pwc-archive/papers-with-abstracts huggingface dataset:
Improving the Performance of R17 Type-II Codebook with Deep Learning
Authors: Ke Ma, Yiliang Sang, Yang Ming, Jin Lian, Chang Tian, Zhaocheng Wang
Date: 2023-09-13 00:00:00
Tasks: Deep Learning

The Type-II codebook in Release 17 (R17) exploits the angular-delay-domain partial reciprocity between uplink and downlink channels to select part of angular-delay-domain ports for measuring and feeding back the downlink channel state information (CSI), where the performance of existing deep learning enhanced CSI feedback methods is limited due to the deficiency of sparse structures. To address this issue, we propose two new perspectives of adopting deep learning to improve the R17 Type-II codebook. Firstly, considering the low signal-to-noise ratio of uplink channels, deep learning is utilized to accurately select the dominant angular-delay-domain ports, where the focal loss is harnessed to solve the class imbalance problem. Secondly, we propose to adopt deep learning to reconstruct the downlink CSI based on the feedback of the R17 Type-II codebook at the base station, where the information of sparse structures can be effectively leveraged. Besides, a weighted shortcut module is designed to facilitate the accurate reconstruction. Simulation results demonstrate that our proposed methods could improve the sum rate performance compared with its traditional R17 Type-II codebook and deep learning benchmarks.
Quantile-based Maximum Likelihood Training for Outlier Detection
Authors: Masoud Taghikhah, Nishant Kumar, Siniša Šegvić, Abouzar Eslami, Stefan Gumhold
Date: 2023-08-20 00:00:00
Tasks: Autonomous Driving, Contrastive Learning, image-classification, Image Classification, Outlier Detection, Self-Supervised Learning

Discriminative learning effectively predicts true object class for image classification. However, it often results in false positives for outliers, posing critical concerns in applications like autonomous driving and video surveillance systems. Previous attempts to address this challenge involved training image classifiers through contrastive learning using actual outlier data or synthesizing outliers for self-supervised learning. Furthermore, unsupervised generative modeling of inliers in pixel space has shown limited success for outlier detection. In this work, we introduce a quantile-based maximum likelihood objective for learning the inlier distribution to improve the outlier separation during inference. Our approach fits a normalizing flow to pre-trained discriminative features and detects the outliers according to the evaluated log-likelihood. The experimental evaluation demonstrates the effectiveness of our method as it surpasses the performance of the state-of-the-art unsupervised methods for outlier detection. The results are also competitive compared with a recent self-supervised approach for outlier detection. Our work allows to reduce dependency on well-sampled negative training data, which is especially important for domains like medical diagnostics or remote sensing.
DEUX: Active Exploration for Learning Unsupervised Depth Perception
Authors: Marvin Chancán, Alex Wong, Ian Abraham
Date: 2023-09-16 00:00:00
Tasks: Depth Completion, Depth Estimation, Motion Planning, Robot Navigation, Zero-shot Generalization

Depth perception models are typically trained on non-interactive datasets with predefined camera trajectories. However, this often introduces systematic biases into the learning process correlated to specific camera paths chosen during data acquisition. In this paper, we investigate the role of how data is collected for learning depth completion, from a robot navigation perspective, by leveraging 3D interactive environments. First, we evaluate four depth completion models trained on data collected using conventional navigation techniques. Our key insight is that existing exploration paradigms do not necessarily provide task-specific data points to achieve competent unsupervised depth completion learning. We then find that data collected with respect to photometric reconstruction has a direct positive influence on model performance. As a result, we develop an active, task-informed, depth uncertainty-based motion planning approach for learning depth completion, which we call DEpth Uncertainty-guided eXploration (DEUX). Training with data collected by our approach improves depth completion by an average greater than 18% across four depth completion models compared to existing exploration methods on the MP3D test set. We show that our approach further improves zero-shot generalization, while offering new insights into integrating robot learning-based depth estimation.
Deep Learning based Tomato Disease Detection and Remedy Suggestions using Mobile Application
Authors: Yagya Raj Pandeya, Samin Karki, Ishan Dangol, Nitesh Rajbanshi
Date: 2023-08-16 00:00:00
Tasks: Data Augmentation, object-detection, Object Detection

We have developed a comprehensive computer system to assist farmers who practice traditional farming methods and have limited access to agricultural experts for addressing crop diseases. Our system utilizes artificial intelligence (AI) to identify and provide remedies for vegetable diseases. To ensure ease of use, we have created a mobile application that offers a user-friendly interface, allowing farmers to inquire about vegetable diseases and receive suitable solutions in their local language. The developed system can be utilized by any farmer with a basic understanding of a smartphone. Specifically, we have designed an AI-enabled mobile application for identifying and suggesting remedies for vegetable diseases, focusing on tomato diseases to benefit the local farming community in Nepal. Our system employs state-of-the-art object detection methodology, namely You Only Look Once (YOLO), to detect tomato diseases. The detected information is then relayed to the mobile application, which provides remedy suggestions guided by domain experts. In order to train our system effectively, we curated a dataset consisting of ten classes of tomato diseases. We utilized various data augmentation methods to address overfitting and trained a YOLOv5 object detector. The proposed method achieved a mean average precision of 0.76 and offers an efficient mobile interface for interacting with the AI system. While our system is currently in the development phase, we are actively working towards enhancing its robustness and real-time usability by accumulating more training samples.
DF-3DFace: One-to-Many Speech Synchronized 3D Face Animation with Diffusion
Authors: Se Jin Park, Joanna Hong, Minsu Kim, Yong Man Ro
Date: 2023-08-23 00:00:00
Tasks: 3D Face Animation

Speech-driven 3D facial animation has gained significant attention for its ability to create realistic and expressive facial animations in 3D space based on speech. Learning-based methods have shown promising progress in achieving accurate facial motion synchronized with speech. However, one-to-many nature of speech-to-3D facial synthesis has not been fully explored: while the lip accurately synchronizes with the speech content, other facial attributes beyond speech-related motions are variable with respect to the speech. To account for the potential variance in the facial attributes within a single speech, we propose DF-3DFace, a diffusion-driven speech-to-3D face mesh synthesis. DF-3DFace captures the complex one-to-many relationships between speech and 3D face based on diffusion. It concurrently achieves aligned lip motion by exploiting audio-mesh synchronization and masked conditioning. Furthermore, the proposed method jointly models identity and pose in addition to facial motions so that it can generate 3D face animation without requiring a reference identity mesh and produce natural head poses. We contribute a new large-scale 3D facial mesh dataset, 3D-HDTF to enable the synthesis of variations in identities, poses, and facial motions of 3D face mesh. Extensive experiments demonstrate that our method successfully generates highly variable facial shapes and motions from speech and simultaneously achieves more realistic facial animation than the state-of-the-art methods.
Component attention network for multimodal dance improvisation recognition
Authors: Jia Fu, Jiarui Tan, Wenjie Yin, Sepideh Pashami, Mårten Björkman
Date: 2023-08-24 00:00:00

Dance improvisation is an active research topic in the arts. Motion analysis of improvised dance can be challenging due to its unique dynamics. Data-driven dance motion analysis, including recognition and generation, is often limited to skeletal data. However, data of other modalities, such as audio, can be recorded and benefit downstream tasks. This paper explores the application and performance of multimodal fusion methods for human motion recognition in the context of dance improvisation. We propose an attention-based model, component attention network (CANet), for multimodal fusion on three levels: 1) feature fusion with CANet, 2) model fusion with CANet and graph convolutional network (GCN), and 3) late fusion with a voting strategy. We conduct thorough experiments to analyze the impact of each modality in different fusion methods and distinguish critical temporal or component features. We show that our proposed model outperforms the two baseline methods, demonstrating its potential for analyzing improvisation in dance.
Reducing the False Positive Rate Using Bayesian Inference in Autonomous Driving Perception
Authors: Gledson Melotti, Johann J. S. Bastos, Bruno L. S. da Silva, Tiago Zanotelli, Cristiano Premebida
Date: 2023-09-09 00:00:00
Tasks: Autonomous Driving, Bayesian Inference, Object, Object Recognition

Object recognition is a crucial step in perception systems for autonomous and intelligent vehicles, as evidenced by the numerous research works in the topic. In this paper, object recognition is explored by using multisensory and multimodality approaches, with the intention of reducing the false positive rate (FPR). The reduction of the FPR becomes increasingly important in perception systems since the misclassification of an object can potentially cause accidents. In particular, this work presents a strategy through Bayesian inference to reduce the FPR considering the likelihood function as a cumulative distribution function from Gaussian kernel density estimations, and the prior probabilities as cumulative functions of normalized histograms. The validation of the proposed methodology is performed on the KITTI dataset using deep networks (DenseNet, NasNet, and EfficientNet), and recent 3D point cloud networks (PointNet, and PintNet++), by considering three object-categories (cars, cyclists, pedestrians) and the RGB and LiDAR sensor modalities.
Efficient Network Representation for GNN-based Intrusion Detection
Authors: Hamdi Friji, Alexis Olivereau, Mireille Sarkiss
Date: 2023-09-11 00:00:00
Tasks: Graph Neural Network, Intrusion Detection, Network Intrusion Detection, Relation

The last decades have seen a growth in the number of cyber-attacks with severe economic and privacy damages, which reveals the need for network intrusion detection approaches to assist in preventing cyber-attacks and reducing their risks. In this work, we propose a novel network representation as a graph of flows that aims to provide relevant topological information for the intrusion detection task, such as malicious behavior patterns, the relation between phases of multi-step attacks, and the relation between spoofed and pre-spoofed attackers activities. In addition, we present a Graph Neural Network (GNN) based framework responsible for exploiting the proposed graph structure to classify communication flows by assigning them a maliciousness score. The framework comprises three main steps that aim to embed nodes features and learn relevant attack patterns from the network representation. Finally, we highlight a potential data leakage issue with classical evaluation procedures and suggest a solution to ensure a reliable validation of intrusion detection systems performance. We implement the proposed framework and prove that exploiting the flow-based graph structure outperforms the classical machine learning-based and the previous GNN-based solutions.
Exploring Embeddings for Measuring Text Relatedness: Unveiling Sentiments and Relationships in Online Comments
Authors: Anthony Olakangil, Cindy Wang, Justin Nguyen, Qunbo Zhou, Kaavya Jethwa, Jason Li, Aryan Narendra, Nishk Patel, Arjun Rajaram
Date: 2023-09-15 00:00:00
Tasks: Word Embeddings

After the COVID-19 pandemic caused internet usage to grow by 70%, there has been an increased number of people all across the world using social media. Applications like Twitter, Meta Threads, YouTube, and Reddit have become increasingly pervasive, leaving almost no digital space where public opinion is not expressed. This paper investigates sentiment and semantic relationships among comments across various social media platforms, as well as discusses the importance of shared opinions across these different media platforms, using word embeddings to analyze components in sentences and documents. It allows researchers, politicians, and business representatives to trace a path of shared sentiment among users across the world. This research paper presents multiple approaches that measure the relatedness of text extracted from user comments on these popular online platforms. By leveraging embeddings, which capture semantic relationships between words and help analyze sentiments across the web, we can uncover connections regarding public opinion as a whole. The study utilizes pre-existing datasets from YouTube, Reddit, Twitter, and more. We made use of popular natural language processing models like Bidirectional Encoder Representations from Transformers (BERT) to analyze sentiments and explore relationships between comment embeddings. Additionally, we aim to utilize clustering and Kl-divergence to find semantic relationships within these comment embeddings across various social media platforms. Our analysis will enable a deeper understanding of the interconnectedness of online comments and will investigate the notion of the internet functioning as a large interconnected brain.
The Unreasonable Effectiveness of Linear Prediction as a Perceptual Metric
Authors: Daniel Severo, Lucas Theis, Johannes Ballé
Date: 2023-10-06 00:00:00
Tasks: Full reference image quality assessment, Full-Reference Image Quality Assessment, Image Quality Assessment, MS-SSIM, SSIM

We show how perceptual embeddings of the visual system can be constructed at inference-time with no training data or deep neural network features. Our perceptual embeddings are solutions to a weighted least squares (WLS) problem, defined at the pixel-level, and solved at inference-time, that can capture global and local image characteristics. The distance in embedding space is used to define a perceptual similarity metric which we call LASI: Linear Autoregressive Similarity Index. Experiments on full-reference image quality assessment datasets show LASI performs competitively with learned deep feature based methods like LPIPS (Zhang et al., 2018) and PIM (Bhardwaj et al., 2020), at a similar computational cost to hand-crafted methods such as MS-SSIM (Wang et al., 2003). We found that increasing the dimensionality of the embedding space consistently reduces the WLS loss while increasing performance on perceptual tasks, at the cost of increasing the computational complexity. LASI is fully differentiable, scales cubically with the number of embedding dimensions, and can be parallelized at the pixel-level. A Maximum Differentiation (MAD) competition (Wang & Simoncelli, 2008) between LASI and LPIPS shows that both methods are capable of finding failure points for the other, suggesting these metrics can be combined.
QE-BEV: Query Evolution for Bird's Eye View Object Detection in Varied Contexts
Authors: Jiawei Yao, Yingxin Lai, Hongrui Kou, Tong Wu, Ruixi Liu
Date: 2023-10-07 00:00:00
Tasks: 3D Object Detection, Autonomous Driving, Computational Efficiency, Diversity, Object, object-detection, Object Detection

3D object detection plays a pivotal role in autonomous driving and robotics, demanding precise interpretation of Bird's Eye View (BEV) images. The dynamic nature of real-world environments necessitates the use of dynamic query mechanisms in 3D object detection to adaptively capture and process the complex spatio-temporal relationships present in these scenes. However, prior implementations of dynamic queries have often faced difficulties in effectively leveraging these relationships, particularly when it comes to integrating temporal information in a computationally efficient manner. Addressing this limitation, we introduce a framework utilizing dynamic query evolution strategy, harnesses K-means clustering and Top-K attention mechanisms for refined spatio-temporal data processing. By dynamically segmenting the BEV space and prioritizing key features through Top-K attention, our model achieves a real-time, focused analysis of pertinent scene elements. Our extensive evaluation on the nuScenes and Waymo dataset showcases a marked improvement in detection accuracy, setting a new benchmark in the domain of query-based BEV object detection. Our dynamic query evolution strategy has the potential to push the boundaries of current BEV methods with enhanced adaptability and computational efficiency. Project page: https://github.com/Jiawei-Yao0812/QE-BEV
Payload-Independent Direct Cost Learning for Image Steganography
Authors: Weixiang Li, Shiang Wu, Bin Li, Weixuan Tang, and XinPeng Zhang
Date: 2023-07-11 00:00:00
Proceeding: journal 2023 7
Tasks: Image Steganography, Reinforcement Learning (RL), Steganalysis, valid

Abstract—Recent research has shown that architectures utilizing reinforcement learning (RL) are effective in cost-based image steganography. However, these architectures only learn embedding probabilities rather than costs, and are trained for a specific embedding payload, making it difficult to extend the trained model to serve other payloads. In this paper, we propose a payload-independent cost learning framework using RL called PICO-RL. This framework directly learns universal costs that can be applied to any payload. PICO-RL incorporates an optimal probability approximation (OPA) module that can calculate the required probability map for embedding simulation directly from a learned cost map for any payload, eliminating the need for time-consuming searches for a valid probability scaling parameter. Additionally, PICO-RL uses an advanced steganalysis environment network to provide more effective reward feedback for learning. During RL training, the learned cost maps of different payloads converge and eventually become similar under the OPA constraint, resulting in payload independence. Experimental results demonstrate that a well-trained PICO-RL model, which acts as a universal cost function, defines costs with superior security performance against steganalysis and has better coding compatibility when encoding with practical steganographic codes.
Can one hear the shape of the Universe?
Authors: R. Aurich, S. Lustig, F. Steiner, H. Then
Date: 2004-12-16 00:00:00

It is shown that the recent observations of NASA's explorer mission "Wilkinson Microwave Anisotropy Probe" (WMAP) hint that our Universe may possess a non-trivial topology. As an example we discuss the Picard space which is stretched out into an infinitely long horn but with finite volume.
Image Watermarking Scheme Using LSB and Image Gradient
Authors: Zaid Bin Faheem, Mubashir Ali, Muhammad Ahsan Raza, Farrukh Arslan, Jehad Ali, Mehedi Masud and Mohammad Shorfuzzaman
Date: 2022-04-21 00:00:00
Proceeding: Appl. Sci. 2022 2022 4

In the modern age, watermarking techniques are mandatory to secure digital communication over the internet. For an optimal technique, a high signal-to-noise ratio and normalized correctional is required. In this paper, a digital watermarking technique is proposed on the basis of the least significant bit through an image gradient and chaotic map. The image is segmented into noncorrelated blocks, and the gradient of each block is calculated. The gradient of the image expresses the rapid changes in an image. A chaotic substitution box (S-Box) is used to scramble the watermark according to a piecewise linear chaotic map (PWLCM). PWLCM has a positive Lyapunov exponent and better balance property as compared to other chaotic maps. This S-Box technique is capable of producing a disperse sequence with high nonlinearity in the generated sequence. Least significant bit is a simple technique for embedding but it has a high payload capacity and direct pixel manipulation. The embedding payload introduces a tradeoff between robustness and imperceptibility; hence, the image gradient is a technique to identify the best-suited place to embed a watermark and avoid image degradation. By modifying the least significant bits of the original image, the watermark signal is embedded according to the image gradient. In the image gradient, the direction and magnitude decide how much embedding can be done. In comparison with other methods, the experimental results show satisfactory progress in robustness against several image processing and geometrical attacks while maintaining the imperceptibility of the watermark signal.
Accurate Dictionary Learning with Direct Sparsity Control
Authors: Hongyu Mou, Adrian Barbu
Date: 2018-10-01 00:00:00
Proceeding: ICIP 2018 10
Tasks: Dictionary Learning, feature selection, image-classification, Image Classification, Variable Selection

Dictionary learning is a popular method for obtaining sparse linear representations for high dimensional data, with many applications in image classification, signal processing and machine learning. 
In this paper, we introduce a novel dictionary learning method based on a recent variable selection algorithm called Feature Selection with Annealing (FSA). 
Because FSA uses an L0 constraint instead of the L1 penalty, it does not introduce any bias in the  coefficients and obtains a more accurate sparse representation. Furthermore, the L0 constraint makes it easy to directly specify the desired sparsity level instead of indirectly through a L1 penalty. Finally, experimental validation on real gray-scale images shows that the proposed method obtains higher accuracy and efficiency in dictionary learning compared to classical methods based on the L1 penalty.
Scalable Learning with Incremental Probabilistic PCA
Authors: Boshi Wang, Adrian Barbu
Date: 2022-12-20 00:00:00
Proceeding: IEEE International Conference on Big Data 2022 12
Tasks: class-incremental learning, Class Incremental Learning, Incremental Learning

Incremental class learning is the classification problem of learning a model where instances from new object classes are added sequentially, and it is desired that the model be retrained only on the new classes with minimal training on the old classes. 
One major problem facing class incremental learning is catastrophic forgetting, where the updated model forgets the old classes and focuses only on the new classes.
This paper proposes a simple and novel incremental class learning method that uses a self-supervised pretrained feature extractor to obtain meaningful features and trains Probabilistic PCA models on the extracted features for each class separately. 
The Mahalanobis distance is used to obtain the classification result, and an equivalent equation is derived to make the approach computationally affordable.
Experiments on standard and large datasets show that the proposed approach outperforms existing state of the art incremental learning methods by a large margin.
The fact that the model is trained on each class separately makes it applicable to training on very large datasets such as the whole ImageNet with more than 10,000 classes.