id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2412.06127 | HSDA: High-frequency Shuffle Data Augmentation for Bird's-Eye-View Map
Segmentation | [
"cs.CV"
] | Autonomous driving has garnered significant attention in recent research, and Bird's-Eye-View (BEV) map segmentation plays a vital role in the field, providing the basis for safe and reliable operation. While data augmentation is a commonly used technique for improving BEV map segmentation networks, existing approaches... | {
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2412.06129 | GCUNet: A GNN-Based Contextual Learning Network for Tertiary Lymphoid
Structure Semantic Segmentation in Whole Slide Image | [
"cs.CV"
] | We focus on tertiary lymphoid structure (TLS) semantic segmentation in whole slide image (WSI). Unlike TLS binary segmentation, TLS semantic segmentation identifies boundaries and maturity, which requires integrating contextual information to discover discriminative features. Due to the extensive scale of WSI (e.g., 10... | {
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2412.06134 | Evaluating and Mitigating Social Bias for Large Language Models in
Open-ended Settings | [
"cs.CL"
] | Current social bias benchmarks for Large Language Models (LLMs) primarily rely on pre-defined question formats like multiple-choice, limiting their ability to reflect the complexity and open-ended nature of real-world interactions. To address this gap, we extend an existing BBQ dataset introduced by incorporating fill-... | {
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2412.06135 | A CT Image Denoising Method Based on Projection Domain Feature | [
"eess.IV",
"cs.CV"
] | In order to improve image quality of projection in industrial applications, generally, a standard method is to increase the current or exposure time, which might cause overexposure of detector units in areas of thin objects or backgrounds. Increasing the projection sampling is a better method to address the issue, but ... | {
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2412.06136 | AIDE: Task-Specific Fine Tuning with Attribute Guided Multi-Hop Data
Expansion | [
"cs.CL"
] | Fine-tuning large language models (LLMs) for specific tasks requires high-quality, diverse training data relevant to the task. Recent research has leveraged LLMs to synthesize training data, but existing approaches either depend on large seed datasets or struggle to ensure both task relevance and data diversity in the ... | {
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2412.06138 | SGIA: Enhancing Fine-Grained Visual Classification with Sequence
Generative Image Augmentation | [
"cs.CV"
] | In Fine-Grained Visual Classification (FGVC), distinguishing highly similar subcategories remains a formidable challenge, often necessitating datasets with extensive variability. The acquisition and annotation of such FGVC datasets are notably difficult and costly, demanding specialized knowledge to identify subtle dis... | {
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2412.06139 | Bounded Exploration with World Model Uncertainty in Soft Actor-Critic
Reinforcement Learning Algorithm | [
"cs.LG",
"cs.SY",
"eess.SY"
] | One of the bottlenecks preventing Deep Reinforcement Learning algorithms (DRL) from real-world applications is how to explore the environment and collect informative transitions efficiently. The present paper describes bounded exploration, a novel exploration method that integrates both 'soft' and intrinsic motivation ... | {
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2412.06140 | Learnable Evolutionary Multi-Objective Combinatorial Optimization via
Sequence-to-Sequence Model | [
"cs.NE"
] | Recent advances in learnable evolutionary algorithms have demonstrated the importance of leveraging population distribution information and historical evolutionary trajectories. While significant progress has been made in continuous optimization domains, combinatorial optimization problems remain challenging due to the... | {
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2412.06141 | MMedPO: Aligning Medical Vision-Language Models with Clinical-Aware
Multimodal Preference Optimization | [
"cs.CV",
"cs.AI",
"cs.CL",
"cs.LG"
] | The advancement of Large Vision-Language Models (LVLMs) has propelled their application in the medical field. However, Medical LVLMs (Med-LVLMs) encounter factuality challenges due to modality misalignment, where the models prioritize textual knowledge over visual input, leading to hallucinations that contradict inform... | {
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2412.06142 | AgentAlign: Misalignment-Adapted Multi-Agent Perception for Resilient
Inter-Agent Sensor Correlations | [
"cs.CV",
"cs.RO"
] | Cooperative perception has attracted wide attention given its capability to leverage shared information across connected automated vehicles (CAVs) and smart infrastructures to address sensing occlusion and range limitation issues. However, existing research overlooks the fragile multi-sensor correlations in multi-agent... | {
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2412.06143 | Precise, Fast, and Low-cost Concept Erasure in Value Space: Orthogonal
Complement Matters | [
"cs.CV",
"cs.AI"
] | The success of text-to-image generation enabled by diffuion models has imposed an urgent need to erase unwanted concepts, e.g., copyrighted, offensive, and unsafe ones, from the pre-trained models in a precise, timely, and low-cost manner. The twofold demand of concept erasure requires a precise removal of the target c... | {
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2412.06144 | Hate Speech According to the Law: An Analysis for Effective Detection | [
"cs.CL"
] | The issue of hate speech extends beyond the confines of the online realm. It is a problem with real-life repercussions, prompting most nations to formulate legal frameworks that classify hate speech as a punishable offence. These legal frameworks differ from one country to another, contributing to the big chaos that on... | {
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2412.06146 | Homogeneous Dynamics Space for Heterogeneous Humans | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Analyses of human motion kinematics have achieved tremendous advances. However, the production mechanism, known as human dynamics, is still undercovered. In this paper, we aim to push data-driven human dynamics understanding forward. We identify a major obstacle to this as the heterogeneity of existing human motion und... | {
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2412.06147 | Advancements in Machine Learning and Deep Learning for Early Detection
and Management of Mental Health Disorder | [
"cs.LG",
"cs.ET"
] | For the early identification, diagnosis, and treatment of mental health illnesses, the integration of deep learning (DL) and machine learning (ML) has started playing a significant role. By evaluating complex data from imaging, genetics, and behavioral assessments, these technologies have the potential to significantly... | {
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2412.06148 | The Computational Limits of State-Space Models and Mamba via the Lens of
Circuit Complexity | [
"cs.CC",
"cs.AI",
"cs.CL",
"cs.LG"
] | In this paper, we analyze the computational limitations of Mamba and State-space Models (SSMs) by using the circuit complexity framework. Despite Mamba's stateful design and recent attention as a strong candidate to outperform Transformers, we have demonstrated that both Mamba and SSMs with $\mathrm{poly}(n)$-precision... | {
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2412.06149 | An Effective and Resilient Backdoor Attack Framework against Deep Neural
Networks and Vision Transformers | [
"cs.CV",
"cs.CR"
] | Recent studies have revealed the vulnerability of Deep Neural Network (DNN) models to backdoor attacks. However, existing backdoor attacks arbitrarily set the trigger mask or use a randomly selected trigger, which restricts the effectiveness and robustness of the generated backdoor triggers. In this paper, we propose a... | {
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2412.06153 | A Hyperdimensional One Place Signature to Represent Them All: Stackable
Descriptors For Visual Place Recognition | [
"cs.CV"
] | Visual Place Recognition (VPR) enables coarse localization by comparing query images to a reference database of geo-tagged images. Recent breakthroughs in deep learning architectures and training regimes have led to methods with improved robustness to factors like environment appearance change, but with the downside th... | {
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2412.06154 | MoSH: Modeling Multi-Objective Tradeoffs with Soft and Hard Bounds | [
"cs.LG",
"cs.AI"
] | Countless science and engineering applications in multi-objective optimization (MOO) necessitate that decision-makers (DMs) select a Pareto-optimal solution which aligns with their preferences. Evaluating individual solutions is often expensive, necessitating cost-sensitive optimization techniques. Due to competing obj... | {
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2412.06158 | Is the neural tangent kernel of PINNs deep learning general partial
differential equations always convergent ? | [
"stat.ML",
"cs.LG",
"math-ph",
"math.MP",
"nlin.PS",
"physics.comp-ph"
] | In this paper, we study the neural tangent kernel (NTK) for general partial differential equations (PDEs) based on physics-informed neural networks (PINNs). As we all know, the training of an artificial neural network can be converted to the evolution of NTK. We analyze the initialization of NTK and the convergence con... | {
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2412.06160 | Obstacle-aware Gaussian Process Regression | [
"cs.LG",
"math.PR",
"stat.ML"
] | Obstacle-aware trajectory navigation is crucial for many systems. For example, in real-world navigation tasks, an agent must avoid obstacles, such as furniture in a room, while planning a trajectory. Gaussian Process (GP) regression, in its current form, fits a curve to a set of data pairs, with each pair consisting of... | {
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2412.06162 | Query-Efficient Planning with Language Models | [
"cs.AI",
"cs.CL"
] | Planning in complex environments requires an agent to efficiently query a world model to find a feasible sequence of actions from start to goal. Recent work has shown that Large Language Models (LLMs), with their rich prior knowledge and reasoning capabilities, can potentially help with planning by searching over promi... | {
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2412.06163 | ASGDiffusion: Parallel High-Resolution Generation with Asynchronous
Structure Guidance | [
"cs.CV"
] | Training-free high-resolution (HR) image generation has garnered significant attention due to the high costs of training large diffusion models. Most existing methods begin by reconstructing the overall structure and then proceed to refine the local details. Despite their advancements, they still face issues with repet... | {
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2412.06165 | Conservative Contextual Bandits: Beyond Linear Representations | [
"cs.LG",
"cs.AI",
"stat.ML"
] | Conservative Contextual Bandits (CCBs) address safety in sequential decision making by requiring that an agent's policy, along with minimizing regret, also satisfies a safety constraint: the performance is not worse than a baseline policy (e.g., the policy that the company has in production) by more than $(1+\alpha)$ f... | {
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2412.06166 | MVD: A Multi-Lingual Software Vulnerability Detection Framework | [
"cs.SE",
"cs.CR",
"cs.LG"
] | Software vulnerabilities can result in catastrophic cyberattacks that increasingly threaten business operations. Consequently, ensuring the safety of software systems has become a paramount concern for both private and public sectors. Recent literature has witnessed increasing exploration of learning-based approaches f... | {
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2412.06167 | ACQ: A Unified Framework for Automated Programmatic Creativity in Online
Advertising | [
"cs.AI"
] | In online advertising, the demand-side platform (a.k.a. DSP) enables advertisers to create different ad creatives for real-time bidding. Intuitively, advertisers tend to create more ad creatives for a single photo to increase the probability of participating in bidding, further enhancing their ad cost. From the perspec... | {
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2412.06168 | Out-of-Distribution Detection with Overlap Index | [
"cs.LG",
"stat.ML"
] | Out-of-distribution (OOD) detection is crucial for the deployment of machine learning models in the open world. While existing OOD detectors are effective in identifying OOD samples that deviate significantly from in-distribution (ID) data, they often come with trade-offs. For instance, deep OOD detectors usually suffe... | {
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2412.06171 | Holmes-VAU: Towards Long-term Video Anomaly Understanding at Any
Granularity | [
"cs.CV"
] | How can we enable models to comprehend video anomalies occurring over varying temporal scales and contexts? Traditional Video Anomaly Understanding (VAU) methods focus on frame-level anomaly prediction, often missing the interpretability of complex and diverse real-world anomalies. Recent multimodal approaches leverage... | {
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2412.06172 | Robust Noisy Correspondence Learning via Self-Drop and Dual-Weight | [
"cs.CV"
] | Many researchers collect data from the internet through crowd-sourcing or web crawling to alleviate the data-hungry challenge associated with cross-modal matching. Although such practice does not require expensive annotations, it inevitably introduces mismatched pairs and results in a noisy correspondence problem. Curr... | {
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2412.06173 | Revisiting the Necessity of Graph Learning and Common Graph Benchmarks | [
"cs.LG",
"stat.ML"
] | Graph machine learning has enjoyed a meteoric rise in popularity since the introduction of deep learning in graph contexts. This is no surprise due to the ubiquity of graph data in large scale industrial settings. Tacitly assumed in all graph learning tasks is the separation of the graph structure and node features: no... | {
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2412.06174 | One-shot Human Motion Transfer via Occlusion-Robust Flow Prediction and
Neural Texturing | [
"cs.CV"
] | Human motion transfer aims at animating a static source image with a driving video. While recent advances in one-shot human motion transfer have led to significant improvement in results, it remains challenging for methods with 2D body landmarks, skeleton and semantic mask to accurately capture correspondences between ... | {
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2412.06176 | AlphaVerus: Bootstrapping Formally Verified Code Generation through
Self-Improving Translation and Treefinement | [
"cs.LG",
"cs.AI"
] | Automated code generation with large language models has gained significant traction, but there remains no guarantee on the correctness of generated code. We aim to use formal verification to provide mathematical guarantees that the generated code is correct. However, generating formally verified code with LLMs is hind... | {
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2412.06177 | Quantum Algorithms for Optimal Power Flow | [
"quant-ph",
"cs.SY",
"eess.SY",
"math.OC"
] | This paper explores the use of quantum computing, specifically the use of HHL and VQLS algorithms, to solve optimal power flow problem in electrical grids. We investigate the effectiveness of these quantum algorithms in comparison to classical methods. The simulation results presented here which substantially improve t... | {
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2412.06178 | Deep Unfolding Beamforming and Power Control Designs for Multi-Port
Matching Networks | [
"cs.IT",
"eess.SP",
"math.IT"
] | The key technologies of sixth generation (6G), such as ultra-massive multiple-input multiple-output (MIMO), enable intricate interactions between antennas and wireless propagation environments. As a result, it becomes necessary to develop joint models that encompass both antennas and wireless propagation channels. To a... | {
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2412.06179 | Annotations for Exploring Food Tweets From Multiple Aspects | [
"cs.CL",
"cs.AI"
] | This research builds upon the Latvian Twitter Eater Corpus (LTEC), which is focused on the narrow domain of tweets related to food, drinks, eating and drinking. LTEC has been collected for more than 12 years and reaching almost 3 million tweets with the basic information as well as extended automatically and manually a... | {
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2412.06181 | Enhancing Adversarial Resistance in LLMs with Recursion | [
"cs.CR",
"cs.AI"
] | The increasing integration of Large Language Models (LLMs) into society necessitates robust defenses against vulnerabilities from jailbreaking and adversarial prompts. This project proposes a recursive framework for enhancing the resistance of LLMs to manipulation through the use of prompt simplification techniques. By... | {
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2412.06182 | Towards Long Video Understanding via Fine-detailed Video Story
Generation | [
"cs.CV"
] | Long video understanding has become a critical task in computer vision, driving advancements across numerous applications from surveillance to content retrieval. Existing video understanding methods suffer from two challenges when dealing with long video understanding: intricate long-context relationship modeling and i... | {
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2412.06184 | Evaluating Model Perception of Color Illusions in Photorealistic Scenes | [
"cs.CV"
] | We study the perception of color illusions by vision-language models. Color illusion, where a person's visual system perceives color differently from actual color, is well-studied in human vision. However, it remains underexplored whether vision-language models (VLMs), trained on large-scale human data, exhibit similar... | {
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2412.06186 | Input-to-State Stability of Newton Methods in Nash Equilibrium Problems
with Applications to Game-Theoretic Model Predictive Control | [
"eess.SY",
"cs.SY"
] | We prove input-to-state stability (ISS) of perturbed Newton methods for generalized equations arising from Nash equilibrium (NE) and generalized NE (GNE) problems. This ISS property allows the use of inexact computation in equilibrium-seeking to enable fast solution tracking in dynamic systems and plays a critical role... | {
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2412.06189 | Fast Matrix Multiplication meets the Submodular Width | [
"cs.DB",
"cs.CC",
"cs.IT",
"math.IT"
] | One fundamental question in database theory is the following: Given a Boolean conjunctive query Q, what is the best complexity for computing the answer to Q in terms of the input database size N? When restricted to the class of combinatorial algorithms, it is known that the best known complexity for any query Q is capt... | {
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2412.06190 | Category-Adaptive Cross-Modal Semantic Refinement and Transfer for
Open-Vocabulary Multi-Label Recognition | [
"cs.CV"
] | Benefiting from the generalization capability of CLIP, recent vision language pre-training (VLP) models have demonstrated an impressive ability to capture virtually any visual concept in daily images. However, due to the presence of unseen categories in open-vocabulary settings, existing algorithms struggle to effectiv... | {
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2412.06191 | Event fields: Capturing light fields at high speed, resolution, and
dynamic range | [
"cs.CV"
] | Event cameras, which feature pixels that independently respond to changes in brightness, are becoming increasingly popular in high-speed applications due to their lower latency, reduced bandwidth requirements, and enhanced dynamic range compared to traditional frame-based cameras. Numerous imaging and vision techniques... | {
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2412.06192 | PoLaRIS Dataset: A Maritime Object Detection and Tracking Dataset in
Pohang Canal | [
"cs.RO"
] | Maritime environments often present hazardous situations due to factors such as moving ships or buoys, which become obstacles under the influence of waves. In such challenging conditions, the ability to detect and track potentially hazardous objects is critical for the safe navigation of marine robots. To address the s... | {
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2412.06195 | Adaptive Resolution Residual Networks -- Generalizing Across Resolutions
Easily and Efficiently | [
"cs.LG",
"cs.CV"
] | The majority of signal data captured in the real world uses numerous sensors with different resolutions. In practice, however, most deep learning architectures are fixed-resolution; they consider a single resolution at training time and inference time. This is convenient to implement but fails to fully take advantage o... | {
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2412.06197 | Modeling, Planning, and Control for Hybrid UAV Transition Maneuvers | [
"cs.RO",
"cs.SY",
"eess.SY"
] | Small unmanned aerial vehicles (UAVs) have become standard tools in reconnaissance and surveying for both civilian and defense applications. In the future, UAVs will likely play a pivotal role in autonomous package delivery, but current multi-rotor candidates suffer from poor energy efficiency leading to insufficient e... | {
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2412.06198 | SparseAccelerate: Efficient Long-Context Inference for Mid-Range GPUs | [
"cs.CL"
] | As Large Language Models (LLMs) scale to longer context windows, the computational cost of attention mechanisms, which traditionally grows quadratically with input length, presents a critical challenge for real-time and memory-constrained deployments. Existing sparse attention techniques have sought to reduce this comp... | {
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2412.06201 | Size-Variable Virtual Try-On with Physical Clothes Size | [
"cs.CV",
"cs.GR"
] | This paper addresses a new virtual try-on problem of fitting any size of clothes to a reference person in the image domain. While previous image-based virtual try-on methods can produce highly natural try-on images, these methods fit the clothes on the person without considering the relative relationship between the ph... | {
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2412.06203 | Applications of Positive Unlabeled (PU) and Negative Unlabeled (NU)
Learning in Cybersecurity | [
"cs.CR",
"cs.LG"
] | This paper explores the relatively underexplored application of Positive Unlabeled (PU) Learning and Negative Unlabeled (NU) Learning in the cybersecurity domain. While these semi-supervised learning methods have been applied successfully in fields like medicine and marketing, their potential in cybersecurity remains l... | {
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2412.06204 | You KAN Do It in a Single Shot: Plug-and-Play Methods with
Single-Instance Priors | [
"cs.CV"
] | The use of Plug-and-Play (PnP) methods has become a central approach for solving inverse problems, with denoisers serving as regularising priors that guide optimisation towards a clean solution. In this work, we introduce KAN-PnP, an optimisation framework that incorporates Kolmogorov-Arnold Networks (KANs) as denoiser... | {
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2412.06205 | Applying Machine Learning Tools for Urban Resilience Against Floods | [
"cs.LG"
] | Floods are among the most prevalent and destructive natural disasters, often leading to severe social and economic impacts in urban areas due to the high concentration of assets and population density. In Iran, particularly in Tehran, recurring flood events underscore the urgent need for robust urban resilience strateg... | {
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2412.06206 | SiReRAG: Indexing Similar and Related Information for Multihop Reasoning | [
"cs.CL",
"cs.AI"
] | Indexing is an important step towards strong performance in retrieval-augmented generation (RAG) systems. However, existing methods organize data based on either semantic similarity (similarity) or related information (relatedness), but do not cover both perspectives comprehensively. Our analysis reveals that modeling ... | {
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2412.06207 | Skill-Enhanced Reinforcement Learning Acceleration from Demonstrations | [
"cs.LG",
"cs.AI"
] | Learning from Demonstration (LfD) aims to facilitate rapid Reinforcement Learning (RL) by leveraging expert demonstrations to pre-train the RL agent. However, the limited availability of expert demonstration data often hinders its ability to effectively aid downstream RL learning. To address this problem, we propose a ... | {
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2412.06208 | Pilot-guided Multimodal Semantic Communication for Audio-Visual Event
Localization | [
"cs.SD",
"cs.CV",
"cs.MM",
"eess.AS"
] | Multimodal semantic communication, which integrates various data modalities such as text, images, and audio, significantly enhances communication efficiency and reliability. It has broad application prospects in fields such as artificial intelligence, autonomous driving, and smart homes. However, current research prima... | {
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2412.06209 | Sound2Vision: Generating Diverse Visuals from Audio through Cross-Modal
Latent Alignment | [
"cs.CV",
"cs.MM",
"cs.SD",
"eess.AS"
] | How does audio describe the world around us? In this work, we propose a method for generating images of visual scenes from diverse in-the-wild sounds. This cross-modal generation task is challenging due to the significant information gap between auditory and visual signals. We address this challenge by designing a mode... | {
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2412.06210 | H-FedSN: Personalized Sparse Networks for Efficient and Accurate
Hierarchical Federated Learning for IoT Applications | [
"cs.LG"
] | The proliferation of Internet of Things (IoT) has increased interest in federated learning (FL) for privacy-preserving distributed data utilization. However, traditional two-tier FL architectures inadequately adapt to multi-tier IoT environments. While Hierarchical Federated Learning (HFL) improves practicality in mult... | {
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2412.06211 | MSCrackMamba: Leveraging Vision Mamba for Crack Detection in Fused
Multispectral Imagery | [
"cs.CV",
"cs.AI",
"cs.MM"
] | Crack detection is a critical task in structural health monitoring, aimed at assessing the structural integrity of bridges, buildings, and roads to prevent potential failures. Vision-based crack detection has become the mainstream approach due to its ease of implementation and effectiveness. Fusing infrared (IR) channe... | {
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2412.06212 | A Self-guided Multimodal Approach to Enhancing Graph Representation
Learning for Alzheimer's Diseases | [
"cs.LG",
"cs.AI"
] | Graph neural networks (GNNs) are powerful machine learning models designed to handle irregularly structured data. However, their generic design often proves inadequate for analyzing brain connectomes in Alzheimer's Disease (AD), highlighting the need to incorporate domain knowledge for optimal performance. Infusing AD-... | {
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2412.06215 | A Real-Time Defense Against Object Vanishing Adversarial Patch Attacks
for Object Detection in Autonomous Vehicles | [
"cs.CV",
"cs.AI"
] | Autonomous vehicles (AVs) increasingly use DNN-based object detection models in vision-based perception. Correct detection and classification of obstacles is critical to ensure safe, trustworthy driving decisions. Adversarial patches aim to fool a DNN with intentionally generated patterns concentrated in a localized re... | {
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2412.06216 | Top-r Influential Community Search in Bipartite Graphs | [
"cs.SI"
] | Community search over bipartite graphs is a fundamental problem, and finding influential communities has attracted significant attention. However, all existing studies have used the minimum weight of vertices as the influence of communities. This leads to an inaccurate assessment of real influence in graphs where there... | {
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2412.06219 | Data Free Backdoor Attacks | [
"cs.CR",
"cs.AI",
"cs.CV"
] | Backdoor attacks aim to inject a backdoor into a classifier such that it predicts any input with an attacker-chosen backdoor trigger as an attacker-chosen target class. Existing backdoor attacks require either retraining the classifier with some clean data or modifying the model's architecture. As a result, they are 1)... | {
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2412.06220 | Discrete-Time Distribution Steering using Monte Carlo Tree Search | [
"eess.SY",
"cs.RO",
"cs.SY"
] | Optimal control problems with state distribution constraints have attracted interest for their expressivity, but solutions rely on linear approximations. We approach the problem of driving the state of a dynamical system in distribution from a sequential decision-making perspective. We formulate the optimal control pro... | {
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2412.06223 | On low-power error-correcting cooling codes with large distances | [
"cs.IT",
"math.CO",
"math.IT"
] | A low-power error-correcting cooling (LPECC) code was introduced as a coding scheme for communication over a bus by Chee et al. to control the peak temperature, the average power consumption of on-chip buses, and error-correction for the transmitted information, simultaneously. Specifically, an $(n, t, w, e)$-LPECC cod... | {
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2412.06224 | Uni-NaVid: A Video-based Vision-Language-Action Model for Unifying
Embodied Navigation Tasks | [
"cs.RO",
"cs.CV"
] | A practical navigation agent must be capable of handling a wide range of interaction demands, such as following instructions, searching objects, answering questions, tracking people, and more. Existing models for embodied navigation fall short of serving as practical generalists in the real world, as they are often con... | {
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2412.06227 | Attention-Enhanced Lightweight Hourglass Network for Human Pose
Estimation | [
"cs.CV"
] | Pose estimation is a critical task in computer vision with a wide range of applications from activity monitoring to human-robot interaction. However,most of the existing methods are computationally expensive or have complex architecture. Here we propose a lightweight attention based pose estimation network that utilize... | {
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2412.06229 | LLMs as Debate Partners: Utilizing Genetic Algorithms and Adversarial
Search for Adaptive Arguments | [
"cs.AI",
"cs.CL",
"cs.CY",
"cs.NE"
] | This paper introduces DebateBrawl, an innovative AI-powered debate platform that integrates Large Language Models (LLMs), Genetic Algorithms (GA), and Adversarial Search (AS) to create an adaptive and engaging debating experience. DebateBrawl addresses the limitations of traditional LLMs in strategic planning by incorp... | {
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2412.06231 | A Scalable Decentralized Reinforcement Learning Framework for UAV Target
Localization Using Recurrent PPO | [
"cs.RO",
"cs.LG"
] | The rapid advancements in unmanned aerial vehicles (UAVs) have unlocked numerous applications, including environmental monitoring, disaster response, and agricultural surveying. Enhancing the collective behavior of multiple decentralized UAVs can significantly improve these applications through more efficient and coord... | {
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2412.06233 | Representational Transfer Learning for Matrix Completion | [
"stat.ML",
"cs.LG"
] | We propose to transfer representational knowledge from multiple sources to a target noisy matrix completion task by aggregating singular subspaces information. Under our representational similarity framework, we first integrate linear representation information by solving a two-way principal component analysis problem ... | {
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2412.06234 | Generative Densification: Learning to Densify Gaussians for
High-Fidelity Generalizable 3D Reconstruction | [
"cs.CV",
"cs.GR"
] | Generalized feed-forward Gaussian models have achieved significant progress in sparse-view 3D reconstruction by leveraging prior knowledge from large multi-view datasets. However, these models often struggle to represent high-frequency details due to the limited number of Gaussians. While the densification strategy use... | {
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2412.06235 | VariFace: Fair and Diverse Synthetic Dataset Generation for Face
Recognition | [
"cs.CV",
"cs.LG"
] | The use of large-scale, web-scraped datasets to train face recognition models has raised significant privacy and bias concerns. Synthetic methods mitigate these concerns and provide scalable and controllable face generation to enable fair and accurate face recognition. However, existing synthetic datasets display limit... | {
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2412.06237 | In Silico Pharmacokinetic and Molecular Docking Studies of Natural
Plants against Essential Protein KRAS for Treatment of Pancreatic Cancer | [
"q-bio.BM",
"cs.LG"
] | A kind of pancreatic cancer called Pancreatic Ductal Adenocarcinoma (PDAC) is anticipated to be one of the main causes of mortality during past years. Evidence from several researches supported the concept that the oncogenic KRAS (Ki-ras2 Kirsten rat sarcoma viral oncogene) mutation is the major cause of pancreatic can... | {
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2412.06239 | Unseen Attack Detection in Software-Defined Networking Using a
BERT-Based Large Language Model | [
"cs.CR",
"cs.AI"
] | Software defined networking (SDN) represents a transformative shift in network architecture by decoupling the control plane from the data plane, enabling centralized and flexible management of network resources. However, this architectural shift introduces significant security challenges, as SDN's centralized control b... | {
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2412.06243 | U-Know-DiffPAN: An Uncertainty-aware Knowledge Distillation Diffusion
Framework with Details Enhancement for PAN-Sharpening | [
"cs.CV",
"eess.IV"
] | Conventional methods for PAN-sharpening often struggle to restore fine details due to limitations in leveraging high-frequency information. Moreover, diffusion-based approaches lack sufficient conditioning to fully utilize Panchromatic (PAN) images and low-resolution multispectral (LRMS) inputs effectively. To address ... | {
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2412.06244 | DenseVLM: A Retrieval and Decoupled Alignment Framework for
Open-Vocabulary Dense Prediction | [
"cs.CV"
] | Pre-trained vision-language models (VLMs), such as CLIP, have demonstrated impressive zero-shot recognition capability, but still underperform in dense prediction tasks. Self-distillation recently is emerging as a promising approach for fine-tuning VLMs to better adapt to local regions without requiring extensive annot... | {
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2412.06245 | A Comparative Study of Learning Paradigms in Large Language Models via
Intrinsic Dimension | [
"cs.CL"
] | The performance of Large Language Models (LLMs) on natural language tasks can be improved through both supervised fine-tuning (SFT) and in-context learning (ICL), which operate via distinct mechanisms. Supervised fine-tuning updates the model's weights by minimizing loss on training data, whereas in-context learning le... | {
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2412.06248 | Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data | [
"cs.CV"
] | Growing privacy concerns and regulations like GDPR and CCPA necessitate pseudonymization techniques that protect identity in image datasets. However, retaining utility is also essential. Traditional methods like masking and blurring degrade quality and obscure critical context, especially in human-centric images. We in... | {
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2412.06249 | Optimizing Multi-Task Learning for Enhanced Performance in Large
Language Models | [
"cs.CL",
"cs.LG"
] | This study aims to explore the performance improvement method of large language models based on GPT-4 under the multi-task learning framework and conducts experiments on two tasks: text classification and automatic summary generation. Through the combined design of shared feature extractors and task-specific modules, w... | {
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2412.06250 | Splatter-360: Generalizable 360$^{\circ}$ Gaussian Splatting for
Wide-baseline Panoramic Images | [
"cs.CV",
"cs.GR"
] | Wide-baseline panoramic images are frequently used in applications like VR and simulations to minimize capturing labor costs and storage needs. However, synthesizing novel views from these panoramic images in real time remains a significant challenge, especially due to panoramic imagery's high resolution and inherent d... | {
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2412.06257 | Advancing Extended Reality with 3D Gaussian Splatting: Innovations and
Prospects | [
"cs.CV",
"cs.GR",
"cs.HC"
] | 3D Gaussian Splatting (3DGS) has attracted significant attention for its potential to revolutionize 3D representation, rendering, and interaction. Despite the rapid growth of 3DGS research, its direct application to Extended Reality (XR) remains underexplored. Although many studies recognize the potential of 3DGS for X... | {
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2412.06258 | Enhanced Multi-Object Tracking Using Pose-based Virtual Markers in 3x3
Basketball | [
"cs.CV"
] | Multi-object tracking (MOT) is crucial for various multi-agent analyses such as evaluating team sports tactics and player movements and performance. While pedestrian tracking has advanced with Tracking-by-Detection MOT, team sports like basketball pose unique challenges. These challenges include players' unpredictable ... | {
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2412.06262 | A Lightweight U-like Network Utilizing Neural Memory Ordinary
Differential Equations for Slimming the Decoder | [
"cs.CV",
"cs.AI",
"eess.IV"
] | In recent years, advanced U-like networks have demonstrated remarkable performance in medical image segmentation tasks. However, their drawbacks, including excessive parameters, high computational complexity, and slow inference speed, pose challenges for practical implementation in scenarios with limited computational ... | {
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2412.06263 | iLLaVA: An Image is Worth Fewer Than 1/3 Input Tokens in Large
Multimodal Models | [
"cs.CV"
] | In this paper, we introduce iLLaVA, a simple method that can be seamlessly deployed upon current Large Vision-Language Models (LVLMs) to greatly increase the throughput with nearly lossless model performance, without a further requirement to train. iLLaVA achieves this by finding and gradually merging the redundant tok... | {
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2412.06264 | Flow Matching Guide and Code | [
"cs.LG"
] | Flow Matching (FM) is a recent framework for generative modeling that has achieved state-of-the-art performance across various domains, including image, video, audio, speech, and biological structures. This guide offers a comprehensive and self-contained review of FM, covering its mathematical foundations, design choic... | {
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2412.06265 | Table2Image: Interpretable Tabular Data Classification with Realistic
Image Transformations | [
"cs.LG"
] | Recent advancements in deep learning for tabular data have shown promise, but challenges remain in achieving interpretable and lightweight models. This paper introduces Table2Image, a novel framework that transforms tabular data into realistic and diverse image representations, enabling deep learning methods to achieve... | {
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2412.06268 | Open-Vocabulary High-Resolution 3D (OVHR3D) Data Segmentation and
Annotation Framework | [
"cs.CV"
] | In the domain of the U.S. Army modeling and simulation, the availability of high quality annotated 3D data is pivotal to creating virtual environments for training and simulations. Traditional methodologies for 3D semantic and instance segmentation, such as KpConv, RandLA, Mask3D, etc., are designed to train on extensi... | {
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2412.06272 | Methods for Legal Citation Prediction in the Age of LLMs: An Australian
Law Case Study | [
"cs.CL",
"cs.AI",
"cs.IR"
] | In recent years, Large Language Models (LLMs) have shown great potential across a wide range of legal tasks. Despite these advances, mitigating hallucination remains a significant challenge, with state-of-the-art LLMs still frequently generating incorrect legal references. In this paper, we focus on the problem of lega... | {
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2412.06273 | Omni-Scene: Omni-Gaussian Representation for Ego-Centric Sparse-View
Scene Reconstruction | [
"cs.CV",
"cs.GR"
] | Prior works employing pixel-based Gaussian representation have demonstrated efficacy in feed-forward sparse-view reconstruction. However, such representation necessitates cross-view overlap for accurate depth estimation, and is challenged by object occlusions and frustum truncations. As a result, these methods require ... | {
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2412.06275 | Performance Analysis and Code Design for Resistive Random-Access Memory
Using Channel Decomposition Approach | [
"cs.IT",
"math.IT"
] | A novel framework for performance analysis and code design is proposed to address the sneak path (SP) problem in resistive random-access memory (ReRAM) arrays. The main idea is to decompose the ReRAM channel, which is both non-ergodic and data-dependent, into multiple stationary memoryless channels. A finite-length per... | {
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2412.06279 | Reconfigurable Holographic Surface-aided Distributed MIMO Radar Systems | [
"eess.SP",
"cs.SY",
"eess.SY"
] | Distributed phased Multiple-Input Multiple-Output (phased-MIMO) radar systems have attracted wide attention in target detection and tracking. However, the phase-shifting circuits in phased subarrays contribute to high power consumption and hardware cost. To address this issue, an energy-efficient and cost-efficient met... | {
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2412.06282 | Recent development of optical electric current transformer and its
obstacles | [
"physics.optics",
"cs.SY",
"eess.SP",
"eess.SY",
"physics.app-ph"
] | Conventional electromagnetic induction-based current transformers suffer from issues such as bulky and complex structures, slow response times, and low safety levels. Consequently, researchers have explored combining various sensing technologies with optical fibers to develop optical current transformers that could bec... | {
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2412.06284 | Your Data Is Not Perfect: Towards Cross-Domain Out-of-Distribution
Detection in Class-Imbalanced Data | [
"cs.CV"
] | Previous OOD detection systems only focus on the semantic gap between ID and OOD samples. Besides the semantic gap, we are faced with two additional gaps: the domain gap between source and target domains, and the class-imbalance gap between different classes. In fact, similar objects from different domains should belon... | {
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2412.06285 | Neural Garment Dynamic Super-Resolution | [
"cs.CV",
"cs.GR"
] | Achieving efficient, high-fidelity, high-resolution garment simulation is challenging due to its computational demands. Conversely, low-resolution garment simulation is more accessible and ideal for low-budget devices like smartphones. In this paper, we introduce a lightweight, learning-based method for garment dynamic... | {
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} |
2412.06286 | No Annotations for Object Detection in Art through Stable Diffusion | [
"cs.CV"
] | Object detection in art is a valuable tool for the digital humanities, as it allows for faster identification of objects in artistic and historical images compared to humans. However, annotating such images poses significant challenges due to the need for specialized domain expertise. We present NADA (no annotations fo... | {
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} |
2412.06287 | PediaBench: A Comprehensive Chinese Pediatric Dataset for Benchmarking
Large Language Models | [
"cs.CL"
] | The emergence of Large Language Models (LLMs) in the medical domain has stressed a compelling need for standard datasets to evaluate their question-answering (QA) performance. Although there have been several benchmark datasets for medical QA, they either cover common knowledge across different departments or are speci... | {
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} |
2412.06289 | S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by
Structured Sparsity | [
"cs.LG",
"cs.AI"
] | Current PEFT methods for LLMs can achieve either high quality, efficient training, or scalable serving, but not all three simultaneously. To address this limitation, we investigate sparse fine-tuning and observe a remarkable improvement in generalization ability. Utilizing this key insight, we propose a family of Struc... | {
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} |
2412.06292 | ZeroKey: Point-Level Reasoning and Zero-Shot 3D Keypoint Detection from
Large Language Models | [
"cs.CV"
] | We propose a novel zero-shot approach for keypoint detection on 3D shapes. Point-level reasoning on visual data is challenging as it requires precise localization capability, posing problems even for powerful models like DINO or CLIP. Traditional methods for 3D keypoint detection rely heavily on annotated 3D datasets a... | {
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} |
2412.06293 | Mastering Collaborative Multi-modal Data Selection: A Focus on
Informativeness, Uniqueness, and Representativeness | [
"cs.CV"
] | Instruction tuning fine-tunes pre-trained Multi-modal Large Language Models (MLLMs) to handle real-world tasks. However, the rapid expansion of visual instruction datasets introduces data redundancy, leading to excessive computational costs. We propose a collaborative framework, DataTailor, which leverages three key pr... | {
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} |
2412.06295 | See Further When Clear: Curriculum Consistency Model | [
"cs.CV"
] | Significant advances have been made in the sampling efficiency of diffusion models and flow matching models, driven by Consistency Distillation (CD), which trains a student model to mimic the output of a teacher model at a later timestep. However, we found that the learning complexity of the student model varies signif... | {
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} |
2412.06299 | 4D Gaussian Splatting with Scale-aware Residual Field and Adaptive
Optimization for Real-time Rendering of Temporally Complex Dynamic Scenes | [
"cs.CV",
"cs.MM"
] | Reconstructing dynamic scenes from video sequences is a highly promising task in the multimedia domain. While previous methods have made progress, they often struggle with slow rendering and managing temporal complexities such as significant motion and object appearance/disappearance. In this paper, we propose SaRO-GS ... | {
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} |
2412.06303 | DSAI: Unbiased and Interpretable Latent Feature Extraction for
Data-Centric AI | [
"cs.LG",
"cs.AI"
] | Large language models (LLMs) often struggle to objectively identify latent characteristics in large datasets due to their reliance on pre-trained knowledge rather than actual data patterns. To address this data grounding issue, we propose Data Scientist AI (DSAI), a framework that enables unbiased and interpretable fea... | {
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} |
2412.06306 | Self-Paced Learning Strategy with Easy Sample Prior Based on Confidence
for the Flying Bird Object Detection Model Training | [
"cs.CV"
] | In order to avoid the impact of hard samples on the training process of the Flying Bird Object Detection model (FBOD model, in our previous work, we designed the FBOD model according to the characteristics of flying bird objects in surveillance video), the Self-Paced Learning strategy with Easy Sample Prior Based on Co... | {
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} |
2412.06308 | PRECISE: Pre-training Sequential Recommenders with Collaborative and
Semantic Information | [
"cs.IR",
"cs.AI"
] | Real-world recommendation systems commonly offer diverse content scenarios for users to interact with. Considering the enormous number of users in industrial platforms, it is infeasible to utilize a single unified recommendation model to meet the requirements of all scenarios. Usually, separate recommendation pipelines... | {
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} |
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