduardo Palhares Junior
Date: 2025-02-25 00:00:00

The automatic detection of human conflicts through videos is a crucial area in computer vision, with significant applications in monitoring and public safety policies. However, the scarcity of public datasets and the complexity of human interactions make this task challenging. This study investigates the integration of advanced deep learning techniques, including Attention Mechanism, Convolutional Neural Networks (CNNs), and Bidirectional Long ShortTerm Memory (BiLSTM), to improve the detection of violent behaviors in videos. The research explores how the use of the attention mechanism can help focus on the most relevant parts of the video, enhancing the accuracy and robustness of the model. The experiments indicate that the combination of CNNs with BiLSTM and the attention mechanism provides a promising solution for conflict monitoring, offering insights into the effectiveness of different strategies. This work opens new possibilities for the development of automated surveillance systems that can operate more efficiently in real-time detection of violent events.
Triple Difference Designs with Heterogeneous Treatment Effects
Authors: Laura Caron
Date: 2025-02-26 00:00:00

Triple difference designs have become increasingly popular in empirical economics. The advantage of a triple difference design is that, within a treatment group, it allows for another subgroup of the population -- potentially less impacted by the treatment -- to serve as a control for the subgroup of interest. While literature on difference-in-differences has discussed heterogeneity in treatment effects between treated and control groups or over time, little attention has been given to the implications of heterogeneity in treatment effects between subgroups. In this paper, I show that the parameter identified under the usual triple difference assumptions does not allow for causal interpretation of differences between subgroups when subgroups may differ in their underlying (unobserved) treatment effects. I propose a new parameter of interest, the causal difference in average treatment effects on the treated, which makes causal comparisons between subgroups. I discuss assumptions for identification and derive the semiparametric efficiency bounds for this parameter. I then propose doubly-robust, efficient estimators for this parameter. I use a simulation study to highlight the desirable finite-sample properties of these estimators, as well as to show the difference between this parameter and the usual triple difference parameter of interest. An empirical application shows the importance of considering treatment effect heterogeneity in practical applications.
MMKE-Bench: A Multimodal Editing Benchmark for Diverse Visual Knowledge
Authors: Yuntao Du, Kailin Jiang, Zhi Gao, Chenrui Shi, Zilong Zheng, Siyuan Qi, Qing Li
Date: 2025-02-27 00:00:00
Tasks: knowledge editing

Knowledge editing techniques have emerged as essential tools for updating the factual knowledge of large language models (LLMs) and multimodal models (LMMs), allowing them to correct outdated or inaccurate information without retraining from scratch. However, existing benchmarks for multimodal knowledge editing primarily focus on entity-level knowledge represented as simple triplets, which fail to capture the complexity of real-world multimodal information. To address this issue, we introduce MMKE-Bench, a comprehensive MultiModal Knowledge Editing Benchmark, designed to evaluate the ability of LMMs to edit diverse visual knowledge in real-world scenarios. MMKE-Bench addresses these limitations by incorporating three types of editing tasks: visual entity editing, visual semantic editing, and user-specific editing. Besides, MMKE-Bench uses free-form natural language to represent and edit knowledge, offering a more flexible and effective format. The benchmark consists of 2,940 pieces of knowledge and 8,363 images across 33 broad categories, with evaluation questions automatically generated and human-verified. We assess five state-of-the-art knowledge editing methods on three prominent LMMs, revealing that no method excels across all criteria, and that visual and user-specific edits are particularly challenging. MMKE-Bench sets a new standard for evaluating the robustness of multimodal knowledge editing techniques, driving progress in this rapidly evolving field.
Language-Informed Hyperspectral Image Synthesis for Imbalanced-Small Sample Classification via Semi-Supervised Conditional Diffusion Model
Authors: Yimin Zhu, Linlin Xu
Date: 2025-02-27 00:00:00
Tasks: Data Augmentation, Hyperspectral Image Classification, image-classification, Image Classification, Image Generation

Although data augmentation is an effective method to address the imbalanced-small sample data (ISSD) problem in hyperspectral image classification (HSIC), most methodologies extend features in the latent space. Few, however, generate realistic and diverse samples using text information to balance the limited number of annotated samples. Recently, text-driven diffusion models have gained significant attention due to their remarkable ability to generate highly diverse images based on given text prompts in natural image synthesis. Therefore, this paper proposes a novel language-informed hyperspectral image synthesis method (Txt2HSI-LDM(VAE)) for addressing the ISSD problem of HSIC. First, for addressing the high-dimensional hyperspectral data, we use universal varitional autoencoeder (VAE) to map the hyperspectral into a low-dimensional latent space and get stable feature representation, which hugely reduce the inference parameter of diffusion model. Next, a semi-supervised diffusion model is designed for fully taking advantage of unlabeled data, beside, random polygon spatial clipping (RPSC) and uncertainty estimation of latent feature (LF-UE) are also used for simulating the varying degrees of mixing of training data. Then, VAE decodes HSI from latent space generated by diffusion model with the conditional language as input, contributing to more realistic and diverse samples. In our experiments, we fully evaluate the effectiveness of synthetic samples from aspect of statistical characteristic and data distribution in 2D-PCA space. Additionally, cross-attention map is visualized on the pixel-level to prove that our proposed model can capture the spatial layout of and geometry of the generated hyperspectral image depend on the visual-linguistic alignment.
Information retrieval system using evolutionary algorithms
Authors: Doaa N. Mhawi, Haider W. Oleiwi, Nagham H. Saeed, Heba L. Al-Taie
Date: 2022-10-28 00:00:00
Proceeding: Advanced Technologies in Network and Service Management 2022 10
Tasks: Evolutionary Algorithms, Information Retrieval, Retrieval

When it comes to web search, information retrieval (IR) represents a critical technique as web pages have been increasingly growing. However, web users face major problems; unrelated user query retrieved documents (i.e., low precision), a lack of relevant document retrieval (i.e., low recall), acceptable retrieval time, and minimum storage space. This paper proposed a novel advanced document-indexing method (ADIM) with an integrated evolutionary algorithm. The proposed IRS includes three main stages; the first stage (i.e., the advanced documents indexing method) is preprocessing, which consists of two steps: dataset documents reading and advanced documents indexing method (ADIM), resulting in a set of two tables. The second stage is the query searching algorithm to produce a set of words or keywords and the related documents retrieving. The third stage (i.e., the searching algorithm) consists of two steps. The modified genetic algorithm (MGA) proposed new fitness functions using a cross-point operator with dynamic length chromosomes with the adaptive function of the culture algorithm (CA). The proposed system ranks the most relevant documents to the user query by adding a simple parameter (∝) to the fitness function to guarantee the convergence solution, retrieving the most relevant user’s document by integrating MGA with the CA algorithm to achieve the best accuracy. This system was simulated using a free dataset called WebKb containing Worldwide Webpages of computer science departments at multiple universities. The dataset is composed of 8280 HTML-programed semi-structured documents. Experimental results and evaluation measurements showed 100% average precision with 98.5236% average recall for 50 test queries, while the average response time was 00.46.74.78 milliseconds with 18.8 MB memory space for document indexing. The proposed work outperforms all the literature, comparatively, representing a remarkable leap in the studied field.
SeisMoLLM: Advancing Seismic Monitoring via Cross-modal Transfer with Pre-trained Large Language Model
Authors: Xinghao Wang, Feng Liu, Rui Su, Zhihui Wang, Lei Bai, Wanli Ouyang
Date: 2025-02-27 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

Recent advances in deep learning have revolutionized seismic monitoring, yet developing a foundation model that performs well across multiple complex tasks remains challenging, particularly when dealing with degraded signals or data scarcity. This work presents SeisMoLLM, the first foundation model that utilizes cross-modal transfer for seismic monitoring, to unleash the power of large-scale pre-training from a large language model without requiring direct pre-training on seismic datasets. Through elaborate waveform tokenization and fine-tuning of pre-trained GPT-2 model, SeisMoLLM achieves state-of-the-art performance on the DiTing and STEAD datasets across five critical tasks: back-azimuth estimation, epicentral distance estimation, magnitude estimation, phase picking, and first-motion polarity classification. It attains 36 best results out of 43 task metrics and 12 top scores out of 16 few-shot generalization metrics, with many relative improvements ranging from 10% to 50%. In addition to its superior performance, SeisMoLLM maintains efficiency comparable to or even better than lightweight models in both training and inference. These findings establish SeisMoLLM as a promising foundation model for practical seismic monitoring and highlight cross-modal transfer as an exciting new direction for earthquake studies, showcasing the potential of advanced deep learning techniques to propel seismology research forward.
DF-RAP: A Robust Adversarial Perturbation for Defending against Deepfakes in Real-world Social Network Scenarios
Authors: Zuomin Qu, Zuping Xi, Wei Lu, Xiangyang Luo, Qian Wang, Bin Li
Date: 2024-03-05 00:00:00
Proceeding: journal 2024 3
Tasks: Adversarial Robustness, Face Swapping

The misuse of Deepfakes to create unauthorized fake facial images and videos poses a growing threat to personal privacy and social stability. Proactive defense algorithms have been proposed to prevent this fraud by injecting adversarial perturbations into facial images. However, these perturbations are sensitive to the lossy compression on online social networks (OSNs). Recent studies have attempted to produce compression resistance by modeling compression at the pixel level. However, accurate modeling is challenging due to the customization of proprietary compression mechanisms by different OSNs. In this paper, we propose a Robust Adversarial Perturbation (DF-RAP) that provides persistent protection for facial images under OSN compression. Specifically, a novel Compression Approximation GAN (ComGAN) is designed to explicitly model OSN compression. The well-trained ComGAN is then incorporated as a sub-module of the target Deepfake model to derive DF-RAP. Furthermore, we reveal a commonality among various OSNs, i.e., that the lossy compression employed tends to destroy perturbations. Based on this, a novel objective-level destruction-aware constraint (DAC) is introduced during ComGAN training. The extensive experimental results show that DF-RAP can effectively protect facial images from Deepfakes under complex OSN compression, especially for OSNs employing more stringent compression. We also investigate the lossy operation mechanisms employed by widely used OSN platforms and build an OSN-transmission dataset based on the CelebA to facilitate future research.
Dynamic Classification: Leveraging Self-Supervised Classification to Enhance Prediction Performance
Authors: Ziyuan Zhong, Junyang Zhou
Date: 2025-02-26 00:00:00
Tasks: Classification

In this paper, we propose an innovative dynamic classification algorithm designed to achieve the objective of zero missed detections and minimal false positives. The algorithm partitions the data into N equivalent training subsets and N prediction subsets using a supervised model, followed by independent predictions from N separate predictive models. This enables each predictive model to operate within a smaller data range, thereby improving overall accuracy. Additionally, the algorithm leverages data generated through supervised learning to further refine prediction results, filtering out predictions that do not meet accuracy requirements without the need to introduce additional models. Experimental results demonstrate that, when data partitioning errors are minimal, the dynamic classification algorithm achieves exceptional performance with zero missed detections and minimal false positives, significantly outperforming existing model ensembles. Even in cases where classification errors are larger, the algorithm remains comparable to state of the art models. The key innovations of this study include self-supervised classification learning, the use of small-range subset predictions, and the direct rejection of substandard predictions. While the current algorithm still has room for improvement in terms of automatic parameter tuning and classification model efficiency, it has demonstrated outstanding performance across multiple datasets. Future research will focus on optimizing the classification component to further enhance the algorithm's robustness and adaptability.
SegLocNet: Multimodal Localization Network for Autonomous Driving via Bird's-Eye-View Segmentation
Authors: Zijie Zhou, Zhangshuo Qi, Luqi Cheng, Guangming Xiong
Date: 2025-02-27 00:00:00
Tasks: Autonomous Driving, BEV Segmentation, Pose Estimation, Semantic Segmentation

Robust and accurate localization is critical for autonomous driving. Traditional GNSS-based localization methods suffer from signal occlusion and multipath effects in urban environments. Meanwhile, methods relying on high-definition (HD) maps are constrained by the high costs associated with the construction and maintenance of HD maps. Standard-definition (SD) maps-based methods, on the other hand, often exhibit unsatisfactory performance or poor generalization ability due to overfitting. To address these challenges, we propose SegLocNet, a multimodal GNSS-free localization network that achieves precise localization using bird's-eye-view (BEV) semantic segmentation. SegLocNet employs a BEV segmentation network to generate semantic maps from multiple sensor inputs, followed by an exhaustive matching process to estimate the vehicle's ego pose. This approach avoids the limitations of regression-based pose estimation and maintains high interpretability and generalization. By introducing a unified map representation, our method can be applied to both HD and SD maps without any modifications to the network architecture, thereby balancing localization accuracy and area coverage. Extensive experiments on the nuScenes and Argoverse datasets demonstrate that our method outperforms the current state-of-the-art methods, and that our method can accurately estimate the ego pose in urban environments without relying on GNSS, while maintaining strong generalization ability. Our code and pre-trained model will be released publicly.
Deep Convolutional Neural Networks for Palm Fruit Maturity Classification
Authors: Mingqiang Han, Chunlin Yi
Date: 2025-02-27 00:00:00
Tasks: Transfer Learning

To maximize palm oil yield and quality, it is essential to harvest palm fruit at the optimal maturity stage. This project aims to develop an automated computer vision system capable of accurately classifying palm fruit images into five ripeness levels. We employ deep Convolutional Neural Networks (CNNs) to classify palm fruit images based on their maturity stage. A shallow CNN serves as the baseline model, while transfer learning and fine-tuning are applied to pre-trained ResNet50 and InceptionV3 architectures. The study utilizes a publicly available dataset of over 8,000 images with significant variations, which is split into 80\% for training and 20\% for testing. The proposed deep CNN models achieve test accuracies exceeding 85\% in classifying palm fruit maturity stages. This research highlights the potential of deep learning for automating palm fruit ripeness assessment, which can contribute to optimizing harvesting decisions and improving palm oil production efficiency.
SPU-IMR: Self-supervised Arbitrary-scale Point Cloud Upsampling via Iterative Mask-recovery Network
Authors: Ziming Nie, Qiao Wu, Chenlei Lv, Siwen Quan, Zhaoshuai Qi, Muze Wang, Jiaqi Yang
Date: 2025-02-26 00:00:00
Tasks: point cloud upsampling

Point cloud upsampling aims to generate dense and uniformly distributed point sets from sparse point clouds. Existing point cloud upsampling methods typically approach the task as an interpolation problem. They achieve upsampling by performing local interpolation between point clouds or in the feature space, then regressing the interpolated points to appropriate positions. By contrast, our proposed method treats point cloud upsampling as a global shape completion problem. Specifically, our method first divides the point cloud into multiple patches. Then, a masking operation is applied to remove some patches, leaving visible point cloud patches. Finally, our custom-designed neural network iterative completes the missing sections of the point cloud through the visible parts. During testing, by selecting different mask sequences, we can restore various complete patches. A sufficiently dense upsampled point cloud can be obtained by merging all the completed patches. We demonstrate the superior performance of our method through both quantitative and qualitative experiments, showing overall superiority against both existing self-supervised and supervised methods.
Sensor-Invariant Tactile Representation
Authors: Harsh Gupta, Yuchen Mo, Shengmiao Jin, Wenzhen Yuan
Date: 2025-02-27 00:00:00

High-resolution tactile sensors have become critical for embodied perception and robotic manipulation. However, a key challenge in the field is the lack of transferability between sensors due to design and manufacturing variations, which result in significant differences in tactile signals. This limitation hinders the ability to transfer models or knowledge learned from one sensor to another. To address this, we introduce a novel method for extracting Sensor-Invariant Tactile Representations (SITR), enabling zero-shot transfer across optical tactile sensors. Our approach utilizes a transformer-based architecture trained on a diverse dataset of simulated sensor designs, allowing it to generalize to new sensors in the real world with minimal calibration. Experimental results demonstrate the method's effectiveness across various tactile sensing applications, facilitating data and model transferability for future advancements in the field.
DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations
Authors: Krishna Sri Ipsit Mantri, Carola-Bibiane Schönlieb, Bruno Ribeiro, Chaim Baskin, Moshe Eliasof
Date: 2025-02-09 00:00:00
Proceeding: CVPR 2025 1
Tasks: Multi-Task Learning

Pre-trained Vision Transformers now serve as powerful tools for computer vision. Yet, efficiently adapting them for multiple tasks remains a challenge that arises from the need to modify the rich hidden representations encoded by the learned weight matrices, without inducing interference between tasks. Current parameter-efficient methods like LoRA, which apply low-rank updates, force tasks to compete within constrained subspaces, ultimately degrading performance. We introduce DiTASK a novel Diffeomorphic Multi-Task Fine-Tuning approach that maintains pre-trained representations by preserving weight matrix singular vectors, while enabling task-specific adaptations through neural diffeomorphic transformations of the singular values. By following this approach, DiTASK enables both shared and task-specific feature modulations with minimal added parameters. Our theoretical analysis shows that DITASK achieves full-rank updates during optimization, preserving the geometric structure of pre-trained features, and establishing a new paradigm for efficient multi-task learning (MTL). Our experiments on PASCAL MTL and NYUD show that DiTASK achieves state-of-the-art performance across four dense prediction tasks, using 75% fewer parameters than existing methods.