id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2412.19331 | CALICO: Part-Focused Semantic Co-Segmentation with Large Vision-Language
Models | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Recent advances in Large Vision-Language Models (LVLMs) have sparked significant progress in general-purpose vision tasks through visual instruction tuning. While some works have demonstrated the capability of LVLMs to generate segmentation masks that align phrases with natural language descriptions in a single image, ... | {
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2412.19340 | A Reinforcement Learning-Based Task Mapping Method to Improve the
Reliability of Clustered Manycores | [
"cs.LG",
"cs.AI"
] | The increasing scale of manycore systems poses significant challenges in managing reliability while meeting performance demands. Simultaneously, these systems become more susceptible to different aging mechanisms such as negative-bias temperature instability (NBTI), hot carrier injection (HCI), and thermal cycling (TC)... | {
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2412.19341 | Sparse recovery from quadratic equations, part II: hardness and
incoherence | [
"cs.IT",
"cs.NA",
"math.IT",
"math.NA"
] | We study the square root bottleneck in the recovery of sparse vectors from quadratic equations. It is acknowledged that a sparse vector $ \mathbf x_0\in \mathbb{R}^n$, $\| \mathbf x_0\|_0 = k$ can in theory be recovered from as few as $O(k)$ generic quadratic equations but no polynomial time algorithm is known for this... | {
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2412.19345 | Advanced Scheduling of Electrolyzer Modules for Grid Flexibility | [
"eess.SY",
"cs.SY"
] | As the transition to sustainable power generation progresses, green hydrogen production via electrolysis is expected to gain importance as a means for energy storage and flexible load to complement variable renewable generation. With the increasing need for cost-effective and efficient hydrogen production, electrolyzer... | {
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2412.19346 | Semi-Supervised Learning from Small Annotated Data and Large Unlabeled
Data for Fine-grained PICO Entity Recognition | [
"cs.CL",
"cs.AI"
] | Objective: Extracting PICO elements -- Participants, Intervention, Comparison, and Outcomes -- from clinical trial literature is essential for clinical evidence retrieval, appraisal, and synthesis. Existing approaches do not distinguish the attributes of PICO entities. This study aims to develop a named entity recognit... | {
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2412.19350 | On the Expressiveness and Length Generalization of Selective State-Space
Models on Regular Languages | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Selective state-space models (SSMs) are an emerging alternative to the Transformer, offering the unique advantage of parallel training and sequential inference. Although these models have shown promising performance on a variety of tasks, their formal expressiveness and length generalization properties remain underexpl... | {
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2412.19351 | ETTA: Elucidating the Design Space of Text-to-Audio Models | [
"cs.SD",
"cs.CL",
"cs.LG",
"eess.AS"
] | Recent years have seen significant progress in Text-To-Audio (TTA) synthesis, enabling users to enrich their creative workflows with synthetic audio generated from natural language prompts. Despite this progress, the effects of data, model architecture, training objective functions, and sampling strategies on target be... | {
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2412.19354 | Federated Hybrid Training and Self-Adversarial Distillation: Towards
Robust Edge Networks | [
"cs.CV",
"cs.LG"
] | Federated learning (FL) is a distributed training technology that enhances data privacy in mobile edge networks by allowing data owners to collaborate without transmitting raw data to the edge server. However, data heterogeneity and adversarial attacks pose challenges to develop an unbiased and robust global model for ... | {
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2412.19360 | Improving the network traffic classification using the Packet Vision
approach | [
"cs.NI",
"cs.AI",
"cs.CR",
"cs.CV"
] | The network traffic classification allows improving the management, and the network services offer taking into account the kind of application. The future network architectures, mainly mobile networks, foresee intelligent mechanisms in their architectural frameworks to deliver application-aware network requirements. Th... | {
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2412.19361 | Dynamic Skill Adaptation for Large Language Models | [
"cs.CL"
] | We present Dynamic Skill Adaptation (DSA), an adaptive and dynamic framework to adapt novel and complex skills to Large Language Models (LLMs). Compared with previous work which learns from human-curated and static data in random orders, we propose to first automatically generate and organize the training data by mimic... | {
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2412.19362 | Evaluating Convolutional Neural Networks for COVID-19 classification in
chest X-ray images | [
"eess.IV",
"cs.CV",
"cs.LG"
] | Coronavirus Disease 2019 (COVID-19) pandemic rapidly spread globally, impacting the lives of billions of people. The effective screening of infected patients is a critical step to struggle with COVID-19, and treating the patients avoiding this quickly disease spread. The need for automated and scalable methods has incr... | {
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2412.19363 | Large Language Models for Market Research: A Data-augmentation Approach | [
"cs.AI",
"cs.LG",
"stat.ME",
"stat.ML"
] | Large Language Models (LLMs) have transformed artificial intelligence by excelling in complex natural language processing tasks. Their ability to generate human-like text has opened new possibilities for market research, particularly in conjoint analysis, where understanding consumer preferences is essential but often ... | {
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2412.19370 | BeSplat: Gaussian Splatting from a Single Blurry Image and Event Stream | [
"cs.CV"
] | Novel view synthesis has been greatly enhanced by the development of radiance field methods. The introduction of 3D Gaussian Splatting (3DGS) has effectively addressed key challenges, such as long training times and slow rendering speeds, typically associated with Neural Radiance Fields (NeRF), while maintaining high-q... | {
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2412.19372 | Minimal Batch Adaptive Learning Policy Engine for Real-Time Mid-Price
Forecasting in High-Frequency Trading | [
"q-fin.ST",
"cs.LG"
] | High-frequency trading (HFT) has transformed modern financial markets, making reliable short-term price forecasting models essential. In this study, we present a novel approach to mid-price forecasting using Level 1 limit order book (LOB) data from NASDAQ, focusing on 100 U.S. stocks from the S&P 500 index during the p... | {
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2412.19374 | A Review of Resilience Enhancement Measures for Hydrogen-penetrated
Multi-energy Systems | [
"eess.SY",
"cs.SY"
] | Energy supply for electricity and heat sectors accounts for more than 40% of global carbon emissions in 2023, which brings great pressure for achieving net-zero carbon emission targets in the future. Under the above background, hydrogen-penetrated multi-energy systems (HMESs) have received wide attention due to their p... | {
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2412.19382 | Preventive Energy Management for Distribution Systems Under Uncertain
Events: A Deep Reinforcement Learning Approach | [
"eess.SY",
"cs.SY"
] | As power systems become more complex with the continuous integration of intelligent distributed energy resources (DERs), new risks and uncertainties arise. Consequently, to enhance system resiliency, it is essential to account for various uncertain events when implementing the optimization problem for the energy manage... | {
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2412.19384 | The Internet of Value: Integrating Blockchain and Lightning Network
Micropayments for Knowledge Markets | [
"cs.CY",
"cs.CE",
"cs.ET"
] | Q&A websites rely on user-generated responses, with incentives such as reputation scores or monetary rewards often offered. While some users may find it intrinsically rewarding to assist others, studies indicate that payment can improve the quality and speed of answers. However, traditional payment processors impose mi... | {
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2412.19391 | An In-Depth Analysis of Adversarial Discriminative Domain Adaptation for
Digit Classification | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Domain adaptation is an active area of research driven by the growing demand for robust machine learning models that perform well on real-world data. Adversarial learning for deep neural networks (DNNs) has emerged as a promising approach to improving generalization ability, particularly for image classification. In th... | {
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2412.19392 | Asymptotically Optimal Search for a Change Point Anomaly under a
Composite Hypothesis Model | [
"stat.ML",
"cs.LG",
"eess.SP"
] | We address the problem of searching for a change point in an anomalous process among a finite set of M processes. Specifically, we address a composite hypothesis model in which each process generates measurements following a common distribution with an unknown parameter (vector). This parameter belongs to either a norm... | {
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2412.19394 | An Engorgio Prompt Makes Large Language Model Babble on | [
"cs.CR",
"cs.AI"
] | Auto-regressive large language models (LLMs) have yielded impressive performance in many real-world tasks. However, the new paradigm of these LLMs also exposes novel threats. In this paper, we explore their vulnerability to inference cost attacks, where a malicious user crafts Engorgio prompts to intentionally increase... | {
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2412.19396 | Comparing Few to Rank Many: Active Human Preference Learning using
Randomized Frank-Wolfe | [
"cs.LG",
"cs.AI",
"cs.IT",
"math.IT",
"math.OC",
"stat.ML"
] | We study learning of human preferences from a limited comparison feedback. This task is ubiquitous in machine learning. Its applications such as reinforcement learning from human feedback, have been transformational. We formulate this problem as learning a Plackett-Luce model over a universe of $N$ choices from $K$-way... | {
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2412.19399 | Online distributed algorithms for mixed equilibrium problems in dynamic
environments | [
"eess.SY",
"cs.SY"
] | In this paper, the mixed equilibrium problem with coupled inequality constraints in dynamic environments is solved by employing a multi-agent system, where each agent only has access to its own bifunction, its own constraint function, and can only communicate with its immediate neighbors via a time-varying digraph. At ... | {
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2412.19401 | Joint Optimization of Multimodal Transit Frequency and Shared Autonomous
Vehicle Fleet Size with Hybrid Metaheuristic and Nonlinear Programming | [
"eess.SY",
"cs.SY",
"math.OC"
] | This paper presents an optimization framework for the joint multimodal transit frequency and shared autonomous vehicle (SAV) fleet size optimization, a problem variant of the transit network frequency setting problem (TNFSP) that explicitly considers mode choice behavior and route selection. To address the non-linear n... | {
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2412.19403 | Fully Data-driven but Interpretable Human Behavioural Modelling with
Differentiable Discrete Choice Model | [
"cs.LG",
"cs.AI",
"cs.HC",
"cs.MA"
] | Discrete choice models are essential for modelling various decision-making processes in human behaviour. However, the specification of these models has depended heavily on domain knowledge from experts, and the fully automated but interpretable modelling of complex human behaviours has been a long-standing challenge. I... | {
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2412.19404 | Spectral-Temporal Fusion Representation for Person-in-Bed Detection | [
"cs.CV",
"cs.LG"
] | This study is based on the ICASSP 2025 Signal Processing Grand Challenge's Accelerometer-Based Person-in-Bed Detection Challenge, which aims to determine bed occupancy using accelerometer signals. The task is divided into two tracks: "in bed" and "not in bed" segmented detection, and streaming detection, facing challen... | {
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2412.19406 | MLLM-SUL: Multimodal Large Language Model for Semantic Scene
Understanding and Localization in Traffic Scenarios | [
"cs.CV"
] | Multimodal large language models (MLLMs) have shown satisfactory effects in many autonomous driving tasks. In this paper, MLLMs are utilized to solve joint semantic scene understanding and risk localization tasks, while only relying on front-view images. In the proposed MLLM-SUL framework, a dual-branch visual encoder ... | {
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2412.19409 | Efficient Feature Mapping Using a Collaborative Team of AUVs | [
"cs.RO"
] | We present the results of experiments performed using a team of small autonomous underwater vehicles (AUVs) to determine the location of an isobath. The primary contributions of this work are (1) the development of a novel objective function for level set estimation that utilizes a rigorous assessment of uncertainty, a... | {
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2412.19412 | MINIMA: Modality Invariant Image Matching | [
"cs.CV"
] | Image matching for both cross-view and cross-modality plays a critical role in multimodal perception. In practice, the modality gap caused by different imaging systems/styles poses great challenges to the matching task. Existing works try to extract invariant features for specific modalities and train on limited datase... | {
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2412.19413 | Multi-scale Latent Point Consistency Models for 3D Shape Generation | [
"cs.CV"
] | Consistency Models (CMs) have significantly accelerated the sampling process in diffusion models, yielding impressive results in synthesizing high-resolution images. To explore and extend these advancements to point-cloud-based 3D shape generation, we propose a novel Multi-scale Latent Point Consistency Model (MLPCM). ... | {
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2412.19415 | DIPS: Optimal Dynamic Index for Poisson $\boldsymbol{\pi}$ps Sampling | [
"cs.DB"
] | This paper addresses the Poisson $\pi$ps sampling problem, a topic of significant academic interest in various domains and with practical data mining applications, such as influence maximization. The problem includes a set $\mathcal{S}$ of $n$ elements, where each element $v$ is assigned a weight $w(v)$ reflecting its ... | {
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2412.19417 | KALAHash: Knowledge-Anchored Low-Resource Adaptation for Deep Hashing | [
"cs.CV"
] | Deep hashing has been widely used for large-scale approximate nearest neighbor search due to its storage and search efficiency. However, existing deep hashing methods predominantly rely on abundant training data, leaving the more challenging scenario of low-resource adaptation for deep hashing relatively underexplored.... | {
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2412.19418 | Generalized Uncertainty-Based Evidential Fusion with Hybrid Multi-Head
Attention for Weak-Supervised Temporal Action Localization | [
"cs.CV",
"cs.AI"
] | Weakly supervised temporal action localization (WS-TAL) is a task of targeting at localizing complete action instances and categorizing them with video-level labels. Action-background ambiguity, primarily caused by background noise resulting from aggregation and intra-action variation, is a significant challenge for ex... | {
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2412.19419 | Introduction to Graph Neural Networks: A Starting Point for Machine
Learning Engineers | [
"cs.LG",
"cs.AI"
] | Graph neural networks are deep neural networks designed for graphs with attributes attached to nodes or edges. The number of research papers in the literature concerning these models is growing rapidly due to their impressive performance on a broad range of tasks. This survey introduces graph neural networks through th... | {
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2412.19420 | A Matrix Logic Approach to Efficient Frequent Itemset Discovery in Large
Data Sets | [
"cs.DB",
"cs.DS"
] | This paper proposes a frequent itemset mining algorithm based on the Boolean matrix method, aiming to solve the storage and computational bottlenecks of traditional frequent pattern mining algorithms in high-dimensional and large-scale transaction databases. By representing the itemsets in the transaction database as B... | {
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2412.19422 | Gx2Mol: De Novo Generation of Hit-like Molecules from Gene Expression
Profiles via Deep Learning | [
"cs.LG",
"cs.AI",
"q-bio.QM"
] | De novo generation of hit-like molecules is a challenging task in the drug discovery process. Most methods in previous studies learn the semantics and syntax of molecular structures by analyzing molecular graphs or simplified molecular input line entry system (SMILES) strings; however, they do not take into account the... | {
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2412.19423 | Revisiting PCA for time series reduction in temporal dimension | [
"cs.LG",
"cs.AI",
"stat.AP"
] | Revisiting PCA for Time Series Reduction in Temporal Dimension; Jiaxin Gao, Wenbo Hu, Yuntian Chen; Deep learning has significantly advanced time series analysis (TSA), enabling the extraction of complex patterns for tasks like classification, forecasting, and regression. Although dimensionality reduction has tradition... | {
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2412.19424 | Temporal Context Consistency Above All: Enhancing Long-Term Anticipation
by Learning and Enforcing Temporal Constraints | [
"cs.CV"
] | This paper proposes a method for long-term action anticipation (LTA), the task of predicting action labels and their duration in a video given the observation of an initial untrimmed video interval. We build on an encoder-decoder architecture with parallel decoding and make two key contributions. First, we introduce a ... | {
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2412.19425 | A Self-Efficacy Theory-based Study on the Teachers Readiness to Teach
Artificial Intelligence in Public Schools in Sri Lanka | [
"cs.AI"
] | This study investigates Sri Lankan ICT teachers' readiness to teach AI in schools, focusing on self-efficacy. A survey of over 1,300 teachers assessed their self-efficacy using a scale developed based on Bandura's theory. PLS-SEM analysis revealed that teachers' self-efficacy was low, primarily influenced by emotional ... | {
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2412.19433 | Residual Feature-Reutilization Inception Network for Image
Classification | [
"cs.CV",
"cs.AI"
] | Capturing feature information effectively is of great importance in the field of computer vision. With the development of convolutional neural networks (CNNs), concepts like residual connection and multiple scales promote continual performance gains in diverse deep learning vision tasks. In this paper, we propose a nov... | {
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2412.19436 | Low-Rank Contextual Reinforcement Learning from Heterogeneous Human
Feedback | [
"stat.ML",
"cs.LG"
] | Reinforcement learning from human feedback (RLHF) has become a cornerstone for aligning large language models with human preferences. However, the heterogeneity of human feedback, driven by diverse individual contexts and preferences, poses significant challenges for reward learning. To address this, we propose a Low-r... | {
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2412.19437 | DeepSeek-V3 Technical Report | [
"cs.CL",
"cs.AI"
] | We present DeepSeek-V3, a strong Mixture-of-Experts (MoE) language model with 671B total parameters with 37B activated for each token. To achieve efficient inference and cost-effective training, DeepSeek-V3 adopts Multi-head Latent Attention (MLA) and DeepSeekMoE architectures, which were thoroughly validated in DeepSe... | {
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2412.19438 | The Rendezvous Between Extreme Value Theory and Next-generation Networks | [
"cs.IT",
"math.IT"
] | Promising technologies such as massive multiple-input and multiple-output, reconfigurable intelligent reflecting surfaces, non-terrestrial networks, millimetre wave communication, ultra-reliable lowlatency communication are envisioned as the enablers for next-generation (NG) networks. In contrast to conventional commun... | {
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2412.19439 | Paleoinspired Vision: From Exploring Colour Vision Evolution to
Inspiring Camera Design | [
"cs.CV"
] | The evolution of colour vision is captivating, as it reveals the adaptive strategies of extinct species while simultaneously inspiring innovations in modern imaging technology. In this study, we present a simplified model of visual transduction in the retina, introducing a novel opsin layer. We quantify evolutionary pr... | {
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2412.19441 | Comparative Performance Analysis of Quantum Machine Learning
Architectures for Credit Card Fraud Detection | [
"quant-ph",
"cs.LG"
] | As financial fraud becomes increasingly complex, effective detection methods are essential. Quantum Machine Learning (QML) introduces certain capabilities that may enhance both accuracy and efficiency in this area. This study examines how different quantum feature map and ansatz configurations affect the performance of... | {
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2412.19442 | A Survey on Large Language Model Acceleration based on KV Cache
Management | [
"cs.AI",
"cs.DC"
] | Large Language Models (LLMs) have revolutionized a wide range of domains such as natural language processing, computer vision, and multi-modal tasks due to their ability to comprehend context and perform logical reasoning. However, the computational and memory demands of LLMs, particularly during inference, pose signif... | {
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2412.19444 | Towards Simple and Provable Parameter-Free Adaptive Gradient Methods | [
"cs.LG",
"math.OC",
"stat.ML"
] | Optimization algorithms such as AdaGrad and Adam have significantly advanced the training of deep models by dynamically adjusting the learning rate during the optimization process. However, adhoc tuning of learning rates poses a challenge, leading to inefficiencies in practice. To address this issue, recent research ha... | {
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2412.19449 | Feature Alignment-Based Knowledge Distillation for Efficient Compression
of Large Language Models | [
"cs.CL"
] | This study proposes a knowledge distillation algorithm based on large language models and feature alignment, aiming to effectively transfer the knowledge of large pre-trained models into lightweight student models, thereby reducing computational costs while maintaining high model performance. Different from the traditi... | {
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2412.19450 | Find the Intention of Instruction: Comprehensive Evaluation of
Instruction Understanding for Large Language Models | [
"cs.AI"
] | One of the key strengths of Large Language Models (LLMs) is their ability to interact with humans by generating appropriate responses to given instructions. This ability, known as instruction-following capability, has established a foundation for the use of LLMs across various fields and serves as a crucial metric for ... | {
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2412.19455 | NijiGAN: Transform What You See into Anime with Contrastive
Semi-Supervised Learning and Neural Ordinary Differential Equations | [
"cs.CV"
] | Generative AI has transformed the animation industry. Several models have been developed for image-to-image translation, particularly focusing on converting real-world images into anime through unpaired translation. Scenimefy, a notable approach utilizing contrastive learning, achieves high fidelity anime scene transla... | {
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2412.19457 | Focusing Image Generation to Mitigate Spurious Correlations | [
"cs.CV"
] | Instance features in images exhibit spurious correlations with background features, affecting the training process of deep neural classifiers. This leads to insufficient attention to instance features by the classifier, resulting in erroneous classification outcomes. In this paper, we propose a data augmentation method... | {
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2412.19458 | DriveEditor: A Unified 3D Information-Guided Framework for Controllable
Object Editing in Driving Scenes | [
"cs.CV"
] | Vision-centric autonomous driving systems require diverse data for robust training and evaluation, which can be augmented by manipulating object positions and appearances within existing scene captures. While recent advancements in diffusion models have shown promise in video editing, their application to object manipu... | {
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2412.19459 | A Prototype Unit for Image De-raining using Time-Lapse Data | [
"cs.CV",
"eess.IV"
] | We address the challenge of single-image de-raining, a task that involves recovering rain-free background information from a single rain image. While recent advancements have utilized real-world time-lapse data for training, enabling the estimation of consistent backgrounds and realistic rain streaks, these methods oft... | {
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2412.19464 | MNet-SAt: A Multiscale Network with Spatial-enhanced Attention for
Segmentation of Polyps in Colonoscopy | [
"cs.CV"
] | Objective: To develop a novel deep learning framework for the automated segmentation of colonic polyps in colonoscopy images, overcoming the limitations of current approaches in preserving precise polyp boundaries, incorporating multi-scale features, and modeling spatial dependencies that accurately reflect the intrica... | {
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2412.19467 | Optimizing Helmet Detection with Hybrid YOLO Pipelines: A Detailed
Analysis | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Helmet detection is crucial for advancing protection levels in public road traffic dynamics. This problem statement translates to an object detection task. Therefore, this paper compares recent You Only Look Once (YOLO) models in the context of helmet detection in terms of reliability and computational load. Specifical... | {
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2412.19469 | Knowledge Graph-Based Multi-Agent Path Planning in Dynamic Environments
using WAITR | [
"cs.MA"
] | This paper addresses the challenge of multi-agent path planning for efficient data collection in dynamic, uncertain environments, exemplified by autonomous underwater vehicles (AUVs) navigating the Gulf of Mexico. Traditional greedy algorithms, though computationally efficient, often fall short in long-term planning du... | {
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2412.19470 | Movable Antenna-Aided Near-Field Integrated Sensing and Communication | [
"cs.IT",
"eess.SP",
"math.IT"
] | Integrated sensing and communication (ISAC) is emerging as a pivotal technology for next-generation wireless networks. However, existing ISAC systems are based on fixed-position antennas (FPAs), which inevitably incur a loss in performance when balancing the trade-off between sensing and communication. Movable antenna ... | {
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2412.19471 | Meta-Learning-Based Delayless Subband Adaptive Filter using Complex
Self-Attention for Active Noise Control | [
"eess.AS",
"cs.LG",
"cs.SD"
] | Active noise control typically employs adaptive filtering to generate secondary noise, where the least mean square algorithm is the most widely used. However, traditional updating rules are linear and exhibit limited effectiveness in addressing nonlinear environments and nonstationary noise. To tackle this challenge, w... | {
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2412.19479 | Generative Adversarial Network on Motion-Blur Image Restoration | [
"cs.CV",
"eess.IV"
] | In everyday life, photographs taken with a camera often suffer from motion blur due to hand vibrations or sudden movements. This phenomenon can significantly detract from the quality of the images captured, making it an interesting challenge to develop a deep learning model that utilizes the principles of adversarial n... | {
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2412.19482 | Pre-training, Fine-tuning and Re-ranking: A Three-Stage Framework for
Legal Question Answering | [
"cs.CL"
] | Legal question answering (QA) has attracted increasing attention from people seeking legal advice, which aims to retrieve the most applicable answers from a large-scale database of question-answer pairs. Previous methods mainly use a dual-encoder architecture to learn dense representations of both questions and answers... | {
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2412.19483 | Learning Radiance Fields from a Single Snapshot Compressive Image | [
"cs.CV"
] | In this paper, we explore the potential of Snapshot Compressive Imaging (SCI) technique for recovering the underlying 3D scene structure from a single temporal compressed image. SCI is a cost-effective method that enables the recording of high-dimensional data, such as hyperspectral or temporal information, into a sing... | {
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2412.19487 | UniBrain: A Unified Model for Cross-Subject Brain Decoding | [
"cs.CV"
] | Brain decoding aims to reconstruct original stimuli from fMRI signals, providing insights into interpreting mental content. Current approaches rely heavily on subject-specific models due to the complex brain processing mechanisms and the variations in fMRI signals across individuals. Therefore, these methods greatly li... | {
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2412.19489 | RAIN: Real-time Animation of Infinite Video Stream | [
"cs.CV"
] | Live animation has gained immense popularity for enhancing online engagement, yet achieving high-quality, real-time, and stable animation with diffusion models remains challenging, especially on consumer-grade GPUs. Existing methods struggle with generating long, consistent video streams efficiently, often being limite... | {
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2412.19490 | User Willingness-aware Sales Talk Dataset | [
"cs.CL",
"cs.HC"
] | User willingness is a crucial element in the sales talk process that affects the achievement of the salesperson's or sales system's objectives. Despite the importance of user willingness, to the best of our knowledge, no previous study has addressed the development of automated sales talk dialogue systems that explicit... | {
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2412.19491 | Multi-label Classification using Deep Multi-order Context-aware Kernel
Networks | [
"cs.CV"
] | Multi-label classification is a challenging task in pattern recognition. Many deep learning methods have been proposed and largely enhanced classification performance. However, most of the existing sophisticated methods ignore context in the models' learning process. Since context may provide additional cues to the lea... | {
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2412.19492 | Towards Open-Vocabulary Remote Sensing Image Semantic Segmentation | [
"cs.CV",
"cs.MM"
] | Recently, deep learning based methods have revolutionized remote sensing image segmentation. However, these methods usually rely on a pre-defined semantic class set, thus needing additional image annotation and model training when adapting to new classes. More importantly, they are unable to segment arbitrary semantic ... | {
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2412.19494 | Retrieval-augmented Generation for GenAI-enabled Semantic Communications | [
"cs.NI",
"cs.IT",
"eess.SP",
"math.IT"
] | Semantic communication (SemCom) is an emerging paradigm aiming at transmitting only task-relevant semantic information to the receiver, which can significantly improve communication efficiency. Recent advancements in generative artificial intelligence (GenAI) have empowered GenAI-enabled SemCom (GenSemCom) to further e... | {
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2412.19495 | Disparate Model Performance and Stability in Machine Learning Clinical
Support for Diabetes and Heart Diseases | [
"cs.LG",
"cs.AI"
] | Machine Learning (ML) algorithms are vital for supporting clinical decision-making in biomedical informatics. However, their predictive performance can vary across demographic groups, often due to the underrepresentation of historically marginalized populations in training datasets. The investigation reveals widespread... | {
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2412.19496 | Multi-P$^2$A: A Multi-perspective Benchmark on Privacy Assessment for
Large Vision-Language Models | [
"cs.CR",
"cs.AI"
] | Large Vision-Language Models (LVLMs) exhibit impressive potential across various tasks but also face significant privacy risks, limiting their practical applications. Current researches on privacy assessment for LVLMs is limited in scope, with gaps in both assessment dimensions and privacy categories. To bridge this ga... | {
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2412.19497 | Multi-Condition Fault Diagnosis of Dynamic Systems: A Survey, Insights,
and Prospects | [
"eess.SY",
"cs.SY"
] | With the increasing complexity of industrial production systems, accurate fault diagnosis is essential to ensure safe and efficient system operation. However, due to changes in production demands, dynamic process adjustments, and complex external environmental disturbances, multiple operating conditions frequently aris... | {
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2412.19498 | Casevo: A Cognitive Agents and Social Evolution Simulator | [
"cs.SI"
] | In this paper, we introduce a multi-agent simulation framework Casevo (Cognitive Agents and Social Evolution Simulator), that integrates large language models (LLMs) to simulate complex social phenomena and decision-making processes. Casevo is designed as a discrete-event simulator driven by agents with features such a... | {
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2412.19500 | RobotDiffuse: Motion Planning for Redundant Manipulator based on
Diffusion Model | [
"cs.RO",
"cs.LG"
] | Redundant manipulators, with their higher Degrees of Freedom (DOFs), offer enhanced kinematic performance and versatility, making them suitable for applications like manufacturing, surgical robotics, and human-robot collaboration. However, motion planning for these manipulators is challenging due to increased DOFs and ... | {
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2412.19504 | Hear the Scene: Audio-Enhanced Text Spotting | [
"cs.CV"
] | Recent advancements in scene text spotting have focused on end-to-end methodologies that heavily rely on precise location annotations, which are often costly and labor-intensive to procure. In this study, we introduce an innovative approach that leverages only transcription annotations for training text spotting models... | {
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2412.19505 | DrivingWorld: Constructing World Model for Autonomous Driving via Video
GPT | [
"cs.CV"
] | Recent successes in autoregressive (AR) generation models, such as the GPT series in natural language processing, have motivated efforts to replicate this success in visual tasks. Some works attempt to extend this approach to autonomous driving by building video-based world models capable of generating realistic future... | {
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2412.19507 | Hybrid Local Causal Discovery | [
"cs.AI"
] | Local causal discovery aims to learn and distinguish the direct causes and effects of a target variable from observed data. Existing constraint-based local causal discovery methods use AND or OR rules in constructing the local causal skeleton, but using either rule alone is prone to produce cascading errors in the lear... | {
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2412.19509 | MBQ: Modality-Balanced Quantization for Large Vision-Language Models | [
"cs.CV",
"cs.AI"
] | Vision-Language Models (VLMs) have enabled a variety of real-world applications. The large parameter size of VLMs brings large memory and computation overhead which poses significant challenges for deployment. Post-Training Quantization (PTQ) is an effective technique to reduce the memory and computation overhead. Exis... | {
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2412.19510 | Parameter Efficient Fine-Tuning for Deep Learning-Based Full-Waveform
Inversion | [
"cs.CE",
"physics.geo-ph"
] | Seismic full waveform inversion (FWI) has seen promising advancements through deep learning. Existing approaches typically focus on task-specific models trained and evaluated in isolation that lead to limited generalization across different geological scenarios. In this work we introduce a task-agnostic foundational mo... | {
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2412.19511 | Uncertainty quantification for improving radiomic-based models in
radiation pneumonitis prediction | [
"cs.LG",
"physics.med-ph"
] | Background and Objective: Radiation pneumonitis (RP) is a side effect of thoracic radiation therapy. Recently, Machine learning (ML) models enhanced with radiomic and dosiomic features provide better predictions by incorporating spatial information beyond DVHs. However, to improve the clinical decision process, we prop... | {
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2412.19512 | Safeguard Fine-Tuned LLMs Through Pre- and Post-Tuning Model Merging | [
"cs.CL"
] | Fine-tuning large language models (LLMs) for downstream tasks is a widely adopted approach, but it often leads to safety degradation in safety-aligned LLMs. Currently, many solutions address this issue by incorporating additional safety data, which can be impractical in many cases. In this paper, we address the questio... | {
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2412.19513 | Confidence v.s. Critique: A Decomposition of Self-Correction Capability
for LLMs | [
"cs.CL"
] | Large Language Models (LLMs) can correct their self-generated responses, but a decline in accuracy after self-correction is also witnessed. To have a deeper understanding of self-correction, we endeavor to decompose, evaluate, and analyze the self-correction behaviors of LLMs. By enumerating and analyzing answer correc... | {
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2412.19515 | Real-time classification of EEG signals using Machine Learning
deployment | [
"cs.LG",
"cs.HC"
] | The prevailing educational methods predominantly rely on traditional classroom instruction or online delivery, often limiting the teachers' ability to engage effectively with all the students simultaneously. A more intrinsic method of evaluating student attentiveness during lectures can enable the educators to tailor t... | {
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2412.19517 | Estimation of System Parameters Including Repeated Cross-Sectional Data
through Emulator-Informed Deep Generative Model | [
"cs.LG",
"cs.AI",
"cs.NA",
"math.NA",
"q-bio.PE",
"stat.ML"
] | Differential equations (DEs) are crucial for modeling the evolution of natural or engineered systems. Traditionally, the parameters in DEs are adjusted to fit data from system observations. However, in fields such as politics, economics, and biology, available data are often independently collected at distinct time poi... | {
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2412.19518 | Dust to Tower: Coarse-to-Fine Photo-Realistic Scene Reconstruction from
Sparse Uncalibrated Images | [
"cs.CV"
] | Photo-realistic scene reconstruction from sparse-view, uncalibrated images is highly required in practice. Although some successes have been made, existing methods are either Sparse-View but require accurate camera parameters (i.e., intrinsic and extrinsic), or SfM-free but need densely captured images. To combine the ... | {
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2412.19522 | Exploiting Domain-Specific Parallel Data on Multilingual Language Models
for Low-resource Language Translation | [
"cs.CL"
] | Neural Machine Translation (NMT) systems built on multilingual sequence-to-sequence Language Models (msLMs) fail to deliver expected results when the amount of parallel data for a language, as well as the language's representation in the model are limited. This restricts the capabilities of domain-specific NMT systems ... | {
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2412.19523 | Attribution for Enhanced Explanation with Transferable Adversarial
eXploration | [
"cs.AI",
"cs.CV"
] | The interpretability of deep neural networks is crucial for understanding model decisions in various applications, including computer vision. AttEXplore++, an advanced framework built upon AttEXplore, enhances attribution by incorporating transferable adversarial attack methods such as MIG and GRA, significantly improv... | {
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2412.19524 | PLN and NARS Often Yield Similar strength $\times$ confidence Given
Highly Uncertain Term Probabilities | [
"cs.AI"
] | We provide a comparative analysis of the deduction, induction, and abduction formulas used in Probabilistic Logic Networks (PLN) and the Non-Axiomatic Reasoning System (NARS), two uncertain reasoning frameworks aimed at AGI. One difference between the two systems is that, at the level of individual inference rules, PLN... | {
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2412.19529 | Nonconvex Stochastic Optimization under Heavy-Tailed Noises: Optimal
Convergence without Gradient Clipping | [
"math.OC",
"cs.LG",
"stat.ML"
] | Recently, the study of heavy-tailed noises in first-order nonconvex stochastic optimization has gotten a lot of attention since it was recognized as a more realistic condition as suggested by many empirical observations. Specifically, the stochastic noise (the difference between the stochastic and true gradient) is con... | {
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2412.19530 | The Value of AI Advice: Personalized and Value-Maximizing AI Advisors
Are Necessary to Reliably Benefit Experts and Organizations | [
"cs.HC",
"cs.LG"
] | Despite advances in AI's performance and interpretability, AI advisors can undermine experts' decisions and increase the time and effort experts must invest to make decisions. Consequently, AI systems deployed in high-stakes settings often fail to consistently add value across contexts and can even diminish the value t... | {
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2412.19531 | Is Your Text-to-Image Model Robust to Caption Noise? | [
"cs.CV",
"cs.AI"
] | In text-to-image (T2I) generation, a prevalent training technique involves utilizing Vision Language Models (VLMs) for image re-captioning. Even though VLMs are known to exhibit hallucination, generating descriptive content that deviates from the visual reality, the ramifications of such caption hallucinations on T2I g... | {
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2412.19533 | P3S-Diffusion:A Selective Subject-driven Generation Framework via Point
Supervision | [
"cs.CV",
"cs.AI"
] | Recent research in subject-driven generation increasingly emphasizes the importance of selective subject features. Nevertheless, accurately selecting the content in a given reference image still poses challenges, especially when selecting the similar subjects in an image (e.g., two different dogs). Some methods attempt... | {
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2412.19535 | StyleRWKV: High-Quality and High-Efficiency Style Transfer with
RWKV-like Architecture | [
"cs.CV"
] | Style transfer aims to generate a new image preserving the content but with the artistic representation of the style source. Most of the existing methods are based on Transformers or diffusion models, however, they suffer from quadratic computational complexity and high inference time. RWKV, as an emerging deep sequenc... | {
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2412.19537 | Finger in Camera Speaks Everything: Unconstrained Air-Writing for
Real-World | [
"cs.CV",
"cs.HC"
] | Air-writing is a challenging task that combines the fields of computer vision and natural language processing, offering an intuitive and natural approach for human-computer interaction. However, current air-writing solutions face two primary challenges: (1) their dependency on complex sensors (e.g., Radar, EEGs and oth... | {
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2412.19538 | Scalable Hierarchical Reinforcement Learning for Hyper Scale Multi-Robot
Task Planning | [
"cs.RO",
"cs.AI",
"cs.MA"
] | To improve the efficiency of warehousing system and meet huge customer orders, we aim to solve the challenges of dimension disaster and dynamic properties in hyper scale multi-robot task planning (MRTP) for robotic mobile fulfillment system (RMFS). Existing research indicates that hierarchical reinforcement learning (H... | {
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} |
2412.19542 | Interacted Object Grounding in Spatio-Temporal Human-Object Interactions | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Spatio-temporal Human-Object Interaction (ST-HOI) understanding aims at detecting HOIs from videos, which is crucial for activity understanding. However, existing whole-body-object interaction video benchmarks overlook the truth that open-world objects are diverse, that is, they usually provide limited and predefined o... | {
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} |
2412.19543 | Diverse Rare Sample Generation with Pretrained GANs | [
"cs.CV"
] | Deep generative models are proficient in generating realistic data but struggle with producing rare samples in low density regions due to their scarcity of training datasets and the mode collapse problem. While recent methods aim to improve the fidelity of generated samples, they often reduce diversity and coverage by ... | {
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} |
2412.19544 | TARGA: Targeted Synthetic Data Generation for Practical Reasoning over
Structured Data | [
"cs.CL",
"cs.AI"
] | Semantic parsing, which converts natural language questions into logic forms, plays a crucial role in reasoning within structured environments. However, existing methods encounter two significant challenges: reliance on extensive manually annotated datasets and limited generalization capability to unseen examples. To t... | {
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} |
2412.19547 | Unprejudiced Training Auxiliary Tasks Makes Primary Better: A Multi-Task
Learning Perspective | [
"cs.CV"
] | Human beings can leverage knowledge from relative tasks to improve learning on a primary task. Similarly, multi-task learning methods suggest using auxiliary tasks to enhance a neural network's performance on a specific primary task. However, previous methods often select auxiliary tasks carefully but treat them as sec... | {
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} |
2412.19550 | Learning states enhanced knowledge tracing: Simulating the diversity in
real-world learning process | [
"cs.AI"
] | The Knowledge Tracing (KT) task focuses on predicting a learner's future performance based on the historical interactions. The knowledge state plays a key role in learning process. However, considering that the knowledge state is influenced by various learning factors in the interaction process, such as the exercises s... | {
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} |
2412.19553 | Structural Similarity in Deep Features: Image Quality Assessment Robust
to Geometrically Disparate Reference | [
"cs.CV",
"eess.IV"
] | Image Quality Assessment (IQA) with references plays an important role in optimizing and evaluating computer vision tasks. Traditional methods assume that all pixels of the reference and test images are fully aligned. Such Aligned-Reference IQA (AR-IQA) approaches fail to address many real-world problems with various g... | {
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} |
2412.19562 | Hindsight Planner: A Closed-Loop Few-Shot Planner for Embodied
Instruction Following | [
"cs.AI",
"cs.RO"
] | This work focuses on building a task planner for Embodied Instruction Following (EIF) using Large Language Models (LLMs). Previous works typically train a planner to imitate expert trajectories, treating this as a supervised task. While these methods achieve competitive performance, they often lack sufficient robustnes... | {
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} |
2412.19563 | Reinforced Label Denoising for Weakly-Supervised Audio-Visual Video
Parsing | [
"cs.CV"
] | Audio-visual video parsing (AVVP) aims to recognize audio and visual event labels with precise temporal boundaries, which is quite challenging since audio or visual modality might include only one event label with only the overall video labels available. Existing label denoising models often treat the denoising process... | {
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} |
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