id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2412.08473 | Multi-perspective Alignment for Increasing Naturalness in Neural Machine
Translation | [
"cs.CL"
] | Neural machine translation (NMT) systems amplify lexical biases present in their training data, leading to artificially impoverished language in output translations. These language-level characteristics render automatic translations different from text originally written in a language and human translations, which hind... | {
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2412.08477 | Accurate Water Level Monitoring in AWD Rice Cultivation Using
Convolutional Neural Networks | [
"cs.CV",
"cs.AI"
] | The Alternate Wetting and Drying (AWD) method is a rice-growing water management technique promoted as a sustainable alternative to Continuous Flooding (CF). Climate change has placed the agricultural sector in a challenging position, particularly as global water resources become increasingly scarce, affecting rice pro... | {
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2412.08479 | CAT: Class Aware Adaptive Thresholding for Semi-Supervised Domain
Generalization | [
"cs.CV"
] | Domain Generalization (DG) seeks to transfer knowledge from multiple source domains to unseen target domains, even in the presence of domain shifts. Achieving effective generalization typically requires a large and diverse set of labeled source data to learn robust representations that can generalize to new, unseen dom... | {
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2412.08480 | InvDiff: Invariant Guidance for Bias Mitigation in Diffusion Models | [
"cs.CV",
"cs.IR",
"cs.LG"
] | As one of the most successful generative models, diffusion models have demonstrated remarkable efficacy in synthesizing high-quality images. These models learn the underlying high-dimensional data distribution in an unsupervised manner. Despite their success, diffusion models are highly data-driven and prone to inherit... | {
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2412.08482 | SAM-Mamba: Mamba Guided SAM Architecture for Generalized Zero-Shot Polyp
Segmentation | [
"cs.CV"
] | Polyp segmentation in colonoscopy is crucial for detecting colorectal cancer. However, it is challenging due to variations in the structure, color, and size of polyps, as well as the lack of clear boundaries with surrounding tissues. Traditional segmentation models based on Convolutional Neural Networks (CNNs) struggle... | {
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2412.08484 | ConvMesh: Reimagining Mesh Quality Through Convex Optimization | [
"cs.GR",
"cs.CV",
"math.OC"
] | Mesh generation has become a critical topic in recent years, forming the foundation of all 3D objects used across various applications, such as virtual reality, gaming, and 3D printing. With advancements in computational resources and machine learning, neural networks have emerged as powerful tools for generating high-... | {
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2412.08486 | Learning Flow Fields in Attention for Controllable Person Image
Generation | [
"cs.CV"
] | Controllable person image generation aims to generate a person image conditioned on reference images, allowing precise control over the person's appearance or pose. However, prior methods often distort fine-grained textural details from the reference image, despite achieving high overall image quality. We attribute the... | {
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2412.08489 | A Dual-Module Denoising Approach with Curriculum Learning for Enhancing
Multimodal Aspect-Based Sentiment Analysis | [
"cs.CV",
"cs.MM"
] | Multimodal Aspect-Based Sentiment Analysis (MABSA) combines text and images to perform sentiment analysis but often struggles with irrelevant or misleading visual information. Existing methodologies typically address either sentence-image denoising or aspect-image denoising but fail to comprehensively tackle both types... | {
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2412.08490 | SuperCode: Sustainability PER AI-driven CO-DEsign | [
"astro-ph.IM",
"cs.AI"
] | Currently, data-intensive scientific applications require vast amounts of compute resources to deliver world-leading science. The climate emergency has made it clear that unlimited use of resources (e.g., energy) for scientific discovery is no longer acceptable. Future computing hardware promises to be much more energy... | {
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2412.08496 | Drift-free Visual SLAM using Digital Twins | [
"cs.RO"
] | Globally-consistent localization in urban environments is crucial for autonomous systems such as self-driving vehicles and drones, as well as assistive technologies for visually impaired people. Traditional Visual-Inertial Odometry (VIO) and Visual Simultaneous Localization and Mapping (VSLAM) methods, though adequate ... | {
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2412.08501 | GradStop: Exploring Training Dynamics in Unsupervised Outlier Detection
through Gradient Cohesion | [
"cs.LG"
] | Unsupervised Outlier Detection (UOD) is a critical task in data mining and machine learning, aiming to identify instances that significantly deviate from the majority. Without any label, deep UOD methods struggle with the misalignment between the model's direct optimization goal and the final performance goal of Outlie... | {
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2412.08503 | StyleStudio: Text-Driven Style Transfer with Selective Control of Style
Elements | [
"cs.CV"
] | Text-driven style transfer aims to merge the style of a reference image with content described by a text prompt. Recent advancements in text-to-image models have improved the nuance of style transformations, yet significant challenges remain, particularly with overfitting to reference styles, limiting stylistic control... | {
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2412.08504 | PointTalk: Audio-Driven Dynamic Lip Point Cloud for 3D Gaussian-based
Talking Head Synthesis | [
"cs.SD",
"cs.AI",
"cs.GR",
"cs.MM",
"eess.AS"
] | Talking head synthesis with arbitrary speech audio is a crucial challenge in the field of digital humans. Recently, methods based on radiance fields have received increasing attention due to their ability to synthesize high-fidelity and identity-consistent talking heads from just a few minutes of training video. Howeve... | {
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2412.08505 | Open-Loop and Model Predictive Control for Electric Vehicle Charging to
Manage Excess Renewable Energy Supply in Texas | [
"eess.SY",
"cs.SY"
] | Modern power grids are evolving to become more interconnected, include more electric vehicles (EVs), and utilize more renewable energy sources (RES). Increased interconnectivity provides an opportunity to manage EVs and RES by using price signaling to shift EV loads towards periods of high RES output. This work uses ER... | {
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2412.08506 | Orchestrating the Symphony of Prompt Distribution Learning for
Human-Object Interaction Detection | [
"cs.CV"
] | Human-object interaction (HOI) detectors with popular query-transformer architecture have achieved promising performance. However, accurately identifying uncommon visual patterns and distinguishing between ambiguous HOIs continue to be difficult for them. We observe that these difficulties may arise from the limited ca... | {
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2412.08508 | Comparative Opinion Mining in Product Reviews: Multi-perspective
Prompt-based Learning | [
"cs.CL"
] | Comparative reviews are pivotal in understanding consumer preferences and influencing purchasing decisions. Comparative Quintuple Extraction (COQE) aims to identify five key components in text: the target entity, compared entities, compared aspects, opinions on these aspects, and polarity. Extracting precise comparativ... | {
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2412.08511 | Combining Neural Fields and Deformation Models for Non-Rigid 3D Motion
Reconstruction from Partial Data | [
"cs.CV"
] | We introduce a novel, data-driven approach for reconstructing temporally coherent 3D motion from unstructured and potentially partial observations of non-rigidly deforming shapes. Our goal is to achieve high-fidelity motion reconstructions for shapes that undergo near-isometric deformations, such as humans wearing loos... | {
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2412.08512 | Galois hulls of constacyclic codes over affine algebra rings | [
"cs.IT",
"math.IT"
] | Let $\mathcal A$ the affine algebra given by the ring $\mathbb{F}_q[X_1,X_2,\ldots,X_\ell]/ I$, where $I$ is the ideal $\langle t_1(X_1), t_2(X_2), \ldots, t_\ell(X_\ell) \rangle$ with each $t_i(X_i)$, $1\leq i\leq \ell$, being a square-free polynomial over $\mathbb{F}_q$. This paper studies the $k$-Galois hulls of $\l... | {
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2412.08513 | REPEAT: Improving Uncertainty Estimation in Representation Learning
Explainability | [
"cs.LG",
"cs.AI"
] | Incorporating uncertainty is crucial to provide trustworthy explanations of deep learning models. Recent works have demonstrated how uncertainty modeling can be particularly important in the unsupervised field of representation learning explainable artificial intelligence (R-XAI). Current R-XAI methods provide uncertai... | {
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2412.08514 | Image-Based Malware Classification Using QR and Aztec Codes | [
"cs.CR",
"cs.LG"
] | In recent years, the use of image-based techniques for malware detection has gained prominence, with numerous studies demonstrating the efficacy of deep learning approaches such as Convolutional Neural Networks (CNN) in classifying images derived from executable files. In this paper, we consider an innovative method th... | {
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2412.08515 | Enhancing Interpretability Through Loss-Defined Classification Objective
in Structured Latent Spaces | [
"cs.LG",
"cs.AI"
] | Supervised machine learning often operates on the data-driven paradigm, wherein internal model parameters are autonomously optimized to converge predicted outputs with the ground truth, devoid of explicitly programming rules or a priori assumptions. Although data-driven methods have yielded notable successes across var... | {
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2412.08516 | AltFS: Agency-light Feature Selection with Large Language Models in Deep
Recommender Systems | [
"cs.IR"
] | Feature selection is crucial in recommender systems for improving model efficiency and predictive performance. Traditional methods rely on agency models, such as decision trees or neural networks, to estimate feature importance. However, this approach is inherently limited, as the agency models may fail to learn effect... | {
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2412.08519 | Bridging Relevance and Reasoning: Rationale Distillation in
Retrieval-Augmented Generation | [
"cs.CL"
] | The reranker and generator are two critical components in the Retrieval-Augmented Generation (i.e., RAG) pipeline, responsible for ranking relevant documents and generating responses. However, due to differences in pre-training data and objectives, there is an inevitable gap between the documents ranked as relevant by ... | {
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2412.08520 | GR-NLP-TOOLKIT: An Open-Source NLP Toolkit for Modern Greek | [
"cs.CL",
"cs.AI",
"cs.SE"
] | We present GR-NLP-TOOLKIT, an open-source natural language processing (NLP) toolkit developed specifically for modern Greek. The toolkit provides state-of-the-art performance in five core NLP tasks, namely part-of-speech tagging, morphological tagging, dependency parsing, named entity recognition, and Greeklishto-Greek... | {
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2412.08521 | EMS: Adaptive Evict-then-Merge Strategy for Head-wise KV Cache
Compression Based on Global-Local Importance | [
"cs.CL"
] | As large language models (LLMs) continue to advance, the demand for higher quality and faster processing of long contexts across various applications is growing. KV cache is widely adopted as it stores previously generated key and value tokens, effectively reducing redundant computations during inference. However, as m... | {
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2412.08522 | Subspace-wise Hybrid RL for Articulated Object Manipulation | [
"cs.RO"
] | Articulated object manipulation is a challenging task, requiring constrained motion and adaptive control to handle the unknown dynamics of the manipulated objects. While reinforcement learning (RL) has been widely employed to tackle various scenarios and types of articulated objects, the complexity of these tasks, stem... | {
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2412.08524 | Learning to Decouple the Lights for 3D Face Texture Modeling | [
"cs.CV"
] | Existing research has made impressive strides in reconstructing human facial shapes and textures from images with well-illuminated faces and minimal external occlusions. Nevertheless, it remains challenging to recover accurate facial textures from scenarios with complicated illumination affected by external occlusions,... | {
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2412.08526 | Spend More to Save More (SM2): An Energy-Aware Implementation of
Successive Halving for Sustainable Hyperparameter Optimization | [
"cs.LG"
] | A fundamental step in the development of machine learning models commonly involves the tuning of hyperparameters, often leading to multiple model training runs to work out the best-performing configuration. As machine learning tasks and models grow in complexity, there is an escalating need for solutions that not only ... | {
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2412.08528 | Continual Learning for Encoder-only Language Models via a Discrete
Key-Value Bottleneck | [
"cs.CL"
] | Continual learning remains challenging across various natural language understanding tasks. When models are updated with new training data, they risk catastrophic forgetting of prior knowledge. In the present work, we introduce a discrete key-value bottleneck for encoder-only language models, allowing for efficient con... | {
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2412.08529 | TECO: Improving Multimodal Intent Recognition with Text Enhancement
through Commonsense Knowledge Extraction | [
"cs.CL"
] | The objective of multimodal intent recognition (MIR) is to leverage various modalities-such as text, video, and audio-to detect user intentions, which is crucial for understanding human language and context in dialogue systems. Despite advances in this field, two main challenges persist: (1) effectively extracting and ... | {
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2412.08534 | Protecting Confidentiality, Privacy and Integrity in Collaborative
Learning | [
"cs.DC",
"cs.CR",
"cs.LG"
] | A collaboration between dataset owners and model owners is needed to facilitate effective machine learning (ML) training. During this collaboration, however, dataset owners and model owners want to protect the confidentiality of their respective assets (i.e., datasets, models and training code), with the dataset owners... | {
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2412.08536 | SenCLIP: Enhancing zero-shot land-use mapping for Sentinel-2 with
ground-level prompting | [
"cs.CV"
] | Pre-trained vision-language models (VLMs), such as CLIP, demonstrate impressive zero-shot classification capabilities with free-form prompts and even show some generalization in specialized domains. However, their performance on satellite imagery is limited due to the underrepresentation of such data in their training ... | {
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2412.08541 | Euclidean Fast Attention: Machine Learning Global Atomic Representations
at Linear Cost | [
"cs.LG"
] | Long-range correlations are essential across numerous machine learning tasks, especially for data embedded in Euclidean space, where the relative positions and orientations of distant components are often critical for accurate predictions. Self-attention offers a compelling mechanism for capturing these global effects,... | {
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2412.08542 | MaestroMotif: Skill Design from Artificial Intelligence Feedback | [
"cs.AI",
"cs.CL",
"cs.LG"
] | Describing skills in natural language has the potential to provide an accessible way to inject human knowledge about decision-making into an AI system. We present MaestroMotif, a method for AI-assisted skill design, which yields high-performing and adaptable agents. MaestroMotif leverages the capabilities of Large Lang... | {
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2412.08544 | Training Data Reconstruction: Privacy due to Uncertainty? | [
"cs.LG",
"cs.CR"
] | Being able to reconstruct training data from the parameters of a neural network is a major privacy concern. Previous works have shown that reconstructing training data, under certain circumstances, is possible. In this work, we analyse such reconstructions empirically and propose a new formulation of the reconstruction... | {
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2412.08545 | Improving Satellite Imagery Masking using Multi-task and Transfer
Learning | [
"cs.CV"
] | Many remote sensing applications employ masking of pixels in satellite imagery for subsequent measurements. For example, estimating water quality variables, such as Suspended Sediment Concentration (SSC) requires isolating pixels depicting water bodies unaffected by clouds, their shadows, terrain shadows, and snow and ... | {
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2412.08548 | Bilevel Joint Unsupervised and Supervised Training for Automatic Speech
Recognition | [
"cs.CL"
] | In this paper, we propose a bilevel joint unsupervised and supervised training (BL-JUST) framework for automatic speech recognition. Compared to the conventional pre-training and fine-tuning strategy which is a disconnected two-stage process, BL-JUST tries to optimize an acoustic model such that it simultaneously minim... | {
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2412.08549 | Watermarking Training Data of Music Generation Models | [
"cs.LG",
"cs.SD",
"eess.AS"
] | Generative Artificial Intelligence (Gen-AI) models are increasingly used to produce content across domains, including text, images, and audio. While these models represent a major technical breakthrough, they gain their generative capabilities from being trained on enormous amounts of human-generated content, which oft... | {
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2412.08555 | Grimm: A Plug-and-Play Perturbation Rectifier for Graph Neural Networks
Defending against Poisoning Attacks | [
"cs.LG"
] | Recent studies have revealed the vulnerability of graph neural networks (GNNs) to adversarial poisoning attacks on node classification tasks. Current defensive methods require substituting the original GNNs with defense models, regardless of the original's type. This approach, while targeting adversarial robustness, co... | {
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2412.08556 | Exact Algorithms for Multiagent Path Finding with Communication
Constraints on Tree-Like Structures | [
"cs.CC",
"cs.AI"
] | Consider the scenario where multiple agents have to move in an optimal way through a network, each one towards their ending position while avoiding collisions. By optimal, we mean as fast as possible, which is evaluated by a measure known as the makespan of the proposed solution. This is the setting studied in the Mult... | {
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2412.08559 | Underestimated Privacy Risks for Minority Populations in Large Language
Model Unlearning | [
"cs.LG"
] | Large Language Models are trained on extensive datasets that often contain sensitive, human-generated information, raising significant concerns about privacy breaches. While certified unlearning approaches offer strong privacy guarantees, they rely on restrictive model assumptions that are not applicable to LLMs. As a ... | {
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2412.08562 | An End-to-End Collaborative Learning Approach for Connected Autonomous
Vehicles in Occluded Scenarios | [
"cs.RO",
"cs.MA"
] | Collaborative navigation becomes essential in situations of occluded scenarios in autonomous driving where independent driving policies are likely to lead to collisions. One promising approach to address this issue is through the use of Vehicle-to-Vehicle (V2V) networks that allow for the sharing of perception informat... | {
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2412.08563 | Physics Based Differentiable Rendering for Inverse Problems and Beyond | [
"cs.CV",
"cs.GR"
] | Physics-based differentiable rendering (PBDR) has become an efficient method in computer vision, graphics, and machine learning for addressing an array of inverse problems. PBDR allows patterns to be generated from perceptions which can be applied to enhance object attributes like geometry, substances, and lighting by ... | {
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2412.08564 | Template-Based Visual Program Distillation | [
"cs.CV",
"cs.CL"
] | For users with limited computational resources, visual programming or prompting large language models (LLMs) to generate executable code for visual tasks, like visual question answering (VQA), remains largely inaccessible. Even with techniques such as distillation, adapting visual programming to smaller models or speci... | {
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2412.08565 | GenPlan: Generative Sequence Models as Adaptive Planners | [
"cs.LG",
"cs.AI"
] | Sequence models have demonstrated remarkable success in behavioral planning by leveraging previously collected demonstrations. However, solving multi-task missions remains a significant challenge, particularly when the planner must adapt to unseen constraints and tasks, such as discovering goals and unlocking doors. Su... | {
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2412.08568 | Real-Time Trajectory Generation for Soft Robot Manipulators Using
Differential Flatness | [
"cs.RO",
"cs.SY",
"eess.SY"
] | Soft robots have the potential to interact with sensitive environments and perform complex tasks effectively. However, motion plans and trajectories for soft manipulators are challenging to calculate due to their deformable nature and nonlinear dynamics. This article introduces a fast real-time trajectory generation ap... | {
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2412.08573 | TryOffAnyone: Tiled Cloth Generation from a Dressed Person | [
"cs.CV"
] | The fashion industry is increasingly leveraging computer vision and deep learning technologies to enhance online shopping experiences and operational efficiencies. In this paper, we address the challenge of generating high-fidelity tiled garment images essential for personalized recommendations, outfit composition, and... | {
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2412.08574 | Learning Sketch Decompositions in Planning via Deep Reinforcement
Learning | [
"cs.AI"
] | In planning and reinforcement learning, the identification of common subgoal structures across problems is important when goals are to be achieved over long horizons. Recently, it has been shown that such structures can be expressed as feature-based rules, called sketches, over a number of classical planning domains. T... | {
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2412.08575 | Annotation-Efficient Task Guidance for Medical Segment Anything | [
"cs.CV"
] | Medical image segmentation is a key task in the imaging workflow, influencing many image-based decisions. Traditional, fully-supervised segmentation models rely on large amounts of labeled training data, typically obtained through manual annotation, which can be an expensive, time-consuming, and error-prone process. Th... | {
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2412.08578 | Machine Learning Information Retrieval and Summarisation to Support
Systematic Review on Outcomes Based Contracting | [
"cs.CL",
"cs.CY",
"cs.DL",
"cs.HC"
] | As academic literature proliferates, traditional review methods are increasingly challenged by the sheer volume and diversity of available research. This article presents a study that aims to address these challenges by enhancing the efficiency and scope of systematic reviews in the social sciences through advanced mac... | {
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2412.08580 | LAION-SG: An Enhanced Large-Scale Dataset for Training Complex
Image-Text Models with Structural Annotations | [
"cs.CV"
] | Recent advances in text-to-image (T2I) generation have shown remarkable success in producing high-quality images from text. However, existing T2I models show decayed performance in compositional image generation involving multiple objects and intricate relationships. We attribute this problem to limitations in existing... | {
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2412.08582 | Utilizing Multi-step Loss for Single Image Reflection Removal | [
"cs.CV",
"eess.IV"
] | Image reflection removal is crucial for restoring image quality. Distorted images can negatively impact tasks like object detection and image segmentation. In this paper, we present a novel approach for image reflection removal using a single image. Instead of focusing on model architecture, we introduce a new training... | {
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2412.08583 | A Principled Solution to the Disjunction Problem of Diagrammatic Query
Representations | [
"cs.DB",
"cs.LO"
] | Finding unambiguous diagrammatic representations for first-order logical formulas and relational queries with arbitrarily nested disjunctions has been a surprisingly long-standing unsolved problem. We refer to this problem as the disjunction problem (of diagrammatic query representations). This work solves the disjun... | {
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2412.08585 | TurboAttention: Efficient Attention Approximation For High Throughputs
LLMs | [
"cs.LG",
"cs.AI",
"cs.AR"
] | Large language model (LLM) inference demands significant amount of computation and memory, especially in the key attention mechanism. While techniques, such as quantization and acceleration algorithms, like FlashAttention, have improved efficiency of the overall inference, they address different aspects of the problem:... | {
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2412.08586 | Asymptotically good CSS-T codes exist | [
"quant-ph",
"cs.IT",
"math.IT"
] | We give a new construction of binary quantum codes that enables the generation of a CSS-T code from any given CSS code. Using this construction, we prove the existence of asymptotically good binary CSS-T codes, resolving a previously open problem in the literature. Furthermore, we demonstrate that the same result holds... | {
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2412.08587 | Advancing Single- and Multi-task Text Classification through Large
Language Model Fine-tuning | [
"cs.CL",
"cs.AI"
] | Both encoder-only models (e.g., BERT, RoBERTa) and large language models (LLMs, e.g., Llama3) have been widely used for text classification tasks. However, there is a lack of systematic studies comparing the performance of encoder-based models and LLMs in text classification, particularly when fine-tuning is involved. ... | {
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2412.08589 | SPACE-SUIT: An Artificial Intelligence based chromospheric feature
extractor and classifier for SUIT | [
"astro-ph.SR",
"astro-ph.IM",
"cs.CV",
"cs.LG"
] | The Solar Ultraviolet Imaging Telescope(SUIT) onboard Aditya-L1 is an imager that observes the solar photosphere and chromosphere through observations in the wavelength range of 200-400 nm. A comprehensive understanding of the plasma and thermodynamic properties of chromospheric and photospheric morphological structure... | {
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2412.08590 | Preventing Conflicting Gradients in Neural Marked Temporal Point
Processes | [
"cs.LG"
] | Neural Marked Temporal Point Processes (MTPP) are flexible models to capture complex temporal inter-dependencies between labeled events. These models inherently learn two predictive distributions: one for the arrival times of events and another for the types of events, also known as marks. In this study, we demonstrate... | {
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2412.08591 | RoomTour3D: Geometry-Aware Video-Instruction Tuning for Embodied
Navigation | [
"cs.CV",
"cs.AI",
"cs.RO"
] | Vision-and-Language Navigation (VLN) suffers from the limited diversity and scale of training data, primarily constrained by the manual curation of existing simulators. To address this, we introduce RoomTour3D, a video-instruction dataset derived from web-based room tour videos that capture real-world indoor spaces and... | {
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2412.08592 | Adaptive Principal Components Allocation with the
$\ell_{2,g}$-regularized Gaussian Graphical Model for Efficient Fine-Tuning
Large Models | [
"cs.LG"
] | In this work, we propose a novel Parameter-Efficient Fine-Tuning (PEFT) approach based on Gaussian Graphical Models (GGMs), marking the first application of GGMs to PEFT tasks, to the best of our knowledge. The proposed method utilizes the $\ell_{2,g}$-norm to effectively select critical parameters and capture global d... | {
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2412.08593 | Leveraging Graph-RAG and Prompt Engineering to Enhance LLM-Based
Automated Requirement Traceability and Compliance Checks | [
"cs.SE",
"cs.IR"
] | Ensuring that Software Requirements Specifications (SRS) align with higher-level organizational or national requirements is vital, particularly in regulated environments such as finance and aerospace. In these domains, maintaining consistency, adhering to regulatory frameworks, minimizing errors, and meeting critical e... | {
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2412.08594 | ASDnB: Merging Face with Body Cues For Robust Active Speaker Detection | [
"cs.CV"
] | State-of-the-art Active Speaker Detection (ASD) approaches mainly use audio and facial features as input. However, the main hypothesis in this paper is that body dynamics is also highly correlated to "speaking" (and "listening") actions and should be particularly useful in wild conditions (e.g., surveillance settings),... | {
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2412.08595 | Numerical Analysis of HiPPO-LegS ODE for Deep State Space Models | [
"math.NA",
"cs.LG",
"cs.NA"
] | In deep learning, the recently introduced state space models utilize HiPPO (High-order Polynomial Projection Operators) memory units to approximate continuous-time trajectories of input functions using ordinary differential equations (ODEs), and these techniques have shown empirical success in capturing long-range depe... | {
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2412.08596 | Quantum-enhanced belief propagation for LDPC decoding | [
"quant-ph",
"cs.IT",
"math.IT"
] | Decoding low-density parity-check codes is critical in many current technologies, such as fifth-generation (5G) wireless networks and satellite communications. The belief propagation algorithm allows for fast decoding due to the low density of these codes. However, there is scope for improvement to this algorithm both ... | {
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2412.08599 | Der Effizienz- und Intelligenzbegriff in der Lexikographie und
kuenstlichen Intelligenz: kann ChatGPT die lexikographische Textsorte
nachbilden? | [
"cs.CL"
] | By means of pilot experiments for the language pair German and Galician, this paper examines the concept of efficiency and intelligence in lexicography and artificial intelligence, AI. The aim of the experiments is to gain empirically and statistically based insights into the lexicographical text type,dictionary articl... | {
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2412.08603 | Design2GarmentCode: Turning Design Concepts to Tangible Garments Through
Program Synthesis | [
"cs.GR",
"cs.CV"
] | Sewing patterns, the essential blueprints for fabric cutting and tailoring, act as a crucial bridge between design concepts and producible garments. However, existing uni-modal sewing pattern generation models struggle to effectively encode complex design concepts with a multi-modal nature and correlate them with vecto... | {
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2412.08604 | Preference Discerning with LLM-Enhanced Generative Retrieval | [
"cs.IR",
"cs.AI",
"cs.LG",
"stat.ML"
] | Sequential recommendation systems aim to provide personalized recommendations for users based on their interaction history. To achieve this, they often incorporate auxiliary information, such as textual descriptions of items and auxiliary tasks, like predicting user preferences and intent. Despite numerous efforts to e... | {
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2412.08608 | AdvWave: Stealthy Adversarial Jailbreak Attack against Large
Audio-Language Models | [
"cs.SD",
"cs.AI",
"cs.CR",
"eess.AS"
] | Recent advancements in large audio-language models (LALMs) have enabled speech-based user interactions, significantly enhancing user experience and accelerating the deployment of LALMs in real-world applications. However, ensuring the safety of LALMs is crucial to prevent risky outputs that may raise societal concerns ... | {
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2412.08610 | Competition and Diversity in Generative AI | [
"cs.GT",
"cs.AI",
"cs.CY"
] | Recent evidence suggests that the use of generative artificial intelligence reduces the diversity of content produced. In this work, we develop a game-theoretic model to explore the downstream consequences of content homogeneity when producers use generative AI to compete with one another. At equilibrium, players indee... | {
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2412.08613 | Fair Primal Dual Splitting Method for Image Inverse Problems | [
"cs.CV",
"math.OC"
] | Image inverse problems have numerous applications, including image processing, super-resolution, and computer vision, which are important areas in image science. These application models can be seen as a three-function composite optimization problem solvable by a variety of primal dual-type methods. We propose a fair p... | {
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2412.08614 | Benchmarking Large Vision-Language Models via Directed Scene Graph for
Comprehensive Image Captioning | [
"cs.CV"
] | Generating detailed captions comprehending text-rich visual content in images has received growing attention for Large Vision-Language Models (LVLMs). However, few studies have developed benchmarks specifically tailored for detailed captions to measure their accuracy and comprehensiveness. In this paper, we introduce a... | {
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2412.08615 | Exploiting the Index Gradients for Optimization-Based Jailbreaking on
Large Language Models | [
"cs.CL"
] | Despite the advancements in training Large Language Models (LLMs) with alignment techniques to enhance the safety of generated content, these models remain susceptible to jailbreak, an adversarial attack method that exposes security vulnerabilities in LLMs. Notably, the Greedy Coordinate Gradient (GCG) method has demon... | {
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2412.08618 | Image Retrieval Methods in the Dissimilarity Space | [
"cs.CV",
"cs.AI"
] | Image retrieval methods rely on metric learning to train backbone feature extraction models that can extract discriminant queries and reference (gallery) feature representations for similarity matching. Although state-of-the-art accuracy has improved considerably with the advent of deep learning (DL) models trained on ... | {
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2412.08619 | Synthetic Vision: Training Vision-Language Models to Understand Physics | [
"cs.CV",
"cs.AI"
] | Physical reasoning, which involves the interpretation, understanding, and prediction of object behavior in dynamic environments, remains a significant challenge for current Vision-Language Models (VLMs). In this work, we propose two methods to enhance VLMs' physical reasoning capabilities using simulated data. First, w... | {
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2412.08628 | EOV-Seg: Efficient Open-Vocabulary Panoptic Segmentation | [
"cs.CV"
] | Open-vocabulary panoptic segmentation aims to segment and classify everything in diverse scenes across an unbounded vocabulary. Existing methods typically employ two-stage or single-stage framework. The two-stage framework involves cropping the image multiple times using masks generated by a mask generator, followed by... | {
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2412.08629 | FlowEdit: Inversion-Free Text-Based Editing Using Pre-Trained Flow
Models | [
"cs.CV",
"cs.LG"
] | Editing real images using a pre-trained text-to-image (T2I) diffusion/flow model often involves inverting the image into its corresponding noise map. However, inversion by itself is typically insufficient for obtaining satisfactory results, and therefore many methods additionally intervene in the sampling process. Such... | {
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2412.08633 | MNIST-Fraction: Enhancing Math Education with AI-Driven Fraction
Detection and Analysis | [
"cs.LG"
] | Mathematics education, a crucial and basic field, significantly influences students' learning in related subjects and their future careers. Utilizing artificial intelligence to interpret and comprehend math problems in education is not yet fully explored. This is due to the scarcity of quality datasets and the intricac... | {
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2412.08635 | Multimodal Latent Language Modeling with Next-Token Diffusion | [
"cs.CL",
"cs.CV",
"cs.LG"
] | Multimodal generative models require a unified approach to handle both discrete data (e.g., text and code) and continuous data (e.g., image, audio, video). In this work, we propose Latent Language Modeling (LatentLM), which seamlessly integrates continuous and discrete data using causal Transformers. Specifically, we e... | {
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2412.08637 | DMin: Scalable Training Data Influence Estimation for Diffusion Models | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Identifying the training data samples that most influence a generated image is a critical task in understanding diffusion models, yet existing influence estimation methods are constrained to small-scale or LoRA-tuned models due to computational limitations. As diffusion models scale up, these methods become impractical... | {
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2412.08639 | Fast Prompt Alignment for Text-to-Image Generation | [
"cs.CL",
"cs.CV"
] | Text-to-image generation has advanced rapidly, yet aligning complex textual prompts with generated visuals remains challenging, especially with intricate object relationships and fine-grained details. This paper introduces Fast Prompt Alignment (FPA), a prompt optimization framework that leverages a one-pass approach, ... | {
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2412.08640 | BLADE: Single-view Body Mesh Learning through Accurate Depth Estimation | [
"cs.CV"
] | Single-image human mesh recovery is a challenging task due to the ill-posed nature of simultaneous body shape, pose, and camera estimation. Existing estimators work well on images taken from afar, but they break down as the person moves close to the camera. Moreover, current methods fail to achieve both accurate 3D pos... | {
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2412.08641 | 3D Mesh Editing using Masked LRMs | [
"cs.CV"
] | We present a novel approach to mesh shape editing, building on recent progress in 3D reconstruction from multi-view images. We formulate shape editing as a conditional reconstruction problem, where the model must reconstruct the input shape with the exception of a specified 3D region, in which the geometry should be ge... | {
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2412.08642 | Generative Semantic Communication: Architectures, Technologies, and
Applications | [
"cs.IT",
"cs.LG",
"cs.NI",
"math.IT"
] | This paper delves into the applications of generative artificial intelligence (GAI) in semantic communication (SemCom) and presents a thorough study. Three popular SemCom systems enabled by classical GAI models are first introduced, including variational autoencoders, generative adversarial networks, and diffusion mode... | {
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2412.08643 | GPD-1: Generative Pre-training for Driving | [
"cs.CV",
"cs.AI",
"cs.LG",
"cs.RO"
] | Modeling the evolutions of driving scenarios is important for the evaluation and decision-making of autonomous driving systems. Most existing methods focus on one aspect of scene evolution such as map generation, motion prediction, and trajectory planning. In this paper, we propose a unified Generative Pre-training for... | {
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2412.08644 | Bilevel Learning for Dual-Quadruped Collaborative Transportation under
Kinematic and Anisotropic Velocity Constraints | [
"cs.RO"
] | Multi-robot collaborative transportation is a critical capability that has attracted significant attention over recent years. To reliably transport a kinematically constrained payload, a team of robots must closely collaborate and coordinate their individual velocities to achieve the desired payload motion. For quadrup... | {
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2412.08645 | ObjectMate: A Recurrence Prior for Object Insertion and Subject-Driven
Generation | [
"cs.CV"
] | This paper introduces a tuning-free method for both object insertion and subject-driven generation. The task involves composing an object, given multiple views, into a scene specified by either an image or text. Existing methods struggle to fully meet the task's challenging objectives: (i) seamlessly composing the obje... | {
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2412.08646 | StreamChat: Chatting with Streaming Video | [
"cs.CV"
] | This paper presents StreamChat, a novel approach that enhances the interaction capabilities of Large Multimodal Models (LMMs) with streaming video content. In streaming interaction scenarios, existing methods rely solely on visual information available at the moment a question is posed, resulting in significant delays ... | {
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2412.08647 | SegFace: Face Segmentation of Long-Tail Classes | [
"cs.CV"
] | Face parsing refers to the semantic segmentation of human faces into key facial regions such as eyes, nose, hair, etc. It serves as a prerequisite for various advanced applications, including face editing, face swapping, and facial makeup, which often require segmentation masks for classes like eyeglasses, hats, earrin... | {
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2412.08648 | Detecting Visual Triggers in Cannabis Imagery: A CLIP-Based
Multi-Labeling Framework with Local-Global Aggregation | [
"cs.CY",
"cs.CV"
] | This study investigates the interplay of visual and textual features in online discussions about cannabis edibles and their impact on user engagement. Leveraging the CLIP model, we analyzed 42,743 images from Facebook (March 1 to August 31, 2021), with a focus on detecting food-related visuals and examining the influen... | {
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2412.08649 | Multi-modal Representation Learning Enables Accurate Protein Function
Prediction in Low-Data Setting | [
"q-bio.BM",
"cs.LG"
] | In this study, we propose HOPER (HOlistic ProtEin Representation), a novel multimodal learning framework designed to enhance protein function prediction (PFP) in low-data settings. The challenge of predicting protein functions is compounded by the limited availability of labeled data. Traditional machine learning model... | {
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2412.08650 | Capacitive Touch Sensor Modeling With a Physics-informed Neural Network
and Maxwell's Equations | [
"physics.comp-ph",
"cs.LG",
"eess.SP"
] | Maxwell's equations are the fundamental equations for understanding electric and magnetic field interactions and play a crucial role in designing and optimizing sensor systems like capacitive touch sensors, which are widely prevalent in automotive switches and smartphones. Ensuring robust functionality and stability of... | {
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} |
2412.08651 | Enhancing Code-Switching ASR Leveraging Non-Peaky CTC Loss and Deep
Language Posterior Injection | [
"eess.AS",
"cs.CL",
"cs.LG",
"cs.SD"
] | Code-switching-where multilingual speakers alternately switch between languages during conversations-still poses significant challenges to end-to-end (E2E) automatic speech recognition (ASR) systems due to phenomena of both acoustic and semantic confusion. This issue arises because ASR systems struggle to handle the ra... | {
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} |
2412.08653 | What AI evaluations for preventing catastrophic risks can and cannot do | [
"cs.CY",
"cs.AI"
] | AI evaluations are an important component of the AI governance toolkit, underlying current approaches to safety cases for preventing catastrophic risks. Our paper examines what these evaluations can and cannot tell us. Evaluations can establish lower bounds on AI capabilities and assess certain misuse risks given suffi... | {
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} |
2412.08654 | A Behavior Tree-inspired programming language for autonomous agents | [
"cs.PL",
"cs.AI",
"cs.RO",
"cs.SE"
] | We propose a design for a functional programming language for autonomous agents, built off the ideas and motivations of Behavior Trees (BTs). BTs are a popular model for designing agents behavior in robotics and AI. However, as their growth has increased dramatically, the simple model of BTs has come to be limiting. Th... | {
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} |
2412.08661 | GeoConformal prediction: a model-agnostic framework of measuring the
uncertainty of spatial prediction | [
"stat.ML",
"cs.LG",
"physics.data-an",
"stat.AP"
] | Spatial prediction is a fundamental task in geography. In recent years, with advances in geospatial artificial intelligence (GeoAI), numerous models have been developed to improve the accuracy of geographic variable predictions. Beyond achieving higher accuracy, it is equally important to obtain predictions with uncert... | {
"Other": 0,
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} |
2412.08670 | A feature refinement module for light-weight semantic segmentation
network | [
"cs.CV",
"cs.LG",
"eess.IV"
] | Low computational complexity and high segmentation accuracy are both essential to the real-world semantic segmentation tasks. However, to speed up the model inference, most existing approaches tend to design light-weight networks with a very limited number of parameters, leading to a considerable degradation in accurac... | {
"Other": 0,
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} |
2412.08671 | A Deep Semantic Segmentation Network with Semantic and Contextual
Refinements | [
"cs.CV",
"cs.LG",
"eess.IV"
] | Semantic segmentation is a fundamental task in multimedia processing, which can be used for analyzing, understanding, editing contents of images and videos, among others. To accelerate the analysis of multimedia data, existing segmentation researches tend to extract semantic information by progressively reducing the sp... | {
"Other": 0,
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} |
2412.08672 | Efficient Gravitational Wave Parameter Estimation via Knowledge
Distillation: A ResNet1D-IAF Approach | [
"gr-qc",
"astro-ph.IM",
"cs.LG",
"physics.data-an"
] | With the rapid development of gravitational wave astronomy, the increasing number of detected events necessitates efficient methods for parameter estimation and model updates. This study presents a novel approach using knowledge distillation techniques to enhance computational efficiency in gravitational wave analysis.... | {
"Other": 0,
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} |
2412.08680 | Distinguishing Scams and Fraud with Ensemble Learning | [
"cs.CR",
"cs.AI",
"cs.HC",
"cs.LG"
] | Users increasingly query LLM-enabled web chatbots for help with scam defense. The Consumer Financial Protection Bureau's complaints database is a rich data source for evaluating LLM performance on user scam queries, but currently the corpus does not distinguish between scam and non-scam fraud. We developed an LLM ensem... | {
"Other": 0,
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"cs.SY": 0
} |
2412.08681 | Learning Physics Informed Neural ODEs With Partial Measurements | [
"cs.LG",
"cs.AI"
] | Learning dynamics governing physical and spatiotemporal processes is a challenging problem, especially in scenarios where states are partially measured. In this work, we tackle the problem of learning dynamics governing these systems when parts of the system's states are not measured, specifically when the dynamics gen... | {
"Other": 0,
"cs.AI": 1,
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"cs.SY": 0
} |
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