id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2411.17077 | Contrastive CFG: Improving CFG in Diffusion Models by Contrasting
Positive and Negative Concepts | [
"cs.LG",
"cs.AI",
"cs.CV"
] | As Classifier-Free Guidance (CFG) has proven effective in conditional diffusion model sampling for improved condition alignment, many applications use a negated CFG term to filter out unwanted features from samples. However, simply negating CFG guidance creates an inverted probability distribution, often distorting sam... | {
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2411.17079 | Zero-order Control Barrier Functions for Sampled-Data Systems with State
and Input Dependent Safety Constraints | [
"eess.SY",
"cs.RO",
"cs.SY"
] | We propose a novel zero-order control barrier function (ZOCBF) for sampled-data systems to ensure system safety. Our formulation generalizes conventional control barrier functions and straightforwardly handles safety constraints with high-relative degrees or those that explicitly depend on both system states and inputs... | {
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2411.17080 | DeepMDV: Learning Global Matching for Multi-depot Vehicle Routing
Problems | [
"cs.DB",
"cs.AI",
"cs.LG"
] | Due to the substantial rise in online retail and e-commerce in recent years, the demand for efficient and fast solutions to Vehicle Routing Problems (VRP) has become critical. To manage the increasing demand, companies have adopted the strategy of adding more depots. However, the presence of multiple depots introduces ... | {
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2411.17083 | A Haptic-Based Proximity Sensing System for Buried Object in Granular
Material | [
"cs.RO",
"physics.flu-dyn",
"physics.geo-ph",
"physics.ins-det"
] | The proximity perception of objects in granular materials is significant, especially for applications like minesweeping. However, due to particles' opacity and complex properties, existing proximity sensors suffer from high costs from sophisticated hardware and high user-cost from unintuitive results. In this paper, we... | {
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2411.17088 | {\Omega}SFormer: Dual-Modal {\Omega}-like Super-Resolution Transformer
Network for Cross-scale and High-accuracy Terraced Field Vectorization
Extraction | [
"cs.CV"
] | Terraced field is a significant engineering practice for soil and water conservation (SWC). Terraced field extraction from remotely sensed imagery is the foundation for monitoring and evaluating SWC. This study is the first to propose a novel dual-modal {\Omega}-like super-resolution Transformer network for intelligent... | {
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2411.17089 | Efficient LLM Inference with I/O-Aware Partial KV Cache Recomputation | [
"cs.LG",
"cs.DC",
"cs.PF"
] | Inference for Large Language Models (LLMs) is computationally demanding. To reduce the cost of auto-regressive decoding, Key-Value (KV) caching is used to store intermediate activations, enabling GPUs to perform only the incremental computation required for each new token. This approach significantly lowers the computa... | {
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2411.17099 | Spatio-Temporal Conformal Prediction for Power Outage Data | [
"stat.ML",
"cs.LG"
] | In recent years, increasingly unpredictable and severe global weather patterns have frequently caused long-lasting power outages. Building resilience, the ability to withstand, adapt to, and recover from major disruptions, has become crucial for the power industry. To enable rapid recovery, accurately predicting future... | {
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2411.17102 | Scholar Name Disambiguation with Search-enhanced LLM Across Language | [
"cs.IR"
] | The task of scholar name disambiguation is crucial in various real-world scenarios, including bibliometric-based candidate evaluation for awards, application material anti-fraud measures, and more. Despite significant advancements, current methods face limitations due to the complexity of heterogeneous data, often nece... | {
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2411.17106 | PassionSR: Post-Training Quantization with Adaptive Scale in One-Step
Diffusion based Image Super-Resolution | [
"cs.CV"
] | Diffusion-based image super-resolution (SR) models have shown superior performance at the cost of multiple denoising steps. However, even though the denoising step has been reduced to one, they require high computational costs and storage requirements, making it difficult for deployment on hardware devices. To address ... | {
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2411.17110 | TabulaX: Leveraging Large Language Models for Multi-Class Table
Transformations | [
"cs.DB",
"cs.LG"
] | The integration of tabular data from diverse sources is often hindered by inconsistencies in formatting and representation, posing significant challenges for data analysts and personal digital assistants. Existing methods for automating tabular data transformations are limited in scope, often focusing on specific types... | {
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2411.17113 | Learning from Noisy Labels via Conditional Distributionally Robust
Optimization | [
"cs.LG"
] | While crowdsourcing has emerged as a practical solution for labeling large datasets, it presents a significant challenge in learning accurate models due to noisy labels from annotators with varying levels of expertise. Existing methods typically estimate the true label posterior, conditioned on the instance and noisy a... | {
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2411.17116 | Star Attention: Efficient LLM Inference over Long Sequences | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Inference with Transformer-based Large Language Models (LLMs) on long sequences is both costly and slow due to the quadratic complexity of the self-attention mechanism. We introduce Star Attention, a two-phase block-sparse approximation that improves computational efficiency by sharding attention across multiple hosts ... | {
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2411.17123 | Advancing Content Moderation: Evaluating Large Language Models for
Detecting Sensitive Content Across Text, Images, and Videos | [
"cs.CV",
"cs.AI"
] | The widespread dissemination of hate speech, harassment, harmful and sexual content, and violence across websites and media platforms presents substantial challenges and provokes widespread concern among different sectors of society. Governments, educators, and parents are often at odds with media platforms about how t... | {
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2411.17124 | DexGrip: Multi-modal Soft Gripper with Dexterous Grasping and In-hand
Manipulation Capacity | [
"cs.RO"
] | The ability of robotic grippers to not only grasp but also re-position and re-orient objects in-hand is crucial for achieving versatile, general-purpose manipulation. While recent advances in soft robotic grasping has greatly improved grasp quality and stability, their manipulation capabilities remain under-explored. T... | {
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2411.17125 | DOGE: Towards Versatile Visual Document Grounding and Referring | [
"cs.CV",
"cs.AI"
] | In recent years, Multimodal Large Language Models (MLLMs) have increasingly emphasized grounding and referring capabilities to achieve detailed understanding and flexible user interaction. However, in the realm of visual document understanding, these capabilities lag behind due to the scarcity of fine-grained datasets ... | {
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2411.17126 | From Machine Learning to Machine Unlearning: Complying with GDPR's Right
to be Forgotten while Maintaining Business Value of Predictive Models | [
"cs.LG"
] | Recent privacy regulations (e.g., GDPR) grant data subjects the `Right to Be Forgotten' (RTBF) and mandate companies to fulfill data erasure requests from data subjects. However, companies encounter great challenges in complying with the RTBF regulations, particularly when asked to erase specific training data from the... | {
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2411.17128 | Enhancing Imbalance Learning: A Novel Slack-Factor Fuzzy SVM Approach | [
"cs.LG",
"cs.NE"
] | In real-world applications, class-imbalanced datasets pose significant challenges for machine learning algorithms, such as support vector machines (SVMs), particularly in effectively managing imbalance, noise, and outliers. Fuzzy support vector machines (FSVMs) address class imbalance by assigning varying fuzzy members... | {
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2411.17130 | TechCoach: Towards Technical Keypoint-Aware Descriptive Action Coaching | [
"cs.CV"
] | To guide a learner to master the action skills, it is crucial for a coach to 1) reason through the learner's action execution and technical keypoints, and 2) provide detailed, understandable feedback on what is done well and what can be improved. However, existing score-based action assessment methods are still far fro... | {
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2411.17132 | Improving Resistance to Noisy Label Fitting by Reweighting Gradient in
SAM | [
"cs.LG"
] | Noisy labels pose a substantial challenge in machine learning, often resulting in overfitting and poor generalization. Sharpness-Aware Minimization (SAM), as demonstrated in Foret et al. (2021), improves generalization over traditional Stochastic Gradient Descent (SGD) in classification tasks with noisy labels by impli... | {
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2411.17134 | TRIP: Terrain Traversability Mapping With Risk-Aware Prediction for
Enhanced Online Quadrupedal Robot Navigation | [
"cs.RO"
] | Accurate traversability estimation using an online dense terrain map is crucial for safe navigation in challenging environments like construction and disaster areas. However, traversability estimation for legged robots on rough terrains faces substantial challenges owing to limited terrain information caused by restric... | {
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2411.17135 | LLM-Based Offline Learning for Embodied Agents via Consistency-Guided
Reward Ensemble | [
"cs.AI",
"cs.CL"
] | Employing large language models (LLMs) to enable embodied agents has become popular, yet it presents several limitations in practice. In this work, rather than using LLMs directly as agents, we explore their use as tools for embodied agent learning. Specifically, to train separate agents via offline reinforcement learn... | {
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2411.17136 | Autoencoder Enhanced Realised GARCH on Volatility Forecasting | [
"q-fin.RM",
"cs.LG",
"econ.EM"
] | Realised volatility has become increasingly prominent in volatility forecasting due to its ability to capture intraday price fluctuations. With a growing variety of realised volatility estimators, each with unique advantages and limitations, selecting an optimal estimator may introduce challenges. In this thesis, aimin... | {
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2411.17137 | Self-reconfiguration Strategies for Space-distributed Spacecraft | [
"cs.RO",
"cs.AI"
] | This paper proposes a distributed on-orbit spacecraft assembly algorithm, where future spacecraft can assemble modules with different functions on orbit to form a spacecraft structure with specific functions. This form of spacecraft organization has the advantages of reconfigurability, fast mission response and easy ma... | {
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2411.17138 | HGC: A hybrid method combining gravity model and cycle structure for
identifying influential spreaders in complex networks | [
"cs.CE"
] | Identifying influential spreaders in complex networks is a critical challenge in network science, with broad applications in disease control, information dissemination, and influence analysis in social networks. The gravity model, a distinctive approach for identifying influential spreaders, has attracted significant a... | {
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2411.17139 | Neural-Network-Enhanced Metalens Camera for High-Definition, Dynamic
Imaging in the Long-Wave Infrared Spectrum | [
"eess.IV",
"cs.CV"
] | To provide a lightweight and cost-effective solution for the long-wave infrared imaging using a singlet, we develop a camera by integrating a High-Frequency-Enhancing Cycle-GAN neural network into a metalens imaging system. The High-Frequency-Enhancing Cycle-GAN improves the quality of the original metalens images by a... | {
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2411.17140 | Crack Detection in Infrastructure Using Transfer Learning, Spatial
Attention, and Genetic Algorithm Optimization | [
"cs.CV"
] | Crack detection plays a pivotal role in the maintenance and safety of infrastructure, including roads, bridges, and buildings, as timely identification of structural damage can prevent accidents and reduce costly repairs. Traditionally, manual inspection has been the norm, but it is labor-intensive, subjective, and haz... | {
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2411.17141 | Learning Robust Anymodal Segmentor with Unimodal and Cross-modal
Distillation | [
"cs.CV"
] | Simultaneously using multimodal inputs from multiple sensors to train segmentors is intuitively advantageous but practically challenging. A key challenge is unimodal bias, where multimodal segmentors over rely on certain modalities, causing performance drops when others are missing, common in real world applications. T... | {
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2411.17150 | Distilling Spectral Graph for Object-Context Aware Open-Vocabulary
Semantic Segmentation | [
"cs.CV"
] | Open-Vocabulary Semantic Segmentation (OVSS) has advanced with recent vision-language models (VLMs), enabling segmentation beyond predefined categories through various learning schemes. Notably, training-free methods offer scalable, easily deployable solutions for handling unseen data, a key goal of OVSS. Yet, a critic... | {
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2411.17152 | On-Road Object Importance Estimation: A New Dataset and A Model with
Multi-Fold Top-Down Guidance | [
"cs.RO",
"cs.CV"
] | This paper addresses the problem of on-road object importance estimation, which utilizes video sequences captured from the driver's perspective as the input. Although this problem is significant for safer and smarter driving systems, the exploration of this problem remains limited. On one hand, publicly-available large... | {
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2411.17154 | Emergenet: A Digital Twin of Sequence Evolution for Scalable Emergence
Risk Assessment of Animal Influenza A Strains | [
"q-bio.PE",
"cs.LG",
"stat.ML"
] | Despite having triggered devastating pandemics in the past, our ability to quantitatively assess the emergence potential of individual strains of animal influenza viruses remains limited. This study introduces Emergenet, a tool to infer a digital twin of sequence evolution to chart how new variants might emerge in the ... | {
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2411.17155 | AUTO-IceNav: A Local Navigation Strategy for Autonomous Surface Ships in
Broken Ice Fields | [
"cs.RO"
] | Ice conditions often require ships to reduce speed and deviate from their main course to avoid damage to the ship. In addition, broken ice fields are becoming the dominant ice conditions encountered in the Arctic, where the effects of collisions with ice are highly dependent on where contact occurs and on the particula... | {
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2411.17160 | Motion Free B-frame Coding for Neural Video Compression | [
"eess.IV",
"cs.CV"
] | Typical deep neural video compression networks usually follow the hybrid approach of classical video coding that contains two separate modules: motion coding and residual coding. In addition, a symmetric auto-encoder is often used as a normal architecture for both motion and residual coding. In this paper, we propose a... | {
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2411.17161 | Enhancing Lane Segment Perception and Topology Reasoning with
Crowdsourcing Trajectory Priors | [
"cs.CV"
] | In autonomous driving, recent advances in lane segment perception provide autonomous vehicles with a comprehensive understanding of driving scenarios. Moreover, incorporating prior information input into such perception model represents an effective approach to ensure the robustness and accuracy. However, utilizing div... | {
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2411.17163 | OSDFace: One-Step Diffusion Model for Face Restoration | [
"cs.CV"
] | Diffusion models have demonstrated impressive performance in face restoration. Yet, their multi-step inference process remains computationally intensive, limiting their applicability in real-world scenarios. Moreover, existing methods often struggle to generate face images that are harmonious, realistic, and consistent... | {
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2411.17164 | X-MeshGraphNet: Scalable Multi-Scale Graph Neural Networks for Physics
Simulation | [
"cs.LG",
"physics.comp-ph"
] | Graph Neural Networks (GNNs) have gained significant traction for simulating complex physical systems, with models like MeshGraphNet demonstrating strong performance on unstructured simulation meshes. However, these models face several limitations, including scalability issues, requirement for meshing at inference, and... | {
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2411.17167 | MRIFE: A Mask-Recovering and Interactive-Feature-Enhancing Semantic
Segmentation Network For Relic Landslide Detection | [
"cs.CV"
] | Relic landslide, formed over a long period, possess the potential for reactivation, making them a hazardous geological phenomenon. While reliable relic landslide detection benefits the effective monitoring and prevention of landslide disaster, semantic segmentation using high-resolution remote sensing images for relic ... | {
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2411.17170 | Learning Monotonic Attention in Transducer for Streaming Generation | [
"cs.CL",
"cs.AI"
] | Streaming generation models are increasingly utilized across various fields, with the Transducer architecture being particularly popular in industrial applications. However, its input-synchronous decoding mechanism presents challenges in tasks requiring non-monotonic alignments, such as simultaneous translation, leadin... | {
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2411.17174 | GMFlow: Global Motion-Guided Recurrent Flow for 6D Object Pose
Estimation | [
"cs.CV"
] | 6D object pose estimation is crucial for robotic perception and precise manipulation. Occlusion and incomplete object visibility are common challenges in this task, but existing pose refinement methods often struggle to handle these issues effectively. To tackle this problem, we propose a global motion-guided recurrent... | {
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2411.17176 | ChatGen: Automatic Text-to-Image Generation From FreeStyle Chatting | [
"cs.CV",
"cs.AI"
] | Despite the significant advancements in text-to-image (T2I) generative models, users often face a trial-and-error challenge in practical scenarios. This challenge arises from the complexity and uncertainty of tedious steps such as crafting suitable prompts, selecting appropriate models, and configuring specific argumen... | {
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2411.17178 | LiteVAR: Compressing Visual Autoregressive Modelling with Efficient
Attention and Quantization | [
"cs.CV"
] | Visual Autoregressive (VAR) has emerged as a promising approach in image generation, offering competitive potential and performance comparable to diffusion-based models. However, current AR-based visual generation models require substantial computational resources, limiting their applicability on resource-constrained d... | {
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2411.17180 | Training a neural netwok for data reduction and better generalization | [
"stat.ML",
"cs.LG"
] | The motivation for sparse learners is to compress the inputs (features) by selecting only the ones needed for good generalization. Linear models with LASSO-type regularization achieve this by setting the weights of irrelevant features to zero, effectively identifying and ignoring them. In artificial neural networks, th... | {
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2411.17181 | A Novel Word Pair-based Gaussian Sentence Similarity Algorithm For
Bengali Extractive Text Summarization | [
"cs.CL"
] | Extractive Text Summarization is the process of selecting the most representative parts of a larger text without losing any key information. Recent attempts at extractive text summarization in Bengali, either relied on statistical techniques like TF-IDF or used naive sentence similarity measures like the word averaging... | {
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2411.17182 | An In-depth Investigation of Sparse Rate Reduction in Transformer-like
Models | [
"cs.LG"
] | Deep neural networks have long been criticized for being black-box. To unveil the inner workings of modern neural architectures, a recent work \cite{yu2024white} proposed an information-theoretic objective function called Sparse Rate Reduction (SRR) and interpreted its unrolled optimization as a Transformer-like model ... | {
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2411.17188 | Interleaved Scene Graph for Interleaved Text-and-Image Generation
Assessment | [
"cs.CV",
"cs.CL"
] | Many real-world user queries (e.g. "How do to make egg fried rice?") could benefit from systems capable of generating responses with both textual steps with accompanying images, similar to a cookbook. Models designed to generate interleaved text and images face challenges in ensuring consistency within and across these... | {
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2411.17189 | PhysMotion: Physics-Grounded Dynamics From a Single Image | [
"cs.CV"
] | We introduce PhysMotion, a novel framework that leverages principled physics-based simulations to guide intermediate 3D representations generated from a single image and input conditions (e.g., applied force and torque), producing high-quality, physically plausible video generation. By utilizing continuum mechanics-bas... | {
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2411.17190 | SelfSplat: Pose-Free and 3D Prior-Free Generalizable 3D Gaussian
Splatting | [
"cs.CV"
] | We propose SelfSplat, a novel 3D Gaussian Splatting model designed to perform pose-free and 3D prior-free generalizable 3D reconstruction from unposed multi-view images. These settings are inherently ill-posed due to the lack of ground-truth data, learned geometric information, and the need to achieve accurate 3D recon... | {
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2411.17195 | Depth-PC: A Visual Servo Framework Integrated with Cross-Modality Fusion
for Sim2Real Transfer | [
"cs.RO"
] | Visual servo techniques guide robotic motion using visual information to accomplish manipulation tasks, requiring high precision and robustness against noise. Traditional methods often require prior knowledge and are susceptible to external disturbances. Learning-driven alternatives, while promising, frequently struggl... | {
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2411.17196 | P2DFlow: A Protein Ensemble Generative Model with SE(3) Flow Matching | [
"physics.bio-ph",
"cs.LG"
] | Biological processes, functions, and properties are intricately linked to the ensemble of protein conformations, rather than being solely determined by a single stable conformation. In this study, we have developed P2DFlow, a generative model based on SE(3) flow matching, to predict the structural ensembles of proteins... | {
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2411.17201 | Learning Hierarchical Polynomials of Multiple Nonlinear Features with
Three-Layer Networks | [
"cs.LG",
"cs.AI",
"math.ST",
"stat.ML",
"stat.TH"
] | In deep learning theory, a critical question is to understand how neural networks learn hierarchical features. In this work, we study the learning of hierarchical polynomials of \textit{multiple nonlinear features} using three-layer neural networks. We examine a broad class of functions of the form $f^{\star}=g^{\star}... | {
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2411.17203 | cWDM: Conditional Wavelet Diffusion Models for Cross-Modality 3D Medical
Image Synthesis | [
"eess.IV",
"cs.CV"
] | This paper contributes to the "BraTS 2024 Brain MR Image Synthesis Challenge" and presents a conditional Wavelet Diffusion Model (cWDM) for directly solving a paired image-to-image translation task on high-resolution volumes. While deep learning-based brain tumor segmentation models have demonstrated clear clinical uti... | {
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2411.17204 | Strategic Prompting for Conversational Tasks: A Comparative Analysis of
Large Language Models Across Diverse Conversational Tasks | [
"cs.CL",
"cs.AI"
] | Given the advancements in conversational artificial intelligence, the evaluation and assessment of Large Language Models (LLMs) play a crucial role in ensuring optimal performance across various conversational tasks. In this paper, we present a comprehensive study that thoroughly evaluates the capabilities and limitati... | {
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2411.17207 | On the Efficiency of NLP-Inspired Methods for Tabular Deep Learning | [
"cs.LG"
] | Recent advancements in tabular deep learning (DL) have led to substantial performance improvements, surpassing the capabilities of traditional models. With the adoption of techniques from natural language processing (NLP), such as language model-based approaches, DL models for tabular data have also grown in complexity... | {
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2411.17209 | LampMark: Proactive Deepfake Detection via Training-Free Landmark
Perceptual Watermarks | [
"cs.CV"
] | Deepfake facial manipulation has garnered significant public attention due to its impacts on enhancing human experiences and posing privacy threats. Despite numerous passive algorithms that have been attempted to thwart malicious Deepfake attacks, they mostly struggle with the generalizability challenge when confronted... | {
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2411.17213 | Scaling nnU-Net for CBCT Segmentation | [
"cs.CV"
] | This paper presents our approach to scaling the nnU-Net framework for multi-structure segmentation on Cone Beam Computed Tomography (CBCT) images, specifically in the scope of the ToothFairy2 Challenge. We leveraged the nnU-Net ResEnc L model, introducing key modifications to patch size, network topology, and data augm... | {
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2411.17214 | MAT: Multi-Range Attention Transformer for Efficient Image
Super-Resolution | [
"cs.CV"
] | Recent advances in image super-resolution (SR) have significantly benefited from the incorporation of Transformer architectures. However, conventional techniques aimed at enlarging the self-attention window to capture broader contexts come with inherent drawbacks, especially the significantly increased computational de... | {
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2411.17215 | Interval-based validation of a nonlinear estimator | [
"cs.RO"
] | In engineering, models are often used to represent the behavior of a system. Estimators are then needed to approximate the values of the model's parameters based on observations. This approximation implies a difference between the values predicted by the model and the observations that have been made. It creates an unc... | {
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2411.17217 | Promptable Anomaly Segmentation with SAM Through Self-Perception Tuning | [
"cs.CV"
] | Segment Anything Model (SAM) has made great progress in anomaly segmentation tasks due to its impressive generalization ability. However, existing methods that directly apply SAM through prompting often overlook the domain shift issue, where SAM performs well on natural images but struggles in industrial scenarios. Par... | {
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2411.17218 | GraphSubDetector: Time Series Subsequence Anomaly Detection via
Density-Aware Adaptive Graph Neural Network | [
"cs.LG",
"cs.AI"
] | Time series subsequence anomaly detection is an important task in a large variety of real-world applications ranging from health monitoring to AIOps, and is challenging due to the following reasons: 1) how to effectively learn complex dynamics and dependencies in time series; 2) diverse and complicated anomalous subseq... | {
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2411.17221 | AIGV-Assessor: Benchmarking and Evaluating the Perceptual Quality of
Text-to-Video Generation with LMM | [
"cs.CV"
] | The rapid advancement of large multimodal models (LMMs) has led to the rapid expansion of artificial intelligence generated videos (AIGVs), which highlights the pressing need for effective video quality assessment (VQA) models designed specifically for AIGVs. Current VQA models generally fall short in accurately assess... | {
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2411.17223 | DreamMix: Decoupling Object Attributes for Enhanced Editability in
Customized Image Inpainting | [
"cs.CV"
] | Subject-driven image inpainting has emerged as a popular task in image editing alongside recent advancements in diffusion models. Previous methods primarily focus on identity preservation but struggle to maintain the editability of inserted objects. In response, this paper introduces DreamMix, a diffusion-based generat... | {
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2411.17226 | MWFormer: Multi-Weather Image Restoration Using Degradation-Aware
Transformers | [
"cs.CV"
] | Restoring images captured under adverse weather conditions is a fundamental task for many computer vision applications. However, most existing weather restoration approaches are only capable of handling a specific type of degradation, which is often insufficient in real-world scenarios, such as rainy-snowy or rainy-haz... | {
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2411.17229 | Efficient Data-aware Distance Comparison Operations for High-Dimensional
Approximate Nearest Neighbor Search | [
"cs.DB",
"cs.IR"
] | High-dimensional approximate $K$ nearest neighbor search (AKNN) is a fundamental task for various applications, including information retrieval. Most existing algorithms for AKNN can be decomposed into two main components, i.e., candidate generation and distance comparison operations (DCOs). While different methods hav... | {
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2411.17235 | MLI-NeRF: Multi-Light Intrinsic-Aware Neural Radiance Fields | [
"cs.CV"
] | Current methods for extracting intrinsic image components, such as reflectance and shading, primarily rely on statistical priors. These methods focus mainly on simple synthetic scenes and isolated objects and struggle to perform well on challenging real-world data. To address this issue, we propose MLI-NeRF, which inte... | {
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2411.17236 | From Graph Diffusion to Graph Classification | [
"cs.LG",
"cs.AI"
] | Generative models such as diffusion models have achieved remarkable success in state-of-the-art image and text tasks. Recently, score-based diffusion models have extended their success beyond image generation, showing competitive performance with discriminative methods in image {\em classification} tasks~\cite{zimmerma... | {
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2411.17237 | Grounding-IQA: Multimodal Language Grounding Model for Image Quality
Assessment | [
"cs.CV"
] | The development of multimodal large language models (MLLMs) enables the evaluation of image quality through natural language descriptions. This advancement allows for more detailed assessments. However, these MLLM-based IQA methods primarily rely on general contextual descriptions, sometimes limiting fine-grained quali... | {
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2411.17240 | Boost 3D Reconstruction using Diffusion-based Monocular Camera
Calibration | [
"cs.CV"
] | In this paper, we present DM-Calib, a diffusion-based approach for estimating pinhole camera intrinsic parameters from a single input image. Monocular camera calibration is essential for many 3D vision tasks. However, most existing methods depend on handcrafted assumptions or are constrained by limited training data, r... | {
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2411.17241 | Divergence Inequalities with Applications in Ergodic Theory | [
"cs.IT",
"math.IT",
"quant-ph"
] | The data processing inequality is central to information theory and motivates the study of monotonic divergences. However, it is not clear operationally we need to consider all such divergences. We establish a simple method for Pinsker inequalities as well as general bounds in terms of $\chi^{2}$-divergences for twice-... | {
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2411.17248 | DiffSLT: Enhancing Diversity in Sign Language Translation via Diffusion
Model | [
"cs.CV"
] | Sign language translation (SLT) is challenging, as it involves converting sign language videos into natural language. Previous studies have prioritized accuracy over diversity. However, diversity is crucial for handling lexical and syntactic ambiguities in machine translation, suggesting it could similarly benefit SLT.... | {
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2411.17249 | Buffer Anytime: Zero-Shot Video Depth and Normal from Image Priors | [
"cs.CV",
"cs.AI"
] | We present Buffer Anytime, a framework for estimation of depth and normal maps (which we call geometric buffers) from video that eliminates the need for paired video--depth and video--normal training data. Instead of relying on large-scale annotated video datasets, we demonstrate high-quality video buffer estimation by... | {
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2411.17251 | DGNN-YOLO: Interpretable Dynamic Graph Neural Networks with YOLO11 for
Detecting and Tracking Small Occluded Objects in Urban Traffic | [
"cs.CV",
"cs.LG"
] | The detection and tracking of small, occluded objects such as pedestrians, cyclists, and motorbikes pose significant challenges for traffic surveillance systems because of their erratic movement, frequent occlusion, and poor visibility in dynamic urban environments. Traditional methods like YOLO11, while proficient in ... | {
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2411.17253 | LHPF: Look back the History and Plan for the Future in Autonomous
Driving | [
"cs.RO",
"cs.CV"
] | Decision-making and planning in autonomous driving critically reflect the safety of the system, making effective planning imperative. Current imitation learning-based planning algorithms often merge historical trajectories with present observations to predict future candidate paths. However, these algorithms typically ... | {
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2411.17254 | Semantic Data Augmentation for Long-tailed Facial Expression Recognition | [
"cs.CV",
"cs.AI"
] | Facial Expression Recognition has a wide application prospect in social robotics, health care, driver fatigue monitoring, and many other practical scenarios. Automatic recognition of facial expressions has been extensively studied by the Computer Vision research society. But Facial Expression Recognition in real-world ... | {
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2411.17255 | APT: Architectural Planning and Text-to-Blueprint Construction Using
Large Language Models for Open-World Agents | [
"cs.LG",
"cs.AI"
] | We present APT, an advanced Large Language Model (LLM)-driven framework that enables autonomous agents to construct complex and creative structures within the Minecraft environment. Unlike previous approaches that primarily concentrate on skill-based open-world tasks or rely on image-based diffusion models for generati... | {
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2411.17257 | Disentangled Interpretable Representation for Efficient Long-term Time
Series Forecasting | [
"cs.LG",
"cs.AI"
] | Industry 5.0 introduces new challenges for Long-term Time Series Forecasting (LTSF), characterized by high-dimensional, high-resolution data and high-stakes application scenarios. Against this backdrop, developing efficient and interpretable models for LTSF becomes a key challenge. Existing deep learning and linear mod... | {
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2411.17260 | MiceBoneChallenge: Micro-CT public dataset and six solutions for
automatic growth plate detection in micro-CT mice bone scans | [
"eess.IV",
"cs.AI",
"cs.CV",
"stat.ML"
] | Detecting and quantifying bone changes in micro-CT scans of rodents is a common task in preclinical drug development studies. However, this task is manual, time-consuming and subject to inter- and intra-observer variability. In 2024, Anonymous Company organized an internal challenge to develop models for automatic bone... | {
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2411.17261 | HEIE: MLLM-Based Hierarchical Explainable AIGC Image Implausibility
Evaluator | [
"cs.CV",
"cs.AI"
] | AIGC images are prevalent across various fields, yet they frequently suffer from quality issues like artifacts and unnatural textures. Specialized models aim to predict defect region heatmaps but face two primary challenges: (1) lack of explainability, failing to provide reasons and analyses for subtle defects, and (2)... | {
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2411.17265 | A Topic-level Self-Correctional Approach to Mitigate Hallucinations in
MLLMs | [
"cs.CL",
"cs.CV"
] | Aligning the behaviors of Multimodal Large Language Models (MLLMs) with human preferences is crucial for developing robust and trustworthy AI systems. While recent attempts have employed human experts or powerful auxiliary AI systems to provide more accurate preference feedback, such as determining the preferable respo... | {
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2411.17270 | An Attempt to Develop a Neural Parser based on Simplified Head-Driven
Phrase Structure Grammar on Vietnamese | [
"cs.CL"
] | In this paper, we aimed to develop a neural parser for Vietnamese based on simplified Head-Driven Phrase Structure Grammar (HPSG). The existing corpora, VietTreebank and VnDT, had around 15% of constituency and dependency tree pairs that did not adhere to simplified HPSG rules. To attempt to address the issue of the co... | {
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2411.17277 | Minimizing Conservatism in Safety-Critical Control for Input-Delayed
Systems via Adaptive Delay Estimation | [
"eess.SY",
"cs.SY"
] | Input delays affect systems such as teleoperation and wirelessly autonomous connected vehicles, and may lead to safety violations. One promising way to ensure safety in the presence of delay is to employ control barrier functions (CBFs), and extensions thereof that account for uncertainty: delay adaptive CBFs (DaCBFs).... | {
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2411.17278 | The Exploration of Neural Collapse under Imbalanced Data | [
"cs.LG",
"math.OC"
] | Neural collapse, a newly identified characteristic, describes a property of solutions during model training. In this paper, we explore neural collapse in the context of imbalanced data. We consider the $L$-extended unconstrained feature model with a bias term and provide a theoretical analysis of global minimizer. Ou... | {
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2411.17282 | Social Distancing Induced Coronavirus Optimization Algorithm (COVO):
Application to Multimodal Function Optimization and Noise Removal | [
"cs.CC",
"cs.AI"
] | The metaheuristic optimization technique attained more awareness for handling complex optimization problems. Over the last few years, numerous optimization techniques have been developed that are inspired by natural phenomena. Recently, the propagation of the new COVID-19 implied a burden on the public health system to... | {
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2411.17283 | BadScan: An Architectural Backdoor Attack on Visual State Space Models | [
"cs.CV"
] | The newly introduced Visual State Space Model (VMamba), which employs \textit{State Space Mechanisms} (SSM) to interpret images as sequences of patches, has shown exceptional performance compared to Vision Transformers (ViT) across various computer vision tasks. However, recent studies have highlighted that deep models... | {
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2411.17284 | AutoElicit: Using Large Language Models for Expert Prior Elicitation in
Predictive Modelling | [
"cs.LG",
"cs.CL",
"stat.ML"
] | Large language models (LLMs) acquire a breadth of information across various domains. However, their computational complexity, cost, and lack of transparency often hinder their direct application for predictive tasks where privacy and interpretability are paramount. In fields such as healthcare, biology, and finance, s... | {
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2411.17287 | Privacy-Preserving Federated Unsupervised Domain Adaptation for
Regression on Small-Scale and High-Dimensional Biological Data | [
"cs.LG"
] | Machine learning models often struggle with generalization in small, heterogeneous datasets due to domain shifts caused by variations in data collection and population differences. This challenge is particularly pronounced in biological data, where data is high-dimensional, small-scale, and decentralized across institu... | {
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2411.17289 | Loosely coupled 4D-Radar-Inertial Odometry for Ground Robots | [
"cs.RO"
] | Accurate robot odometry is essential for autonomous navigation. While numerous techniques have been developed based on various sensor suites, odometry estimation using only radar and IMU remains an underexplored area. Radar proves particularly valuable in environments where traditional sensors, like cameras or LiDAR, m... | {
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2411.17290 | A "Breathing" Mobile Communication Network | [
"eess.SY",
"cs.SY",
"math.OC"
] | The frequent migration of large-scale users leads to the load imbalance of mobile communication networks, which causes resource waste and decreases user experience. To address the load balancing problem, this paper proposes a dynamic optimization framework for mobile communication networks inspired by the average conse... | {
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2411.17291 | Interpretable label-free self-guided subspace clustering | [
"cs.LG",
"cs.CV"
] | Majority subspace clustering (SC) algorithms depend on one or more hyperparameters that need to be carefully tuned for the SC algorithms to achieve high clustering performance. Hyperparameter optimization (HPO) is often performed using grid-search, assuming that some labeled data is available. In some domains, such as ... | {
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2411.17292 | Task Progressive Curriculum Learning for Robust Visual Question
Answering | [
"cs.CV",
"cs.LG"
] | Visual Question Answering (VQA) systems are known for their poor performance in out-of-distribution datasets. An issue that was addressed in previous works through ensemble learning, answer re-ranking, or artificially growing the training set. In this work, we show for the first time that robust Visual Question Answeri... | {
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2411.17293 | SIL-RRT*: Learning Sampling Distribution through Self Imitation Learning | [
"cs.RO"
] | Efficiently finding safe and feasible trajectories for mobile objects is a critical field in robotics and computer science. In this paper, we propose SIL-RRT*, a novel learning-based motion planning algorithm that extends the RRT* algorithm by using a deep neural network to predict a distribution for sampling at each i... | {
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2411.17296 | GrokFormer: Graph Fourier Kolmogorov-Arnold Transformers | [
"cs.LG",
"cs.AI"
] | Graph Transformers (GTs) have demonstrated remarkable performance in graph representation learning over popular graph neural networks (GNNs). However, self--attention, the core module of GTs, preserves only low-frequency signals in graph features, leading to ineffectiveness in capturing other important signals like hig... | {
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2411.17299 | 2D Matryoshka Training for Information Retrieval | [
"cs.IR",
"cs.CL"
] | 2D Matryoshka Training is an advanced embedding representation training approach designed to train an encoder model simultaneously across various layer-dimension setups. This method has demonstrated higher effectiveness in Semantic Text Similarity (STS) tasks over traditional training approaches when using sub-layers f... | {
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} |
2411.17301 | ReFINE: A Reward-Based Framework for Interpretable and Nuanced
Evaluation of Radiology Report Generation | [
"cs.CL",
"cs.AI"
] | Automated radiology report generation (R2Gen) has advanced significantly, introducing challenges in accurate evaluation due to its complexity. Traditional metrics often fall short by relying on rigid word-matching or focusing only on pathological entities, leading to inconsistencies with human assessments. To bridge th... | {
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} |
2411.17304 | Meaningless is better: hashing bias-inducing words in LLM prompts
improves performance in logical reasoning and statistical learning | [
"cs.CL",
"cs.AI"
] | This paper introduces a novel method, referred to as "hashing", which involves masking potentially bias-inducing words in large language models (LLMs) with hash-like meaningless identifiers to reduce cognitive biases and reliance on external knowledge. The method was tested across three sets of experiments involving a ... | {
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} |
2411.17305 | in-Car Biometrics (iCarB) Datasets for Driver Recognition: Face,
Fingerprint, and Voice | [
"cs.CV"
] | We present three biometric datasets (iCarB-Face, iCarB-Fingerprint, iCarB-Voice) containing face videos, fingerprint images, and voice samples, collected inside a car from 200 consenting volunteers. The data was acquired using a near-infrared camera, two fingerprint scanners, and two microphones, while the volunteers w... | {
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} |
2411.17309 | PIM-AI: A Novel Architecture for High-Efficiency LLM Inference | [
"cs.AR",
"cs.AI",
"cs.DC",
"cs.ET"
] | Large Language Models (LLMs) have become essential in a variety of applications due to their advanced language understanding and generation capabilities. However, their computational and memory requirements pose significant challenges to traditional hardware architectures. Processing-in-Memory (PIM), which integrates c... | {
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} |
2411.17310 | Reward Incremental Learning in Text-to-Image Generation | [
"cs.CV",
"cs.LG"
] | The recent success of denoising diffusion models has significantly advanced text-to-image generation. While these large-scale pretrained models show excellent performance in general image synthesis, downstream objectives often require fine-tuning to meet specific criteria such as aesthetics or human preference. Reward ... | {
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} |
2411.17313 | Event Ellipsometer: Event-based Mueller-Matrix Video Imaging | [
"cs.CV"
] | Light-matter interactions modify both the intensity and polarization state of light. Changes in polarization, represented by a Mueller matrix, encode detailed scene information. Existing optical ellipsometers capture Mueller-matrix images; however, they are often limited to capturing static scenes due to long acquisiti... | {
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} |
2411.17323 | InsightEdit: Towards Better Instruction Following for Image Editing | [
"cs.CV"
] | In this paper, we focus on the task of instruction-based image editing. Previous works like InstructPix2Pix, InstructDiffusion, and SmartEdit have explored end-to-end editing. However, two limitations still remain: First, existing datasets suffer from low resolution, poor background consistency, and overly simplistic i... | {
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} |
2411.17326 | Towards Intention Recognition for Robotic Assistants Through Online
POMDP Planning | [
"cs.AI",
"cs.RO"
] | Intention recognition, or the ability to anticipate the actions of another agent, plays a vital role in the design and development of automated assistants that can support humans in their daily tasks. In particular, industrial settings pose interesting challenges that include potential distractions for a decision-maker... | {
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} |
2411.17332 | On the Generalization of Handwritten Text Recognition Models | [
"cs.LG"
] | Recent advances in Handwritten Text Recognition (HTR) have led to significant reductions in transcription errors on standard benchmarks under the i.i.d. assumption, thus focusing on minimizing in-distribution (ID) errors. However, this assumption does not hold in real-world applications, which has motivated HTR researc... | {
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} |
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