id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2411.19322 | SAMa: Material-aware 3D Selection and Segmentation | [
"cs.CV",
"cs.GR"
] | Decomposing 3D assets into material parts is a common task for artists and creators, yet remains a highly manual process. In this work, we introduce Select Any Material (SAMa), a material selection approach for various 3D representations. Building on the recently introduced SAM2 video selection model, we extend its cap... | {
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2411.19324 | Trajectory Attention for Fine-grained Video Motion Control | [
"cs.CV"
] | Recent advancements in video generation have been greatly driven by video diffusion models, with camera motion control emerging as a crucial challenge in creating view-customized visual content. This paper introduces trajectory attention, a novel approach that performs attention along available pixel trajectories for f... | {
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2411.19325 | GEOBench-VLM: Benchmarking Vision-Language Models for Geospatial Tasks | [
"cs.CV"
] | While numerous recent benchmarks focus on evaluating generic Vision-Language Models (VLMs), they fall short in addressing the unique demands of geospatial applications. Generic VLM benchmarks are not designed to handle the complexities of geospatial data, which is critical for applications such as environmental monitor... | {
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2411.19331 | Talking to DINO: Bridging Self-Supervised Vision Backbones with Language
for Open-Vocabulary Segmentation | [
"cs.CV",
"cs.AI",
"cs.CL"
] | Open-Vocabulary Segmentation (OVS) aims at segmenting images from free-form textual concepts without predefined training classes. While existing vision-language models such as CLIP can generate segmentation masks by leveraging coarse spatial information from Vision Transformers, they face challenges in spatial localiza... | {
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2411.19334 | Reconfigurable Holographic Surface: A New Paradigm for Ultra-Massive
MIMO | [
"cs.IT",
"eess.SP",
"math.IT"
] | Evolving from massive multiple-input multiple-output (MIMO) in current 5G communications, ultra-massive MIMO emerges as a seminal technology for fulfilling more stringent requirements of future 6G communications. However, widely-utilized phased arrays relying on active components make the implementation of ultra-massiv... | {
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2411.19335 | PEFT-as-an-Attack! Jailbreaking Language Models during Federated
Parameter-Efficient Fine-Tuning | [
"cs.CR",
"cs.AI"
] | Federated Parameter-Efficient Fine-Tuning (FedPEFT) has emerged as a promising paradigm for privacy-preserving and efficient adaptation of Pre-trained Language Models (PLMs) in Federated Learning (FL) settings. It preserves data privacy by keeping the data decentralized and training the model on local devices, ensuring... | {
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2411.19339 | Towards a Mechanistic Explanation of Diffusion Model Generalization | [
"cs.LG",
"cs.AI",
"cs.CV"
] | We propose a simple, training-free mechanism which explains the generalization behaviour of diffusion models. By comparing pre-trained diffusion models to their theoretically optimal empirical counterparts, we identify a shared local inductive bias across a variety of network architectures. From this observation, we hy... | {
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2411.19341 | An Adversarial Learning Approach to Irregular Time-Series Forecasting | [
"cs.LG",
"cs.AI"
] | Forecasting irregular time series presents significant challenges due to two key issues: the vulnerability of models to mean regression, driven by the noisy and complex nature of the data, and the limitations of traditional error-based evaluation metrics, which fail to capture meaningful patterns and penalize unrealist... | {
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2411.19345 | 3D Wasserstein generative adversarial network with dense U-Net based
discriminator for preclinical fMRI denoising | [
"eess.IV",
"cs.CV",
"cs.LG"
] | Functional magnetic resonance imaging (fMRI) is extensively used in clinical and preclinical settings to study brain function, however, fMRI data is inherently noisy due to physiological processes, hardware, and external noise. Denoising is one of the main preprocessing steps in any fMRI analysis pipeline. This process... | {
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2411.19346 | CLIP meets DINO for Tuning Zero-Shot Classifier using Unlabeled Image
Collections | [
"cs.CV",
"cs.CL",
"cs.LG"
] | In the era of foundation models, CLIP has emerged as a powerful tool for aligning text and visual modalities into a common embedding space. However, the alignment objective used to train CLIP often results in subpar visual features for fine-grained tasks. In contrast, SSL-pretrained models like DINO excel at extracting... | {
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2411.19352 | OMuleT: Orchestrating Multiple Tools for Practicable Conversational
Recommendation | [
"cs.AI"
] | In this paper, we present a systematic effort to design, evaluate, and implement a realistic conversational recommender system (CRS). The objective of our system is to allow users to input free-form text to request recommendations, and then receive a list of relevant and diverse items. While previous work on synthetic ... | {
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2411.19356 | Mapping Public Perception of Artificial Intelligence: Expectations,
Risk-Benefit Tradeoffs, and Value As Determinants for Societal Acceptance | [
"cs.CY",
"cs.AI",
"cs.HC"
] | Understanding public perception of artificial intelligence (AI) and the tradeoffs between potential risks and benefits is crucial, as these perceptions might shape policy decisions, influence innovation trajectories for successful market strategies, and determine individual and societal acceptance of AI technologies. U... | {
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2411.19359 | Integrating Transit Signal Priority into Multi-Agent Reinforcement
Learning based Traffic Signal Control | [
"cs.AI",
"cs.MA",
"cs.SY",
"eess.SY"
] | This study integrates Transit Signal Priority (TSP) into multi-agent reinforcement learning (MARL) based traffic signal control. The first part of the study develops adaptive signal control based on MARL for a pair of coordinated intersections in a microscopic simulation environment. The two agents, one for each inters... | {
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2411.19360 | DENIAHL: In-Context Features Influence LLM Needle-In-A-Haystack
Abilities | [
"cs.CL",
"cs.AI",
"cs.LG"
] | The Needle-in-a-haystack (NIAH) test is a general task used to assess language models' (LMs') abilities to recall particular information from long input context. This framework however does not provide a means of analyzing what factors, beyond context length, contribute to LMs' abilities or inabilities to separate and ... | {
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2411.19370 | Machine learning the Ising transition: A comparison between
discriminative and generative approaches | [
"cond-mat.dis-nn",
"cond-mat.stat-mech",
"cs.LG"
] | The detection of phase transitions is a central task in many-body physics. To automate this process, the task can be phrased as a classification problem. Classification problems can be approached in two fundamentally distinct ways: through either a discriminative or a generative method. In general, it is unclear which ... | {
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2411.19371 | Parameter-Efficient Transfer Learning for Music Foundation Models | [
"cs.SD",
"cs.LG",
"eess.AS"
] | More music foundation models are recently being released, promising a general, mostly task independent encoding of musical information. Common ways of adapting music foundation models to downstream tasks are probing and fine-tuning. These common transfer learning approaches, however, face challenges. Probing might lead... | {
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2411.19374 | Performance Evaluation of Single-step Explicit Exponential Integration
Methods on Stiff Ordinary Differential Equations | [
"math.NA",
"cs.NA",
"cs.SY",
"eess.SY"
] | Stiff systems of ordinary differential equations (ODEs) arise in a wide range of scientific and engineering disciplines and are traditionally solved using implicit integration methods due to their stability and efficiency. However, these methods are computationally expensive, particularly for applications requiring rep... | {
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2411.19376 | Prying Pedestrian Surveillance-Evasion: Minumum-Time Evasion from an
Agile Pursuer | [
"eess.SY",
"cs.SY"
] | A new surveillance-evasion differential game is posed and solved in which an agile pursuer (the prying pedestrian) seeks to remain within a given surveillance range of a less agile evader that aims to escape. In contrast to previous surveillance-evasion games, the pursuer is agile in the sense of being able to instanta... | {
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2411.19377 | Feedback Nash equilibria for scalar N-player linear quadratic dynamic
games | [
"math.OC",
"cs.SY",
"eess.SY"
] | Considering infinite-horizon, discrete-time, linear quadratic, N-player dynamic games with scalar dynamics, a graphical representation of feedback Nash equilibrium solutions is provided. This representation is utilised to derive conditions for the number and properties of different feedback Nash equilibria a game may a... | {
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2411.19378 | Libra: Leveraging Temporal Images for Biomedical Radiology Analysis | [
"cs.CV",
"cs.AI",
"cs.CL",
"cs.LG"
] | Radiology report generation (RRG) requires advanced medical image analysis, effective temporal reasoning, and accurate text generation. While multimodal large language models (MLLMs) align with pre-trained vision encoders to enhance visual-language understanding, most existing methods rely on single-image analysis or r... | {
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2411.19379 | Marconi: Prefix Caching for the Era of Hybrid LLMs | [
"cs.DC",
"cs.AI",
"cs.LG"
] | Hybrid models that combine the language modeling capabilities of Attention layers with the efficiency of Recurrent layers (e.g., State Space Models) have gained traction in practically supporting long contexts in Large Language Model serving. Yet, the unique properties of these models complicate the usage of complement... | {
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2411.19381 | Enhancing Sketch Animation: Text-to-Video Diffusion Models with Temporal
Consistency and Rigidity Constraints | [
"cs.CV",
"cs.GR"
] | Animating hand-drawn sketches using traditional tools is challenging and complex. Sketches provide a visual basis for explanations, and animating these sketches offers an experience of real-time scenarios. We propose an approach for animating a given input sketch based on a descriptive text prompt. Our method utilizes ... | {
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2411.19385 | Zero-Forget Preservation of Semantic Communication Alignment in
Distributed AI Networks | [
"cs.LG",
"cs.AI",
"eess.SP"
] | Future communication networks are expected to connect massive distributed artificial intelligence (AI). Exploiting aligned priori knowledge of AI pairs, it is promising to convert high-dimensional data transmission into highly-compressed semantic communications (SC). However, to accommodate the local data distribution ... | {
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2411.19387 | Enhancing Accuracy and Efficiency in Calibration of Drinking Water
Distribution Networks Through Evolutionary Artificial Neural Networks and
Expert Systems | [
"cs.CE"
] | The importance of drinking water distribution networks (DWDNs) as critical urban infrastructures has led to the development and utilization of models for the analysis, design, operation, and management of DWDNs, to ensure optimal efficiency and water quality. In order to provide models that accurately represent real-wo... | {
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2411.19390 | DreamBlend: Advancing Personalized Fine-tuning of Text-to-Image
Diffusion Models | [
"cs.CV",
"cs.GR",
"cs.LG"
] | Given a small number of images of a subject, personalized image generation techniques can fine-tune large pre-trained text-to-image diffusion models to generate images of the subject in novel contexts, conditioned on text prompts. In doing so, a trade-off is made between prompt fidelity, subject fidelity and diversity.... | {
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2411.19392 | Scale Invariance of Graph Neural Networks | [
"cs.LG"
] | We address two fundamental challenges in Graph Neural Networks (GNNs): (1) the lack of theoretical support for invariance learning, a critical property in image processing, and (2) the absence of a unified model capable of excelling on both homophilic and heterophilic graph datasets. To tackle these issues, we establis... | {
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2411.19393 | Global Tensor Motion Planning | [
"cs.RO",
"cs.AI",
"cs.LG",
"cs.SY",
"eess.SY"
] | Batch planning is increasingly necessary to quickly produce diverse and high-quality motion plans for downstream learning applications, such as distillation and imitation learning. This paper presents Global Tensor Motion Planning (GTMP) -- a sampling-based motion planning algorithm comprising only tensor operations. W... | {
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2411.19395 | Concept-driven Off Policy Evaluation | [
"stat.ML",
"cs.AI",
"cs.LG"
] | Evaluating off-policy decisions using batch data poses significant challenges due to limited sample sizes leading to high variance. To improve Off-Policy Evaluation (OPE), we must identify and address the sources of this variance. Recent research on Concept Bottleneck Models (CBMs) shows that using human-explainable co... | {
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2411.19402 | On the effectiveness of discrete representations in sparse mixture of
experts | [
"cs.LG"
] | Sparse mixture of experts (SMoE) is an effective solution for scaling up model capacity without increasing the computational costs. A crucial component of SMoE is the router, responsible for directing the input to relevant experts; however, it also presents a major weakness, leading to routing inconsistencies and repre... | {
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2411.19408 | SoGraB: A Visual Method for Soft Grasping Benchmarking and Evaluation | [
"cs.RO"
] | Recent years have seen soft robotic grippers gain increasing attention due to their ability to robustly grasp soft and fragile objects. However, a commonly available standardised evaluation protocol has not yet been developed to assess the performance of varying soft robotic gripper designs. This work introduces a nove... | {
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2411.19415 | AMO Sampler: Enhancing Text Rendering with Overshooting | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Achieving precise alignment between textual instructions and generated images in text-to-image generation is a significant challenge, particularly in rendering written text within images. Sate-of-the-art models like Stable Diffusion 3 (SD3), Flux, and AuraFlow still struggle with accurate text depiction, resulting in m... | {
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2411.19417 | Any-Resolution AI-Generated Image Detection by Spectral Learning | [
"cs.CV",
"cs.LG",
"eess.IV"
] | Recent works have established that AI models introduce spectral artifacts into generated images and propose approaches for learning to capture them using labeled data. However, the significant differences in such artifacts among different generative models hinder these approaches from generalizing to generators not see... | {
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2411.19418 | Proto Successor Measure: Representing the Space of All Possible
Solutions of Reinforcement Learning | [
"cs.LG",
"cs.AI"
] | Having explored an environment, intelligent agents should be able to transfer their knowledge to most downstream tasks within that environment. Referred to as "zero-shot learning," this ability remains elusive for general-purpose reinforcement learning algorithms. While recent works have attempted to produce zero-shot ... | {
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2411.19419 | A Simple Sparse Matrix Vector Multiplication Approach to Padded
Convolution | [
"cs.LG",
"cs.DS"
] | We introduce an algorithm for efficiently representing convolution with zero-padding and stride as a sparse transformation matrix, applied to a vectorized input through sparse matrix-vector multiplication (SpMV). We provide a theoretical contribution with an explicit expression for the number of non-zero multiplication... | {
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2411.19422 | Wafer2Spike: Spiking Neural Network for Wafer Map Pattern Classification | [
"cs.NE"
] | In integrated circuit design, the analysis of wafer map patterns is critical to improve yield and detect manufacturing issues. We develop Wafer2Spike, an architecture for wafer map pattern classification using a spiking neural network (SNN), and demonstrate that a well-trained SNN achieves superior performance compared... | {
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2411.19430 | Core Placement Optimization of Many-core Brain-Inspired Near-Storage
Systems for Spiking Neural Network Training | [
"cs.AR",
"cs.NE"
] | With the increasing application scope of spiking neural networks (SNN), the complexity of SNN models has surged, leading to an exponential growth in demand for AI computility. As the new generation computing architecture of the neural networks, the efficiency and power consumption of distributed storage and parallel co... | {
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2411.19434 | Actions and Objects Pathways for Domain Adaptation in Video Question
Answering | [
"cs.CV",
"cs.CL"
] | In this paper, we introduce the Actions and Objects Pathways (AOPath) for out-of-domain generalization in video question answering tasks. AOPath leverages features from a large pretrained model to enhance generalizability without the need for explicit training on the unseen domains. Inspired by human brain, AOPath diss... | {
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2411.19440 | Gradient Inversion Attack on Graph Neural Networks | [
"cs.LG",
"cs.AI"
] | Graph federated learning is of essential importance for training over large graph datasets while protecting data privacy, where each client stores a subset of local graph data, while the server collects the local gradients and broadcasts only the aggregated gradients. Recent studies reveal that a malicious attacker can... | {
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2411.19442 | MCUCoder: Adaptive Bitrate Learned Video Compression for IoT Devices | [
"eess.IV",
"cs.CV"
] | The rapid growth of camera-based IoT devices demands the need for efficient video compression, particularly for edge applications where devices face hardware constraints, often with only 1 or 2 MB of RAM and unstable internet connections. Traditional and deep video compression methods are designed for high-end hardware... | {
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2411.19443 | Auto-RAG: Autonomous Retrieval-Augmented Generation for Large Language
Models | [
"cs.CL"
] | Iterative retrieval refers to the process in which the model continuously queries the retriever during generation to enhance the relevance of the retrieved knowledge, thereby improving the performance of Retrieval-Augmented Generation (RAG). Existing work typically employs few-shot prompting or manually constructed rul... | {
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2411.19447 | Adaptive Interactive Segmentation for Multimodal Medical Imaging via
Selection Engine | [
"cs.CV",
"cs.AI",
"cs.LG"
] | In medical image analysis, achieving fast, efficient, and accurate segmentation is essential for automated diagnosis and treatment. Although recent advancements in deep learning have significantly improved segmentation accuracy, current models often face challenges in adaptability and generalization, particularly when ... | {
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2411.19450 | Unsupervised Learning Approach to Anomaly Detection in Gravitational
Wave Data | [
"gr-qc",
"astro-ph.IM",
"cs.LG"
] | Gravitational waves (GW), predicted by Einstein's General Theory of Relativity, provide a powerful probe of astrophysical phenomena and fundamental physics. In this work, we propose an unsupervised anomaly detection method using variational autoencoders (VAEs) to analyze GW time-series data. By training on noise-only d... | {
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2411.19451 | Learning Visual Abstract Reasoning through Dual-Stream Networks | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Visual abstract reasoning tasks present challenges for deep neural networks, exposing limitations in their capabilities. In this work, we present a neural network model that addresses the challenges posed by Raven's Progressive Matrices (RPM). Inspired by the two-stream hypothesis of visual processing, we introduce the... | {
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2411.19454 | GausSurf: Geometry-Guided 3D Gaussian Splatting for Surface
Reconstruction | [
"cs.CV"
] | 3D Gaussian Splatting has achieved impressive performance in novel view synthesis with real-time rendering capabilities. However, reconstructing high-quality surfaces with fine details using 3D Gaussians remains a challenging task. In this work, we introduce GausSurf, a novel approach to high-quality surface reconstruc... | {
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2411.19455 | Autocorrelation Matters: Understanding the Role of Initialization
Schemes for State Space Models | [
"cs.LG"
] | Current methods for initializing state space model (SSM) parameters primarily rely on the HiPPO framework \citep{gu2023how}, which is based on online function approximation with the SSM kernel basis. However, the HiPPO framework does not explicitly account for the effects of the temporal structures of input sequences o... | {
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2411.19456 | Beyond Surface Structure: A Causal Assessment of LLMs' Comprehension
Ability | [
"cs.CL",
"cs.AI"
] | Large language models (LLMs) have shown remarkable capability in natural language tasks, yet debate persists on whether they truly comprehend deep structure (i.e., core semantics) or merely rely on surface structure (e.g., presentation format). Prior studies observe that LLMs' performance declines when intervening on s... | {
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2411.19457 | Multi-task CNN Behavioral Embedding Model For Transaction Fraud
Detection | [
"cs.LG"
] | The burgeoning e-Commerce sector requires advanced solutions for the detection of transaction fraud. With an increasing risk of financial information theft and account takeovers, deep learning methods have become integral to the embedding of behavior sequence data in fraud detection. However, these methods often strugg... | {
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2411.19458 | Multiview Equivariance Improves 3D Correspondence Understanding with
Minimal Feature Finetuning | [
"cs.CV"
] | Vision foundation models, particularly the ViT family, have revolutionized image understanding by providing rich semantic features. However, despite their success in 2D comprehension, their abilities on grasping 3D spatial relationships are still unclear. In this work, we evaluate and enhance the 3D awareness of ViT-ba... | {
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2411.19459 | Fleximo: Towards Flexible Text-to-Human Motion Video Generation | [
"cs.CV",
"cs.LG"
] | Current methods for generating human motion videos rely on extracting pose sequences from reference videos, which restricts flexibility and control. Additionally, due to the limitations of pose detection techniques, the extracted pose sequences can sometimes be inaccurate, leading to low-quality video outputs. We intro... | {
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2411.19460 | Look Every Frame All at Once: Video-Ma$^2$mba for Efficient Long-form
Video Understanding with Multi-Axis Gradient Checkpointing | [
"cs.CV",
"cs.AI",
"cs.LG"
] | With the growing scale and complexity of video data, efficiently processing long video sequences poses significant challenges due to the quadratic increase in memory and computational demands associated with existing transformer-based Large Multi-modal Models (LMMs). To address these issues, we introduce Video-Ma$^2$mb... | {
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2411.19461 | Robust Bayesian Scene Reconstruction by Leveraging Retrieval-Augmented
Priors | [
"cs.CV",
"cs.RO"
] | Constructing 3D representations of object geometry is critical for many downstream robotics tasks, particularly tabletop manipulation problems. These representations must be built from potentially noisy partial observations. In this work, we focus on the problem of reconstructing a multi-object scene from a single RGBD... | {
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2411.19463 | Towards Understanding Retrieval Accuracy and Prompt Quality in RAG
Systems | [
"cs.SE",
"cs.AI"
] | Retrieval-Augmented Generation (RAG) is a pivotal technique for enhancing the capability of large language models (LLMs) and has demonstrated promising efficacy across a diverse spectrum of tasks. While LLM-driven RAG systems show superior performance, they face unique challenges in stability and reliability. Their com... | {
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2411.19466 | ForgerySleuth: Empowering Multimodal Large Language Models for Image
Manipulation Detection | [
"cs.CV",
"cs.LG"
] | Multimodal large language models have unlocked new possibilities for various multimodal tasks. However, their potential in image manipulation detection remains unexplored. When directly applied to the IMD task, M-LLMs often produce reasoning texts that suffer from hallucinations and overthinking. To address this, in th... | {
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2411.19468 | Random Feature Models with Learnable Activation Functions | [
"cs.LG"
] | Current random feature models typically rely on fixed activation functions, limiting their ability to capture diverse patterns in data. To address this, we introduce the Random Feature model with Learnable Activation Functions (RFLAF), a novel model that significantly enhances the expressivity and interpretability of t... | {
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2411.19474 | Blurred LiDAR for Sharper 3D: Robust Handheld 3D Scanning with Diffuse
LiDAR and RGB | [
"eess.IV",
"cs.CV",
"cs.LG"
] | 3D surface reconstruction is essential across applications of virtual reality, robotics, and mobile scanning. However, RGB-based reconstruction often fails in low-texture, low-light, and low-albedo scenes. Handheld LiDARs, now common on mobile devices, aim to address these challenges by capturing depth information from... | {
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2411.19475 | Effective Fine-Tuning of Vision-Language Models for Accurate Galaxy
Morphology Analysis | [
"cs.CV",
"astro-ph.GA",
"cs.AI",
"cs.LG"
] | Galaxy morphology analysis involves classifying galaxies by their shapes and structures. For this task, directly training domain-specific models on large, annotated astronomical datasets is effective but costly. In contrast, fine-tuning vision foundation models on a smaller set of astronomical images is more resource-e... | {
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2411.19477 | Simple and Provable Scaling Laws for the Test-Time Compute of Large
Language Models | [
"cs.CL",
"cs.AI",
"cs.LG"
] | We propose two simple yet principled algorithms that enjoy provable scaling laws for the test-time compute of large language models (LLMs), which require a black-box LLM and nothing else (e.g., no external verifier or reward model) for a minimalistic implementation. (i) The first one is a two-stage knockout-style algor... | {
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2411.19478 | Zero-Indexing Internet Search Augmented Generation for Large Language
Models | [
"cs.IR"
] | Retrieval augmented generation has emerged as an effective method to enhance large language model performance. This approach typically relies on an internal retrieval module that uses various indexing mechanisms to manage a static pre-processed corpus. However, such a paradigm often falls short when it is necessary to ... | {
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2411.19479 | FLARE: Towards Universal Dataset Purification against Backdoor Attacks | [
"cs.CR",
"cs.AI",
"cs.CV",
"cs.LG"
] | Deep neural networks (DNNs) are susceptible to backdoor attacks, where adversaries poison datasets with adversary-specified triggers to implant hidden backdoors, enabling malicious manipulation of model predictions. Dataset purification serves as a proactive defense by removing malicious training samples to prevent bac... | {
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2411.19485 | Action Engine: An LLM-based Framework for Automatic FaaS Workflow
Generation | [
"cs.DC",
"cs.AI",
"cs.LG",
"cs.SE"
] | Function as a Service (FaaS) is poised to become the foundation of the next generation of cloud systems due to its inherent advantages in scalability, cost-efficiency, and ease of use. However, challenges such as the need for specialized knowledge and difficulties in building function workflows persist for cloud-native... | {
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2411.19486 | V2SFlow: Video-to-Speech Generation with Speech Decomposition and
Rectified Flow | [
"cs.CV",
"cs.SD",
"eess.AS"
] | In this paper, we introduce V2SFlow, a novel Video-to-Speech (V2S) framework designed to generate natural and intelligible speech directly from silent talking face videos. While recent V2S systems have shown promising results on constrained datasets with limited speakers and vocabularies, their performance often degrad... | {
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2411.19488 | Interleaved-Modal Chain-of-Thought | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Chain-of-Thought (CoT) prompting elicits large language models (LLMs) to produce a series of intermediate reasoning steps before arriving at the final answer. However, when transitioning to vision-language models (VLMs), their text-only rationales struggle to express the fine-grained associations with the original imag... | {
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2411.19492 | Diorama: Unleashing Zero-shot Single-view 3D Scene Modeling | [
"cs.CV",
"cs.LG"
] | Reconstructing structured 3D scenes from RGB images using CAD objects unlocks efficient and compact scene representations that maintain compositionality and interactability. Existing works propose training-heavy methods relying on either expensive yet inaccurate real-world annotations or controllable yet monotonous syn... | {
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2411.19493 | Diffusion Models Meet Network Management: Improving Traffic Matrix
Analysis with Diffusion-based Approach | [
"cs.NI",
"cs.LG"
] | Due to network operation and maintenance relying heavily on network traffic monitoring, traffic matrix analysis has been one of the most crucial issues for network management related tasks. However, it is challenging to reliably obtain the precise measurement in computer networks because of the high measurement cost, a... | {
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2411.19495 | Loop Shaping of Hybrid Motion Control with Contact Transition | [
"eess.SY",
"cs.RO",
"cs.SY"
] | A standard (stiff) motion control with output displacement feedback cannot handle unforeseen contact with environment without penetrating into soft, i.e. viscoelastic, materials or even damaging brittle or fragile materials. Robotics and mechatronics with tactile and haptic capabilities, and medical assistance systems ... | {
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2411.19496 | An Approach Towards Learning K-means-friendly Deep Latent Representation | [
"cs.LG",
"cs.CV"
] | Clustering is a long-standing problem area in data mining. The centroid-based classical approaches to clustering mainly face difficulty in the case of high dimensional inputs such as images. With the advent of deep neural networks, a common approach to this problem is to map the data to some latent space of comparative... | {
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2411.19497 | SANGO: Socially Aware Navigation through Grouped Obstacles | [
"cs.RO",
"cs.LG"
] | This paper introduces SANGO (Socially Aware Navigation through Grouped Obstacles), a novel method that ensures socially appropriate behavior by dynamically grouping obstacles and adhering to social norms. Using deep reinforcement learning, SANGO trains agents to navigate complex environments leveraging the DBSCAN algor... | {
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2411.19498 | Protecting Multiple Types of Privacy Simultaneously in EEG-based
Brain-Computer Interfaces | [
"cs.HC",
"cs.AI",
"cs.LG"
] | A brain-computer interface (BCI) enables direct communication between the brain and an external device. Electroencephalogram (EEG) is the preferred input signal in non-invasive BCIs, due to its convenience and low cost. EEG-based BCIs have been successfully used in many applications, such as neurological rehabilitation... | {
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2411.19500 | COLD: Causal reasOning in cLosed Daily activities | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Large Language Models (LLMs) have shown state-of-the-art performance in a variety of tasks, including arithmetic and reasoning; however, to gauge the intellectual capabilities of LLMs, causal reasoning has become a reliable proxy for validating a general understanding of the mechanics and intricacies of the world simil... | {
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2411.19502 | Knowledge-Data Fusion Based Source-Free Semi-Supervised Domain
Adaptation for Seizure Subtype Classification | [
"cs.LG",
"cs.AI",
"cs.HC"
] | Electroencephalogram (EEG)-based seizure subtype classification enhances clinical diagnosis efficiency. Source-free semi-supervised domain adaptation (SF-SSDA), which transfers a pre-trained model to a new dataset with no source data and limited labeled target data, can be used for privacy-preserving seizure subtype cl... | {
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2411.19504 | TQA-Bench: Evaluating LLMs for Multi-Table Question Answering with
Scalable Context and Symbolic Extension | [
"cs.AI",
"cs.CL",
"cs.IR"
] | The advent of large language models (LLMs) has unlocked great opportunities in complex data management tasks, particularly in question answering (QA) over complicated multi-table relational data. Despite significant progress, systematically evaluating LLMs on multi-table QA remains a critical challenge due to the inher... | {
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2411.19506 | Real-time Anomaly Detection at the L1 Trigger of CMS Experiment | [
"hep-ex",
"cs.LG",
"physics.data-an"
] | We present the preparation, deployment, and testing of an autoencoder trained for unbiased detection of new physics signatures in the CMS experiment Global Trigger (GT) test crate FPGAs during LHC Run 3. The GT makes the final decision whether to readout or discard the data from each LHC collision, which occur at a rat... | {
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2411.19507 | Graph-Enhanced EEG Foundation Model | [
"cs.LG",
"eess.SP"
] | Electroencephalography (EEG) signals provide critical insights for applications in disease diagnosis and healthcare. However, the scarcity of labeled EEG data poses a significant challenge. Foundation models offer a promising solution by leveraging large-scale unlabeled data through pre-training, enabling strong perfor... | {
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2411.19509 | Ditto: Motion-Space Diffusion for Controllable Realtime Talking Head
Synthesis | [
"cs.CV",
"cs.LG",
"cs.SD",
"eess.AS"
] | Recent advances in diffusion models have revolutionized audio-driven talking head synthesis. Beyond precise lip synchronization, diffusion-based methods excel in generating subtle expressions and natural head movements that are well-aligned with the audio signal. However, these methods are confronted by slow inference ... | {
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2411.19510 | Retrieval-guided Cross-view Image Synthesis | [
"cs.CV",
"cs.LG"
] | Information retrieval techniques have demonstrated exceptional capabilities in identifying semantic similarities across diverse domains through robust feature representations. However, their potential in guiding synthesis tasks, particularly cross-view image synthesis, remains underexplored. Cross-view image synthesis ... | {
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2411.19511 | Scalable Order-Preserving Pattern Mining | [
"cs.DS",
"cs.DB"
] | Time series are ubiquitous in domains ranging from medicine to marketing and finance. Frequent Pattern Mining (FPM) from a time series has thus received much attention. Recently, it has been studied under the order-preserving (OP) matching relation stating that a match occurs when two time series have the same relative... | {
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2411.19512 | Topology-Preserving Scaling in Data Augmentation | [
"math.AT",
"cs.IT",
"cs.LG",
"math.IT"
] | We propose an algorithmic framework for dataset normalization in data augmentation pipelines that preserves topological stability under non-uniform scaling transformations. Given a finite metric space \( X \subset \mathbb{R}^n \) with Euclidean distance \( d_X \), we consider scaling transformations defined by scaling ... | {
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2411.19513 | ContextGNN: Beyond Two-Tower Recommendation Systems | [
"cs.IR",
"cs.LG"
] | Recommendation systems predominantly utilize two-tower architectures, which evaluate user-item rankings through the inner product of their respective embeddings. However, one key limitation of two-tower models is that they learn a pair-agnostic representation of users and items. In contrast, pair-wise representations e... | {
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2411.19514 | Enhancing AI microscopy for foodborne bacterial classification via
adversarial domain adaptation across optical and biological variability | [
"eess.IV",
"cs.CV",
"cs.LG"
] | Rapid detection of foodborne bacteria is critical for food safety and quality, yet traditional culture-based methods require extended incubation and specialized sample preparation. This study addresses these challenges by i) enhancing the generalizability of AI-enabled microscopy for bacterial classification using adve... | {
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2411.19515 | Leveraging Large Language Models for Institutional Portfolio Management:
Persona-Based Ensembles | [
"cs.CE",
"cs.MA"
] | Large language models (LLMs) have demonstrated promising performance in various financial applications, though their potential in complex investment strategies remains underexplored. To address this gap, we investigate how LLMs can predict price movements in stock and bond portfolios using economic indicators, enabling... | {
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2411.19517 | RL-MILP Solver: A Reinforcement Learning Approach for Solving
Mixed-Integer Linear Programs with Graph Neural Networks | [
"cs.LG",
"cs.AI"
] | Mixed-integer linear programming (MILP) is a widely used optimization technique across various fields. Existing $\textit{end-to-end learning}$ methods for MILP generate values for a subset of decision variables and delegate the remaining problem to traditional MILP solvers. However, this approach often fails to guarant... | {
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2411.19522 | Subjective and Objective Quality Assessment Methods of Stereoscopic
Videos with Visibility Affecting Distortions | [
"cs.MM",
"cs.CV"
] | We present two major contributions in this work: 1) we create a full HD resolution stereoscopic (S3D) video dataset comprised of 12 reference and 360 distorted videos. The test stimuli are produced by simulating the five levels of fog and haze ambiances on the pristine left and right video sequences. We perform subject... | {
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2411.19523 | Density-Calibrated Conformal Quantile Regression | [
"stat.ME",
"cs.AI",
"cs.LG",
"stat.ML"
] | This paper introduces the Density-Calibrated Conformal Quantile Regression (CQR-d) method, a novel approach for constructing prediction intervals that adapts to varying uncertainty across the feature space. Building upon conformal quantile regression, CQR-d incorporates local information through a weighted combination ... | {
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2411.19525 | LokiTalk: Learning Fine-Grained and Generalizable Correspondences to
Enhance NeRF-based Talking Head Synthesis | [
"cs.CV",
"cs.LG"
] | Despite significant progress in talking head synthesis since the introduction of Neural Radiance Fields (NeRF), visual artifacts and high training costs persist as major obstacles to large-scale commercial adoption. We propose that identifying and establishing fine-grained and generalizable correspondences between driv... | {
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2411.19526 | A Local Information Aggregation based Multi-Agent Reinforcement Learning
for Robot Swarm Dynamic Task Allocation | [
"cs.AI",
"cs.MA",
"cs.RO"
] | In this paper, we explore how to optimize task allocation for robot swarms in dynamic environments, emphasizing the necessity of formulating robust, flexible, and scalable strategies for robot cooperation. We introduce a novel framework using a decentralized partially observable Markov decision process (Dec_POMDP), spe... | {
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2411.19527 | DisCoRD: Discrete Tokens to Continuous Motion via Rectified Flow
Decoding | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Human motion, inherently continuous and dynamic, presents significant challenges for generative models. Despite their dominance, discrete quantization methods, such as VQ-VAEs, suffer from inherent limitations, including restricted expressiveness and frame-wise noise artifacts. Continuous approaches, while producing sm... | {
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2411.19528 | RAGDiffusion: Faithful Cloth Generation via External Knowledge
Assimilation | [
"cs.CV",
"cs.AI",
"cs.GR",
"cs.LG"
] | Standard clothing asset generation involves creating forward-facing flat-lay garment images displayed on a clear background by extracting clothing information from diverse real-world contexts, which presents significant challenges due to highly standardized sampling distributions and precise structural requirements in ... | {
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2411.19530 | Quantized Delta Weight Is Safety Keeper | [
"cs.CR",
"cs.AI",
"cs.LG"
] | Recent advancements in fine-tuning proprietary language models enable customized applications across various domains but also introduce two major challenges: high resource demands and security risks. Regarding resource demands, recent work proposes novel partial compression, such as BitDelta, to quantize the delta weig... | {
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2411.19534 | QUOTA: Quantifying Objects with Text-to-Image Models for Any Domain | [
"cs.CV",
"cs.LG"
] | We tackle the problem of quantifying the number of objects by a generative text-to-image model. Rather than retraining such a model for each new image domain of interest, which leads to high computational costs and limited scalability, we are the first to consider this problem from a domain-agnostic perspective. We pro... | {
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"cs.SY": 0
} |
2411.19536 | Development of Low-Cost IoT Units for Thermal Comfort Measurement and AC
Energy Consumption Prediction System | [
"cs.LG"
] | In response to the substantial energy consumption in buildings, the Japanese government initiated the BI-Tech (Behavioral Insights X Technology) project in 2019, aimed at promoting voluntary energy-saving behaviors through the utilization of AI and IoT technologies. Our study aimed at small and medium-sized office buil... | {
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} |
2411.19537 | Deepfake Media Generation and Detection in the Generative AI Era: A
Survey and Outlook | [
"cs.CV",
"cs.AI",
"cs.LG",
"cs.MM",
"cs.SD",
"eess.AS"
] | With the recent advancements in generative modeling, the realism of deepfake content has been increasing at a steady pace, even reaching the point where people often fail to detect manipulated media content online, thus being deceived into various kinds of scams. In this paper, we survey deepfake generation and detecti... | {
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} |
2411.19539 | Knowledge Management for Automobile Failure Analysis Using Graph RAG | [
"cs.AI",
"cs.CL",
"cs.IR"
] | This paper presents a knowledge management system for automobile failure analysis using retrieval-augmented generation (RAG) with large language models (LLMs) and knowledge graphs (KGs). In the automotive industry, there is a growing demand for knowledge transfer of failure analysis from experienced engineers to young ... | {
"Other": 0,
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} |
2411.19544 | SkelMamba: A State Space Model for Efficient Skeleton Action Recognition
of Neurological Disorders | [
"cs.CV",
"cs.AI",
"cs.LG"
] | We introduce a novel state-space model (SSM)-based framework for skeleton-based human action recognition, with an anatomically-guided architecture that improves state-of-the-art performance in both clinical diagnostics and general action recognition tasks. Our approach decomposes skeletal motion analysis into spatial, ... | {
"Other": 0,
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"cs.SD": 0,
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"cs.SY": 0
} |
2411.19545 | A Unified Interaction Control Framework for Safe Robotic Ultrasound
Scanning with Human-Intention-Aware Compliance | [
"cs.RO"
] | The ultrasound scanning robot operates in environments where frequent human-robot interactions occur. Most existing control methods for ultrasound scanning address only one specific interaction situation or implement hard switches between controllers for different situations, which compromises both safety and efficienc... | {
"Other": 0,
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"cs.RO": 1,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.19547 | Training Agents with Weakly Supervised Feedback from Large Language
Models | [
"cs.CL",
"cs.AI"
] | Large Language Models (LLMs) offer a promising basis for creating agents that can tackle complex tasks through iterative environmental interaction. Existing methods either require these agents to mimic expert-provided trajectories or rely on definitive environmental feedback for reinforcement learning which limits thei... | {
"Other": 0,
"cs.AI": 1,
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"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.19548 | ReconDreamer: Crafting World Models for Driving Scene Reconstruction via
Online Restoration | [
"cs.CV",
"cs.AI",
"cs.LG",
"cs.RO"
] | Closed-loop simulation is crucial for end-to-end autonomous driving. Existing sensor simulation methods (e.g., NeRF and 3DGS) reconstruct driving scenes based on conditions that closely mirror training data distributions. However, these methods struggle with rendering novel trajectories, such as lane changes. Recent wo... | {
"Other": 0,
"cs.AI": 1,
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"cs.RO": 1,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.19549 | Contextual Checkerboard Denoise -- A Novel Neural Network-Based Approach
for Classification-Aware OCT Image Denoising | [
"eess.IV",
"cs.CV",
"cs.LG"
] | In contrast to non-medical image denoising, where enhancing image clarity is the primary goal, medical image denoising warrants preservation of crucial features without introduction of new artifacts. However, many denoising methods that improve the clarity of the image, inadvertently alter critical information of the d... | {
"Other": 0,
"cs.AI": 0,
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"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.19551 | Bootstraping Clustering of Gaussians for View-consistent 3D Scene
Understanding | [
"cs.CV",
"cs.LG"
] | Injecting semantics into 3D Gaussian Splatting (3DGS) has recently garnered significant attention. While current approaches typically distill 3D semantic features from 2D foundational models (e.g., CLIP and SAM) to facilitate novel view segmentation and semantic understanding, their heavy reliance on 2D supervision can... | {
"Other": 0,
"cs.AI": 0,
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"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.19553 | Analysis of High-dimensional Gaussian Labeled-unlabeled Mixture Model
via Message-passing Algorithm | [
"cs.LG",
"stat.ML"
] | Semi-supervised learning (SSL) is a machine learning methodology that leverages unlabeled data in conjunction with a limited amount of labeled data. Although SSL has been applied in various applications and its effectiveness has been empirically demonstrated, it is still not fully understood when and why SSL performs w... | {
"Other": 0,
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"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.19554 | Unimib Assistant: designing a student-friendly RAG-based chatbot for all
their needs | [
"cs.HC",
"cs.AI",
"cs.CY"
] | Natural language processing skills of Large Language Models (LLMs) are unprecedented, having wide diffusion and application in different tasks. This pilot study focuses on specializing ChatGPT behavior through a Retrieval-Augmented Generation (RAG) system using the OpenAI custom GPTs feature. The purpose of our chatbot... | {
"Other": 0,
"cs.AI": 1,
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"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
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