id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2411.10814 | DEAL: Decoupled Classifier with Adaptive Linear Modulation for Group
Robust Early Diagnosis of MCI to AD Conversion | [
"cs.CV"
] | While deep learning-based Alzheimer's disease (AD) diagnosis has recently made significant advancements, particularly in predicting the conversion of mild cognitive impairment (MCI) to AD based on MRI images, there remains a critical gap in research regarding the group robustness of the diagnosis. Although numerous stu... | {
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2411.10817 | Conformation Generation using Transformer Flows | [
"cs.LG",
"q-bio.QM",
"stat.ML"
] | Estimating three-dimensional conformations of a molecular graph allows insight into the molecule's biological and chemical functions. Fast generation of valid conformations is thus central to molecular modeling. Recent advances in graph-based deep networks have accelerated conformation generation from hours to seconds.... | {
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2411.10818 | FlipSketch: Flipping Static Drawings to Text-Guided Sketch Animations | [
"cs.GR",
"cs.CV"
] | Sketch animations offer a powerful medium for visual storytelling, from simple flip-book doodles to professional studio productions. While traditional animation requires teams of skilled artists to draw key frames and in-between frames, existing automation attempts still demand significant artistic effort through preci... | {
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2411.10819 | An Oversampling-enhanced Multi-class Imbalanced Classification Framework
for Patient Health Status Prediction Using Patient-reported Outcomes | [
"cs.LG"
] | Patient-reported outcomes (PROs) directly collected from cancer patients being treated with radiation therapy play a vital role in assisting clinicians in counseling patients regarding likely toxicities. Precise prediction and evaluation of symptoms or health status associated with PROs are fundamental to enhancing dec... | {
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2411.10820 | Molecular Dynamics Study of Liquid Condensation on Nano-structured
Sinusoidal Hybrid Wetting Surfaces | [
"cond-mat.mtrl-sci",
"cs.SY",
"eess.SY"
] | Although real surfaces exhibit intricate topologies at the nanoscale, rough surface consideration is often overlooked in nanoscale heat transfer studies. Superimposed sinusoidal functions effectively model the complexity of these surfaces. This study investigates the impact of sinusoidal roughness on liquid argon conde... | {
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2411.10821 | GeomCLIP: Contrastive Geometry-Text Pre-training for Molecules | [
"cs.LG",
"q-bio.BM"
] | Pretraining molecular representations is crucial for drug and material discovery. Recent methods focus on learning representations from geometric structures, effectively capturing 3D position information. Yet, they overlook the rich information in biomedical texts, which detail molecules' properties and substructures. ... | {
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2411.10822 | A Data-Efficient Sequential Learning Framework for Melt Pool Defect
Classification in Laser Powder Bed Fusion | [
"cs.LG",
"cond-mat.mtrl-sci",
"cs.CE"
] | Ensuring the quality and reliability of Metal Additive Manufacturing (MAM) components is crucial, especially in the Laser Powder Bed Fusion (L-PBF) process, where melt pool defects such as keyhole, balling, and lack of fusion can significantly compromise structural integrity. This study presents SL-RF+ (Sequentially Le... | {
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2411.10825 | ARM: Appearance Reconstruction Model for Relightable 3D Generation | [
"cs.CV",
"cs.GR"
] | Recent image-to-3D reconstruction models have greatly advanced geometry generation, but they still struggle to faithfully generate realistic appearance. To address this, we introduce ARM, a novel method that reconstructs high-quality 3D meshes and realistic appearance from sparse-view images. The core of ARM lies in de... | {
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2411.10828 | Bilingual Text-dependent Speaker Verification with Pre-trained Models
for TdSV Challenge 2024 | [
"eess.AS",
"cs.CL",
"cs.LG"
] | This paper presents our submissions to the Iranian division of the Text-dependent Speaker Verification Challenge (TdSV) 2024. TdSV aims to determine if a specific phrase was spoken by a target speaker. We developed two independent subsystems based on pre-trained models: For phrase verification, a phrase classifier reje... | {
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2411.10830 | One-Layer Transformer Provably Learns One-Nearest Neighbor In Context | [
"cs.LG",
"cs.AI",
"math.OC"
] | Transformers have achieved great success in recent years. Interestingly, transformers have shown particularly strong in-context learning capability -- even without fine-tuning, they are still able to solve unseen tasks well purely based on task-specific prompts. In this paper, we study the capability of one-layer trans... | {
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2411.10831 | Neighboring Slice Noise2Noise: Self-Supervised Medical Image Denoising
from Single Noisy Image Volume | [
"eess.IV",
"cs.CV"
] | In the last few years, with the rapid development of deep learning technologies, supervised methods based on convolutional neural networks have greatly enhanced the performance of medical image denoising. However, these methods require large quantities of noisy-clean image pairs for training, which greatly limits their... | {
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2411.10832 | Small-signal stability of power systems with voltage droop | [
"math.OC",
"cs.SY",
"eess.SY",
"math.DS"
] | The small-signal stability of power grids is a well-studied topic. In this work, we give new sufficient conditions for highly heterogeneous mixes of grid-forming inverters (and other machines) that implement a $V$-$q$ droop to stabilize viable operating states of lossless grids. Assuming the edges are not overloaded, a... | {
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2411.10836 | AnimateAnything: Consistent and Controllable Animation for Video
Generation | [
"cs.CV"
] | We present a unified controllable video generation approach AnimateAnything that facilitates precise and consistent video manipulation across various conditions, including camera trajectories, text prompts, and user motion annotations. Specifically, we carefully design a multi-scale control feature fusion network to co... | {
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2411.10841 | Adaptive Learning of Design Strategies over Non-Hierarchical
Multi-Fidelity Models via Policy Alignment | [
"cs.LG",
"cs.AI"
] | Multi-fidelity Reinforcement Learning (RL) frameworks significantly enhance the efficiency of engineering design by leveraging analysis models with varying levels of accuracy and computational costs. The prevailing methodologies, characterized by transfer learning, human-inspired strategies, control variate techniques,... | {
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2411.10842 | CODECLEANER: Elevating Standards with A Robust Data Contamination
Mitigation Toolkit | [
"cs.SE",
"cs.AI"
] | Data contamination presents a critical barrier preventing widespread industrial adoption of advanced software engineering techniques that leverage code language models (CLMs). This phenomenon occurs when evaluation data inadvertently overlaps with the public code repositories used to train CLMs, severely undermining th... | {
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2411.10843 | A Novel Adaptive Hybrid Focal-Entropy Loss for Enhancing Diabetic
Retinopathy Detection Using Convolutional Neural Networks | [
"eess.IV",
"cs.AI",
"cs.CV",
"cs.LG"
] | Diabetic retinopathy is a leading cause of blindness around the world and demands precise AI-based diagnostic tools. Traditional loss functions in multi-class classification, such as Categorical Cross-Entropy (CCE), are very common but break down with class imbalance, especially in cases with inherently challenging or ... | {
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2411.10845 | Automatic Discovery and Assessment of Interpretable Systematic Errors in
Semantic Segmentation | [
"cs.CV"
] | This paper presents a novel method for discovering systematic errors in segmentation models. For instance, a systematic error in the segmentation model can be a sufficiently large number of misclassifications from the model as a parking meter for a target class of pedestrians. With the rapid deployment of these models ... | {
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2411.10848 | NeuroNURBS: Learning Efficient Surface Representations for 3D Solids | [
"cs.CV",
"cs.CE"
] | Boundary Representation (B-Rep) is the de facto representation of 3D solids in Computer-Aided Design (CAD). B-Rep solids are defined with a set of NURBS (Non-Uniform Rational B-Splines) surfaces forming a closed volume. To represent a surface, current works often employ the UV-grid approximation, i.e., sample points un... | {
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2411.10857 | Large Vision-Language Models for Remote Sensing Visual Question
Answering | [
"cs.CV",
"cs.CL"
] | Remote Sensing Visual Question Answering (RSVQA) is a challenging task that involves interpreting complex satellite imagery to answer natural language questions. Traditional approaches often rely on separate visual feature extractors and language processing models, which can be computationally intensive and limited in ... | {
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2411.10861 | See-Saw Generative Mechanism for Scalable Recursive Code Generation with
Generative AI | [
"cs.LG",
"cs.AI",
"cs.SE"
] | The generation of complex, large-scale code projects using generative AI models presents challenges due to token limitations, dependency management, and iterative refinement requirements. This paper introduces the See-Saw generative mechanism, a novel methodology for dynamic and recursive code generation. The proposed ... | {
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2411.10863 | Improvement in Facial Emotion Recognition using Synthetic Data Generated
by Diffusion Model | [
"cs.CV",
"cs.HC",
"eess.IV"
] | Facial Emotion Recognition (FER) plays a crucial role in computer vision, with significant applications in human-computer interaction, affective computing, and areas such as mental health monitoring and personalized learning environments. However, a major challenge in FER task is the class imbalance commonly found in a... | {
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2411.10867 | ViBe: A Text-to-Video Benchmark for Evaluating Hallucination in Large
Multimodal Models | [
"cs.CV",
"cs.AI"
] | Latest developments in Large Multimodal Models (LMMs) have broadened their capabilities to include video understanding. Specifically, Text-to-video (T2V) models have made significant progress in quality, comprehension, and duration, excelling at creating videos from simple textual prompts. Yet, they still frequently pr... | {
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2411.10868 | Destabilizing a Social Network Model via Intrinsic Feedback
Vulnerabilities | [
"cs.SI",
"math.OC",
"physics.soc-ph"
] | Social influence plays a significant role in shaping individual opinions and actions, particularly in a world of ubiquitous digital interconnection. The rapid development of generative AI has engendered well-founded concerns regarding the potential scalable implementation of radicalization techniques in social media. M... | {
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2411.10869 | Large Language Models (LLMs) as Traffic Control Systems at Urban
Intersections: A New Paradigm | [
"cs.CL",
"cs.CE",
"cs.CY",
"cs.HC"
] | This study introduces a novel approach for traffic control systems by using Large Language Models (LLMs) as traffic controllers. The study utilizes their logical reasoning, scene understanding, and decision-making capabilities to optimize throughput and provide feedback based on traffic conditions in real-time. LLMs ce... | {
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2411.10877 | Developer Perspectives on Licensing and Copyright Issues Arising from
Generative AI for Coding | [
"cs.SE",
"cs.AI"
] | Generative AI (GenAI) tools have already started to transform software development practices. Despite their utility in tasks such as writing code, the use of these tools raises important legal questions and potential risks, particularly those associated with copyright law. In the midst of this uncertainty, this paper p... | {
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2411.10878 | Empowering Meta-Analysis: Leveraging Large Language Models for
Scientific Synthesis | [
"cs.CL",
"cs.AI",
"cs.IR"
] | This study investigates the automation of meta-analysis in scientific documents using large language models (LLMs). Meta-analysis is a robust statistical method that synthesizes the findings of multiple studies support articles to provide a comprehensive understanding. We know that a meta-article provides a structured ... | {
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2411.10879 | BanglaDialecto: An End-to-End AI-Powered Regional Speech Standardization | [
"cs.CL",
"cs.AI",
"cs.LG",
"cs.SD",
"eess.AS"
] | This study focuses on recognizing Bangladeshi dialects and converting diverse Bengali accents into standardized formal Bengali speech. Dialects, often referred to as regional languages, are distinctive variations of a language spoken in a particular location and are identified by their phonetics, pronunciations, and le... | {
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2411.10881 | FIAS: Feature Imbalance-Aware Medical Image Segmentation with Dynamic
Fusion and Mixing Attention | [
"cs.CV"
] | With the growing application of transformer in computer vision, hybrid architecture that combine convolutional neural networks (CNNs) and transformers demonstrates competitive ability in medical image segmentation. However, direct fusion of features from CNNs and transformers often leads to feature imbalance and redund... | {
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2411.10882 | Adaptive Soft Actor-Critic Framework for RIS-Assisted and UAV-Aided
Communication | [
"eess.SP",
"cs.SY",
"eess.SY"
] | In this work, we explore UAV-assisted reconfigurable intelligent surface (RIS) technology to enhance downlink communications in wireless networks. By integrating RIS on both UAVs and ground infrastructure, we aim to boost network coverage, fairness, and resilience against challenges such as UAV jitter. To maximize the ... | {
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2411.10886 | MetricGold: Leveraging Text-To-Image Latent Diffusion Models for Metric
Depth Estimation | [
"cs.CV",
"cs.AI",
"cs.GR",
"cs.RO"
] | Recovering metric depth from a single image remains a fundamental challenge in computer vision, requiring both scene understanding and accurate scaling. While deep learning has advanced monocular depth estimation, current models often struggle with unfamiliar scenes and layouts, particularly in zero-shot scenarios and ... | {
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2411.10888 | MpoxVLM: A Vision-Language Model for Diagnosing Skin Lesions from Mpox
Virus Infection | [
"eess.IV",
"cs.AI",
"cs.CV"
] | In the aftermath of the COVID-19 pandemic and amid accelerating climate change, emerging infectious diseases, particularly those arising from zoonotic spillover, remain a global threat. Mpox (caused by the monkeypox virus) is a notable example of a zoonotic infection that often goes undiagnosed, especially as its rash ... | {
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2411.10889 | Neuc-MDS: Non-Euclidean Multidimensional Scaling Through Bilinear Forms | [
"cs.LG",
"stat.ML"
] | We introduce Non-Euclidean-MDS (Neuc-MDS), an extension of classical Multidimensional Scaling (MDS) that accommodates non-Euclidean and non-metric inputs. The main idea is to generalize the standard inner product to symmetric bilinear forms to utilize the negative eigenvalues of dissimilarity Gram matrices. Neuc-MDS ef... | {
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2411.10891 | ChannelDropBack: Forward-Consistent Stochastic Regularization for Deep
Networks | [
"cs.CV"
] | Incorporating stochasticity into the training process of deep convolutional networks is a widely used technique to reduce overfitting and improve regularization. Existing techniques often require modifying the architecture of the network by adding specialized layers, are effective only to specific network topologies or... | {
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2411.10894 | Deep BI-RADS Network for Improved Cancer Detection from Mammograms | [
"cs.CV"
] | While state-of-the-art models for breast cancer detection leverage multi-view mammograms for enhanced diagnostic accuracy, they often focus solely on visual mammography data. However, radiologists document valuable lesion descriptors that contain additional information that can enhance mammography-based breast cancer s... | {
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2411.10895 | Evolution of IVR building techniques: from code writing to AI-powered
automation | [
"cs.SE",
"cs.AI"
] | Interactive Voice Response (IVR) systems have undergone significant transformation in recent years, moving from traditional code-based development to more user-friendly approaches leveraging widgets and, most recently, harnessing the power of Artificial Intelligence (AI) for automated IVR flow creation. This paper expl... | {
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2411.10896 | Targeting Negative Flips in Active Learning using Validation Sets | [
"cs.LG",
"cs.CV"
] | The performance of active learning algorithms can be improved in two ways. The often used and intuitive way is by reducing the overall error rate within the test set. The second way is to ensure that correct predictions are not forgotten when the training set is increased in between rounds. The former is measured by th... | {
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2411.10898 | Watermarking Generative Categorical Data | [
"cs.CR",
"cs.LG"
] | In this paper, we propose a novel statistical framework for watermarking generative categorical data. Our method systematically embeds pre-agreed secret signals by splitting the data distribution into two components and modifying one distribution based on a deterministic relationship with the other, ensuring the waterm... | {
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2411.10899 | Planning for Tabletop Object Rearrangement | [
"cs.RO"
] | Finding an high-quality solution for the tabletop object rearrangement planning is a challenging problem. Compared to determining a goal arrangement, rearrangement planning is challenging due to the dependencies between objects and the buffer capacity available to hold objects. Although orla* has proposed an A* based s... | {
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2411.10902 | Attention-based U-Net Method for Autonomous Lane Detection | [
"cs.CV"
] | Lane detection involves identifying lanes on the road and accurately determining their location and shape. This is a crucial technique for modern assisted and autonomous driving systems. However, several unique properties of lanes pose challenges for detection methods. The lack of distinctive features can cause lane de... | {
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2411.10906 | Efficient, Low-Regret, Online Reinforcement Learning for Linear MDPs | [
"cs.LG",
"cs.DS"
] | Reinforcement learning algorithms are usually stated without theoretical guarantees regarding their performance. Recently, Jin, Yang, Wang, and Jordan (COLT 2020) showed a polynomial-time reinforcement learning algorithm (namely, LSVI-UCB) for the setting of linear Markov decision processes, and provided theoretical gu... | {
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2411.10911 | Constructing accurate machine-learned potentials and performing highly
efficient atomistic simulations to predict structural and thermal properties | [
"cond-mat.mtrl-sci",
"cs.LG",
"physics.chem-ph"
] | The $\text{Cu}_7\text{P}\text{S}_6$ compound has garnered significant attention due to its potential in thermoelectric applications. In this study, we introduce a neuroevolution potential (NEP), trained on a dataset generated from ab initio molecular dynamics (AIMD) simulations, using the moment tensor potential (MTP) ... | {
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2411.10912 | SPICA: Retrieving Scenarios for Pluralistic In-Context Alignment | [
"cs.CL"
] | When different groups' values differ, one approach to model alignment is to steer models at inference time towards each group's preferences. However, techniques like in-context learning only consider similarity when drawing few-shot examples and not cross-group differences in values. We propose SPICA, a framework that ... | {
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2411.10913 | Generating Compositional Scenes via Text-to-image RGBA Instance
Generation | [
"cs.CV",
"cs.LG"
] | Text-to-image diffusion generative models can generate high quality images at the cost of tedious prompt engineering. Controllability can be improved by introducing layout conditioning, however existing methods lack layout editing ability and fine-grained control over object attributes. The concept of multi-layer gener... | {
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2411.10914 | BPO: Towards Balanced Preference Optimization between Knowledge Breadth
and Depth in Alignment | [
"cs.CL"
] | Reinforcement Learning with Human Feedback (RLHF) is the key to the success of large language models (LLMs) in recent years. In this work, we first introduce the concepts of knowledge breadth and knowledge depth, which measure the comprehensiveness and depth of an LLM or knowledge source respectively. We reveal that th... | {
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2411.10915 | Bias in Large Language Models: Origin, Evaluation, and Mitigation | [
"cs.CL",
"cs.LG"
] | Large Language Models (LLMs) have revolutionized natural language processing, but their susceptibility to biases poses significant challenges. This comprehensive review examines the landscape of bias in LLMs, from its origins to current mitigation strategies. We categorize biases as intrinsic and extrinsic, analyzing t... | {
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2411.10918 | LLM-assisted Physical Invariant Extraction for Cyber-Physical Systems
Anomaly Detection | [
"cs.CR",
"cs.AI"
] | Modern industrial infrastructures rely heavily on Cyber-Physical Systems (CPS), but these are vulnerable to cyber-attacks with potentially catastrophic effects. To reduce these risks, anomaly detection methods based on physical invariants have been developed. However, these methods often require domain-specific experti... | {
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2411.10919 | Multi-Modal Self-Supervised Learning for Surgical Feedback Effectiveness
Assessment | [
"cs.LG",
"cs.AI",
"cs.CV"
] | During surgical training, real-time feedback from trainers to trainees is important for preventing errors and enhancing long-term skill acquisition. Accurately predicting the effectiveness of this feedback, specifically whether it leads to a change in trainee behavior, is crucial for developing methods for improving su... | {
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2411.10921 | Distributed solar generation forecasting using attention-based deep
neural networks for cloud movement prediction | [
"cs.LG",
"cs.CV"
] | Accurate forecasts of distributed solar generation are necessary to reduce negative impacts resulting from the increased uptake of distributed solar photovoltaic (PV) systems. However, the high variability of solar generation over short time intervals (seconds to minutes) caused by cloud movement makes this forecasting... | {
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2411.10922 | Exploiting VLM Localizability and Semantics for Open Vocabulary Action
Detection | [
"cs.CV"
] | Action detection aims to detect (recognize and localize) human actions spatially and temporally in videos. Existing approaches focus on the closed-set setting where an action detector is trained and tested on videos from a fixed set of action categories. However, this constrained setting is not viable in an open world ... | {
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2411.10924 | Hyperspectral Imaging-Based Grain Quality Assessment With Limited
Labelled Data | [
"cs.CV",
"cs.AI"
] | Recently hyperspectral imaging (HSI)-based grain quality assessment has gained research attention. However, unlike other imaging modalities, HSI data lacks sufficient labelled samples required to effectively train deep convolutional neural network (DCNN)-based classifiers. In this paper, we present a novel approach to ... | {
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2411.10925 | A Resilience Perspective on C-V2X Communication Networks under Imperfect
CSI | [
"cs.IT",
"math.IT"
] | Cellular vehicle-to-everything (C-V2X) networks provide a promising solution to improve road safety and traffic efficiency. One key challenge in such systems lies in meeting different quality-of-service (QoS) requirements of coexisting vehicular communication links, particularly under imperfect channel state informatio... | {
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2411.10927 | Inter-linguistic Phonetic Composition (IPC): A Theoretical and
Computational Approach to Enhance Second Language Pronunciation | [
"cs.CL",
"cs.SD",
"eess.AS"
] | Learners of a second language (L2) often unconsciously substitute unfamiliar L2 phonemes with similar phonemes from their native language (L1), even though native speakers of the L2 perceive these sounds as distinct and non-interchangeable. This phonemic substitution leads to deviations from the standard phonological p... | {
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} |
2411.10928 | Learn from Downstream and Be Yourself in Multimodal Large Language Model
Fine-Tuning | [
"cs.CL",
"cs.AI"
] | Multimodal Large Language Model (MLLM) have demonstrated strong generalization capabilities across diverse distributions and tasks, largely due to extensive pre-training datasets. Fine-tuning MLLM has become a common practice to improve performance on specific downstream tasks. However, during fine-tuning, MLLM often f... | {
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2411.10929 | Wildfire Risk Metric Impact on Public Safety Power Shut-off Cost Savings | [
"eess.SY",
"cs.SY"
] | Public Safety Power Shutoffs (PSPS) are a proactive strategy to mitigate fire hazards from power system infrastructure failures. System operators employ PSPS to deactivate portions of the electric grid with heightened wildfire risks to prevent wildfire ignition and redispatch generators to minimize load shedding. A mea... | {
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2411.10932 | Constrained Diffusion with Trust Sampling | [
"cs.LG",
"cs.CV"
] | Diffusion models have demonstrated significant promise in various generative tasks; however, they often struggle to satisfy challenging constraints. Our approach addresses this limitation by rethinking training-free loss-guided diffusion from an optimization perspective. We formulate a series of constrained optimizatio... | {
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2411.10934 | Analyzing Pok\'emon and Mario Streamers' Twitch Chat with LLM-based User
Embeddings | [
"cs.CL"
] | We present a novel digital humanities method for representing our Twitch chatters as user embeddings created by a large language model (LLM). We cluster these embeddings automatically using affinity propagation and further narrow this clustering down through manual analysis. We analyze the chat of one stream by each Tw... | {
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2411.10935 | Exciting Contact Modes in Differentiable Simulations for Robot Learning | [
"cs.RO",
"cs.IT",
"math.IT"
] | In this paper, we explore an approach to actively plan and excite contact modes in differentiable simulators as a means to tighten the sim-to-real gap. We propose an optimal experimental design approach derived from information-theoretic methods to identify and search for information-rich contact modes through the use ... | {
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2411.10936 | Iterative Camera-LiDAR Extrinsic Optimization via Surrogate Diffusion | [
"cs.CV"
] | Cameras and LiDAR are essential sensors for autonomous vehicles. Camera-LiDAR data fusion compensate for deficiencies of stand-alone sensors but relies on precise extrinsic calibration. Many learning-based calibration methods predict extrinsic parameters in a single step. Driven by the growing demand for higher accurac... | {
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2411.10937 | Memory-Augmented Multimodal LLMs for Surgical VQA via Self-Contained
Inquiry | [
"cs.CV",
"cs.CL"
] | Comprehensively understanding surgical scenes in Surgical Visual Question Answering (Surgical VQA) requires reasoning over multiple objects. Previous approaches address this task using cross-modal fusion strategies to enhance reasoning ability. However, these methods often struggle with limited scene understanding and ... | {
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2411.10940 | A Monocular SLAM-based Multi-User Positioning System with Image
Occlusion in Augmented Reality | [
"cs.HC",
"cs.CV"
] | In recent years, with the rapid development of augmented reality (AR) technology, there is an increasing demand for multi-user collaborative experiences. Unlike for single-user experiences, ensuring the spatial localization of every user and maintaining synchronization and consistency of positioning and orientation acr... | {
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2411.10941 | Efficient Estimation of Relaxed Model Parameters for Robust UAV
Trajectory Optimization | [
"math.OC",
"cs.RO",
"cs.SY",
"eess.SY"
] | Online trajectory optimization and optimal control methods are crucial for enabling sustainable unmanned aerial vehicle (UAV) services, such as agriculture, environmental monitoring, and transportation, where available actuation and energy are limited. However, optimal controllers are highly sensitive to model mismatch... | {
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2411.10943 | Generalist Virtual Agents: A Survey on Autonomous Agents Across Digital
Platforms | [
"cs.MA"
] | In this paper, we introduce the Generalist Virtual Agent (GVA), an autonomous entity engineered to function across diverse digital platforms and environments, assisting users by executing a variety of tasks. This survey delves into the evolution of GVAs, tracing their progress from early intelligent assistants to conte... | {
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2411.10945 | Anomaly Detection for People with Visual Impairments Using an Egocentric
360-Degree Camera | [
"cs.CV"
] | Recent advancements in computer vision have led to a renewed interest in developing assistive technologies for individuals with visual impairments. Although extensive research has been conducted in the field of computer vision-based assistive technologies, most of the focus has been on understanding contexts in images,... | {
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2411.10947 | Direct and Explicit 3D Generation from a Single Image | [
"cs.CV"
] | Current image-to-3D approaches suffer from high computational costs and lack scalability for high-resolution outputs. In contrast, we introduce a novel framework to directly generate explicit surface geometry and texture using multi-view 2D depth and RGB images along with 3D Gaussian features using a repurposed Stable ... | {
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2411.10948 | Towards Accurate and Efficient Sub-8-Bit Integer Training | [
"cs.LG",
"cs.CV"
] | Neural network training is a memory- and compute-intensive task. Quantization, which enables low-bitwidth formats in training, can significantly mitigate the workload. To reduce quantization error, recent methods have developed new data formats and additional pre-processing operations on quantizers. However, it remains... | {
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2411.10950 | Understanding Multimodal LLMs: the Mechanistic Interpretability of Llava
in Visual Question Answering | [
"cs.CL"
] | Understanding the mechanisms behind Large Language Models (LLMs) is crucial for designing improved models and strategies. While recent studies have yielded valuable insights into the mechanisms of textual LLMs, the mechanisms of Multi-modal Large Language Models (MLLMs) remain underexplored. In this paper, we apply mec... | {
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2411.10951 | TSFormer: A Robust Framework for Efficient UHD Image Restoration | [
"cs.CV"
] | Ultra-high-definition (UHD) image restoration is vital for applications demanding exceptional visual fidelity, yet existing methods often face a trade-off between restoration quality and efficiency, limiting their practical deployment. In this paper, we propose TSFormer, an all-in-one framework that integrates \textbf{... | {
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2411.10954 | Dialectal Toxicity Detection: Evaluating LLM-as-a-Judge Consistency
Across Language Varieties | [
"cs.CL"
] | There has been little systematic study on how dialectal differences affect toxicity detection by modern LLMs. Furthermore, although using LLMs as evaluators ("LLM-as-a-judge") is a growing research area, their sensitivity to dialectal nuances is still underexplored and requires more focused attention. In this paper, we... | {
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2411.10955 | A Topic-aware Comparable Corpus of Chinese Variations | [
"cs.CL"
] | This study aims to fill the gap by constructing a topic-aware comparable corpus of Mainland Chinese Mandarin and Taiwanese Mandarin from the social media in Mainland China and Taiwan, respectively. Using Dcard for Taiwanese Mandarin and Sina Weibo for Mainland Chinese, we create a comparable corpus that updates regular... | {
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2411.10956 | IVE: Enhanced Probabilistic Forecasting of Intraday Volume Ratio with
Transformers | [
"q-fin.CP",
"cs.CE"
] | This paper presents a new approach to volume ratio prediction in financial markets, specifically targeting the execution of Volume-Weighted Average Price (VWAP) strategies. Recognizing the importance of accurate volume profile forecasting, our research leverages the Transformer architecture to predict intraday volume r... | {
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2411.10957 | IMPaCT GNN: Imposing invariance with Message Passing in Chronological
split Temporal Graphs | [
"cs.LG",
"cs.AI",
"stat.ML"
] | This paper addresses domain adaptation challenges in graph data resulting from chronological splits. In a transductive graph learning setting, where each node is associated with a timestamp, we focus on the task of Semi-Supervised Node Classification (SSNC), aiming to classify recent nodes using labels of past nodes. T... | {
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2411.10958 | SageAttention2: Efficient Attention with Thorough Outlier Smoothing and
Per-thread INT4 Quantization | [
"cs.LG",
"cs.AI",
"cs.CV",
"cs.NE",
"cs.PF"
] | Although quantization for linear layers has been widely used, its application to accelerate the attention process remains limited. To further enhance the efficiency of attention computation compared to SageAttention while maintaining precision, we propose SageAttention2, which utilizes significantly faster 4-bit matrix... | {
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2411.10959 | Program Evaluation with Remotely Sensed Outcomes | [
"econ.EM",
"cs.LG",
"math.ST",
"stat.AP",
"stat.ME",
"stat.ML",
"stat.TH"
] | While traditional program evaluations typically rely on surveys to measure outcomes, certain economic outcomes such as living standards or environmental quality may be infeasible or costly to collect. As a result, recent empirical work estimates treatment effects using remotely sensed variables (RSVs), such mobile phon... | {
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2411.10960 | Beamforming Design and Multi-User Scheduling in Transmissive RIS Enabled
Distributed Cooperative ISAC Networks with RSMA | [
"cs.IT",
"math.IT"
] | In this paper, we propose a novel transmissive reconfigurable intelligent surface (TRIS) transceiver-empowered distributed cooperative integrated sensing and communication (ISAC) network to enhance coverage as well as to enhance wireless environment understanding. Based on the network requirements, the users are catego... | {
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2411.10961 | Map-Free Trajectory Prediction with Map Distillation and Hierarchical
Encoding | [
"cs.CV"
] | Reliable motion forecasting of surrounding agents is essential for ensuring the safe operation of autonomous vehicles. Many existing trajectory prediction methods rely heavily on high-definition (HD) maps as strong driving priors. However, the availability and accuracy of these priors are not guaranteed due to substant... | {
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2411.10962 | V2X-Radar: A Multi-modal Dataset with 4D Radar for Cooperative
Perception | [
"cs.CV"
] | Modern autonomous vehicle perception systems often struggle with occlusions and limited perception range. Previous studies have demonstrated the effectiveness of cooperative perception in extending the perception range and overcoming occlusions, thereby improving the safety of autonomous driving. In recent years, a ser... | {
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2411.10965 | Immersion of General Nonlinear Systems Into State-Affine Ones for the
Design of Generalized Parameter Estimation-Based Observers: A Simple
Algebraic Procedure | [
"eess.SY",
"cs.SY"
] | Generalized parameter estimation-based observers have proven very successful to deal with systems described in state-affine form. In this paper, we enlarge the domain of applicability of this method proposing an algebraic procedure to immerse} an $n$-dimensional general nonlinear system into and $n_z$-dimensional syste... | {
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2411.10966 | Avian-Inspired High-Precision Tracking Control for Aerial Manipulators | [
"cs.RO"
] | Aerial manipulators, composed of multirotors and robotic arms, have a structure and function highly reminiscent of avian species. This paper studies the tracking control problem for aerial manipulators. This paper studies the tracking control problem for aerial manipulators. We propose an avian-inspired aerial manipula... | {
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2411.10974 | CropNav: a Framework for Autonomous Navigation in Real Farms | [
"cs.RO"
] | Small robots that can operate under the plant canopy can enable new possibilities in agriculture. However, unlike larger autonomous tractors, autonomous navigation for such under canopy robots remains an open challenge because Global Navigation Satellite System (GNSS) is unreliable under the plant canopy. We present a ... | {
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2411.10979 | VidComposition: Can MLLMs Analyze Compositions in Compiled Videos? | [
"cs.CV",
"cs.AI"
] | The advancement of Multimodal Large Language Models (MLLMs) has enabled significant progress in multimodal understanding, expanding their capacity to analyze video content. However, existing evaluation benchmarks for MLLMs primarily focus on abstract video comprehension, lacking a detailed assessment of their ability t... | {
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2411.10982 | Towards a framework on tabular synthetic data generation: a minimalist
approach: theory, use cases, and limitations | [
"cs.LG",
"stat.ME",
"stat.ML"
] | We propose and study a minimalist approach towards synthetic tabular data generation. The model consists of a minimalistic unsupervised SparsePCA encoder (with contingent clustering step or log transformation to handle nonlinearity) and XGboost decoder which is SOTA for structured data regression and classification tas... | {
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2411.10983 | Framework for developing and evaluating ethical collaboration between
expert and machine | [
"cs.CV"
] | Precision medicine is a promising approach for accessible disease diagnosis and personalized intervention planning in high-mortality diseases such as coronary artery disease (CAD), drug-resistant epilepsy (DRE), and chronic illnesses like Type 1 diabetes (T1D). By leveraging artificial intelligence (AI), precision medi... | {
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2411.10988 | AppSign: Multi-level Approximate Computing for Real-Time Traffic Sign
Recognition in Autonomous Vehicles | [
"cs.AR",
"cs.CV"
] | This paper presents a multi-level approximate computing approach for real-time traffic sign recognition in autonomous vehicles called AppSign. Since autonomous vehicles are real-time systems, they must gather environmental information and process them instantaneously to respond properly. However, due to the limited res... | {
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2411.10991 | Modulating Reservoir Dynamics via Reinforcement Learning for Efficient
Robot Skill Synthesis | [
"cs.RO",
"cs.AI"
] | A random recurrent neural network, called a reservoir, can be used to learn robot movements conditioned on context inputs that encode task goals. The Learning is achieved by mapping the random dynamics of the reservoir modulated by context to desired trajectories via linear regression. This makes the reservoir computin... | {
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2411.10997 | Beyond Normal: Learning Spatial Density Models of Node Mobility | [
"cs.NI",
"cs.LG",
"stat.ML"
] | Learning models of complex spatial density functions, representing the steady-state density of mobile nodes moving on a two-dimensional terrain, can assist in network design and optimization problems, e.g., by accelerating the computation of the density function during a parameter sweep. We address the question of appl... | {
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2411.10998 | Image-Based RKPM for Accessing Failure Mechanisms in Composite Materials | [
"cs.CE"
] | Stress distributions and the corresponding fracture patterns and evolutions in the microstructures strongly influence the load-carrying capabilities of composite structures. This work introduces an enhanced phase-field fracture model incorporating interface decohesion to simulate fracture propagation and interactions a... | {
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2411.11002 | Unveiling the Hidden: Online Vectorized HD Map Construction with
Clip-Level Token Interaction and Propagation | [
"cs.CV",
"cs.AI"
] | Predicting and constructing road geometric information (e.g., lane lines, road markers) is a crucial task for safe autonomous driving, while such static map elements can be repeatedly occluded by various dynamic objects on the road. Recent studies have shown significantly improved vectorized high-definition (HD) map co... | {
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2411.11003 | TeG: Temporal-Granularity Method for Anomaly Detection with Attention in
Smart City Surveillance | [
"cs.CV"
] | Anomaly detection in video surveillance has recently gained interest from the research community. Temporal duration of anomalies vary within video streams, leading to complications in learning the temporal dynamics of specific events. This paper presents a temporal-granularity method for an anomaly detection model (TeG... | {
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2411.11004 | EROAM: Event-based Camera Rotational Odometry and Mapping in Real-time | [
"cs.CV",
"cs.RO"
] | This paper presents EROAM, a novel event-based rotational odometry and mapping system that achieves real-time, accurate camera rotation estimation. Unlike existing approaches that rely on event generation models or contrast maximization, EROAM employs a spherical event representation by projecting events onto a unit sp... | {
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2411.11006 | BackdoorMBTI: A Backdoor Learning Multimodal Benchmark Tool Kit for
Backdoor Defense Evaluation | [
"cs.CR",
"cs.AI"
] | We introduce BackdoorMBTI, the first backdoor learning toolkit and benchmark designed for multimodal evaluation across three representative modalities from eleven commonly used datasets. BackdoorMBTI provides a systematic backdoor learning pipeline, encompassing data processing, data poisoning, backdoor training, and e... | {
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2411.11011 | CCi-YOLOv8n: Enhanced Fire Detection with CARAFE and Context-Guided
Modules | [
"cs.CV"
] | Fire incidents in urban and forested areas pose serious threats,underscoring the need for more effective detection technologies. To address these challenges, we present CCi-YOLOv8n, an enhanced YOLOv8 model with targeted improvements for detecting small fires and smoke. The model integrates the CARAFE up-sampling opera... | {
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} |
2411.11016 | Time Step Generating: A Universal Synthesized Deepfake Image Detector | [
"cs.CV",
"cs.AI"
] | Currently, high-fidelity text-to-image models are developed in an accelerating pace. Among them, Diffusion Models have led to a remarkable improvement in the quality of image generation, making it vary challenging to distinguish between real and synthesized images. It simultaneously raises serious concerns regarding pr... | {
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} |
2411.11020 | Training a Label-Noise-Resistant GNN with Reduced Complexity | [
"cs.LG",
"cs.SI"
] | Graph Neural Networks (GNNs) have been widely employed for semi-supervised node classification tasks on graphs. However, the performance of GNNs is significantly affected by label noise, that is, a small amount of incorrectly labeled nodes can substantially misguide model training. Mainstream solutions define node clas... | {
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} |
2411.11024 | VeGaS: Video Gaussian Splatting | [
"cs.CV"
] | Implicit Neural Representations (INRs) employ neural networks to approximate discrete data as continuous functions. In the context of video data, such models can be utilized to transform the coordinates of pixel locations along with frame occurrence times (or indices) into RGB color values. Although INRs facilitate eff... | {
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} |
2411.11027 | BianCang: A Traditional Chinese Medicine Large Language Model | [
"cs.CL",
"cs.AI"
] | The rise of large language models (LLMs) has driven significant progress in medical applications, including traditional Chinese medicine (TCM). However, current medical LLMs struggle with TCM diagnosis and syndrome differentiation due to substantial differences between TCM and modern medical theory, and the scarcity of... | {
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} |
2411.11029 | Wafer Map Defect Classification Using Autoencoder-Based Data
Augmentation and Convolutional Neural Network | [
"cs.CV",
"cs.AI",
"eess.IV"
] | In semiconductor manufacturing, wafer defect maps (WDMs) play a crucial role in diagnosing issues and enhancing process yields by revealing critical defect patterns. However, accurately categorizing WDM defects presents significant challenges due to noisy data, unbalanced defect classes, and the complexity of failure m... | {
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} |
2411.11038 | EfQAT: An Efficient Framework for Quantization-Aware Training | [
"cs.LG"
] | Quantization-aware training (QAT) schemes have been shown to achieve near-full precision accuracy. They accomplish this by training a quantized model for multiple epochs. This is computationally expensive, mainly because of the full precision backward pass. On the other hand, post-training quantization (PTQ) schemes do... | {
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2411.11039 | FedUHB: Accelerating Federated Unlearning via Polyak Heavy Ball Method | [
"cs.LG",
"cs.DC"
] | Federated learning facilitates collaborative machine learning, enabling multiple participants to collectively develop a shared model while preserving the privacy of individual data. The growing importance of the "right to be forgotten" calls for effective mechanisms to facilitate data removal upon request. In response,... | {
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} |
2411.11044 | Efficient Federated Unlearning with Adaptive Differential Privacy
Preservation | [
"cs.CR",
"cs.LG"
] | Federated unlearning (FU) offers a promising solution to effectively address the need to erase the impact of specific clients' data on the global model in federated learning (FL), thereby granting individuals the ``Right to be Forgotten". The most straightforward approach to achieve unlearning is to train the model fro... | {
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} |
2411.11045 | StableV2V: Stablizing Shape Consistency in Video-to-Video Editing | [
"cs.CV"
] | Recent advancements of generative AI have significantly promoted content creation and editing, where prevailing studies further extend this exciting progress to video editing. In doing so, these studies mainly transfer the inherent motion patterns from the source videos to the edited ones, where results with inferior c... | {
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} |
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