id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2412.06606 | Vulnerability of Text-Matching in ML/AI Conference Reviewer Assignments
to Collusions | [
"cs.LG",
"cs.CR",
"cs.DL"
] | In the peer review process of top-tier machine learning (ML) and artificial intelligence (AI) conferences, reviewers are assigned to papers through automated methods. These assignment algorithms consider two main factors: (1) reviewers' expressed interests indicated by their bids for papers, and (2) reviewers' domain e... | {
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2412.06613 | 3D Spatial Understanding in MLLMs: Disambiguation and Evaluation | [
"cs.CV"
] | Multimodal Large Language Models (MLLMs) have made significant progress in tasks such as image captioning and question answering. However, while these models can generate realistic captions, they often struggle with providing precise instructions, particularly when it comes to localizing and disambiguating objects in c... | {
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2412.06614 | MVReward: Better Aligning and Evaluating Multi-View Diffusion Models
with Human Preferences | [
"cs.CV"
] | Recent years have witnessed remarkable progress in 3D content generation. However, corresponding evaluation methods struggle to keep pace. Automatic approaches have proven challenging to align with human preferences, and the mixed comparison of text- and image-driven methods often leads to unfair evaluations. In this p... | {
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2412.06617 | AI TrackMate: Finally, Someone Who Will Give Your Music More Than Just
"Sounds Great!" | [
"cs.SD",
"cs.HC",
"cs.LG",
"cs.MM",
"eess.AS"
] | The rise of "bedroom producers" has democratized music creation, while challenging producers to objectively evaluate their work. To address this, we present AI TrackMate, an LLM-based music chatbot designed to provide constructive feedback on music productions. By combining LLMs' inherent musical knowledge with direct ... | {
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2412.06619 | Copyright-Protected Language Generation via Adaptive Model Fusion | [
"cs.LG",
"cs.CL",
"cs.CR"
] | The risk of language models reproducing copyrighted material from their training data has led to the development of various protective measures. Among these, inference-time strategies that impose constraints via post-processing have shown promise in addressing the complexities of copyright regulation. However, they oft... | {
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2412.06624 | Fundus Image-based Visual Acuity Assessment with PAC-Guarantees | [
"eess.IV",
"cs.AI",
"cs.CV"
] | Timely detection and treatment are essential for maintaining eye health. Visual acuity (VA), which measures the clarity of vision at a distance, is a crucial metric for managing eye health. Machine learning (ML) techniques have been introduced to assist in VA measurement, potentially alleviating clinicians' workloads. ... | {
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2412.06629 | PolytopeWalk: Sparse MCMC Sampling over Polytopes | [
"stat.CO",
"cs.LG",
"stat.ML"
] | High dimensional sampling is an important computational tool in statistics and other computational disciplines, with applications ranging from Bayesian statistical uncertainty quantification, metabolic modeling in systems biology to volume computation. We present $\textsf{PolytopeWalk}$, a new scalable Python library d... | {
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2412.06632 | MAVias: Mitigate any Visual Bias | [
"cs.CV"
] | Mitigating biases in computer vision models is an essential step towards the trustworthiness of artificial intelligence models. Existing bias mitigation methods focus on a small set of predefined biases, limiting their applicability in visual datasets where multiple, possibly unknown biases exist. To address this limit... | {
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2412.06636 | Neo-FREE: Policy Composition Through Thousand Brains And Free Energy
Optimization | [
"math.OC",
"cs.RO",
"cs.SY",
"eess.SY"
] | We consider the problem of optimally composing a set of primitives to tackle control tasks. To address this problem, we introduce Neo-FREE: a control architecture inspired by the Thousand Brains Theory and Free Energy Principle from cognitive sciences. In accordance with the neocortical (Neo) processes postulated by th... | {
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2412.06637 | FREYJA: Efficient Join Discovery in Data Lakes | [
"cs.DB"
] | Data lakes are massive repositories of raw and heterogeneous data, designed to meet the requirements of modern data storage. Nonetheless, this same philosophy increases the complexity of performing discovery tasks to find relevant data for subsequent processing. As a response to these growing challenges, we present FRE... | {
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2412.06639 | Beyond Scalars: Concept-Based Alignment Analysis in Vision Transformers | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Vision transformers (ViTs) can be trained using various learning paradigms, from fully supervised to self-supervised. Diverse training protocols often result in significantly different feature spaces, which are usually compared through alignment analysis. However, current alignment measures quantify this relationship i... | {
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2412.06642 | Class Balance Matters to Active Class-Incremental Learning | [
"cs.CV"
] | Few-Shot Class-Incremental Learning has shown remarkable efficacy in efficient learning new concepts with limited annotations. Nevertheless, the heuristic few-shot annotations may not always cover the most informative samples, which largely restricts the capability of incremental learner. We aim to start from a pool of... | {
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2412.06643 | Detecting Facial Image Manipulations with Multi-Layer CNN Models | [
"cs.CV",
"cs.AI"
] | The rapid evolution of digital image manipulation techniques poses significant challenges for content verification, with models such as stable diffusion and mid-journey producing highly realistic, yet synthetic, images that can deceive human perception. This research develops and evaluates convolutional neural networks... | {
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2412.06646 | The Narrow Gate: Localized Image-Text Communication in Vision-Language
Models | [
"cs.CV",
"cs.LG"
] | Recent advances in multimodal training have significantly improved the integration of image understanding and generation within a unified model. This study investigates how vision-language models (VLMs) handle image-understanding tasks, specifically focusing on how visual information is processed and transferred to the... | {
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2412.06647 | Object Detection using Event Camera: A MoE Heat Conduction based
Detector and A New Benchmark Dataset | [
"cs.CV",
"cs.NE"
] | Object detection in event streams has emerged as a cutting-edge research area, demonstrating superior performance in low-light conditions, scenarios with motion blur, and rapid movements. Current detectors leverage spiking neural networks, Transformers, or convolutional neural networks as their core architectures, each... | {
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2412.06649 | Semantic Search and Recommendation Algorithm | [
"cs.IR",
"cs.AI",
"cs.DB",
"cs.LG"
] | This paper introduces a new semantic search algorithm that uses Word2Vec and Annoy Index to improve the efficiency of information retrieval from large datasets. The proposed approach addresses the limitations of traditional search methods by offering enhanced speed, accuracy, and scalability. Testing on datasets up to ... | {
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2412.06651 | Chatbots im Schulunterricht: Wir testen das Fobizz-Tool zur
automatischen Bewertung von Hausaufgaben | [
"cs.CY",
"cs.AI",
"cs.CL",
"cs.ET"
] | This study examines the AI-powered grading tool "AI Grading Assistant" by the German company Fobizz, designed to support teachers in evaluating and providing feedback on student assignments. Against the societal backdrop of an overburdened education system and rising expectations for artificial intelligence as a soluti... | {
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2412.06653 | A Novel Approach to Process Discovery with Enhanced Loop Handling | [
"cs.DB"
] | Automated process discovery from event logs is a key component of process mining, allowing companies to acquire meaningful insights into their business processes. Despite significant research, present methods struggle to balance important quality dimensions: fitness, precision, generalization, and complexity, but is li... | {
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2412.06654 | GEAR: A Simple GENERATE, EMBED, AVERAGE AND RANK Approach for
Unsupervised Reverse Dictionary | [
"cs.CL"
] | Reverse Dictionary (RD) is the task of obtaining the most relevant word or set of words given a textual description or dictionary definition. Effective RD methods have applications in accessibility, translation or writing support systems. Moreover, in NLP research we find RD to be used to benchmark text encoders at var... | {
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2412.06655 | Off-Policy Maximum Entropy RL with Future State and Action Visitation
Measures | [
"cs.LG",
"stat.ML"
] | We introduce a new maximum entropy reinforcement learning framework based on the distribution of states and actions visited by a policy. More precisely, an intrinsic reward function is added to the reward function of the Markov decision process that shall be controlled. For each state and action, this intrinsic reward ... | {
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2412.06661 | Efficiency Meets Fidelity: A Novel Quantization Framework for Stable
Diffusion | [
"cs.CV"
] | Text-to-image generation of Stable Diffusion models has achieved notable success due to its remarkable generation ability. However, the repetitive denoising process is computationally intensive during inference, which renders Diffusion models less suitable for real-world applications that require low latency and scalab... | {
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2412.06662 | Stochastic LQR Design With Disturbance Preview | [
"math.OC",
"cs.SY",
"eess.SY"
] | This paper considers the discrete-time, stochastic LQR problem with $p$ steps of disturbance preview information where $p$ is finite. We first derive the solution for this problem on a finite horizon with linear, time-varying dynamics and time-varying costs. Next, we derive the solution on the infinite horizon with lin... | {
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2412.06664 | Knowledge Transfer and Domain Adaptation for Fine-Grained Remote Sensing
Image Segmentation | [
"cs.CV"
] | Fine-grained remote sensing image segmentation is essential for accurately identifying detailed objects in remote sensing images. Recently, vision transformer models (VTMs) pre-trained on large-scale datasets have demonstrated strong zero-shot generalization. However, directly applying them to specific tasks may lead t... | {
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2412.06666 | Diff5T: Benchmarking Human Brain Diffusion MRI with an Extensive 5.0
Tesla K-Space and Spatial Dataset | [
"eess.IV",
"cs.CV",
"physics.med-ph"
] | Diffusion magnetic resonance imaging (dMRI) provides critical insights into the microstructural and connectional organization of the human brain. However, the availability of high-field, open-access datasets that include raw k-space data for advanced research remains limited. To address this gap, we introduce Diff5T, a... | {
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2412.06673 | ILLUME: Illuminating Your LLMs to See, Draw, and Self-Enhance | [
"cs.CV"
] | In this paper, we introduce ILLUME, a unified multimodal large language model (MLLM) that seamlessly integrates multimodal understanding and generation capabilities within a single large language model through a unified next-token prediction formulation. To address the large dataset size typically required for image-te... | {
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2412.06674 | EMOv2: Pushing 5M Vision Model Frontier | [
"cs.CV"
] | This work focuses on developing parameter-efficient and lightweight models for dense predictions while trading off parameters, FLOPs, and performance. Our goal is to set up the new frontier of the 5M magnitude lightweight model on various downstream tasks. Inverted Residual Block (IRB) serves as the infrastructure for ... | {
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2412.06676 | I Don't Know: Explicit Modeling of Uncertainty with an [IDK] Token | [
"cs.LG",
"cs.CL"
] | Large Language Models are known to capture real-world knowledge, allowing them to excel in many downstream tasks. Despite recent advances, these models are still prone to what are commonly known as hallucinations, causing them to emit unwanted and factually incorrect text. In this work, we propose a novel calibration m... | {
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2412.06681 | Toward LLM-Agent-Based Modeling of Transportation Systems: A Conceptual
Framework | [
"cs.AI",
"cs.MA"
] | In transportation system demand modeling and simulation, agent-based models and microsimulations are current state-of-the-art approaches. However, existing agent-based models still have some limitations on behavioral realism and resource demand that limit their applicability. In this study, leveraging the emerging tech... | {
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2412.06684 | Exploring Critical Testing Scenarios for Decision-Making Policies: An
LLM Approach | [
"cs.LG"
] | Recent advances in decision-making policies have led to significant progress in fields such as autonomous driving and robotics. However, testing these policies remains crucial with the existence of critical scenarios that may threaten their reliability. Despite ongoing research, challenges such as low testing efficienc... | {
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2412.06685 | Policy Agnostic RL: Offline RL and Online RL Fine-Tuning of Any Class
and Backbone | [
"cs.LG",
"cs.AI"
] | Recent advances in learning decision-making policies can largely be attributed to training expressive policy models, largely via imitation learning. While imitation learning discards non-expert data, reinforcement learning (RL) can still learn from suboptimal data. However, instantiating RL training of a new policy cla... | {
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2412.06686 | Some Best Practices in Operator Learning | [
"cs.LG",
"physics.comp-ph"
] | Hyperparameters searches are computationally expensive. This paper studies some general choices of hyperparameters and training methods specifically for operator learning. It considers the architectures DeepONets, Fourier neural operators and Koopman autoencoders for several differential equations to find robust trends... | {
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2412.06689 | Impact of Privacy Parameters on Deep Learning Models for Image
Classification | [
"cs.LG",
"cs.CR"
] | The project aims to develop differentially private deep learning models for image classification on CIFAR-10 datasets \cite{cifar10} and analyze the impact of various privacy parameters on model accuracy. We have implemented five different deep learning models, namely ConvNet, ResNet18, EfficientNet, ViT, and DenseNet1... | {
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2412.06690 | FedSynthCT-Brain: A Federated Learning Framework for Multi-Institutional
Brain MRI-to-CT Synthesis | [
"eess.IV",
"cs.CV"
] | The generation of Synthetic Computed Tomography (sCT) images has become a pivotal methodology in modern clinical practice, particularly in the context of Radiotherapy (RT) treatment planning. The use of sCT enables the calculation of doses, pushing towards Magnetic Resonance Imaging (MRI) guided radiotherapy treatments... | {
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2412.06693 | OmniEvalKit: A Modular, Lightweight Toolbox for Evaluating Large
Language Model and its Omni-Extensions | [
"cs.CL",
"cs.AI",
"cs.CV",
"cs.LG",
"cs.MM"
] | The rapid advancements in Large Language Models (LLMs) have significantly expanded their applications, ranging from multilingual support to domain-specific tasks and multimodal integration. In this paper, we present OmniEvalKit, a novel benchmarking toolbox designed to evaluate LLMs and their omni-extensions across mul... | {
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2412.06694 | Digital Transformation in the Water Distribution System based on the
Digital Twins Concept | [
"cs.CY",
"cs.AI"
] | Digital Twins have emerged as a disruptive technology with great potential; they can enhance WDS by offering real-time monitoring, predictive maintenance, and optimization capabilities. This paper describes the development of a state-of-the-art DT platform for WDS, introducing advanced technologies such as the Internet... | {
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2412.06695 | DEEPER: Dense Electroencephalography Passage Retrieval | [
"cs.IR"
] | A fundamental challenge in Information Retrieval (IR) is the cognitive burden of translating internal information needs into explicit textual queries. This translation barrier particularly affects users with undefined information needs or those who face physical constraints in traditional text input methods. While Brai... | {
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2412.06698 | Gen-3Diffusion: Realistic Image-to-3D Generation via 2D & 3D Diffusion
Synergy | [
"cs.CV"
] | Creating realistic 3D objects and clothed avatars from a single RGB image is an attractive yet challenging problem. Due to its ill-posed nature, recent works leverage powerful prior from 2D diffusion models pretrained on large datasets. Although 2D diffusion models demonstrate strong generalization capability, they can... | {
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2412.06699 | You See it, You Got it: Learning 3D Creation on Pose-Free Videos at
Scale | [
"cs.CV"
] | Recent 3D generation models typically rely on limited-scale 3D `gold-labels' or 2D diffusion priors for 3D content creation. However, their performance is upper-bounded by constrained 3D priors due to the lack of scalable learning paradigms. In this work, we present See3D, a visual-conditional multi-view diffusion mode... | {
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2412.06702 | CHOICE: Coordinated Human-Object Interaction in Cluttered Environments
for Pick-and-Place Actions | [
"cs.GR",
"cs.RO"
] | Animating human-scene interactions such as pick-and-place tasks in cluttered, complex layouts is a challenging task, with objects of a wide variation of geometries and articulation under scenarios with various obstacles. The main difficulty lies in the sparsity of the motion data compared to the wide variation of the o... | {
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2412.06703 | Source Separation & Automatic Transcription for Music | [
"cs.SD",
"cs.AI",
"eess.AS"
] | Source separation is the process of isolating individual sounds in an auditory mixture of multiple sounds [1], and has a variety of applications ranging from speech enhancement and lyric transcription [2] to digital audio production for music. Furthermore, Automatic Music Transcription (AMT) is the process of convertin... | {
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2412.06706 | Asynchronous Agents with Perfect Recall: Model Reductions,
Knowledge-Based Construction, and Model Checking for Coalitional Strategies | [
"cs.MA",
"cs.LO"
] | Model checking of strategic abilities for agents with memory is a notoriously hard problem, and very few attempts have been made to tackle it. In this paper, we present two important steps towards this goal. First, we take the partial-order reduction scheme that was recently proved to preserve individual and coalitiona... | {
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2412.06708 | FlexEvent: Event Camera Object Detection at Arbitrary Frequencies | [
"cs.CV",
"cs.RO"
] | Event cameras offer unparalleled advantages for real-time perception in dynamic environments, thanks to their microsecond-level temporal resolution and asynchronous operation. Existing event-based object detection methods, however, are limited by fixed-frequency paradigms and fail to fully exploit the high-temporal res... | {
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2412.06709 | Parkinson's Disease Diagnosis Through Deep Learning: A Novel LSTM-Based
Approach for Freezing of Gait Detection | [
"cs.CV",
"cs.AI"
] | Deep learning holds tremendous potential in healthcare for uncovering hidden patterns within extensive clinical datasets, aiding in the diagnosis of various diseases. Parkinson's disease (PD) is a neurodegenerative condition characterized by the deterioration of brain function. In the initial stages of PD, automatic di... | {
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2412.06711 | MISFEAT: Feature Selection for Subgroups with Systematic Missing Data | [
"cs.LG",
"cs.DB",
"stat.ML"
] | We investigate the problem of selecting features for datasets that can be naturally partitioned into subgroups (e.g., according to socio-demographic groups and age), each with its own dominant set of features. Within this subgroup-oriented framework, we address the challenge of systematic missing data, a scenario in wh... | {
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2412.06712 | How to Merge Your Multimodal Models Over Time? | [
"cs.LG",
"cs.CL",
"cs.CV"
] | Model merging combines multiple expert models - finetuned from a base foundation model on diverse tasks and domains - into a single, more capable model. However, most existing model merging approaches assume that all experts are available simultaneously. In reality, new tasks and domains emerge progressively over time,... | {
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2412.06716 | On Pooling-Based Track Fusion Strategies : Harmonic Mean Density | [
"eess.SP",
"cs.SY",
"eess.SY"
] | In a distributed sensor fusion architecture, using standard Kalman filter (naive fusion) can lead to degraded results as track correlations are ignored and conservative fusion strategies are employed as a sub-optimal alternative to the problem. Since, Gaussian mixtures provide a flexible means of modeling any density, ... | {
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2412.06717 | Toward Non-Invasive Diagnosis of Bankart Lesions with Deep Learning | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Bankart lesions, or anterior-inferior glenoid labral tears, are diagnostically challenging on standard MRIs due to their subtle imaging features-often necessitating invasive MRI arthrograms (MRAs). This study develops deep learning (DL) models to detect Bankart lesions on both standard MRIs and MRAs, aiming to improve ... | {
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2412.06720 | VP-MEL: Visual Prompts Guided Multimodal Entity Linking | [
"cs.CV",
"cs.CL"
] | Multimodal entity linking (MEL), a task aimed at linking mentions within multimodal contexts to their corresponding entities in a knowledge base (KB), has attracted much attention due to its wide applications in recent years. However, existing MEL methods often rely on mention words as retrieval cues, which limits thei... | {
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2412.06724 | AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and
Benchmark | [
"cs.DB",
"cs.CL"
] | We investigate the reasoning capabilities of large language models (LLMs) for automatically generating data-cleaning workflows. To evaluate LLMs' ability to complete data-cleaning tasks, we implemented a pipeline for LLM-based Auto Data Cleaning Workflow (AutoDCWorkflow), prompting LLMs on data cleaning operations to r... | {
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2412.06725 | Harmonic Mean Density Fusion in Distributed Tracking: Performance and
Comparison | [
"eess.SP",
"cs.SY",
"eess.SY"
] | A distributed sensor fusion architecture is preferred in a real target-tracking scenario as compared to a centralized scheme since it provides many practical advantages in terms of computation load, communication bandwidth, fault-tolerance, and scalability. In multi-sensor target-tracking literature, such systems are b... | {
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2412.06727 | Take Fake as Real: Realistic-like Robust Black-box Adversarial Attack to
Evade AIGC Detection | [
"cs.CV"
] | The security of AI-generated content (AIGC) detection is crucial for ensuring multimedia content credibility. To enhance detector security, research on adversarial attacks has become essential. However, most existing adversarial attacks focus only on GAN-generated facial images detection, struggle to be effective on mu... | {
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} |
2412.06728 | Byzantine-Eavesdropper Alliance: How to Achieve Symmetric Privacy in
Quantum $X$-Secure $B$-Byzantine $E$-Eavesdropped $U$-Unresponsive
$T$-Colluding PIR? | [
"cs.IT",
"cs.CR",
"cs.NI",
"eess.SP",
"math.IT",
"quant-ph"
] | We consider the quantum \emph{symmetric} private information retrieval (QSPIR) problem in a system with $N$ databases and $K$ messages, with $U$ unresponsive servers, $T$-colluding servers, and $X$-security parameter, under several fundamental threat models. In the first model, there are $\mathcal{E}_1$ eavesdropped li... | {
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2412.06732 | Analyzing the Scalability of Bi-static Backscatter Networks for Large
Scale Applications | [
"eess.SY",
"cs.SY"
] | Backscatter radio is a promising technology for low-cost and low-power Internet-of-Things (IoT) networks. The conventional monostatic backscatter radio is constrained by its limited communication range, which restricts its utility in wide-area applications. An alternative bi-static backscatter radio architecture, chara... | {
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2412.06735 | Partially Observed Optimal Stochastic Control: Regularity, Optimality,
Approximations, and Learning | [
"math.OC",
"cs.SY",
"eess.SY"
] | In this review/tutorial article, we present recent progress on optimal control of partially observed Markov Decision Processes (POMDPs). We first present regularity and continuity conditions for POMDPs and their belief-MDP reductions, where these constitute weak Feller and Wasserstein regularity and controlled filter s... | {
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2412.06738 | JAPAGEN: Efficient Few/Zero-shot Learning via Japanese Training Dataset
Generation with LLM | [
"cs.CL"
] | Recently some studies have highlighted the potential of Large Language Models (LLMs) as effective generators of supervised training data, offering advantages such as enhanced inference efficiency and reduced costs associated with data collection. However, these studies have predominantly focused on English language tas... | {
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2412.06740 | Convolution goes higher-order: a biologically inspired mechanism
empowers image classification | [
"cs.CV",
"cs.LG",
"q-bio.NC"
] | We propose a novel approach to image classification inspired by complex nonlinear biological visual processing, whereby classical convolutional neural networks (CNNs) are equipped with learnable higher-order convolutions. Our model incorporates a Volterra-like expansion of the convolution operator, capturing multiplica... | {
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2412.06742 | ContRail: A Framework for Realistic Railway Image Synthesis using
ControlNet | [
"cs.CV",
"cs.AI"
] | Deep Learning became an ubiquitous paradigm due to its extraordinary effectiveness and applicability in numerous domains. However, the approach suffers from the high demand of data required to achieve the potential of this type of model. An ever-increasing sub-field of Artificial Intelligence, Image Synthesis, aims to ... | {
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2412.06743 | 3D Graph Attention Networks for High Fidelity Pediatric Glioma
Segmentation | [
"cs.CV"
] | Pediatric brain tumors, particularly gliomas, represent a significant cause of cancer related mortality in children with complex infiltrative growth patterns that complicate treatment. Early, accurate segmentation of these tumors in neuroimaging data is crucial for effective diagnosis and intervention planning. This st... | {
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2412.06745 | ONEBench to Test Them All: Sample-Level Benchmarking Over Open-Ended
Capabilities | [
"cs.LG",
"cs.CL",
"cs.CV"
] | Traditional fixed test sets fall short in evaluating open-ended capabilities of foundation models. To address this, we propose ONEBench(OpeN-Ended Benchmarking), a new testing paradigm that consolidates individual evaluation datasets into a unified, ever-expanding sample pool. ONEBench allows users to generate custom, ... | {
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2412.06748 | Refusal Tokens: A Simple Way to Calibrate Refusals in Large Language
Models | [
"cs.LG",
"cs.CL"
] | A key component of building safe and reliable language models is enabling the models to appropriately refuse to follow certain instructions or answer certain questions. We may want models to output refusal messages for various categories of user queries, for example, ill-posed questions, instructions for committing ill... | {
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2412.06753 | InstantRestore: Single-Step Personalized Face Restoration with
Shared-Image Attention | [
"cs.CV"
] | Face image restoration aims to enhance degraded facial images while addressing challenges such as diverse degradation types, real-time processing demands, and, most crucially, the preservation of identity-specific features. Existing methods often struggle with slow processing times and suboptimal restoration, especiall... | {
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2412.06759 | XRZoo: A Large-Scale and Versatile Dataset of Extended Reality (XR)
Applications | [
"cs.SE",
"cs.AI",
"cs.CR",
"cs.HC"
] | The rapid advancement of Extended Reality (XR, encompassing AR, MR, and VR) and spatial computing technologies forms a foundational layer for the emerging Metaverse, enabling innovative applications across healthcare, education, manufacturing, and entertainment. However, research in this area is often limited by the la... | {
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2412.06760 | Ranking-aware adapter for text-driven image ordering with CLIP | [
"cs.CV"
] | Recent advances in vision-language models (VLMs) have made significant progress in downstream tasks that require quantitative concepts such as facial age estimation and image quality assessment, enabling VLMs to explore applications like image ranking and retrieval. However, existing studies typically focus on the reas... | {
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2412.06767 | MAtCha Gaussians: Atlas of Charts for High-Quality Geometry and
Photorealism From Sparse Views | [
"cs.CV",
"cs.GR"
] | We present a novel appearance model that simultaneously realizes explicit high-quality 3D surface mesh recovery and photorealistic novel view synthesis from sparse view samples. Our key idea is to model the underlying scene geometry Mesh as an Atlas of Charts which we render with 2D Gaussian surfels (MAtCha Gaussians).... | {
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2412.06769 | Training Large Language Models to Reason in a Continuous Latent Space | [
"cs.CL"
] | Large language models (LLMs) are restricted to reason in the "language space", where they typically express the reasoning process with a chain-of-thought (CoT) to solve a complex reasoning problem. However, we argue that language space may not always be optimal for reasoning. For example, most word tokens are primarily... | {
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2412.06770 | Dynamic EventNeRF: Reconstructing General Dynamic Scenes from Multi-view
Event Cameras | [
"cs.CV"
] | Volumetric reconstruction of dynamic scenes is an important problem in computer vision. It is especially challenging in poor lighting and with fast motion. It is partly due to the limitations of RGB cameras: To capture fast motion without much blur, the framerate must be increased, which in turn requires more lighting.... | {
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2412.06771 | Proactive Agents for Multi-Turn Text-to-Image Generation Under
Uncertainty | [
"cs.AI",
"cs.CV",
"cs.LG"
] | User prompts for generative AI models are often underspecified, leading to sub-optimal responses. This problem is particularly evident in text-to-image (T2I) generation, where users commonly struggle to articulate their precise intent. This disconnect between the user's vision and the model's interpretation often force... | {
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2412.06774 | Visual Lexicon: Rich Image Features in Language Space | [
"cs.CV",
"cs.AI",
"cs.LG"
] | We present Visual Lexicon, a novel visual language that encodes rich image information into the text space of vocabulary tokens while retaining intricate visual details that are often challenging to convey in natural language. Unlike traditional methods that prioritize either high-level semantics (e.g., CLIP) or pixel-... | {
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2412.06775 | Delve into Visual Contrastive Decoding for Hallucination Mitigation of
Large Vision-Language Models | [
"cs.CV",
"cs.AI",
"cs.CL"
] | While large vision-language models (LVLMs) have shown impressive capabilities in generating plausible responses correlated with input visual contents, they still suffer from hallucinations, where the generated text inaccurately reflects visual contents. To address this, recent approaches apply contrastive decoding to c... | {
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2412.06776 | Enhancing Robotic System Robustness via Lyapunov Exponent-Based
Optimization | [
"cs.RO"
] | We present a novel approach to quantifying and optimizing stability in robotic systems based on the Lyapunov exponents addressing an open challenge in the field of robot analysis, design, and optimization. Our method leverages differentiable simulation over extended time horizons. The proposed metric offers several pro... | {
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2412.06777 | Driv3R: Learning Dense 4D Reconstruction for Autonomous Driving | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Realtime 4D reconstruction for dynamic scenes remains a crucial challenge for autonomous driving perception. Most existing methods rely on depth estimation through self-supervision or multi-modality sensor fusion. In this paper, we propose Driv3R, a DUSt3R-based framework that directly regresses per-frame point maps fr... | {
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2412.06779 | AnyBimanual: Transferring Unimanual Policy for General Bimanual
Manipulation | [
"cs.RO",
"cs.AI"
] | Performing general language-conditioned bimanual manipulation tasks is of great importance for many applications ranging from household service to industrial assembly. However, collecting bimanual manipulation data is expensive due to the high-dimensional action space, which poses challenges for conventional methods to... | {
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2412.06780 | Diverse Score Distillation | [
"cs.CV"
] | Score distillation of 2D diffusion models has proven to be a powerful mechanism to guide 3D optimization, for example enabling text-based 3D generation or single-view reconstruction. A common limitation of existing score distillation formulations, however, is that the outputs of the (mode-seeking) optimization are limi... | {
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2412.06781 | Around the World in 80 Timesteps: A Generative Approach to Global Visual
Geolocation | [
"cs.CV",
"cs.LG"
] | Global visual geolocation predicts where an image was captured on Earth. Since images vary in how precisely they can be localized, this task inherently involves a significant degree of ambiguity. However, existing approaches are deterministic and overlook this aspect. In this paper, we aim to close the gap between trad... | {
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2412.06782 | CARP: Visuomotor Policy Learning via Coarse-to-Fine Autoregressive
Prediction | [
"cs.RO",
"cs.CV"
] | In robotic visuomotor policy learning, diffusion-based models have achieved significant success in improving the accuracy of action trajectory generation compared to traditional autoregressive models. However, they suffer from inefficiency due to multiple denoising steps and limited flexibility from complex constraints... | {
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2412.06784 | P3-PO: Prescriptive Point Priors for Visuo-Spatial Generalization of
Robot Policies | [
"cs.RO",
"cs.AI",
"cs.CV",
"cs.LG"
] | Developing generalizable robot policies that can robustly handle varied environmental conditions and object instances remains a fundamental challenge in robot learning. While considerable efforts have focused on collecting large robot datasets and developing policy architectures to learn from such data, naively learnin... | {
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2412.06785 | Tactile DreamFusion: Exploiting Tactile Sensing for 3D Generation | [
"cs.CV",
"cs.GR"
] | 3D generation methods have shown visually compelling results powered by diffusion image priors. However, they often fail to produce realistic geometric details, resulting in overly smooth surfaces or geometric details inaccurately baked in albedo maps. To address this, we introduce a new method that incorporates touch ... | {
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2412.06786 | Retrieving Semantics from the Deep: an RAG Solution for Gesture
Synthesis | [
"cs.CV"
] | Non-verbal communication often comprises of semantically rich gestures that help convey the meaning of an utterance. Producing such semantic co-speech gestures has been a major challenge for the existing neural systems that can generate rhythmic beat gestures, but struggle to produce semantically meaningful gestures. T... | {
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2412.06787 | [MASK] is All You Need | [
"cs.CV",
"cs.AI"
] | In generative models, two paradigms have gained attraction in various applications: next-set prediction-based Masked Generative Models and next-noise prediction-based Non-Autoregressive Models, e.g., Diffusion Models. In this work, we propose using discrete-state models to connect them and explore their scalability in ... | {
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2412.06791 | Enhancing Prediction Models with Reinforcement Learning | [
"cs.IR",
"cs.LG"
] | We present a large-scale news recommendation system implemented at Ringier Axel Springer Polska, focusing on enhancing prediction models with reinforcement learning techniques. The system, named Aureus, integrates a variety of algorithms, including multi-armed bandit methods and deep learning models based on large lang... | {
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2412.06793 | FEAD: Figma-Enhanced App Design Framework for Improving UI/UX in
Educational App Development | [
"cs.HC",
"cs.AI"
] | Designing user-centric mobile applications is increasingly essential in educational technology. However, platforms like MIT App Inventor-one of the world's largest educational app development tools-face inherent limitations in supporting modern UI/UX design. This study introduces the Figma-Enhanced App Design (FEAD) Me... | {
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2412.06794 | Understanding the Impact of News Articles on the Movement of Market
Index: A Case on Nifty 50 | [
"cs.CL",
"cs.LG",
"q-fin.ST"
] | In the recent past, there were several works on the prediction of stock price using different methods. Sentiment analysis of news and tweets and relating them to the movement of stock prices have already been explored. But, when we talk about the news, there can be several topics such as politics, markets, sports etc. ... | {
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2412.06795 | SpikeFI: A Fault Injection Framework for Spiking Neural Networks | [
"cs.NE",
"cs.AI"
] | Neuromorphic computing and spiking neural networks (SNNs) are gaining traction across various artificial intelligence (AI) tasks thanks to their potential for efficient energy usage and faster computation speed. This comparative advantage comes from mimicking the structure, function, and efficiency of the biological br... | {
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2412.06803 | Reinforcement learning-enhanced genetic algorithm for wind farm layout
optimization | [
"cs.NE"
] | A reinforcement learning-enhanced genetic algorithm (RLGA) is proposed for wind farm layout optimization (WFLO) problems. While genetic algorithms (GAs) are among the most effective and accessible methods for WFLO, their performance and convergence are highly sensitive to parameter selections. To address the issue, rei... | {
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2412.06805 | HiCat: A Semi-Supervised Approach for Cell Type Annotation | [
"q-bio.BM",
"cs.LG"
] | We introduce HiCat (Hybrid Cell Annotation using Transformative embeddings), a novel semi-supervised pipeline for annotating cell types from single-cell RNA sequencing data. HiCat fuses the strengths of supervised learning for known cell types with unsupervised learning to identify novel types. This hybrid approach inc... | {
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2412.06806 | Ptychoformer: A Physics-Guided Deep Learning Framework for Ptychographic
Imaging | [
"physics.optics",
"cs.CV"
] | Ptychographic imaging confronts limitations in applying deep learning (DL) for retrieval from diffraction patterns. Conventional neural architectures are optimized for natural images, overlooking the unique physical characteristics of diffraction data, including radial intensity decay and coherent information distribut... | {
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2412.06808 | Effect of Adaptive Communication Support on LLM-powered Human-Robot
Collaboration | [
"cs.HC",
"cs.AI",
"cs.RO"
] | Effective human-robot collaboration requires robot to adopt their roles and levels of support based on human needs, task requirements, and complexity. Traditional human-robot teaming often relies on a pre-determined robot communication scheme, restricting teamwork adaptability in complex tasks. Leveraging strong commun... | {
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2412.06809 | Generating Diverse Synthetic Datasets for Evaluation of Real-life
Recommender Systems | [
"cs.IR",
"cs.AI"
] | Synthetic datasets are important for evaluating and testing machine learning models. When evaluating real-life recommender systems, high-dimensional categorical (and sparse) datasets are often considered. Unfortunately, there are not many solutions that would allow generation of artificial datasets with such characteri... | {
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2412.06810 | I See, Therefore I Do: Estimating Causal Effects for Image Treatments | [
"cs.AI",
"stat.AP",
"stat.ML"
] | Causal effect estimation under observational studies is challenging due to the lack of ground truth data and treatment assignment bias. Though various methods exist in literature for addressing this problem, most of them ignore multi-dimensional treatment information by considering it as scalar, either continuous or di... | {
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2412.06815 | Federated Block-Term Tensor Regression for decentralised data analysis
in healthcare | [
"cs.LG"
] | Block-Term Tensor Regression (BTTR) has proven to be a powerful tool for modeling complex, high-dimensional data by leveraging multilinear relationships, making it particularly well-suited for applications in healthcare and neuroscience. However, traditional implementations of BTTR rely on centralized datasets, which p... | {
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} |
2412.06816 | Autonomy in the Real-World: Autonomous Trajectory Planning for Asteroid
Reconnaissance via Stochastic Optimization | [
"physics.space-ph",
"cs.SY",
"eess.SY",
"math.OC",
"math.ST",
"stat.TH"
] | This paper presents the development and evaluation of an optimization-based autonomous trajectory planning algorithm for the asteroid reconnaissance phase of a deep-space exploration mission. The reconnaissance phase is a low-altitude flyby to collect detailed information around a potential landing site. Although such ... | {
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} |
2412.06818 | A Study on Quantum Neural Networks in Healthcare 5.0 | [
"quant-ph",
"cs.LG"
] | The working environment in healthcare analytics is transforming with the emergence of healthcare 5.0 and the advancements in quantum neural networks. In addition to analyzing a comprehensive set of case studies, we also review relevant literature from the fields of quantum computing applications and smart healthcare an... | {
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} |
2412.06819 | A Physics-Constrained Neural Differential Equation Framework for
Data-Driven Snowpack Simulation | [
"cs.LG",
"physics.ao-ph"
] | This paper presents a physics-constrained neural differential equation framework for parameterization, and employs it to model the time evolution of seasonal snow depth given hydrometeorological forcings. When trained on data from multiple SNOTEL sites, the parameterization predicts daily snow depth with under 9% media... | {
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} |
2412.06820 | Artificial Intelligence without Restriction Surpassing Human
Intelligence with Probability One: Theoretical Insight into Secrets of the
Brain with AI Twins of the Brain | [
"cs.AI"
] | Artificial Intelligence (AI) has apparently become one of the most important techniques discovered by humans in history while the human brain is widely recognized as one of the most complex systems in the universe. One fundamental critical question which would affect human sustainability remains open: Will artificial i... | {
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} |
2412.06822 | Guidance is All You Need: Temperature-Guided Reasoning in Large Language
Models | [
"cs.CL",
"cs.AI",
"cs.LG"
] | We present Quasar-1, a novel architecture that introduces temperature-guided reasoning to large language models through the Token Temperature Mechanism (TTM) and Guided Sequence of Thought (GSoT). Our approach leverages the concept of hot and cold tokens, where hot tokens are prioritized for their contextual relevance,... | {
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} |
2412.06823 | Bio-Inspired Pneumatic Modular Actuator for Peristaltic Transport | [
"cs.RO"
] | While its biological significance is well-documented, its application in soft robotics, particularly for the transport of fragile and irregularly shaped objects, remains underexplored. This study presents a modular soft robotic actuator system that addresses these challenges through a scalable, adaptable, and repairabl... | {
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} |
2412.06825 | Feature Group Tabular Transformer: A Novel Approach to Traffic Crash
Modeling and Causality Analysis | [
"cs.LG",
"cs.AI",
"stat.AP"
] | Reliable and interpretable traffic crash modeling is essential for understanding causality and improving road safety. This study introduces a novel approach to predicting collision types by utilizing a comprehensive dataset fused from multiple sources, including weather data, crash reports, high-resolution traffic info... | {
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} |
2412.06827 | Enhancing LLMs for Physics Problem-Solving using Reinforcement Learning
with Human-AI Feedback | [
"cs.LG",
"cs.AI"
] | Large Language Models (LLMs) have demonstrated strong capabilities in text-based tasks but struggle with the complex reasoning required for physics problems, particularly in advanced arithmetic and conceptual understanding. While some research has explored ways to enhance LLMs in physics education using techniques such... | {
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} |
2412.06828 | Enhancing LLMs for Impression Generation in Radiology Reports through a
Multi-Agent System | [
"cs.CL",
"cs.AI"
] | This study introduces "RadCouncil," a multi-agent Large Language Model (LLM) framework designed to enhance the generation of impressions in radiology reports from the finding section. RadCouncil comprises three specialized agents: 1) a "Retrieval" Agent that identifies and retrieves similar reports from a vector databa... | {
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} |
2412.06829 | Stably unactivated neurons in ReLU neural networks | [
"cs.LG",
"math.PR",
"stat.ML"
] | The choice of architecture of a neural network influences which functions will be realizable by that neural network and, as a result, studying the expressiveness of a chosen architecture has received much attention. In ReLU neural networks, the presence of stably unactivated neurons can reduce the network's expressiven... | {
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} |
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