id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2412.09477 | Bayesian Optimization via Continual Variational Last Layer Training | [
"cs.LG",
"stat.ML"
] | Gaussian Processes (GPs) are widely seen as the state-of-the-art surrogate models for Bayesian optimization (BO) due to their ability to model uncertainty and their performance on tasks where correlations are easily captured (such as those defined by Euclidean metrics) and their ability to be efficiently updated online... | {
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2412.09480 | The Parameters of Educability | [
"cs.AI",
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] | The educability model is a computational model that has been recently proposed to describe the cognitive capability that makes humans unique among existing biological species on Earth in being able to create advanced civilizations. Educability is defined as a capability for acquiring and applying knowledge. It is inten... | {
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2412.09483 | Early Detection of At-Risk Students Using Machine Learning | [
"cs.LG"
] | This research presents preliminary work to address the challenge of identifying at-risk students using supervised machine learning and three unique data categories: engagement, demographics, and performance data collected from Fall 2023 using Canvas and the California State University, Fullerton dashboard. We aim to ta... | {
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2412.09486 | Regression and Classification with Single-Qubit Quantum Neural Networks | [
"quant-ph",
"cs.AI",
"cs.LG"
] | Since classical machine learning has become a powerful tool for developing data-driven algorithms, quantum machine learning is expected to similarly impact the development of quantum algorithms. The literature reflects a mutually beneficial relationship between machine learning and quantum computing, where progress in ... | {
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2412.09492 | Video Seal: Open and Efficient Video Watermarking | [
"cs.MM",
"cs.AI",
"cs.CV"
] | The proliferation of AI-generated content and sophisticated video editing tools has made it both important and challenging to moderate digital platforms. Video watermarking addresses these challenges by embedding imperceptible signals into videos, allowing for identification. However, the rare open tools and methods of... | {
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2412.09496 | iKap: Kinematics-aware Planning with Imperative Learning | [
"cs.RO"
] | Trajectory planning in robotics aims to generate collision-free pose sequences that can be reliably executed. Recently, vision-to-planning systems have garnered increasing attention for their efficiency and ability to interpret and adapt to surrounding environments. However, traditional modular systems suffer from incr... | {
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2412.09498 | Gradient descent inference in empirical risk minimization | [
"math.ST",
"cs.IT",
"math.IT",
"math.OC",
"stat.ME",
"stat.ML",
"stat.TH"
] | Gradient descent is one of the most widely used iterative algorithms in modern statistical learning. However, its precise algorithmic dynamics in high-dimensional settings remain only partially understood, which has therefore limited its broader potential for statistical inference applications. This paper provides a ... | {
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2412.09499 | A novel ML-fuzzy control system for optimizing PHEV fuel efficiency and
extending electric range under diverse driving conditions | [
"eess.SY",
"cs.LG",
"cs.SY"
] | Aiming for a greener transportation future, this study introduces an innovative control system for plug-in hybrid electric vehicles (PHEVs) that utilizes machine learning (ML) techniques to forecast energy usage in the pure electric mode of the vehicle and optimize power allocation across different operational modes, i... | {
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2412.09500 | Loss function to optimise signal significance in particle physics | [
"hep-ph",
"cs.LG",
"hep-ex"
] | We construct a surrogate loss to directly optimise the significance metric used in particle physics. We evaluate our loss function for a simple event classification task using a linear model and show that it produces decision boundaries that change according to the cross sections of the processes involved. We find that... | {
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2412.09501 | Lyra: An Efficient and Speech-Centric Framework for Omni-Cognition | [
"cs.CV",
"cs.MM"
] | As Multi-modal Large Language Models (MLLMs) evolve, expanding beyond single-domain capabilities is essential to meet the demands for more versatile and efficient AI. However, previous omni-models have insufficiently explored speech, neglecting its integration with multi-modality. We introduce Lyra, an efficient MLLM t... | {
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2412.09502 | Nonlinear control and stability analysis of a unified Tethered
UAV-winder system | [
"eess.SY",
"cs.SY"
] | This paper presents the development of a comprehensive dynamics and stabilizing control architecture for Tethered Unmanned Aerial Vehicle (TUAV) systems. The proposed architecture integrates both onboard and ground-based controllers, employing nonlinear backstepping control techniques to achieve asymptotic stability of... | {
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2412.09505 | Integrating Vision Systems and STPA for Robust Landing and Take-Off in
VTOL Aircraft | [
"cs.RO",
"cs.ET"
] | Vertical take-off and landing (VTOL) unmanned aerial vehicles (UAVs) are versatile platforms widely used in applications such as surveillance, search and rescue, and urban air mobility. Despite their potential, the critical phases of take-off and landing in uncertain and dynamic environments pose significant safety cha... | {
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2412.09507 | Vision Transformers for Efficient Indoor Pathloss Radio Map Prediction | [
"cs.CV",
"cs.AI",
"cs.NI"
] | Vision Transformers (ViTs) have demonstrated remarkable success in achieving state-of-the-art performance across various image-based tasks and beyond. In this study, we employ a ViT-based neural network to address the problem of indoor pathloss radio map prediction. The network's generalization ability is evaluated acr... | {
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2412.09511 | GEAL: Generalizable 3D Affordance Learning with Cross-Modal Consistency | [
"cs.CV"
] | Identifying affordance regions on 3D objects from semantic cues is essential for robotics and human-machine interaction. However, existing 3D affordance learning methods struggle with generalization and robustness due to limited annotated data and a reliance on 3D backbones focused on geometric encoding, which often la... | {
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2412.09513 | Agent-based Video Trimming | [
"cs.CV"
] | As information becomes more accessible, user-generated videos are increasing in length, placing a burden on viewers to sift through vast content for valuable insights. This trend underscores the need for an algorithm to extract key video information efficiently. Despite significant advancements in highlight detection, ... | {
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2412.09520 | GainAdaptor: Learning Quadrupedal Locomotion with Dual Actors for
Adaptable and Energy-Efficient Walking on Various Terrains | [
"cs.RO",
"cs.LG"
] | Deep reinforcement learning (DRL) has emerged as an innovative solution for controlling legged robots in challenging environments using minimalist architectures. Traditional control methods for legged robots, such as inverse dynamics, either directly manage joint torques or use proportional-derivative (PD) controllers ... | {
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2412.09521 | Efficient and Comprehensive Feature Extraction in Large Vision-Language
Model for Clinical Pathology Analysis | [
"cs.CV",
"cs.AI"
] | Pathological diagnosis is vital for determining disease characteristics, guiding treatment, and assessing prognosis, relying heavily on detailed, multi-scale analysis of high-resolution whole slide images (WSI). However, traditional pure vision models face challenges of redundant feature extraction, whereas existing la... | {
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2412.09529 | Can Modern LLMs Act as Agent Cores in Radiology Environments? | [
"cs.CV"
] | Advancements in large language models (LLMs) have paved the way for LLM-based agent systems that offer enhanced accuracy and interpretability across various domains. Radiology, with its complex analytical requirements, is an ideal field for the application of these agents. This paper aims to investigate the pre-requisi... | {
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2412.09530 | Dynamic-VLM: Simple Dynamic Visual Token Compression for VideoLLM | [
"cs.CV"
] | The application of Large Vision-Language Models (LVLMs) for analyzing images and videos is an exciting and rapidly evolving field. In recent years, we've seen significant growth in high-quality image-text datasets for fine-tuning image understanding, but there is still a lack of comparable datasets for videos. Addition... | {
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2412.09538 | Capturing the Temporal Dependence of Training Data Influence | [
"cs.LG",
"stat.ML"
] | Traditional data influence estimation methods, like influence function, assume that learning algorithms are permutation-invariant with respect to training data. However, modern training paradigms, especially for foundation models using stochastic algorithms and multi-stage curricula, are sensitive to data ordering, thu... | {
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2412.09544 | Sail into the Headwind: Alignment via Robust Rewards and Dynamic Labels
against Reward Hacking | [
"cs.LG",
"cs.AI",
"math.OC",
"math.ST",
"stat.ML",
"stat.TH"
] | Aligning AI systems with human preferences typically suffers from the infamous reward hacking problem, where optimization of an imperfect reward model leads to undesired behaviors. In this paper, we investigate reward hacking in offline preference optimization, which aims to improve an initial model using a preference ... | {
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2412.09545 | SimAvatar: Simulation-Ready Avatars with Layered Hair and Clothing | [
"cs.CV",
"cs.GR",
"cs.LG"
] | We introduce SimAvatar, a framework designed to generate simulation-ready clothed 3D human avatars from a text prompt. Current text-driven human avatar generation methods either model hair, clothing, and the human body using a unified geometry or produce hair and garments that are not easily adaptable for simulation wi... | {
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2412.09548 | Meshtron: High-Fidelity, Artist-Like 3D Mesh Generation at Scale | [
"cs.GR",
"cs.CV"
] | Meshes are fundamental representations of 3D surfaces. However, creating high-quality meshes is a labor-intensive task that requires significant time and expertise in 3D modeling. While a delicate object often requires over $10^4$ faces to be accurately modeled, recent attempts at generating artist-like meshes are limi... | {
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2412.09549 | Exemplar Masking for Multimodal Incremental Learning | [
"cs.CV"
] | Multimodal incremental learning needs to digest the information from multiple modalities while concurrently learning new knowledge without forgetting the previously learned information. There are numerous challenges for this task, mainly including the larger storage size of multimodal data in exemplar-based methods and... | {
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2412.09551 | Video Creation by Demonstration | [
"cs.CV"
] | We explore a novel video creation experience, namely Video Creation by Demonstration. Given a demonstration video and a context image from a different scene, we generate a physically plausible video that continues naturally from the context image and carries out the action concepts from the demonstration. To enable thi... | {
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2412.09556 | Enhancing Convergence of Decentralized Gradient Tracking under the KL
Property | [
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"cs.LG",
"cs.SY",
"eess.SY",
"stat.ML"
] | We study decentralized multiagent optimization over networks, modeled as undirected graphs. The optimization problem consists of minimizing a nonconvex smooth function plus a convex extended-value function, which enforces constraints or extra structure on the solution (e.g., sparsity, low-rank). We further assume that ... | {
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2412.09557 | Experimental Machine Learning with Classical and Quantum Data via NMR
Quantum Kernels | [
"quant-ph",
"cs.LG",
"physics.app-ph"
] | Kernel methods map data into high-dimensional spaces, enabling linear algorithms to learn nonlinear functions without explicitly storing the feature vectors. Quantum kernel methods promise efficient learning by encoding feature maps into exponentially large Hilbert spaces inherent in quantum systems. In this work we im... | {
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2412.09560 | Foundational Large Language Models for Materials Research | [
"cond-mat.mtrl-sci",
"cs.CL",
"cs.IR"
] | Materials discovery and development are critical for addressing global challenges. Yet, the exponential growth in materials science literature comprising vast amounts of textual data has created significant bottlenecks in knowledge extraction, synthesis, and scientific reasoning. Large Language Models (LLMs) offer unpr... | {
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2412.09563 | Does Representation Matter? Exploring Intermediate Layers in Large
Language Models | [
"cs.LG",
"cs.CL"
] | Understanding what defines a good representation in large language models (LLMs) is fundamental to both theoretical understanding and practical applications. In this paper, we investigate the quality of intermediate representations in various LLM architectures, including Transformers and State Space Models (SSMs). We f... | {
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2412.09564 | Improving the Reliability of Cable Broadband Networks via Proactive
Network Maintenance | [
"cs.NI",
"cs.LG"
] | Cable broadband networks are one of the few "last-mile" broadband technologies widely available in the U.S. Unfortunately, they have poor reliability after decades of deployment. The cable industry proposed a framework called Proactive Network Maintenance (PNM) to diagnose the cable networks. However, there is little p... | {
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2412.09565 | Obfuscated Activations Bypass LLM Latent-Space Defenses | [
"cs.LG"
] | Recent latent-space monitoring techniques have shown promise as defenses against LLM attacks. These defenses act as scanners that seek to detect harmful activations before they lead to undesirable actions. This prompts the question: Can models execute harmful behavior via inconspicuous latent states? Here, we study suc... | {
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2412.09567 | Temporal Triadic Closure: Finding Dense Structures in Social Networks
That Evolve | [
"cs.DS",
"cs.DM",
"cs.SI"
] | A graph G is c-closed if every two vertices with at least c common neighbors are adjacent to each other. Introduced by Fox, Roughgarden, Seshadhri, Wei and Wein [ICALP 2018, SICOMP 2020], this definition is an abstraction of the triadic closure property exhibited by many real-world social networks, namely, friends of f... | {
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2412.09569 | JuStRank: Benchmarking LLM Judges for System Ranking | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Given the rapid progress of generative AI, there is a pressing need to systematically compare and choose between the numerous models and configurations available. The scale and versatility of such evaluations make the use of LLM-based judges a compelling solution for this challenge. Crucially, this approach requires fi... | {
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2412.09572 | DiverseAgentEntropy: Quantifying Black-Box LLM Uncertainty through
Diverse Perspectives and Multi-Agent Interaction | [
"cs.CL"
] | Quantifying the uncertainty in the factual parametric knowledge of Large Language Models (LLMs), especially in a black-box setting, poses a significant challenge. Existing methods, which gauge a model's uncertainty through evaluating self-consistency in responses to the original query, do not always capture true uncert... | {
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2412.09573 | FreeSplatter: Pose-free Gaussian Splatting for Sparse-view 3D
Reconstruction | [
"cs.CV"
] | Existing sparse-view reconstruction models heavily rely on accurate known camera poses. However, deriving camera extrinsics and intrinsics from sparse-view images presents significant challenges. In this work, we present FreeSplatter, a highly scalable, feed-forward reconstruction framework capable of generating high-q... | {
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2412.09578 | DISHONEST: Dissecting misInformation Spread using Homogeneous sOcial
NEtworks and Semantic Topic classification | [
"cs.SI",
"cs.AI",
"cs.CL"
] | The emergence of the COVID-19 pandemic resulted in a significant rise in the spread of misinformation on online platforms such as Twitter. Oftentimes this growth is blamed on the idea of the "echo chamber." However, the behavior said to characterize these echo chambers exists in two dimensions. The first is in a user's... | {
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2412.09579 | A Theoretical Analysis of Soft-Label vs Hard-Label Training in Neural
Networks | [
"cs.LG",
"cs.AI"
] | Knowledge distillation, where a small student model learns from a pre-trained large teacher model, has achieved substantial empirical success since the seminal work of \citep{hinton2015distilling}. Despite prior theoretical studies exploring the benefits of knowledge distillation, an important question remains unanswer... | {
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2412.09581 | Probabilistic Shaping for Nonlinearity Tolerance | [
"eess.SP",
"cs.IT",
"math.IT"
] | Optimizing the input probability distribution of a discrete-time channel is a standard step in the information-theoretic analysis of digital communication systems. Nevertheless, many practical communication systems transmit uniformly and independently distributed symbols drawn from regular constellation sets. The intro... | {
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2412.09582 | Neptune: The Long Orbit to Benchmarking Long Video Understanding | [
"cs.LG",
"cs.AI",
"cs.CV"
] | We introduce Neptune, a benchmark for long video understanding that requires reasoning over long time horizons and across different modalities. Many existing video datasets and models are focused on short clips (10s-30s). While some long video datasets do exist, they can often be solved by powerful image models applied... | {
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2412.09584 | BaB-ND: Long-Horizon Motion Planning with Branch-and-Bound and Neural
Dynamics | [
"cs.RO"
] | Neural-network-based dynamics models learned from observational data have shown strong predictive capabilities for scene dynamics in robotic manipulation tasks. However, their inherent non-linearity presents significant challenges for effective planning. Current planning methods, often dependent on extensive sampling o... | {
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2412.09585 | OLA-VLM: Elevating Visual Perception in Multimodal LLMs with Auxiliary
Embedding Distillation | [
"cs.CV"
] | The standard practice for developing contemporary MLLMs is to feed features from vision encoder(s) into the LLM and train with natural language supervision. In this work, we posit an overlooked opportunity to optimize the intermediate LLM representations through a vision perspective (objective), i.e., solely natural la... | {
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2412.09586 | Gaze-LLE: Gaze Target Estimation via Large-Scale Learned Encoders | [
"cs.CV"
] | We address the problem of gaze target estimation, which aims to predict where a person is looking in a scene. Predicting a person's gaze target requires reasoning both about the person's appearance and the contents of the scene. Prior works have developed increasingly complex, hand-crafted pipelines for gaze target est... | {
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2412.09587 | OpenNER 1.0: Standardized Open-Access Named Entity Recognition Datasets
in 50+ Languages | [
"cs.CL"
] | We present OpenNER 1.0, a standardized collection of openly available named entity recognition (NER) datasets. OpenNER contains 34 datasets spanning 51 languages, annotated in varying named entity ontologies. We correct annotation format issues, standardize the original datasets into a uniform representation, map entit... | {
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2412.09593 | Neural LightRig: Unlocking Accurate Object Normal and Material
Estimation with Multi-Light Diffusion | [
"cs.CV"
] | Recovering the geometry and materials of objects from a single image is challenging due to its under-constrained nature. In this paper, we present Neural LightRig, a novel framework that boosts intrinsic estimation by leveraging auxiliary multi-lighting conditions from 2D diffusion priors. Specifically, 1) we first lev... | {
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2412.09594 | Wait-Less Offline Tuning and Re-solving for Online Decision Making | [
"stat.ML",
"cs.LG",
"math.OC"
] | Online linear programming (OLP) has found broad applications in revenue management and resource allocation. State-of-the-art OLP algorithms achieve low regret by repeatedly solving linear programming (LP) subproblems that incorporate updated resource information. However, LP-based methods are computationally expensive ... | {
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2412.09596 | InternLM-XComposer2.5-OmniLive: A Comprehensive Multimodal System for
Long-term Streaming Video and Audio Interactions | [
"cs.CV",
"cs.AI",
"cs.CL"
] | Creating AI systems that can interact with environments over long periods, similar to human cognition, has been a longstanding research goal. Recent advancements in multimodal large language models (MLLMs) have made significant strides in open-world understanding. However, the challenge of continuous and simultaneous s... | {
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2412.09597 | LiftImage3D: Lifting Any Single Image to 3D Gaussians with Video
Generation Priors | [
"cs.CV",
"cs.GR"
] | Single-image 3D reconstruction remains a fundamental challenge in computer vision due to inherent geometric ambiguities and limited viewpoint information. Recent advances in Latent Video Diffusion Models (LVDMs) offer promising 3D priors learned from large-scale video data. However, leveraging these priors effectively ... | {
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2412.09599 | RatBodyFormer: Rodent Body Surface from Keypoints | [
"cs.CV"
] | Rat behavior modeling goes to the heart of many scientific studies, yet the textureless body surface evades automatic analysis as it literally has no keypoints that detectors can find. The movement of the body surface, however, is a rich source of information for deciphering the rat behavior. We introduce two key contr... | {
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2412.09600 | Owl-1: Omni World Model for Consistent Long Video Generation | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Video generation models (VGMs) have received extensive attention recently and serve as promising candidates for general-purpose large vision models. While they can only generate short videos each time, existing methods achieve long video generation by iteratively calling the VGMs, using the last-frame output as the con... | {
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2412.09601 | TimeRefine: Temporal Grounding with Time Refining Video LLM | [
"cs.CV",
"cs.AI",
"cs.CL"
] | Video temporal grounding aims to localize relevant temporal boundaries in a video given a textual prompt. Recent work has focused on enabling Video LLMs to perform video temporal grounding via next-token prediction of temporal timestamps. However, accurately localizing timestamps in videos remains challenging for Video... | {
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2412.09602 | Hidden Biases of End-to-End Driving Datasets | [
"cs.CV",
"cs.AI",
"cs.LG",
"cs.RO"
] | End-to-end driving systems have made rapid progress, but have so far not been applied to the challenging new CARLA Leaderboard 2.0. Further, while there is a large body of literature on end-to-end architectures and training strategies, the impact of the training dataset is often overlooked. In this work, we make a firs... | {
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2412.09603 | Do Multimodal Large Language Models See Like Humans? | [
"cs.CV"
] | Multimodal Large Language Models (MLLMs) have achieved impressive results on various vision tasks, leveraging recent advancements in large language models. However, a critical question remains unaddressed: do MLLMs perceive visual information similarly to humans? Current benchmarks lack the ability to evaluate MLLMs fr... | {
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2412.09604 | SynerGen-VL: Towards Synergistic Image Understanding and Generation with
Vision Experts and Token Folding | [
"cs.CV"
] | The remarkable success of Large Language Models (LLMs) has extended to the multimodal domain, achieving outstanding performance in image understanding and generation. Recent efforts to develop unified Multimodal Large Language Models (MLLMs) that integrate these capabilities have shown promising results. However, exist... | {
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2412.09605 | AgentTrek: Agent Trajectory Synthesis via Guiding Replay with Web
Tutorials | [
"cs.CL"
] | Graphical User Interface (GUI) agents hold great potential for automating complex tasks across diverse digital environments, from web applications to desktop software. However, the development of such agents is hindered by the lack of high-quality, multi-step trajectory data required for effective training. Existing ap... | {
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2412.09606 | Feat2GS: Probing Visual Foundation Models with Gaussian Splatting | [
"cs.CV"
] | Given that visual foundation models (VFMs) are trained on extensive datasets but often limited to 2D images, a natural question arises: how well do they understand the 3D world? With the differences in architecture and training protocols (i.e., objectives, proxy tasks), a unified framework to fairly and comprehensively... | {
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2412.09607 | Spectral Image Tokenizer | [
"cs.CV",
"cs.LG"
] | Image tokenizers map images to sequences of discrete tokens, and are a crucial component of autoregressive transformer-based image generation. The tokens are typically associated with spatial locations in the input image, arranged in raster scan order, which is not ideal for autoregressive modeling. In this paper, we p... | {
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2412.09608 | Representing Long Volumetric Video with Temporal Gaussian Hierarchy | [
"cs.CV",
"cs.GR",
"cs.MM"
] | This paper aims to address the challenge of reconstructing long volumetric videos from multi-view RGB videos. Recent dynamic view synthesis methods leverage powerful 4D representations, like feature grids or point cloud sequences, to achieve high-quality rendering results. However, they are typically limited to short (... | {
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2412.09611 | FluxSpace: Disentangled Semantic Editing in Rectified Flow Transformers | [
"cs.CV"
] | Rectified flow models have emerged as a dominant approach in image generation, showcasing impressive capabilities in high-quality image synthesis. However, despite their effectiveness in visual generation, rectified flow models often struggle with disentangled editing of images. This limitation prevents the ability to ... | {
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2412.09612 | Olympus: A Universal Task Router for Computer Vision Tasks | [
"cs.CV",
"cs.AI",
"cs.CL"
] | We introduce Olympus, a new approach that transforms Multimodal Large Language Models (MLLMs) into a unified framework capable of handling a wide array of computer vision tasks. Utilizing a controller MLLM, Olympus delegates over 20 specialized tasks across images, videos, and 3D objects to dedicated modules. This inst... | {
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2412.09613 | PVC: Progressive Visual Token Compression for Unified Image and Video
Processing in Large Vision-Language Models | [
"cs.CV"
] | Large Vision-Language Models (VLMs) have been extended to understand both images and videos. Visual token compression is leveraged to reduce the considerable token length of visual inputs. To meet the needs of different tasks, existing high-performance models usually process images and videos separately with different ... | {
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2412.09614 | Context Canvas: Enhancing Text-to-Image Diffusion Models with Knowledge
Graph-Based RAG | [
"cs.CV",
"cs.CL"
] | We introduce a novel approach to enhance the capabilities of text-to-image models by incorporating a graph-based RAG. Our system dynamically retrieves detailed character information and relational data from the knowledge graph, enabling the generation of visually accurate and contextually rich images. This capability s... | {
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2412.09616 | V2PE: Improving Multimodal Long-Context Capability of Vision-Language
Models with Variable Visual Position Encoding | [
"cs.CV"
] | Vision-Language Models (VLMs) have shown promising capabilities in handling various multimodal tasks, yet they struggle in long-context scenarios, particularly in tasks involving videos, high-resolution images, or lengthy image-text documents. In our work, we first conduct an empirical analysis of the long-context capa... | {
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2412.09617 | NormalFlow: Fast, Robust, and Accurate Contact-based Object 6DoF Pose
Tracking with Vision-based Tactile Sensors | [
"cs.RO"
] | Tactile sensing is crucial for robots aiming to achieve human-level dexterity. Among tactile-dependent skills, tactile-based object tracking serves as the cornerstone for many tasks, including manipulation, in-hand manipulation, and 3D reconstruction. In this work, we introduce NormalFlow, a fast, robust, and real-time... | {
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2412.09618 | EasyRef: Omni-Generalized Group Image Reference for Diffusion Models via
Multimodal LLM | [
"cs.CV"
] | Significant achievements in personalization of diffusion models have been witnessed. Conventional tuning-free methods mostly encode multiple reference images by averaging their image embeddings as the injection condition, but such an image-independent operation cannot perform interaction among images to capture consist... | {
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2412.09619 | SnapGen: Taming High-Resolution Text-to-Image Models for Mobile Devices
with Efficient Architectures and Training | [
"cs.CV"
] | Existing text-to-image (T2I) diffusion models face several limitations, including large model sizes, slow runtime, and low-quality generation on mobile devices. This paper aims to address all of these challenges by developing an extremely small and fast T2I model that generates high-resolution and high-quality images o... | {
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2412.09620 | Learning Camera Movement Control from Real-World Drone Videos | [
"cs.CV",
"cs.RO"
] | This study seeks to automate camera movement control for filming existing subjects into attractive videos, contrasting with the creation of non-existent content by directly generating the pixels. We select drone videos as our test case due to their rich and challenging motion patterns, distinctive viewing angles, and p... | {
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2412.09621 | Stereo4D: Learning How Things Move in 3D from Internet Stereo Videos | [
"cs.CV"
] | Learning to understand dynamic 3D scenes from imagery is crucial for applications ranging from robotics to scene reconstruction. Yet, unlike other problems where large-scale supervised training has enabled rapid progress, directly supervising methods for recovering 3D motion remains challenging due to the fundamental d... | {
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2412.09622 | LoRACLR: Contrastive Adaptation for Customization of Diffusion Models | [
"cs.CV"
] | Recent advances in text-to-image customization have enabled high-fidelity, context-rich generation of personalized images, allowing specific concepts to appear in a variety of scenarios. However, current methods struggle with combining multiple personalized models, often leading to attribute entanglement or requiring s... | {
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2412.09623 | OmniDrag: Enabling Motion Control for Omnidirectional Image-to-Video
Generation | [
"cs.CV"
] | As virtual reality gains popularity, the demand for controllable creation of immersive and dynamic omnidirectional videos (ODVs) is increasing. While previous text-to-ODV generation methods achieve impressive results, they struggle with content inaccuracies and inconsistencies due to reliance solely on textual inputs. ... | {
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2412.09624 | GenEx: Generating an Explorable World | [
"cs.CV",
"cs.RO"
] | Understanding, navigating, and exploring the 3D physical real world has long been a central challenge in the development of artificial intelligence. In this work, we take a step toward this goal by introducing GenEx, a system capable of planning complex embodied world exploration, guided by its generative imagination t... | {
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2412.09625 | Illusion3D: 3D Multiview Illusion with 2D Diffusion Priors | [
"cs.CV"
] | Automatically generating multiview illusions is a compelling challenge, where a single piece of visual content offers distinct interpretations from different viewing perspectives. Traditional methods, such as shadow art and wire art, create interesting 3D illusions but are limited to simple visual outputs (i.e., figure... | {
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2412.09626 | FreeScale: Unleashing the Resolution of Diffusion Models via Tuning-Free
Scale Fusion | [
"cs.CV"
] | Visual diffusion models achieve remarkable progress, yet they are typically trained at limited resolutions due to the lack of high-resolution data and constrained computation resources, hampering their ability to generate high-fidelity images or videos at higher resolutions. Recent efforts have explored tuning-free str... | {
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2412.09627 | Doe-1: Closed-Loop Autonomous Driving with Large World Model | [
"cs.CV",
"cs.AI",
"cs.LG"
] | End-to-end autonomous driving has received increasing attention due to its potential to learn from large amounts of data. However, most existing methods are still open-loop and suffer from weak scalability, lack of high-order interactions, and inefficient decision-making. In this paper, we explore a closed-loop framewo... | {
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2412.09628 | Bridging AI and Science: Implications from a Large-Scale Literature
Analysis of AI4Science | [
"cs.AI",
"cs.DL",
"cs.IR"
] | Artificial Intelligence has proven to be a transformative tool for advancing scientific research across a wide range of disciplines. However, a significant gap still exists between AI and scientific communities, limiting the full potential of AI methods in driving broad scientific discovery. Existing efforts in identif... | {
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2412.09629 | Online Adaptive Real-Time Beamforming Design for Dynamic Environments in
Cell-Free Systems | [
"eess.SP",
"cs.IT",
"cs.NI",
"math.IT"
] | In this paper, we consider real-time beamforming design for dynamic wireless environments with varying channels and different numbers of access points (APs) and users in cell-free systems. Specifically, a sum-rate maximization optimization problem is formulated for the beamforming design in dynamic wireless environment... | {
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2412.09632 | Methods to Assess the UK Government's Current Role as a Data Provider
for AI | [
"cs.CY",
"cs.AI",
"cs.IR"
] | Governments typically collect and steward a vast amount of high-quality data on their citizens and institutions, and the UK government is exploring how it can better publish and provision this data to the benefit of the AI landscape. However, the compositions of generative AI training corpora remain closely guarded sec... | {
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2412.09633 | A Novel Wavelet-base Algorithm for Reconstruction of the Time-Domain
Impulse Response from Band-limited Scattering Parameters with Applications | [
"eess.SP",
"cs.IT",
"math.IT"
] | In this paper, we introduce a novel waveletbased algorithm for reconstructing time-domain impulse responses from band-limited scattering parameters (frequencydomain data) with a particular focus on ship hull applications. We establish the algorithm and demonstrate its convergence, as well as its efficiency for a class ... | {
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2412.09634 | NERsocial: Efficient Named Entity Recognition Dataset Construction for
Human-Robot Interaction Utilizing RapidNER | [
"cs.CL",
"cs.RO"
] | Adapting named entity recognition (NER) methods to new domains poses significant challenges. We introduce RapidNER, a framework designed for the rapid deployment of NER systems through efficient dataset construction. RapidNER operates through three key steps: (1) extracting domain-specific sub-graphs and triples from a... | {
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2412.09635 | Integrating Functionalities To A System Via Autoencoder Hippocampus
Network | [
"cs.NE",
"cs.LG"
] | Integrating multiple functionalities into a system poses a fascinating challenge to the field of deep learning. While the precise mechanisms by which the brain encodes and decodes information, and learns diverse skills, remain elusive, memorization undoubtedly plays a pivotal role in this process. In this article, we d... | {
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2412.09638 | Predicting Organic-Inorganic Halide Perovskite Photovoltaic Performance
from Optical Properties of Constituent Films through Machine Learning | [
"cond-mat.mtrl-sci",
"cs.LG",
"physics.comp-ph"
] | We demonstrate a machine learning (ML) approach that accurately predicts the current-voltage behavior of 3D/2D-structured (FAMA)Pb(IBr)3/OABr hybrid organic-inorganic halide perovskite (HOIP) solar cells under AM1.5 illumination. Our neural network algorithm is trained on measured responses from several hundred HOIP so... | {
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2412.09640 | Blockchain Data Analysis in the Era of Large-Language Models | [
"cs.CR",
"cs.AI"
] | Blockchain data analysis is essential for deriving insights, tracking transactions, identifying patterns, and ensuring the integrity and security of decentralized networks. It plays a key role in various areas, such as fraud detection, regulatory compliance, smart contract auditing, and decentralized finance (DeFi) ris... | {
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2412.09641 | Machine Learning Driven Smishing Detection Framework for Mobile Security | [
"cs.CR",
"cs.LG"
] | The increasing reliance on smartphones for communication, financial transactions, and personal data management has made them prime targets for cyberattacks, particularly smishing, a sophisticated variant of phishing conducted via SMS. Despite the growing threat, traditional detection methods often struggle with the inf... | {
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2412.09642 | A Practical Exercise in Adapting SIFT Using FHE Primitives | [
"cs.CR",
"cs.CV"
] | An exercise in implementing Scale Invariant Feature Transform using CKKS Fully Homomorphic encryption quickly reveals some glaring limitations in the current FHE paradigm. These limitations include the lack of a standard comparison operator and certain operations that depend on it (like array max, histogram binning etc... | {
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2412.09644 | Combining knowledge graphs and LLMs for hazardous chemical information
management and reuse | [
"cs.IR",
"cs.AI"
] | Human health is increasingly threatened by exposure to hazardous substances, particularly persistent and toxic chemicals. The link between these substances, often encountered in complex mixtures, and various diseases are demonstrated in scientific studies. However, this information is scattered across several sources a... | {
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2412.09645 | Evaluation Agent: Efficient and Promptable Evaluation Framework for
Visual Generative Models | [
"cs.CV",
"cs.AI",
"cs.CL"
] | Recent advancements in visual generative models have enabled high-quality image and video generation, opening diverse applications. However, evaluating these models often demands sampling hundreds or thousands of images or videos, making the process computationally expensive, especially for diffusion-based models with ... | {
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2412.09646 | RealOSR: Latent Unfolding Boosting Diffusion-based Real-world
Omnidirectional Image Super-Resolution | [
"eess.IV",
"cs.CV",
"cs.GR",
"cs.LG"
] | Omnidirectional image super-resolution (ODISR) aims to upscale low-resolution (LR) omnidirectional images (ODIs) to high-resolution (HR), addressing the growing demand for detailed visual content across a $180^{\circ}\times360^{\circ}$ viewport. Existing methods are limited by simple degradation assumptions (e.g., bicu... | {
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2412.09647 | Bench2Drive-R: Turning Real World Data into Reactive Closed-Loop
Autonomous Driving Benchmark by Generative Model | [
"cs.RO",
"cs.CV",
"cs.LG"
] | For end-to-end autonomous driving (E2E-AD), the evaluation system remains an open problem. Existing closed-loop evaluation protocols usually rely on simulators like CARLA being less realistic; while NAVSIM using real-world vision data, yet is limited to fixed planning trajectories in short horizon and assumes other age... | {
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2412.09648 | DSplats: 3D Generation by Denoising Splats-Based Multiview Diffusion
Models | [
"eess.IV",
"cs.CV",
"cs.GR",
"cs.LG"
] | Generating high-quality 3D content requires models capable of learning robust distributions of complex scenes and the real-world objects within them. Recent Gaussian-based 3D reconstruction techniques have achieved impressive results in recovering high-fidelity 3D assets from sparse input images by predicting 3D Gaussi... | {
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2412.09649 | Pole-based Vehicle Localization with Vector Maps: A Camera-LiDAR
Comparative Study | [
"cs.CV",
"cs.LG"
] | For autonomous navigation, accurate localization with respect to a map is needed. In urban environments, infrastructure such as buildings or bridges cause major difficulties to Global Navigation Satellite Systems (GNSS) and, despite advances in inertial navigation, it is necessary to support them with other sources of ... | {
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2412.09651 | Assisted morbidity coding: the SISCO.web use case for identifying the
main diagnosis in Hospital Discharge Records | [
"cs.OH",
"cs.AI"
] | Coding morbidity data using international standard diagnostic classifications is increasingly important and still challenging. Clinical coders and physicians assign codes to patient episodes based on their interpretation of case notes or electronic patient records. Therefore, accurate coding relies on the legibility of... | {
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} |
2412.09656 | From Noise to Nuance: Advances in Deep Generative Image Models | [
"cs.CV",
"cs.AI"
] | Deep learning-based image generation has undergone a paradigm shift since 2021, marked by fundamental architectural breakthroughs and computational innovations. Through reviewing architectural innovations and empirical results, this paper analyzes the transition from traditional generative methods to advanced architect... | {
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2412.09658 | SEGT: A General Spatial Expansion Group Transformer for nuScenes
Lidar-based Object Detection Task | [
"cs.CV"
] | In the technical report, we present a novel transformer-based framework for nuScenes lidar-based object detection task, termed Spatial Expansion Group Transformer (SEGT). To efficiently handle the irregular and sparse nature of point cloud, we propose migrating the voxels into distinct specialized ordered fields with t... | {
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2412.09661 | Language model driven: a PROTAC generation pipeline with dual
constraints of structure and property | [
"q-bio.QM",
"cs.AI"
] | The imperfect modeling of ternary complexes has limited the application of computer-aided drug discovery tools in PROTAC research and development. In this study, an AI-assisted approach for PROTAC molecule design pipeline named LM-PROTAC was developed, which stands for language model driven Proteolysis Targeting Chimer... | {
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2412.09663 | Revisiting Graph Homophily Measures | [
"cs.LG",
"cs.DM",
"cs.SI"
] | Homophily is a graph property describing the tendency of edges to connect similar nodes. There are several measures used for assessing homophily but all are known to have certain drawbacks: in particular, they cannot be reliably used for comparing datasets with varying numbers of classes and class size balance. To show... | {
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} |
2412.09666 | Systematic Analysis of LLM Contributions to Planning: Solver, Verifier,
Heuristic | [
"cs.AI",
"cs.CL"
] | In this work, we provide a systematic analysis of how large language models (LLMs) contribute to solving planning problems. In particular, we examine how LLMs perform when they are used as problem solver, solution verifier, and heuristic guidance to improve intermediate solutions. Our analysis reveals that although it ... | {
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2412.09668 | Vision-Language Models Represent Darker-Skinned Black Individuals as
More Homogeneous than Lighter-Skinned Black Individuals | [
"cs.CV"
] | Vision-Language Models (VLMs) combine Large Language Model (LLM) capabilities with image processing, enabling tasks like image captioning and text-to-image generation. Yet concerns persist about their potential to amplify human-like biases, including skin tone bias. Skin tone bias, where darker-skinned individuals face... | {
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} |
2412.09680 | PBR-NeRF: Inverse Rendering with Physics-Based Neural Fields | [
"cs.CV"
] | We tackle the ill-posed inverse rendering problem in 3D reconstruction with a Neural Radiance Field (NeRF) approach informed by Physics-Based Rendering (PBR) theory, named PBR-NeRF. Our method addresses a key limitation in most NeRF and 3D Gaussian Splatting approaches: they estimate view-dependent appearance without m... | {
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} |
2412.09686 | The Cost of Replicability in Active Learning | [
"cs.LG"
] | Active learning aims to reduce the required number of labeled data for machine learning algorithms by selectively querying the labels of initially unlabeled data points. Ensuring the replicability of results, where an algorithm consistently produces the same outcome across different runs, is essential for the reliabili... | {
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} |
2412.09687 | DQA: An Efficient Method for Deep Quantization of Deep Neural Network
Activations | [
"cs.LG",
"cs.CV",
"stat.ML"
] | Quantization of Deep Neural Network (DNN) activations is a commonly used technique to reduce compute and memory demands during DNN inference, which can be particularly beneficial on resource-constrained devices. To achieve high accuracy, existing methods for quantizing activations rely on complex mathematical computati... | {
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} |
2412.09688 | Formal Languages and TQFTs with Defects | [
"math-ph",
"cs.CL",
"math.MP",
"math.QA"
] | A construction that assigns a Boolean 1D TQFT with defects to a finite state automaton was recently developed by Gustafson, Im, Kaldawy, Khovanov, and Lihn. We show that the construction is functorial with respect to the category of finite state automata with transducers as morphisms. Certain classes of subregular lang... | {
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} |
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