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272599837 | 2409.07694 | 2024-09-12 | Learn from Balance: Rectifying Knowledge Transfer for Long-Tailed Scenarios | Knowledge Distillation (KD) transfers knowledge from a large pre-trained teacher network to a compact and efficient student network, making it suitable for deployment on resource-limited media terminals. However, traditional KD methods require balanced data to ensure robust training, which is often unavailable in pract... | [
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272599883 | 2409.07960 | 2024-09-12 | Do Vision Foundation Models Enhance Domain Generalization in Medical Image Segmentation? | Neural networks achieve state-of-the-art performance in many supervised learning tasks when the training data distribution matches the test data distribution. However, their performance drops significantly under domain (covariate) shift, a prevalent issue in medical image segmentation due to varying acquisition setting... | [
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272599754 | 2409.08000 | 2024-09-12 | OCTAMamba: A State-Space Model Approach for Precision OCTA Vasculature Segmentation | Optical Coherence Tomography Angiography (OCTA) is a crucial imaging technique for visualizing retinal vasculature and diagnosing eye diseases such as diabetic retinopathy and glaucoma. However, precise segmentation of OCTA vasculature remains challenging due to the multi-scale vessel structures and noise from poor ima... | [
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272600167 | 2409.08081 | 2024-09-12 | SoVAR: Building Generalizable Scenarios from Accident Reports for Autonomous Driving Testing | Autonomous driving systems (ADSs) have undergone remarkable development and are increasingly employed in safety-critical applications. However, recently reported data on fatal accidents involving ADSs suggests that the desired level of safety has not yet been fully achieved. Consequently, there is a growing need for mo... | [
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272911034 | 2409.17275 | 2024-09-12 | On the Vulnerability of Applying Retrieval-Augmented Generation within Knowledge-Intensive Application Domains | Retrieval-Augmented Generation (RAG) has been empirically shown to enhance the performance of large language models (LLMs) in knowledge-intensive domains such as healthcare, finance, and legal contexts. Given a query, RAG retrieves relevant documents from a corpus and integrates them into the LLMs' generation process. ... | [
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272599772 | 2409.08211 | 2024-09-12 | Graph Laplacian-based Bayesian Multi-fidelity Modeling | We present a novel probabilistic approach for generating multi-fidelity data while accounting for errors inherent in both low- and high-fidelity data. In this approach a graph Laplacian constructed from the low-fidelity data is used to define a multivariate Gaussian prior density for the coordinates of the true data po... | [
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272599915 | 2409.07904 | 2024-09-12 | FACT: Feature Adaptive Continual-learning Tracker for Multiple Object Tracking | Multiple object tracking (MOT) involves identifying multiple targets and assigning them corresponding IDs within a video sequence, where occlusions are often encountered. Recent methods address occlusions using appearance cues through online learning techniques to improve adaptivity or offline learning techniques to ut... | [
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272599785 | 2409.07925 | 2024-09-12 | A framework for measuring the training efficiency of a neural architecture | Measuring Efficiency in neural network system development is an open research problem. This paper presents an experimental framework to measure the training efficiency of a neural architecture. To demonstrate our approach, we analyze the training efficiency of Convolutional Neural Networks and Bayesian equivalents on t... | [
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272599887 | 2409.08046 | 2024-09-12 | On the challenges of studying bias in Recommender Systems: A UserKNN case study | Statements on the propagation of bias by recommender systems are often hard to verify or falsify. Research on bias tends to draw from a small pool of publicly available datasets and is therefore bound by their specific properties. Additionally, implementation choices are often not explicitly described or motivated in r... | [
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272600452 | 2409.07995 | 2024-09-12 | Depth Matters: Exploring Deep Interactions of RGB-D for Semantic Segmentation in Traffic Scenes | RGB-D has gradually become a crucial data source for understanding complex scenes in assisted driving. However, existing studies have paid insufficient attention to the intrinsic spatial properties of depth maps. This oversight significantly impacts the attention representation, leading to prediction errors caused by a... | [
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272600347 | 2409.07949 | 2024-09-12 | Edge States with Hidden Topology in Spinner Lattices | Symmetries -- whether explicit, latent, or hidden -- are fundamental to understanding topological materials. This work introduces a prototypical spring-mass model that extends beyond established canonical models, revealing topological edge states with distinct profiles at opposite edges. These edge states originate fro... | [
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272599746 | 2409.07780 | 2024-09-12 | Supporting Online Discussions: Integrating AI Into the adhocracy+ Participation Platform To Enhance Deliberation | Online spaces provide individuals with the opportunity to engage in discussions on important topics and make collective decisions, regardless of their geographic location or time zone. However, without adequate support and careful design, such discussions often suffer from a lack of structure and civility in the exchan... | [
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272600126 | 2409.08251 | 2024-09-12 | Dynamic Prompting of Frozen Text-to-Image Diffusion Models for Panoptic Narrative Grounding | Panoptic narrative grounding (PNG), whose core target is fine-grained image-text alignment, requires a panoptic segmentation of referred objects given a narrative caption. Previous discriminative methods achieve only weak or coarse-grained alignment by panoptic segmentation pretraining or CLIP model adaptation. Given t... | [
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282912397 | 2409.07843 | 2024-09-12 | Real-time Multi-view Omnidirectional Depth Estimation for Real Scenarios based on Teacher-Student Learning with Unlabeled Data | Omnidirectional depth estimation enables efficient 3D perception over a full 360-degree range. However, in real-world applications such as autonomous driving and robotics, achieving real-time performance and robust cross-scene generalization remains a significant challenge for existing algorithms. In this paper, we pro... | [
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272599829 | 2409.08045 | 2024-09-12 | Unleashing Worms and Extracting Data: Escalating the Outcome of Attacks against RAG-based Inference in Scale and Severity Using Jailbreaking | In this paper, we show that with the ability to jailbreak a GenAI model, attackers can escalate the outcome of attacks against RAG-based GenAI-powered applications in severity and scale. In the first part of the paper, we show that attackers can escalate RAG membership inference attacks and RAG entity extraction attack... | [
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272653954 | 2409.08331 | 2024-09-12 | Digital Volumetric Biopsy Cores Improve Gleason Grading of Prostate Cancer Using Deep Learning | Prostate cancer (PCa) was the most frequently diagnosed cancer among American men in 2023. The histological grading of biopsies is essential for diagnosis, and various deep learning-based solutions have been developed to assist with this task. Existing deep learning frameworks are typically applied to individual 2D cro... | [
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272653691 | 2409.08406 | 2024-09-12 | Knowledge Tagging with Large Language Model based Multi-Agent System | Knowledge tagging for questions is vital in modern intelligent educational applications, including learning progress diagnosis, practice question recommendations, and course content organization. Traditionally, these annotations have been performed by pedagogical experts, as the task demands not only a deep semantic un... | [
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272654101 | 2409.08346 | 2024-09-12 | Towards Quantifying and Reducing Language Mismatch Effects in Cross-Lingual Speech Anti-Spoofing | The effects of language mismatch impact speech anti-spoofing systems, while investigations and quantification of these effects remain limited. Existing anti-spoofing datasets are mainly in English, and the high cost of acquiring multilingual datasets hinders training language-independent models. We initiate this work b... | [
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272599907 | 2409.08087 | 2024-09-12 | Securing Large Language Models: Addressing Bias, Misinformation, and Prompt Attacks | Large Language Models (LLMs) demonstrate impressive capabilities across various fields, yet their increasing use raises critical security concerns. This article reviews recent literature addressing key issues in LLM security, with a focus on accuracy, bias, content detection, and vulnerability to attacks. Issues relate... | [
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272600084 | 2409.07794 | 2024-09-12 | Efficient Learning of Balanced Signed Graphs via Iterative Linear Programming | Signed graphs are equipped with both positive and negative edge weights, encoding pairwise correlations as well as anti-correlations in data. A balanced signed graph has no cycles of odd number of negative edges. Laplacian of a balanced signed graph has eigenvectors that map simply to ones in a similarity-transformed p... | [
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272600234 | 2409.07753 | 2024-09-12 | Relevance for Human Robot Collaboration | Inspired by the human ability to selectively focus on relevant information, this paper introduces relevance, a novel dimensionality reduction process for human-robot collaboration (HRC). Our approach incorporates a continuously operating perception module, evaluates cue sufficiency within the scene, and applies a flexi... | [
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272599824 | 2409.07957 | 2024-09-12 | Rapid Parameter Estimation for Extreme Mass Ratio Inspirals Using Machine Learning | Extreme-mass-ratio inspiral (EMRI) signals pose significant challenges in gravitational wave (GW) astronomy owing to their low-frequency nature and highly complex waveforms, which occupy a high-dimensional parameter space with numerous variables. Given their extended inspiral timescales and low signal-to-noise ratios, ... | [
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272599905 | 2409.07809 | 2024-09-12 | Controllable Synthetic Clinical Note Generation with Privacy Guarantees | In the field of machine learning, domain-specific annotated data is an invaluable resource for training effective models. However, in the medical domain, this data often includes Personal Health Information (PHI), raising significant privacy concerns. The stringent regulations surrounding PHI limit the availability and... | [
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272599742 | 2409.08207 | 2024-09-12 | VI3DRM:Towards meticulous 3D Reconstruction from Sparse Views via Photo-Realistic Novel View Synthesis | Recently, methods like Zero-1-2-3 have focused on single-view based 3D reconstruction and have achieved remarkable success. However, their predictions for unseen areas heavily rely on the inductive bias of large-scale pretrained diffusion models. Although subsequent work, such as DreamComposer, attempts to make predict... | [
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272600430 | 2409.08098 | 2024-09-12 | The CLC-UKET Dataset: Benchmarking Case Outcome Prediction for the UK Employment Tribunal | This paper explores the intersection of technological innovation and access to justice by developing a benchmark for predicting case outcomes in the UK Employment Tribunal (UKET). To address the challenge of extensive manual annotation, the study employs a large language model (LLM) for automatic annotation, resulting ... | [
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272654034 | 2409.08396 | 2024-09-12 | Federated One-Shot Ensemble Clustering | Cluster analysis across multiple institutions poses significant challenges due to data-sharing restrictions. To overcome these limitations, we introduce the Federated One-shot Ensemble Clustering (FONT) algorithm, a novel solution tailored for multi-site analyses under such constraints. FONT requires only a single roun... | [
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272599799 | 2409.07880 | 2024-09-12 | Non-negative Weighted DAG Structure Learning | We address the problem of learning the topology of directed acyclic graphs (DAGs) from nodal observations, which adhere to a linear structural equation model. Recent advances framed the combinatorial DAG structure learning task as a continuous optimization problem, yet existing methods must contend with the complexitie... | [
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272600494 | 2409.08269 | 2024-09-12 | Touch2Touch: Cross-Modal Tactile Generation for Object Manipulation | Today's touch sensors come in many shapes and sizes. This has made it challenging to develop general-purpose touch processing methods since models are generally tied to one specific sensor design. We address this problem by performing cross-modal prediction between touch sensors: given the tactile signal from one senso... | [
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272599798 | 2409.07808 | 2024-09-12 | FedHide: Federated Learning by Hiding in the Neighbors | We propose a prototype-based federated learning method designed for embedding networks in classification or verification tasks. Our focus is on scenarios where each client has data from a single class. The main challenge is to develop an embedding network that can distinguish between different classes while adhering to... | [
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272600403 | 2409.07691 | 2024-09-12 | Enhancing Q&A Text Retrieval with Ranking Models: Benchmarking, fine-tuning and deploying Rerankers for RAG | Ranking models play a crucial role in enhancing overall accuracy of text retrieval systems. These multi-stage systems typically utilize either dense embedding models or sparse lexical indices to retrieve relevant passages based on a given query, followed by ranking models that refine the ordering of the candidate passa... | [
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272599838 | 2409.07887 | 2024-09-12 | UNIT: Unsupervised Online Instance Segmentation through Time | Online object segmentation and tracking in Lidar point clouds enables autonomous agents to understand their surroundings and make safe decisions. Unfortunately, manual annotations for these tasks are prohibitively costly. We tackle this problem with the task of class-agnostic unsupervised online instance segmentation a... | [
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272600500 | 2409.07853 | 2024-09-12 | Improve Machine Learning carbon footprint using Nvidia GPU and Mixed Precision training for classification models -- Part I | This is the 1st part of the dissertation for my master degree and compares the power consumption using the default floating point (32bit) and Nvidia mixed precision (16bit and 32bit) while training a classification ML model. A custom PC with specific hardware was built to perform the experiments, and different ML hyper... | [
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272988137 | 2409.18970 | 2024-09-12 | Portfolio Stress Testing and Value at Risk (VaR) Incorporating Current Market Conditions | Value at Risk (VaR) and stress testing are two of the most widely used approaches in portfolio risk management to estimate potential market value losses under adverse market moves. VaR quantifies potential loss in value over a specified horizon (such as one day or ten days) at a desired confidence level (such as 95'th ... | [
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272600119 | 2409.07710 | 2024-09-12 | Surprising Resilience of Science During a Global Pandemic: A Large-Scale Descriptive Analysis | The COVID-19 pandemic profoundly impacted people globally, yet its effect on scientists and research institutions has yet to be fully examined. To address this knowledge gap, we use a newly available bibliographic dataset covering tens of millions of papers and authors to investigate changes in research activity and co... | [
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272600309 | 2409.07825 | 2024-09-12 | Deep Multimodal Learning with Missing Modality: A Survey | During multimodal model training and testing, certain data modalities may be absent due to sensor limitations, cost constraints, privacy concerns, or data loss, negatively affecting performance. Multimodal learning techniques designed to handle missing modalities can mitigate this by ensuring model robustness even when... | [
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272653727 | 2409.08400 | 2024-09-12 | Scores as Actions: a framework of fine-tuning diffusion models by continuous-time reinforcement learning | Reinforcement Learning from human feedback (RLHF) has been shown a promising direction for aligning generative models with human intent and has also been explored in recent works for alignment of diffusion generative models. In this work, we provide a rigorous treatment by formulating the task of fine-tuning diffusion ... | [
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272600078 | 2409.07684 | 2024-09-12 | Modeling Information Narrative Detection and Evolution on Telegram during the Russia-Ukraine War | Following the Russian Federation's full-scale invasion of Ukraine in February 2022, a multitude of information narratives emerged within both pro-Russian and pro-Ukrainian communities online. As the conflict progresses, so too do the information narratives, constantly adapting and influencing local and global community... | [
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272600002 | 2409.07701 | 2024-09-12 | TMFNet: Two-Stream Multi-Channels Fusion Networks for Color Image Operation Chain Detection | Image operation chain detection techniques have gained increasing attention recently in the field of multimedia forensics. However, existing detection methods suffer from the generalization problem. Moreover, the channel correlation of color images that provides additional forensic evidence is often ignored. To solve t... | [
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272599986 | 2409.07708 | 2024-09-12 | Dataset-Free Weight-Initialization on Restricted Boltzmann Machine | In feed-forward neural networks, dataset-free weight-initialization methods such as LeCun, Xavier (or Glorot), and He initializations have been developed. These methods randomly determine the initial values of weight parameters based on specific distributions (e.g., Gaussian or uniform distributions) without using trai... | [
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272653985 | 2409.08351 | 2024-09-12 | Bayesian Inverse Graphics for Few-Shot Concept Learning | Humans excel at building generalizations of new concepts from just one single example. Contrary to this, current computer vision models typically require large amount of training samples to achieve a comparable accuracy. In this work we present a Bayesian model of perception that learns using only minimal data, a proto... | [
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272600074 | 2409.07801 | 2024-09-12 | SURGIVID: Annotation-Efficient Surgical Video Object Discovery | Surgical scenes convey crucial information about the quality of surgery. Pixel-wise localization of tools and anatomical structures is the first task towards deeper surgical analysis for microscopic or endoscopic surgical views. This is typically done via fully-supervised methods which are annotation greedy and in seve... | [
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272599902 | 2409.08276 | 2024-09-12 | AnySkin: Plug-and-play Skin Sensing for Robotic Touch | While tactile sensing is widely accepted as an important and useful sensing modality, its use pales in comparison to other sensory modalities like vision and proprioception. AnySkin addresses the critical challenges that impede the use of tactile sensing -- versatility, replaceability, and data reusability. Building on... | [
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272600278 | 2409.07672 | 2024-09-12 | An Unsupervised Dialogue Topic Segmentation Model Based on Utterance Rewriting | Dialogue topic segmentation plays a crucial role in various types of dialogue modeling tasks. The state-of-the-art unsupervised DTS methods learn topic-aware discourse representations from conversation data through adjacent discourse matching and pseudo segmentation to further mine useful clues in unlabeled conversatio... | [
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272600358 | 2409.07884 | 2024-09-12 | Graph Neural Networks for Parkinsons Disease Detection | Despite the promising performance of state of the art approaches for Parkinsons Disease (PD) detection, these approaches often analyze individual speech segments in isolation, which can lead to suboptimal results. Dysarthric cues that characterize speech impairments from PD patients are expected to be related across se... | [
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272600368 | 2409.08111 | 2024-09-12 | Towards a graph-based foundation model for network traffic analysis | Foundation models have shown great promise in various fields of study. A potential application of such models is in computer network traffic analysis, where these models can grasp the complexities of network traffic dynamics and adapt to any specific task or network environment with minimal fine-tuning. Previous approa... | [
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272653975 | 2409.08382 | 2024-09-12 | Stochastic Reinforcement Learning with Stability Guarantees for Control of Unknown Nonlinear Systems | Designing a stabilizing controller for nonlinear systems is a challenging task, especially for high-dimensional problems with unknown dynamics. Traditional reinforcement learning algorithms applied to stabilization tasks tend to drive the system close to the equilibrium point. However, these approaches often fall short... | [
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272600352 | 2409.07839 | 2024-09-12 | FPMT: Enhanced Semi-Supervised Model for Traffic Incident Detection | For traffic incident detection, the acquisition of data and labels is notably resource-intensive, rendering semi-supervised traffic incident detection both a formidable and consequential challenge. Thus, this paper focuses on traffic incident detection with a semi-supervised learning way. It proposes a semi-supervised ... | [
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272521549 | 2409.07797 | 2024-09-12 | Quaternion Nuclear Norm minus Frobenius Norm Minimization for color image reconstruction | Color image restoration methods typically represent images as vectors in Euclidean space or combinations of three monochrome channels. However, they often overlook the correlation between these channels, leading to color distortion and artifacts in the reconstructed image. To address this, we present Quaternion Nuclear... | [
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272600076 | 2409.07774 | 2024-09-12 | ROCAS: Root Cause Analysis of Autonomous Driving Accidents via Cyber-Physical Co-mutation | As Autonomous driving systems (ADS) have transformed our daily life, safety of ADS is of growing significance. While various testing approaches have emerged to enhance the ADS reliability, a crucial gap remains in understanding the accidents causes. Such post-accident analysis is paramount and beneficial for enhancing ... | [
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272653622 | 2409.08434 | 2024-09-13 | Predictive Control and Regret Analysis of Non-Stationary MDP with Look-ahead Information | Policy design in non-stationary Markov Decision Processes (MDPs) is inherently challenging due to the complexities introduced by time-varying system transition and reward, which make it difficult for learners to determine the optimal actions for maximizing cumulative future rewards. Fortunately, in many practical appli... | [
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272654170 | 2409.08885 | 2024-09-13 | Interactive Masked Image Modeling for Multimodal Object Detection in Remote Sensing | Object detection in remote sensing imagery plays a vital role in various Earth observation applications. However, unlike object detection in natural scene images, this task is particularly challenging due to the abundance of small, often barely visible objects across diverse terrains. To address these challenges, multi... | [
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272827371 | 2409.13749 | 2024-09-13 | KodeXv0.1: A Family of State-of-the-Art Financial Large Language Models | Although powerful, current cutting-edge LLMs may not fulfil the needs of highly specialised sectors. We introduce KodeXv0.1, a family of large language models that outclass GPT-4 in financial question answering. We utilise the base variants of Llama 3.1 8B and 70B and adapt them to the financial domain through a custom... | [
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272653775 | 2409.08806 | 2024-09-13 | TabKANet: Tabular Data Modeling with Kolmogorov-Arnold Network and Transformer | Tabular data is the most common type of data in real-life scenarios. In this study, we propose the TabKANet model for tabular data modeling, which targets the bottlenecks in learning from numerical content. We constructed a Kolmogorov-Arnold Network (KAN) based Numerical Embedding Module and unified numerical and categ... | [
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272653644 | 2409.08887 | 2024-09-13 | Visual Language Tracking with Multi-modal Interaction: A Robust Benchmark | Visual Language Tracking (VLT) enhances tracking by mitigating the limitations of relying solely on the visual modality, utilizing high-level semantic information through language. This integration of the language enables more advanced human-machine interaction. The essence of interaction is cognitive alignment, which ... | [
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272654252 | 2409.08691 | 2024-09-13 | Autoregressive Sequence Modeling for 3D Medical Image Representation | Three-dimensional (3D) medical images, such as Computed Tomography (CT) and Magnetic Resonance Imaging (MRI), are essential for clinical applications. However, the need for diverse and comprehensive representations is particularly pronounced when considering the variability across different organs, diagnostic tasks, an... | [
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272653689 | 2409.08601 | 2024-09-13 | STA-V2A: Video-to-Audio Generation with Semantic and Temporal Alignment | Visual and auditory perception are two crucial ways humans experience the world. Text-to-video generation has made remarkable progress over the past year, but the absence of harmonious audio in generated video limits its broader applications. In this paper, we propose Semantic and Temporal Aligned Video-to-Audio (STA-V... | [
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272653904 | 2409.08688 | 2024-09-13 | GenMapping: Unleashing the Potential of Inverse Perspective Mapping for Robust Online HD Map Construction | Online High-Definition (HD) maps have emerged as the preferred option for autonomous driving, overshadowing the counterpart offline HD maps due to flexible update capability and lower maintenance costs. However, contemporary online HD map models embed parameters of visual sensors into training, resulting in a significa... | [
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272653665 | 2409.08673 | 2024-09-13 | Acoustic identification of individual animals with hierarchical contrastive learning | Acoustic identification of individual animals (AIID) is closely related to audio-based species classification but requires a finer level of detail to distinguish between individual animals within the same species. In this work, we frame AIID as a hierarchical multi-label classification task and propose the use of hiera... | [
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272653986 | 2409.08907 | 2024-09-13 | Affective Computing Has Changed: The Foundation Model Disruption | The dawn of Foundation Models has on the one hand revolutionised a wide range of research problems, and, on the other hand, democratised the access and use of AI-based tools by the general public. We even observe an incursion of these models into disciplines related to human psychology, such as the Affective Computing ... | [
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272689329 | 2409.09099 | 2024-09-13 | S-STE: Continuous Pruning Function for Efficient 2:4 Sparse Pre-training | Training deep neural networks (DNNs) is costly. Fortunately, Nvidia Ampere and Hopper GPUs can accelerate matrix multiplications twice as fast as a dense equivalent by implementing 2:4 sparsity. However, previous STE-based 2:4 pre-training methods (e.g. STE with hard-thresholding, SR-STE) suffer from optimization diffi... | [
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272654254 | 2409.08596 | 2024-09-13 | Large Language Model Can Transcribe Speech in Multi-Talker Scenarios with Versatile Instructions | Recent advancements in large language models (LLMs) have revolutionized various domains, bringing significant progress and new opportunities. Despite progress in speech-related tasks, LLMs have not been sufficiently explored in multi-talker scenarios. In this work, we present a pioneering effort to investigate the capa... | [
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272653703 | 2409.08964 | 2024-09-13 | IMMERTWIN: A Mixed Reality Framework for Enhanced Robotic Arm Teleoperation | We present IMMERTWIN, a mixed reality framework for enhance robotic arm teleoperation using a closed-loop digital twin as a bridge for interaction between the user and the robotic system. We evaluated IMMERTWIN by performing a medium-scale user survey with 26 participants on two robots. Users were asked to teleoperate ... | [
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272690302 | 2409.09177 | 2024-09-13 | Transformer with Controlled Attention for Synchronous Motion Captioning | In this paper, we address a challenging task, synchronous motion captioning, that aim to generate a language description synchronized with human motion sequences. This task pertains to numerous applications, such as aligned sign language transcription, unsupervised action segmentation and temporal grounding. Our method... | [
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272427239 | 2409.08580 | 2024-09-13 | Molecular Graph Representation Learning via Structural Similarity Information | Graph Neural Networks (GNNs) have been widely employed for feature representation learning in molecular graphs. Therefore, it is crucial to enhance the expressiveness of feature representation to ensure the effectiveness of GNNs. However, a significant portion of current research primarily focuses on the structural fea... | [
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272689208 | 2409.09144 | 2024-09-13 | PrimeDepth: Efficient Monocular Depth Estimation with a Stable Diffusion Preimage | This work addresses the task of zero-shot monocular depth estimation. A recent advance in this field has been the idea of utilising Text-to-Image foundation models, such as Stable Diffusion. Foundation models provide a rich and generic image representation, and therefore, little training data is required to reformulate... | [
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272653762 | 2409.08566 | 2024-09-13 | Hybrid-TTA: Continual Test-time Adaptation via Dynamic Domain Shift Detection | Continual Test Time Adaptation (CTTA) has emerged as a critical approach for bridging the domain gap between the controlled training environments and the real-world scenarios, enhancing model adaptability and robustness. Existing CTTA methods, typically categorized into Full-Tuning (FT) and Efficient-Tuning (ET), strug... | [
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] | [
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"Domain adaptation in vision",
"Object detection, segmentation, and tracking in vision"
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