id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2412.17504 | An Evaluation Framework for Product Images Background Inpainting based
on Human Feedback and Product Consistency | [
"cs.CV",
"cs.AI"
] | In product advertising applications, the automated inpainting of backgrounds utilizing AI techniques in product images has emerged as a significant task. However, the techniques still suffer from issues such as inappropriate background and inconsistent product in generated product images, and existing approaches for ev... | {
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2412.17505 | More is Less? A Simulation-Based Approach to Dynamic Interactions
between Biases in Multimodal Models | [
"stat.ML",
"cs.LG"
] | Multimodal machine learning models, such as those that combine text and image modalities, are increasingly used in critical domains including public safety, security, and healthcare. However, these systems inherit biases from their single modalities. This study proposes a systemic framework for analyzing dynamic multim... | {
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2412.17506 | Uncertainties of Satellite-based Essential Climate Variables from Deep
Learning | [
"physics.geo-ph",
"cs.LG"
] | Accurate uncertainty information associated with essential climate variables (ECVs) is crucial for reliable climate modeling and understanding the spatiotemporal evolution of the Earth system. In recent years, geoscience and climate scientists have benefited from rapid progress in deep learning to advance the estimatio... | {
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2412.17508 | An efficient search-and-score algorithm for ancestral graphs using
multivariate information scores | [
"cs.LG",
"cs.IT",
"math.IT",
"stat.ME",
"stat.ML"
] | We propose a greedy search-and-score algorithm for ancestral graphs, which include directed as well as bidirected edges, originating from unobserved latent variables. The normalized likelihood score of ancestral graphs is estimated in terms of multivariate information over relevant ``ac-connected subsets'' of vertices,... | {
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2412.17512 | BEE: Metric-Adapted Explanations via Baseline Exploration-Exploitation | [
"cs.LG",
"cs.AI",
"cs.CV"
] | Two prominent challenges in explainability research involve 1) the nuanced evaluation of explanations and 2) the modeling of missing information through baseline representations. The existing literature introduces diverse evaluation metrics, each scrutinizing the quality of explanations through distinct lenses. Additio... | {
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2412.17517 | Dataset for Real-World Human Action Detection Using FMCW mmWave Radar | [
"cs.CV"
] | Human action detection using privacy-preserving mmWave radar sensors is studied for its applications in healthcare and home automation. Unlike existing research, limited to simulations in controlled environments, we present a real-world mmWave radar dataset with baseline results for human action detection. | {
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2412.17518 | Optimal Convergence Rates for Neural Operators | [
"stat.ML",
"cs.LG"
] | We introduce the neural tangent kernel (NTK) regime for two-layer neural operators and analyze their generalization properties. For early-stopped gradient descent (GD), we derive fast convergence rates that are known to be minimax optimal within the framework of non-parametric regression in reproducing kernel Hilbert s... | {
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2412.17522 | DiffusionAttacker: Diffusion-Driven Prompt Manipulation for LLM
Jailbreak | [
"cs.CL"
] | Large Language Models (LLMs) are susceptible to generating harmful content when prompted with carefully crafted inputs, a vulnerability known as LLM jailbreaking. As LLMs become more powerful, studying jailbreak methods is critical to enhancing security and aligning models with human values. Traditionally, jailbreak te... | {
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2412.17523 | Constructing Fair Latent Space for Intersection of Fairness and
Explainability | [
"cs.LG",
"cs.AI",
"cs.CV"
] | As the use of machine learning models has increased, numerous studies have aimed to enhance fairness. However, research on the intersection of fairness and explainability remains insufficient, leading to potential issues in gaining the trust of actual users. Here, we propose a novel module that constructs a fair latent... | {
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2412.17524 | STAHGNet: Modeling Hybrid-grained Heterogenous Dependency Efficiently
for Traffic Prediction | [
"cs.AI"
] | Traffic flow prediction plays a critical role in the intelligent transportation system, and it is also a challenging task because of the underlying complex Spatio-temporal patterns and heterogeneities evolving across time. However, most present works mostly concentrate on solely capturing Spatial-temporal dependency or... | {
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2412.17527 | Enhancing Cancer Diagnosis with Explainable & Trustworthy Deep Learning
Models | [
"cs.AI"
] | This research presents an innovative approach to cancer diagnosis and prediction using explainable Artificial Intelligence (XAI) and deep learning techniques. With cancer causing nearly 10 million deaths globally in 2020, early and accurate diagnosis is crucial. Traditional methods often face challenges in cost, accura... | {
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2412.17531 | Double Landmines: Invisible Textual Backdoor Attacks based on
Dual-Trigger | [
"cs.CR",
"cs.AI"
] | At present, all textual backdoor attack methods are based on single triggers: for example, inserting specific content into the text to activate the backdoor; or changing the abstract text features. The former is easier to be identified by existing defense strategies due to its obvious characteristics; the latter, altho... | {
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2412.17532 | Exploring Dynamic Novel View Synthesis Technologies for Cinematography | [
"cs.CV"
] | Novel view synthesis (NVS) has shown significant promise for applications in cinematographic production, particularly through the exploitation of Neural Radiance Fields (NeRF) and Gaussian Splatting (GS). These methods model real 3D scenes, enabling the creation of new shots that are challenging to capture in the real ... | {
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2412.17533 | Behind Closed Words: Creating and Investigating the forePLay Annotated
Dataset for Polish Erotic Discourse | [
"cs.CL"
] | The surge in online content has created an urgent demand for robust detection systems, especially in non-English contexts where current tools demonstrate significant limitations. We present forePLay, a novel Polish language dataset for erotic content detection, featuring over 24k annotated sentences with a multidimensi... | {
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2412.17534 | CiteBART: Learning to Generate Citations for Local Citation
Recommendation | [
"cs.IR",
"cs.AI",
"cs.CL"
] | Citations are essential building blocks in scientific writing. The scientific community is longing for support in their generation. Citation generation involves two complementary subtasks: Determining the citation worthiness of a context and, if it's worth it, proposing the best candidate papers for the citation placeh... | {
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2412.17537 | Domain adapted machine translation: What does catastrophic forgetting
forget and why? | [
"cs.CL"
] | Neural Machine Translation (NMT) models can be specialized by domain adaptation, often involving fine-tuning on a dataset of interest. This process risks catastrophic forgetting: rapid loss of generic translation quality. Forgetting has been widely observed, with many mitigation methods proposed. However, the causes of... | {
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2412.17540 | WildPPG: A Real-World PPG Dataset of Long Continuous Recordings | [
"cs.CV"
] | Reflective photoplethysmography (PPG) has become the default sensing technique in wearable devices to monitor cardiac activity via a person's heart rate (HR). However, PPG-based HR estimates can be substantially impacted by factors such as the wearer's activities, sensor placement and resulting motion artifacts, as wel... | {
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2412.17541 | Concept Discovery in Deep Neural Networks for Explainable Face
Anti-Spoofing | [
"cs.CV",
"cs.AI"
] | With the rapid growth usage of face recognition in people's daily life, face anti-spoofing becomes increasingly important to avoid malicious attacks. Recent face anti-spoofing models can reach a high classification accuracy on multiple datasets but these models can only tell people "this face is fake" while lacking the... | {
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2412.17542 | Leveraging Cardiovascular Simulations for In-Vivo Prediction of Cardiac
Biomarkers | [
"cs.LG",
"cs.CE",
"physics.bio-ph"
] | Whole-body hemodynamics simulators, which model blood flow and pressure waveforms as functions of physiological parameters, are now essential tools for studying cardiovascular systems. However, solving the corresponding inverse problem of mapping observations (e.g., arterial pressure waveforms at specific locations in ... | {
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2412.17544 | Retention Score: Quantifying Jailbreak Risks for Vision Language Models | [
"cs.AI"
] | The emergence of Vision-Language Models (VLMs) is a significant advancement in integrating computer vision with Large Language Models (LLMs) to enhance multi-modal machine learning capabilities. However, this progress has also made VLMs vulnerable to sophisticated adversarial attacks, raising concerns about their relia... | {
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2412.17547 | Probability-density-aware Semi-supervised Learning | [
"stat.ML",
"cs.LG"
] | Semi-supervised learning (SSL) assumes that neighbor points lie in the same category (neighbor assumption), and points in different clusters belong to various categories (cluster assumption). Existing methods usually rely on similarity measures to retrieve the similar neighbor points, ignoring cluster assumption, which... | {
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2412.17548 | Resource-Aware Arabic LLM Creation: Model Adaptation, Integration, and
Multi-Domain Testing | [
"cs.CL",
"cs.AI"
] | This paper presents a novel approach to fine-tuning the Qwen2-1.5B model for Arabic language processing using Quantized Low-Rank Adaptation (QLoRA) on a system with only 4GB VRAM. We detail the process of adapting this large language model to the Arabic domain, using diverse datasets including Bactrian, OpenAssistant, ... | {
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2412.17552 | Comparative Analysis of Document-Level Embedding Methods for Similarity
Scoring on Shakespeare Sonnets and Taylor Swift Lyrics | [
"cs.CL",
"cs.IR"
] | This study evaluates the performance of TF-IDF weighting, averaged Word2Vec embeddings, and BERT embeddings for document similarity scoring across two contrasting textual domains. By analysing cosine similarity scores, the methods' strengths and limitations are highlighted. The findings underscore TF-IDF's reliance on ... | {
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2412.17554 | Growth-Optimal E-Variables and an extension to the multivariate
Csisz\'ar-Sanov-Chernoff Theorem | [
"cs.IT",
"math.IT",
"math.ST",
"stat.TH"
] | We consider growth-optimal e-variables with maximal e-power, both in an absolute and relative sense, for simple null hypotheses for a $d$-dimensional random vector, and multivariate composite alternatives represented as a set of $d$-dimensional means $\meanspace_1$. These include, among others, the set of all distribut... | {
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2412.17558 | A Survey of Query Optimization in Large Language Models | [
"cs.CL"
] | \textit{Query Optimization} (QO) refers to techniques aimed at enhancing the efficiency and quality of Large Language Models (LLMs) in understanding and answering queries, especially complex ones in scenarios like Retrieval-Augmented Generation (RAG). Specifically, RAG mitigates the limitations of LLMs by dynamically r... | {
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2412.17560 | GQSA: Group Quantization and Sparsity for Accelerating Large Language
Model Inference | [
"cs.LG"
] | Model compression has emerged as a mainstream solution to reduce memory usage and computational overhead. This paper presents Group Quantization and Sparse Acceleration (GQSA), a novel compression technique tailored for LLMs. Traditional methods typically focus exclusively on either quantization or sparsification, but ... | {
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2412.17561 | S-INF: Towards Realistic Indoor Scene Synthesis via Scene Implicit
Neural Field | [
"cs.CV"
] | Learning-based methods have become increasingly popular in 3D indoor scene synthesis (ISS), showing superior performance over traditional optimization-based approaches. These learning-based methods typically model distributions on simple yet explicit scene representations using generative models. However, due to the ov... | {
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2412.17562 | ERUPD -- English to Roman Urdu Parallel Dataset | [
"cs.CL"
] | Bridging linguistic gaps fosters global growth and cultural exchange. This study addresses the challenges of Roman Urdu -- a Latin-script adaptation of Urdu widely used in digital communication -- by creating a novel parallel dataset comprising 75,146 sentence pairs. Roman Urdu's lack of standardization, phonetic varia... | {
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2412.17565 | Evaluation of Bio-Inspired Models under Different Learning Settings For
Energy Efficiency in Network Traffic Prediction | [
"cs.LG",
"cs.AI"
] | Cellular traffic forecasting is a critical task that enables network operators to efficiently allocate resources and address anomalies in rapidly evolving environments. The exponential growth of data collected from base stations poses significant challenges to processing and analysis. While machine learning (ML) algori... | {
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2412.17566 | The Dynamic Duo of Collaborative Masking and Target for Advanced Masked
Autoencoder Learning | [
"cs.CV",
"cs.AI",
"cs.LG",
"eess.IV",
"eess.SP"
] | Masked autoencoders (MAE) have recently succeeded in self-supervised vision representation learning. Previous work mainly applied custom-designed (e.g., random, block-wise) masking or teacher (e.g., CLIP)-guided masking and targets. However, they ignore the potential role of the self-training (student) model in giving ... | {
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2412.17568 | A reaction network approach to modeling carbon dioxide removal systems | [
"eess.SY",
"cs.SY",
"math.DS"
] | This paper focuses on what we call Reaction Network Cardon Dioxide Removal (RNDCR) framework to analyze several proposed negative emissions technologies (NETs) so as to determine when present-day Earth carbon cycle system would exhibit multistationarity (steady-state multiplicity) or possibly monostationarity, that wil... | {
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2412.17571 | HPCNeuroNet: A Neuromorphic Approach Merging SNN Temporal Dynamics with
Transformer Attention for FPGA-based Particle Physics | [
"cs.LG",
"cs.AR",
"physics.comp-ph"
] | This paper presents the innovative HPCNeuroNet model, a pioneering fusion of Spiking Neural Networks (SNNs), Transformers, and high-performance computing tailored for particle physics, particularly in particle identification from detector responses. Our approach leverages SNNs' intrinsic temporal dynamics and Transform... | {
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2412.17572 | Empathetic Response in Audio-Visual Conversations Using Emotion
Preference Optimization and MambaCompressor | [
"cs.CV",
"cs.AI"
] | Chatbot research is advancing with the growing importance of chatbots in fields that require human interactions, such as customer support and mental health care. Despite these advancements, chatbots still face significant challenges in understanding subtle nuances and managing long conversation histories. To address th... | {
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2412.17573 | URoadNet: Dual Sparse Attentive U-Net for Multiscale Road Network
Extraction | [
"cs.CV"
] | The challenges of road network segmentation demand an algorithm capable of adapting to the sparse and irregular shapes, as well as the diverse context, which often leads traditional encoding-decoding methods and simple Transformer embeddings to failure. We introduce a computationally efficient and powerful framework fo... | {
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2412.17574 | HumanVBench: Exploring Human-Centric Video Understanding Capabilities of
MLLMs with Synthetic Benchmark Data | [
"cs.CV",
"cs.AI"
] | In the domain of Multimodal Large Language Models (MLLMs), achieving human-centric video understanding remains a formidable challenge. Existing benchmarks primarily emphasize object and action recognition, often neglecting the intricate nuances of human emotions, behaviors, and speech visual alignment within video cont... | {
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2412.17580 | Quantum Time-Series Learning with Evolutionary Algorithms | [
"quant-ph",
"cs.NE"
] | Variational quantum circuits have arisen as an important method in quantum computing. A crucial step of it is parameter optimization, which is typically tackled through gradient-descent techniques. We advantageously explore instead the use of evolutionary algorithms for such optimization, specifically for time-series f... | {
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2412.17586 | Enhancing Reconstruction-Based Out-of-Distribution Detection in Brain
MRI with Model and Metric Ensembles | [
"eess.IV",
"cs.CV",
"cs.LG"
] | Out-of-distribution (OOD) detection is crucial for safely deploying automated medical image analysis systems, as abnormal patterns in images could hamper their performance. However, OOD detection in medical imaging remains an open challenge, and we address three gaps: the underexplored potential of a simple OOD detecti... | {
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2412.17587 | Improved Cotton Leaf Disease Classification Using Parameter-Efficient
Deep Learning Framework | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Cotton crops, often called "white gold," face significant production challenges, primarily due to various leaf-affecting diseases. As a major global source of fiber, timely and accurate disease identification is crucial to ensure optimal yields and maintain crop health. While deep learning and machine learning techniqu... | {
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2412.17589 | PC Agent: While You Sleep, AI Works -- A Cognitive Journey into Digital
World | [
"cs.AI",
"cs.LG"
] | Imagine a world where AI can handle your work while you sleep - organizing your research materials, drafting a report, or creating a presentation you need for tomorrow. However, while current digital agents can perform simple tasks, they are far from capable of handling the complex real-world work that humans routinely... | {
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2412.17591 | Graph Size-imbalanced Learning with Energy-guided Structural Smoothing | [
"cs.LG",
"stat.ML"
] | Graph is a prevalent data structure employed to represent the relationships between entities, frequently serving as a tool to depict and simulate numerous systems, such as molecules and social networks. However, real-world graphs usually suffer from the size-imbalanced problem in the multi-graph classification, i.e., a... | {
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2412.17592 | Investigating Length Issues in Document-level Machine Translation | [
"cs.CL"
] | Transformer architectures are increasingly effective at processing and generating very long chunks of texts, opening new perspectives for document-level machine translation (MT). In this work, we challenge the ability of MT systems to handle texts comprising up to several thousands of tokens. We design and implement a ... | {
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2412.17593 | Leveraging Memory Retrieval to Enhance LLM-based Generative
Recommendation | [
"cs.IR"
] | Leveraging Large Language Models (LLMs) to harness user-item interaction histories for item generation has emerged as a promising paradigm in generative recommendation. However, the limited context window of LLMs often restricts them to focusing on recent user interactions only, leading to the neglect of long-term inte... | {
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2412.17595 | V$^2$-SfMLearner: Learning Monocular Depth and Ego-motion for Multimodal
Wireless Capsule Endoscopy | [
"cs.CV",
"cs.AI",
"cs.RO"
] | Deep learning can predict depth maps and capsule ego-motion from capsule endoscopy videos, aiding in 3D scene reconstruction and lesion localization. However, the collisions of the capsule endoscopies within the gastrointestinal tract cause vibration perturbations in the training data. Existing solutions focus solely o... | {
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2412.17596 | LiveIdeaBench: Evaluating LLMs' Scientific Creativity and Idea
Generation with Minimal Context | [
"cs.CL",
"cs.AI"
] | While Large Language Models (LLMs) have demonstrated remarkable capabilities in scientific tasks, existing evaluation frameworks primarily assess their performance using rich contextual inputs, overlooking their ability to generate novel ideas from minimal information. We introduce LiveIdeaBench, a comprehensive benchm... | {
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2412.17601 | AFANet: Adaptive Frequency-Aware Network for Weakly-Supervised Few-Shot
Semantic Segmentation | [
"cs.CV",
"cs.AI"
] | Few-shot learning aims to recognize novel concepts by leveraging prior knowledge learned from a few samples. However, for visually intensive tasks such as few-shot semantic segmentation, pixel-level annotations are time-consuming and costly. Therefore, in this paper, we utilize the more challenging image-level annotati... | {
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2412.17603 | EasyTime: Time Series Forecasting Made Easy | [
"cs.LG",
"stat.ML"
] | Time series forecasting has important applications across diverse domains. EasyTime, the system we demonstrate, facilitates easy use of time-series forecasting methods by researchers and practitioners alike. First, EasyTime enables one-click evaluation, enabling researchers to evaluate new forecasting methods using the... | {
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2412.17606 | SBS Figures: Pre-training Figure QA from Stage-by-Stage Synthesized
Images | [
"cs.CV"
] | Building a large-scale figure QA dataset requires a considerable amount of work, from gathering and selecting figures to extracting attributes like text, numbers, and colors, and generating QAs. Although recent developments in LLMs have led to efforts to synthesize figures, most of these focus primarily on QA generatio... | {
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2412.17609 | Towards Foundation Models on Graphs: An Analysis on Cross-Dataset
Transfer of Pretrained GNNs | [
"cs.LG",
"cs.NE"
] | To develop a preliminary understanding towards Graph Foundation Models, we study the extent to which pretrained Graph Neural Networks can be applied across datasets, an effort requiring to be agnostic to dataset-specific features and their encodings. We build upon a purely structural pretraining approach and propose an... | {
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2412.17610 | Personalized Large Vision-Language Models | [
"cs.CV"
] | The personalization model has gained significant attention in image generation yet remains underexplored for large vision-language models (LVLMs). Beyond generic ones, with personalization, LVLMs handle interactive dialogues using referential concepts (e.g., ``Mike and Susan are talking.'') instead of the generic form ... | {
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2412.17612 | CoSurfGS:Collaborative 3D Surface Gaussian Splatting with Distributed
Learning for Large Scene Reconstruction | [
"cs.CV"
] | 3D Gaussian Splatting (3DGS) has demonstrated impressive performance in scene reconstruction. However, most existing GS-based surface reconstruction methods focus on 3D objects or limited scenes. Directly applying these methods to large-scale scene reconstruction will pose challenges such as high memory costs, excessiv... | {
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2412.17613 | Can Stability be Detrimental? Better Generalization through Gradient
Descent Instabilities | [
"cs.LG",
"stat.ML"
] | Traditional analyses of gradient descent optimization show that, when the largest eigenvalue of the loss Hessian - often referred to as the sharpness - is below a critical learning-rate threshold, then training is 'stable' and training loss decreases monotonically. Recent studies, however, have suggested that the major... | {
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2412.17614 | Emerging Security Challenges of Large Language Models | [
"cs.CR",
"cs.AI"
] | Large language models (LLMs) have achieved record adoption in a short period of time across many different sectors including high importance areas such as education [4] and healthcare [23]. LLMs are open-ended models trained on diverse data without being tailored for specific downstream tasks, enabling broad applicabil... | {
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2412.17616 | Facial Expression Analysis and Its Potentials in IoT Systems: A
Contemporary Survey | [
"cs.AI"
] | Facial expressions convey human emotions and can be categorized into macro-expressions (MaEs) and micro-expressions (MiEs) based on duration and intensity. While MaEs are voluntary and easily recognized, MiEs are involuntary, rapid, and can reveal concealed emotions. The integration of facial expression analysis with I... | {
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2412.17619 | Kernel-Aware Graph Prompt Learning for Few-Shot Anomaly Detection | [
"cs.CV"
] | Few-shot anomaly detection (FSAD) aims to detect unseen anomaly regions with the guidance of very few normal support images from the same class. Existing FSAD methods usually find anomalies by directly designing complex text prompts to align them with visual features under the prevailing large vision-language model par... | {
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2412.17622 | Be More Diverse than the Most Diverse: Online Selection of Diverse
Mixtures of Generative Models | [
"cs.LG",
"stat.ML"
] | The availability of multiple training algorithms and architectures for generative models requires a selection mechanism to form a single model over a group of well-trained generation models. The selection task is commonly addressed by identifying the model that maximizes an evaluation score based on the diversity and q... | {
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2412.17623 | Towards An Unsupervised Learning Scheme for Efficiently Solving
Parameterized Mixed-Integer Programs | [
"math.OC",
"cs.LG"
] | In this paper, we describe a novel unsupervised learning scheme for accelerating the solution of a family of mixed integer programming (MIP) problems. Distinct substantially from existing learning-to-optimize methods, our proposal seeks to train an autoencoder (AE) for binary variables in an unsupervised learning fashi... | {
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2412.17626 | Tracking the Feature Dynamics in LLM Training: A Mechanistic Study | [
"cs.LG",
"cs.CL"
] | Understanding training dynamics and feature evolution is crucial for the mechanistic interpretability of large language models (LLMs). Although sparse autoencoders (SAEs) have been used to identify features within LLMs, a clear picture of how these features evolve during training remains elusive. In this study, we: (1)... | {
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2412.17628 | Editing Implicit and Explicit Representations of Radiance Fields: A
Survey | [
"cs.CV"
] | Neural Radiance Fields (NeRF) revolutionized novel view synthesis in recent years by offering a new volumetric representation, which is compact and provides high-quality image rendering. However, the methods to edit those radiance fields developed slower than the many improvements to other aspects of NeRF. With the rec... | {
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2412.17629 | Graph Neural Networks Are Evolutionary Algorithms | [
"cs.NE",
"cs.AI"
] | In this paper, we reveal the intrinsic duality between graph neural networks (GNNs) and evolutionary algorithms (EAs), bridging two traditionally distinct fields. Building on this insight, we propose Graph Neural Evolution (GNE), a novel evolutionary algorithm that models individuals as nodes in a graph and leverages d... | {
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2412.17630 | Detail-Preserving Latent Diffusion for Stable Shadow Removal | [
"cs.CV"
] | Achieving high-quality shadow removal with strong generalizability is challenging in scenes with complex global illumination. Due to the limited diversity in shadow removal datasets, current methods are prone to overfitting training data, often leading to reduced performance on unseen cases. To address this, we leverag... | {
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2412.17632 | ANID: How Far Are We? Evaluating the Discrepancies Between
AI-synthesized Images and Natural Images through Multimodal Guidance | [
"cs.AI",
"cs.CV",
"cs.MM"
] | In the rapidly evolving field of Artificial Intelligence Generated Content (AIGC), one of the key challenges is distinguishing AI-synthesized images from natural images. Despite the remarkable capabilities of advanced AI generative models in producing visually compelling images, significant discrepancies remain when th... | {
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2412.17635 | LangSurf: Language-Embedded Surface Gaussians for 3D Scene Understanding | [
"cs.CV"
] | Applying Gaussian Splatting to perception tasks for 3D scene understanding is becoming increasingly popular. Most existing works primarily focus on rendering 2D feature maps from novel viewpoints, which leads to an imprecise 3D language field with outlier languages, ultimately failing to align objects in 3D space. By u... | {
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2412.17637 | SCBench: A Sports Commentary Benchmark for Video LLMs | [
"cs.CV",
"cs.AI"
] | Recently, significant advances have been made in Video Large Language Models (Video LLMs) in both academia and industry. However, methods to evaluate and benchmark the performance of different Video LLMs, especially their fine-grained, temporal visual capabilities, remain very limited. On one hand, current benchmarks u... | {
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2412.17640 | Hierarchical Vector Quantization for Unsupervised Action Segmentation | [
"cs.CV"
] | In this work, we address unsupervised temporal action segmentation, which segments a set of long, untrimmed videos into semantically meaningful segments that are consistent across videos. While recent approaches combine representation learning and clustering in a single step for this task, they do not cope with large v... | {
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2412.17643 | Advances in Machine Learning Research Using Knowledge Graphs | [
"cs.AI"
] | The study uses CSSCI-indexed literature from the China National Knowledge Infrastructure (CNKI) database as the data source. It utilizes the CiteSpace visualization software to draw knowledge graphs on aspects such as institutional collaboration and keyword co-occurrence. This analysis provides insights into the curren... | {
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2412.17644 | DreamFit: Garment-Centric Human Generation via a Lightweight
Anything-Dressing Encoder | [
"cs.CV"
] | Diffusion models for garment-centric human generation from text or image prompts have garnered emerging attention for their great application potential. However, existing methods often face a dilemma: lightweight approaches, such as adapters, are prone to generate inconsistent textures; while finetune-based methods inv... | {
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2412.17646 | Rate of Model Collapse in Recursive Training | [
"cs.LG",
"cs.IT",
"math.IT",
"stat.ML"
] | Given the ease of creating synthetic data from machine learning models, new models can be potentially trained on synthetic data generated by previous models. This recursive training process raises concerns about the long-term impact on model quality. As models are recursively trained on generated data from previous rou... | {
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2412.17647 | An Adaptive Framework for Multi-View Clustering Leveraging Conditional
Entropy Optimization | [
"cs.AI"
] | Multi-view clustering (MVC) has emerged as a powerful technique for extracting valuable insights from data characterized by multiple perspectives or modalities. Despite significant advancements, existing MVC methods struggle with effectively quantifying the consistency and complementarity among views, and are particula... | {
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2412.17651 | Detecting anxiety and depression in dialogues: a multi-label and
explainable approach | [
"cs.AI"
] | Anxiety and depression are the most common mental health issues worldwide, affecting a non-negligible part of the population. Accordingly, stakeholders, including governments' health systems, are developing new strategies to promote early detection and prevention from a holistic perspective (i.e., addressing several di... | {
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2412.17652 | Benchmarking Generative AI Models for Deep Learning Test Input
Generation | [
"cs.LG",
"cs.SE"
] | Test Input Generators (TIGs) are crucial to assess the ability of Deep Learning (DL) image classifiers to provide correct predictions for inputs beyond their training and test sets. Recent advancements in Generative AI (GenAI) models have made them a powerful tool for creating and manipulating synthetic images, althoug... | {
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2412.17654 | Enhanced Temporal Processing in Spiking Neural Networks for Static
Object Detection Using 3D Convolutions | [
"cs.AI",
"cs.CV",
"cs.NE"
] | Spiking Neural Networks (SNNs) are a class of network models capable of processing spatiotemporal information, with event-driven characteristics and energy efficiency advantages. Recently, directly trained SNNs have shown potential to match or surpass the performance of traditional Artificial Neural Networks (ANNs) in ... | {
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2412.17658 | Private Semantic Communications with Separate Blind Encoders | [
"cs.IT",
"math.IT"
] | We study a semantic communication problem with a privacy constraint where an encoder consists of two separate parts, e.g., encoder 1 and encoder 2. The first encoder has access to information source $X=(X_1,\ldots,X_N)$ which is arbitrarily correlated with private data $S$. The private data is not accessible by encoder... | {
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2412.17669 | Generating Completions for Fragmented Broca's Aphasic Sentences Using
Large Language Models | [
"cs.CL"
] | Broca's aphasia is a type of aphasia characterized by non-fluent, effortful and fragmented speech production with relatively good comprehension. Since traditional aphasia treatment methods are often time-consuming, labour-intensive, and do not reflect real-world conversations, applying natural language processing based... | {
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2412.17671 | A Bias-Free Training Paradigm for More General AI-generated Image
Detection | [
"cs.CV"
] | Successful forensic detectors can produce excellent results in supervised learning benchmarks but struggle to transfer to real-world applications. We believe this limitation is largely due to inadequate training data quality. While most research focuses on developing new algorithms, less attention is given to training ... | {
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2412.17677 | EPE-P: Evidence-based Parameter-efficient Prompting for Multimodal
Learning with Missing Modalities | [
"cs.CV"
] | Missing modalities are a common challenge in real-world multimodal learning scenarios, occurring during both training and testing. Existing methods for managing missing modalities often require the design of separate prompts for each modality or missing case, leading to complex designs and a substantial increase in the... | {
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2412.17684 | COBRA: COmBinatorial Retrieval Augmentation for Few-Shot Learning | [
"cs.LG"
] | Retrieval augmentation, the practice of retrieving additional data from large auxiliary pools, has emerged as an effective technique for enhancing model performance in the low-data regime, e.g. few-shot learning. Prior approaches have employed only nearest-neighbor based strategies for data selection, which retrieve au... | {
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2412.17685 | A Reproducible Method for Mapping Electricity Transmission
Infrastructure for Space Weather Risk Assessment | [
"physics.geo-ph",
"cs.SY",
"eess.SY"
] | Space weather impact assessment is constrained by the lack of available asset information to undertake modeling of Geomagnetically Induced Currents (GICs) in Extra High Voltage electricity infrastructure networks. The U.S. National Space Weather Strategy and Action Plan identifies underutilized data as a central issue ... | {
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2412.17686 | Large Language Model Safety: A Holistic Survey | [
"cs.AI",
"cs.CL"
] | The rapid development and deployment of large language models (LLMs) have introduced a new frontier in artificial intelligence, marked by unprecedented capabilities in natural language understanding and generation. However, the increasing integration of these models into critical applications raises substantial safety ... | {
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2412.17690 | RAGONITE: Iterative Retrieval on Induced Databases and Verbalized RDF
for Conversational QA over KGs with RAG | [
"cs.CL",
"cs.IR"
] | Conversational question answering (ConvQA) is a convenient means of searching over RDF knowledge graphs (KGs), where a prevalent approach is to translate natural language questions to SPARQL queries. However, SPARQL has certain shortcomings: (i) it is brittle for complex intents and conversational questions, and (ii) i... | {
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2412.17692 | FedTLU: Federated Learning with Targeted Layer Updates | [
"cs.LG",
"cs.AI",
"cs.DC"
] | Federated learning (FL) addresses privacy concerns in training language models by enabling multiple clients to contribute to the training, without sending their data to others. However, non-IID (identically and independently distributed) data across clients often limits FL's performance. This issue is especially challe... | {
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2412.17696 | Understanding the Logic of Direct Preference Alignment through Logic | [
"cs.CL"
] | Recent direct preference alignment algorithms (DPA), such as DPO, have shown great promise in aligning large language models to human preferences. While this has motivated the development of many new variants of the original DPO loss, understanding the differences between these recent proposals, as well as developing n... | {
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2412.17699 | Establishing Reality-Virtuality Interconnections in Urban Digital Twins
for Superior Intelligent Road Inspection | [
"cs.CV"
] | Road inspection is essential for ensuring road maintenance and traffic safety, as road defects gradually emerge and compromise road functionality. Traditional methods, which rely on manual evaluations, are labor-intensive, costly, and time-consuming. Although data-driven approaches are gaining traction, the scarcity an... | {
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2412.17700 | MRANet: A Modified Residual Attention Networks for Lung and Colon Cancer
Classification | [
"eess.IV",
"cs.CV",
"cs.LG"
] | Lung and colon cancers are predominant contributors to cancer mortality. Early and accurate diagnosis is crucial for effective treatment. By utilizing imaging technology in different image detection, learning models have shown promise in automating cancer classification from histopathological images. This includes the ... | {
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2412.17701 | From Models to Microtheories: Distilling a Model's Topical Knowledge for
Grounded Question Answering | [
"cs.CL"
] | Recent reasoning methods (e.g., chain-of-thought, entailment reasoning) help users understand how language models (LMs) answer a single question, but they do little to reveal the LM's overall understanding, or "theory," about the question's topic, making it still hard to trust the model. Our goal is to materialize such... | {
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2412.17707 | SMAC-Hard: Enabling Mixed Opponent Strategy Script and Self-play on SMAC | [
"cs.AI"
] | The availability of challenging simulation environments is pivotal for advancing the field of Multi-Agent Reinforcement Learning (MARL). In cooperative MARL settings, the StarCraft Multi-Agent Challenge (SMAC) has gained prominence as a benchmark for algorithms following centralized training with decentralized executio... | {
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2412.17715 | GaussianPainter: Painting Point Cloud into 3D Gaussians with Normal
Guidance | [
"cs.CV"
] | In this paper, we present GaussianPainter, the first method to paint a point cloud into 3D Gaussians given a reference image. GaussianPainter introduces an innovative feed-forward approach to overcome the limitations of time-consuming test-time optimization in 3D Gaussian splatting. Our method addresses a critical chal... | {
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2412.17717 | Fast Causal Discovery by Approximate Kernel-based Generalized Score
Functions with Linear Computational Complexity | [
"cs.LG",
"stat.ML"
] | Score-based causal discovery methods can effectively identify causal relationships by evaluating candidate graphs and selecting the one with the highest score. One popular class of scores is kernel-based generalized score functions, which can adapt to a wide range of scenarios and work well in practice because they cir... | {
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2412.17723 | Asynchronous Federated Learning: A Scalable Approach for Decentralized
Machine Learning | [
"cs.LG",
"cs.SY",
"eess.SY"
] | Federated Learning (FL) has emerged as a powerful paradigm for decentralized machine learning, enabling collaborative model training across diverse clients without sharing raw data. However, traditional FL approaches often face limitations in scalability and efficiency due to their reliance on synchronous client update... | {
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2412.17726 | VidTwin: Video VAE with Decoupled Structure and Dynamics | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Recent advancements in video autoencoders (Video AEs) have significantly improved the quality and efficiency of video generation. In this paper, we propose a novel and compact video autoencoder, VidTwin, that decouples video into two distinct latent spaces: Structure latent vectors, which capture overall content and gl... | {
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2412.17727 | Knowledge Editing through Chain-of-Thought | [
"cs.CL"
] | Large Language Models (LLMs) have demonstrated exceptional capabilities across a wide range of natural language processing (NLP) tasks. However, keeping these models up-to-date with evolving world knowledge remains a significant challenge due to the high costs of frequent retraining. To address this challenge, knowledg... | {
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2412.17729 | Chumor 2.0: Towards Benchmarking Chinese Humor Understanding | [
"cs.CL",
"cs.AI"
] | Existing humor datasets and evaluations predominantly focus on English, leaving limited resources for culturally nuanced humor in non-English languages like Chinese. To address this gap, we construct Chumor, the first Chinese humor explanation dataset that exceeds the size of existing humor datasets. Chumor is sourced ... | {
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} |
2412.17730 | Mimicking-Bench: A Benchmark for Generalizable Humanoid-Scene
Interaction Learning via Human Mimicking | [
"cs.RO",
"cs.CV"
] | Learning generic skills for humanoid robots interacting with 3D scenes by mimicking human data is a key research challenge with significant implications for robotics and real-world applications. However, existing methodologies and benchmarks are constrained by the use of small-scale, manually collected demonstrations, ... | {
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} |
2412.17734 | LASE: Learned Adjacency Spectral Embeddings | [
"cs.LG",
"stat.ML"
] | We put forth a principled design of a neural architecture to learn nodal Adjacency Spectral Embeddings (ASE) from graph inputs. By bringing to bear the gradient descent (GD) method and leveraging the principle of algorithm unrolling, we truncate and re-interpret each GD iteration as a layer in a graph neural network (G... | {
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} |
2412.17737 | Contextual Feedback Loops: Amplifying Deep Reasoning with Iterative
Top-Down Feedback | [
"cs.LG"
] | Conventional deep networks rely on one-way backpropagation that overlooks reconciling high-level predictions with lower-level representations. We propose \emph{Contextual Feedback Loops} (CFLs), a lightweight mechanism that re-injects top-down context into earlier layers for iterative refinement. Concretely, CFLs map t... | {
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} |
2412.17739 | Fourier Position Embedding: Enhancing Attention's Periodic Extension for
Length Generalization | [
"cs.AI",
"cs.CL"
] | Extending the context length of Language Models (LMs) by improving Rotary Position Embedding (RoPE) has become a trend. While existing works mainly address RoPE's limitations within attention mechanism, this paper provides an analysis across nearly all parts of LMs, uncovering their adverse effects on length generaliza... | {
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} |
2412.17740 | Sensitivity Curve Maximization: Attacking Robust Aggregators in
Distributed Learning | [
"cs.LG",
"eess.SP"
] | In distributed learning agents aim at collaboratively solving a global learning problem. It becomes more and more likely that individual agents are malicious or faulty with an increasing size of the network. This leads to a degeneration or complete breakdown of the learning process. Classical aggregation schemes are pr... | {
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} |
2412.17741 | Reasoning to Attend: Try to Understand How <SEG> Token Works | [
"cs.CV"
] | Current Large Multimodal Models (LMMs) empowered visual grounding typically rely on $\texttt{<SEG>}$ token as a text prompt to jointly optimize the vision-language model (e.g., LLaVA) and the downstream task-specified model (\eg, SAM). However, we observe that little research has looked into how it works. In this work,... | {
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} |
2412.17743 | YuLan-Mini: An Open Data-efficient Language Model | [
"cs.CL"
] | Effective pre-training of large language models (LLMs) has been challenging due to the immense resource demands and the complexity of the technical processes involved. This paper presents a detailed technical report on YuLan-Mini, a highly capable base model with 2.42B parameters that achieves top-tier performance amon... | {
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} |
2412.17744 | RepoTransBench: A Real-World Benchmark for Repository-Level Code
Translation | [
"cs.SE",
"cs.AI",
"cs.CL"
] | Repository-level code translation refers to translating an entire code repository from one programming language to another while preserving the functionality of the source repository. Many benchmarks have been proposed to evaluate the performance of such code translators. However, previous benchmarks mostly provide fin... | {
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} |
2412.17747 | Deliberation in Latent Space via Differentiable Cache Augmentation | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Techniques enabling large language models (LLMs) to "think more" by generating and attending to intermediate reasoning steps have shown promise in solving complex problems. However, the standard approaches generate sequences of discrete tokens immediately before responding, and so they can incur significant latency cos... | {
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} |
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