id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2412.16828 | Technical Report: Towards Spatial Feature Regularization in
Deep-Learning-Based Array-SAR Reconstruction | [
"eess.IV",
"cs.CV"
] | Array synthetic aperture radar (Array-SAR), also known as tomographic SAR (TomoSAR), has demonstrated significant potential for high-quality 3D mapping, particularly in urban areas.While deep learning (DL) methods have recently shown strengths in reconstruction, most studies rely on pixel-by-pixel reconstruction, negle... | {
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2412.16829 | Visual Prompting with Iterative Refinement for Design Critique
Generation | [
"cs.AI"
] | Feedback is crucial for every design process, such as user interface (UI) design, and automating design critiques can significantly improve the efficiency of the design workflow. Although existing multimodal large language models (LLMs) excel in many tasks, they often struggle with generating high-quality design critiq... | {
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2412.16830 | Algorithm Design for Continual Learning in IoT Networks | [
"cs.LG",
"cs.DS",
"cs.NI"
] | Continual learning (CL) is a new online learning technique over sequentially generated streaming data from different tasks, aiming to maintain a small forgetting loss on previously-learned tasks. Existing work focuses on reducing the forgetting loss under a given task sequence. However, if similar tasks continuously ap... | {
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2412.16832 | RealisID: Scale-Robust and Fine-Controllable Identity Customization via
Local and Global Complementation | [
"cs.CV"
] | Recently, the success of text-to-image synthesis has greatly advanced the development of identity customization techniques, whose main goal is to produce realistic identity-specific photographs based on text prompts and reference face images. However, it is difficult for existing identity customization methods to simul... | {
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2412.16833 | KG4Diagnosis: A Hierarchical Multi-Agent LLM Framework with Knowledge
Graph Enhancement for Medical Diagnosis | [
"cs.AI",
"cs.LG"
] | Integrating Large Language Models (LLMs) in healthcare diagnosis demands systematic frameworks that can handle complex medical scenarios while maintaining specialized expertise. We present KG4Diagnosis, a novel hierarchical multi-agent framework that combines LLMs with automated knowledge graph construction, encompassi... | {
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2412.16834 | Online Learning from Strategic Human Feedback in LLM Fine-Tuning | [
"cs.AI",
"cs.GT"
] | Reinforcement learning from human feedback (RLHF) has become an essential step in fine-tuning large language models (LLMs) to align them with human preferences. However, human labelers are selfish and have diverse preferences. They may strategically misreport their online feedback to influence the system's aggregation ... | {
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2412.16838 | Ask-Before-Detection: Identifying and Mitigating Conformity Bias in
LLM-Powered Error Detector for Math Word Problem Solutions | [
"cs.CL"
] | The rise of large language models (LLMs) offers new opportunities for automatic error detection in education, particularly for math word problems (MWPs). While prior studies demonstrate the promise of LLMs as error detectors, they overlook the presence of multiple valid solutions for a single MWP. Our preliminary analy... | {
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2412.16839 | Human-Guided Image Generation for Expanding Small-Scale Training Image
Datasets | [
"cs.CV",
"cs.AI"
] | The performance of computer vision models in certain real-world applications (e.g., rare wildlife observation) is limited by the small number of available images. Expanding datasets using pre-trained generative models is an effective way to address this limitation. However, since the automatic generation process is unc... | {
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2412.16840 | Seamless Detection: Unifying Salient Object Detection and Camouflaged
Object Detection | [
"cs.CV"
] | Achieving joint learning of Salient Object Detection (SOD) and Camouflaged Object Detection (COD) is extremely challenging due to their distinct object characteristics, i.e., saliency and camouflage. The only preliminary research treats them as two contradictory tasks, training models on large-scale labeled data altern... | {
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2412.16842 | Graph Learning-based Regional Heavy Rainfall Prediction Using Low-Cost
Rain Gauges | [
"cs.LG",
"cs.AI",
"cs.NE"
] | Accurate and timely prediction of heavy rainfall events is crucial for effective flood risk management and disaster preparedness. By monitoring, analysing, and evaluating rainfall data at a local level, it is not only possible to take effective actions to prevent any severe climate variation but also to improve the pla... | {
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2412.16844 | Sim911: Towards Effective and Equitable 9-1-1 Dispatcher Training with
an LLM-Enabled Simulation | [
"cs.CL",
"cs.AI"
] | Emergency response services are vital for enhancing public safety by safeguarding the environment, property, and human lives. As frontline members of these services, 9-1-1 dispatchers have a direct impact on response times and the overall effectiveness of emergency operations. However, traditional dispatcher training m... | {
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2412.16846 | Autoregressive Speech Synthesis with Next-Distribution Prediction | [
"eess.AS",
"cs.CL",
"cs.SD"
] | We introduce KALL-E, a novel autoregressive (AR) language modeling approach with next-distribution prediction for text-to-speech (TTS) synthesis. Unlike existing methods, KALL-E directly models and predicts the continuous speech distribution conditioned on text without relying on VAE- or diffusion-based components. Spe... | {
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2412.16848 | ACL-QL: Adaptive Conservative Level in Q-Learning for Offline
Reinforcement Learning | [
"cs.LG",
"cs.AI",
"cs.RO"
] | Offline Reinforcement Learning (RL), which operates solely on static datasets without further interactions with the environment, provides an appealing alternative to learning a safe and promising control policy. The prevailing methods typically learn a conservative policy to mitigate the problem of Q-value overestimati... | {
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2412.16849 | OpenRFT: Adapting Reasoning Foundation Model for Domain-specific Tasks
with Reinforcement Fine-Tuning | [
"cs.AI"
] | OpenAI's recent introduction of Reinforcement Fine-Tuning (RFT) showcases the potential of reasoning foundation model and offers a new paradigm for fine-tuning beyond simple pattern imitation. This technical report presents \emph{OpenRFT}, our attempt to fine-tune generalist reasoning models for domain-specific tasks u... | {
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2412.16854 | Sharpness-Aware Minimization with Adaptive Regularization for Training
Deep Neural Networks | [
"cs.LG",
"cs.CV"
] | Sharpness-Aware Minimization (SAM) has proven highly effective in improving model generalization in machine learning tasks. However, SAM employs a fixed hyperparameter associated with the regularization to characterize the sharpness of the model. Despite its success, research on adaptive regularization methods based on... | {
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2412.16855 | GME: Improving Universal Multimodal Retrieval by Multimodal LLMs | [
"cs.CL",
"cs.IR"
] | Universal Multimodal Retrieval (UMR) aims to enable search across various modalities using a unified model, where queries and candidates can consist of pure text, images, or a combination of both. Previous work has attempted to adopt multimodal large language models (MLLMs) to realize UMR using only text data. However,... | {
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2412.16859 | Adversarial Diffusion Model for Unsupervised Domain-Adaptive Semantic
Segmentation | [
"cs.CV",
"cs.AI"
] | Semantic segmentation requires labour-intensive labelling tasks to obtain the supervision signals, and because of this issue, it is encouraged that using domain adaptation, which transfers information from the existing labelled source domains to unlabelled or weakly labelled target domains, is essential. However, it is... | {
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2412.16860 | Diffusion-Based Approaches in Medical Image Generation and Analysis | [
"eess.IV",
"cs.CV"
] | Data scarcity in medical imaging poses significant challenges due to privacy concerns. Diffusion models, a recent generative modeling technique, offer a potential solution by generating synthetic and realistic data. However, questions remain about the performance of convolutional neural network (CNN) models on original... | {
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2412.16861 | SoundLoc3D: Invisible 3D Sound Source Localization and Classification
Using a Multimodal RGB-D Acoustic Camera | [
"cs.SD",
"cs.CV",
"cs.MM",
"eess.AS"
] | Accurately localizing 3D sound sources and estimating their semantic labels -- where the sources may not be visible, but are assumed to lie on the physical surface of objects in the scene -- have many real applications, including detecting gas leak and machinery malfunction. The audio-visual weak-correlation in such se... | {
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2412.16864 | Efficient Row-Level Lineage Leveraging Predicate Pushdown | [
"cs.DB"
] | Row-level lineage explains what input rows produce an output row through a data processing pipeline, having many applications like data debugging, auditing, data integration, etc. Prior work on lineage falls in two lines: eager lineage tracking and lazy lineage inference. Eager tracking integrates lineage tracing tight... | {
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2412.16867 | A Parameter-Efficient Quantum Anomaly Detection Method on a
Superconducting Quantum Processor | [
"quant-ph",
"cs.LG",
"math.ST",
"stat.TH"
] | Quantum machine learning has gained attention for its potential to address computational challenges. However, whether those algorithms can effectively solve practical problems and outperform their classical counterparts, especially on current quantum hardware, remains a critical question. In this work, we propose a nov... | {
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2412.16869 | CoF: Coarse to Fine-Grained Image Understanding for Multi-modal Large
Language Models | [
"cs.CV"
] | The impressive performance of Large Language Model (LLM) has prompted researchers to develop Multi-modal LLM (MLLM), which has shown great potential for various multi-modal tasks. However, current MLLM often struggles to effectively address fine-grained multi-modal challenges. We argue that this limitation is closely l... | {
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2412.16871 | Teaching LLMs to Refine with Tools | [
"cs.CL"
] | Large language models (LLMs) can refine their responses based on feedback, enabling self-improvement through iterative training or test-time refinement. However, existing methods predominantly focus on refinement within the same reasoning format, which may lead to non-correcting behaviors. We propose CaP, a novel appro... | {
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2412.16874 | A Multi-modal Approach to Dysarthria Detection and Severity Assessment
Using Speech and Text Information | [
"cs.AI",
"eess.AS"
] | Automatic detection and severity assessment of dysarthria are crucial for delivering targeted therapeutic interventions to patients. While most existing research focuses primarily on speech modality, this study introduces a novel approach that leverages both speech and text modalities. By employing cross-attention mech... | {
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2412.16875 | Swept Volume-Aware Trajectory Planning and MPC Tracking for Multi-Axle
Swerve-Drive AMRs | [
"cs.RO"
] | Multi-axle autonomous mobile robots (AMRs) are set to revolutionize the future of robotics in logistics. As the backbone of next-generation solutions, these robots face a critical challenge: managing and minimizing the swept volume during turns while maintaining precise control. Traditional systems designed for standar... | {
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2412.16876 | MAGIC++: Efficient and Resilient Modality-Agnostic Semantic Segmentation
via Hierarchical Modality Selection | [
"cs.CV"
] | In this paper, we address the challenging modality-agnostic semantic segmentation (MaSS), aiming at centering the value of every modality at every feature granularity. Training with all available visual modalities and effectively fusing an arbitrary combination of them is essential for robust multi-modal fusion in sema... | {
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2412.16877 | Reconsidering SMT Over NMT for Closely Related Languages: A Case Study
of Persian-Hindi Pair | [
"cs.CL"
] | This paper demonstrates that Phrase-Based Statistical Machine Translation (PBSMT) can outperform Transformer-based Neural Machine Translation (NMT) in moderate-resource scenarios, specifically for structurally similar languages, like the Persian-Hindi pair. Despite the Transformer architecture's typical preference for ... | {
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2412.16878 | Online Preference-based Reinforcement Learning with Self-augmented
Feedback from Large Language Model | [
"cs.LG",
"cs.AI"
] | Preference-based reinforcement learning (PbRL) provides a powerful paradigm to avoid meticulous reward engineering by learning rewards based on human preferences. However, real-time human feedback is hard to obtain in online tasks. Most work suppose there is a "scripted teacher" that utilizes privileged predefined rewa... | {
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2412.16880 | Large-Scale UWB Anchor Calibration and One-Shot Localization Using
Gaussian Process | [
"cs.RO"
] | Ultra-wideband (UWB) is gaining popularity with devices like AirTags for precise home item localization but faces significant challenges when scaled to large environments like seaports. The main challenges are calibration and localization in obstructed conditions, which are common in logistics environments. Traditional... | {
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2412.16881 | Predicting the Reliability of an Image Classifier under Image Distortion | [
"cs.CV"
] | In image classification tasks, deep learning models are vulnerable to image distortions i.e. their accuracy significantly drops if the input images are distorted. An image-classifier is considered "reliable" if its accuracy on distorted images is above a user-specified threshold. For a quality control purpose, it is im... | {
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2412.16882 | PsychAdapter: Adapting LLM Transformers to Reflect Traits, Personality
and Mental Health | [
"cs.AI",
"cs.CL"
] | Artificial intelligence-based language generators are now a part of most people's lives. However, by default, they tend to generate "average" language without reflecting the ways in which people differ. Here, we propose a lightweight modification to the standard language model transformer architecture - "PsychAdapter" ... | {
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2412.16884 | Out-of-Distribution Detection with Prototypical Outlier Proxy | [
"cs.CV"
] | Out-of-distribution (OOD) detection is a crucial task for deploying deep learning models in the wild. One of the major challenges is that well-trained deep models tend to perform over-confidence on unseen test data. Recent research attempts to leverage real or synthetic outliers to mitigate the issue, which may signifi... | {
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2412.16886 | Lightweight Design and Optimization methods for DCNNs: Progress and
Futures | [
"cs.CV"
] | Lightweight design, as a key approach to mitigate disparity between computational requirements of deep learning models and hardware performance, plays a pivotal role in advancing application of deep learning technologies on mobile and embedded devices, alongside rapid development of smart home, telemedicine, and autono... | {
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2412.16888 | Rethinking Performance Analysis for Configurable Software Systems: A
Case Study from a Fitness Landscape Perspective | [
"cs.PF",
"cs.DC",
"cs.LG",
"cs.SE"
] | Modern software systems are often highly configurable to tailor varied requirements from diverse stakeholders. Understanding the mapping between configurations and the desired performance attributes plays a fundamental role in advancing the controllability and tuning of the underlying system, yet has long been a dark h... | {
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2412.16889 | Anchor3DLane++: 3D Lane Detection via Sample-Adaptive Sparse 3D Anchor
Regression | [
"cs.CV"
] | In this paper, we focus on the challenging task of monocular 3D lane detection. Previous methods typically adopt inverse perspective mapping (IPM) to transform the Front-Viewed (FV) images or features into the Bird-Eye-Viewed (BEV) space for lane detection. However, IPM's dependence on flat ground assumption and contex... | {
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2412.16893 | Preventing Non-intrusive Load Monitoring Privacy Invasion: A Precise
Adversarial Attack Scheme for Networked Smart Meters | [
"cs.CR",
"cs.AI"
] | Smart grid, through networked smart meters employing the non-intrusive load monitoring (NILM) technique, can considerably discern the usage patterns of residential appliances. However, this technique also incurs privacy leakage. To address this issue, we propose an innovative scheme based on adversarial attack in this ... | {
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2412.16894 | Unsupervised Bilingual Lexicon Induction for Low Resource Languages | [
"cs.CL"
] | Bilingual lexicons play a crucial role in various Natural Language Processing tasks. However, many low-resource languages (LRLs) do not have such lexicons, and due to the same reason, cannot benefit from the supervised Bilingual Lexicon Induction (BLI) techniques. To address this, unsupervised BLI (UBLI) techniques wer... | {
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2412.16895 | Adaptive Dataset Quantization | [
"cs.CV"
] | Contemporary deep learning, characterized by the training of cumbersome neural networks on massive datasets, confronts substantial computational hurdles. To alleviate heavy data storage burdens on limited hardware resources, numerous dataset compression methods such as dataset distillation (DD) and coreset selection ha... | {
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2412.16897 | MVREC: A General Few-shot Defect Classification Model Using Multi-View
Region-Context | [
"cs.CV",
"cs.AI"
] | Few-shot defect multi-classification (FSDMC) is an emerging trend in quality control within industrial manufacturing. However, current FSDMC research often lacks generalizability due to its focus on specific datasets. Additionally, defect classification heavily relies on contextual information within images, and existi... | {
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2412.16899 | Integrating Random Effects in Variational Autoencoders for
Dimensionality Reduction of Correlated Data | [
"stat.ML",
"cs.LG"
] | Variational Autoencoders (VAE) are widely used for dimensionality reduction of large-scale tabular and image datasets, under the assumption of independence between data observations. In practice, however, datasets are often correlated, with typical sources of correlation including spatial, temporal and clustering struc... | {
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2412.16900 | Speech-Based Depression Prediction Using Encoder-Weight-Only Transfer
Learning and a Large Corpus | [
"eess.AS",
"cs.CL"
] | Speech-based algorithms have gained interest for the management of behavioral health conditions such as depression. We explore a speech-based transfer learning approach that uses a lightweight encoder and that transfers only the encoder weights, enabling a simplified run-time model. Our study uses a large data set cont... | {
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2412.16901 | Learning to Generate Gradients for Test-Time Adaptation via Test-Time
Training Layers | [
"cs.LG",
"cs.CV"
] | Test-time adaptation (TTA) aims to fine-tune a trained model online using unlabeled testing data to adapt to new environments or out-of-distribution data, demonstrating broad application potential in real-world scenarios. However, in this optimization process, unsupervised learning objectives like entropy minimization ... | {
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2412.16905 | A Backdoor Attack Scheme with Invisible Triggers Based on Model
Architecture Modification | [
"cs.CR",
"cs.AI"
] | Machine learning systems are vulnerable to backdoor attacks, where attackers manipulate model behavior through data tampering or architectural modifications. Traditional backdoor attacks involve injecting malicious samples with specific triggers into the training data, causing the model to produce targeted incorrect ou... | {
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2412.16906 | Self-Corrected Flow Distillation for Consistent One-Step and Few-Step
Text-to-Image Generation | [
"cs.CV"
] | Flow matching has emerged as a promising framework for training generative models, demonstrating impressive empirical performance while offering relative ease of training compared to diffusion-based models. However, this method still requires numerous function evaluations in the sampling process. To address these limit... | {
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2412.16908 | Map Imagination Like Blind Humans: Group Diffusion Model for Robotic Map
Generation | [
"cs.RO",
"cs.AI"
] | Can robots imagine or generate maps like humans do, especially when only limited information can be perceived like blind people? To address this challenging task, we propose a novel group diffusion model (GDM) based architecture for robots to generate point cloud maps with very limited input information.Inspired from t... | {
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2412.16915 | FADA: Fast Diffusion Avatar Synthesis with Mixed-Supervised Multi-CFG
Distillation | [
"cs.CV",
"cs.AI",
"cs.GR",
"cs.SD",
"eess.AS"
] | Diffusion-based audio-driven talking avatar methods have recently gained attention for their high-fidelity, vivid, and expressive results. However, their slow inference speed limits practical applications. Despite the development of various distillation techniques for diffusion models, we found that naive diffusion dis... | {
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2412.16918 | Detect Changes like Humans: Incorporating Semantic Priors for Improved
Change Detection | [
"cs.CV"
] | When given two similar images, humans identify their differences by comparing the appearance ({\it e.g., color, texture}) with the help of semantics ({\it e.g., objects, relations}). However, mainstream change detection models adopt a supervised training paradigm, where the annotated binary change map is the main const... | {
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2412.16919 | TAR3D: Creating High-Quality 3D Assets via Next-Part Prediction | [
"cs.CV"
] | We present TAR3D, a novel framework that consists of a 3D-aware Vector Quantized-Variational AutoEncoder (VQ-VAE) and a Generative Pre-trained Transformer (GPT) to generate high-quality 3D assets. The core insight of this work is to migrate the multimodal unification and promising learning capabilities of the next-toke... | {
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2412.16922 | Enhancing Supply Chain Transparency in Emerging Economies Using Online
Contents and LLMs | [
"cs.IR",
"cs.AI"
] | In the current global economy, supply chain transparency plays a pivotal role in ensuring this security by enabling companies to monitor supplier performance and fostering accountability and responsibility. Despite the advancements in supply chain relationship datasets like Bloomberg and FactSet, supply chain transpare... | {
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2412.16923 | Leveraging Consistent Spatio-Temporal Correspondence for Robust Visual
Odometry | [
"cs.CV"
] | Recent approaches to VO have significantly improved performance by using deep networks to predict optical flow between video frames. However, existing methods still suffer from noisy and inconsistent flow matching, making it difficult to handle challenging scenarios and long-sequence estimation. To overcome these chall... | {
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2412.16924 | Learning an Adaptive Fall Recovery Controller for Quadrupeds on Complex
Terrains | [
"cs.RO"
] | Legged robots have shown promise in locomotion complex environments, but recovery from falls on challenging terrains remains a significant hurdle. This paper presents an Adaptive Fall Recovery (AFR) controller for quadrupedal robots on challenging terrains such as rocky, breams, steep slopes, and irregular stones. We l... | {
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2412.16925 | Quantifying Public Response to COVID-19 Events: Introducing the
Community Sentiment and Engagement Index | [
"cs.SI",
"cs.AI",
"cs.CL",
"cs.CY",
"cs.LG"
] | This study introduces the Community Sentiment and Engagement Index (CSEI), developed to capture nuanced public sentiment and engagement variations on social media, particularly in response to major events related to COVID-19. Constructed with diverse sentiment indicators, CSEI integrates features like engagement, daily... | {
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2412.16926 | Revisiting In-Context Learning with Long Context Language Models | [
"cs.CL",
"cs.AI",
"cs.LG"
] | In-Context Learning (ICL) is a technique by which language models make predictions based on examples provided in their input context. Previously, their context window size imposed a limit on the number of examples that can be shown, making example selection techniques crucial for identifying the maximally effective set... | {
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2412.16928 | AV-DTEC: Self-Supervised Audio-Visual Fusion for Drone Trajectory
Estimation and Classification | [
"cs.SD",
"cs.CV",
"cs.MM",
"eess.AS"
] | The increasing use of compact UAVs has created significant threats to public safety, while traditional drone detection systems are often bulky and costly. To address these challenges, we propose AV-DTEC, a lightweight self-supervised audio-visual fusion-based anti-UAV system. AV-DTEC is trained using self-supervised le... | {
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2412.16932 | GSemSplat: Generalizable Semantic 3D Gaussian Splatting from
Uncalibrated Image Pairs | [
"cs.CV"
] | Modeling and understanding the 3D world is crucial for various applications, from augmented reality to robotic navigation. Recent advancements based on 3D Gaussian Splatting have integrated semantic information from multi-view images into Gaussian primitives. However, these methods typically require costly per-scene op... | {
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2412.16933 | Towards a Unified Paradigm: Integrating Recommendation Systems as a New
Language in Large Models | [
"cs.IR",
"cs.AI",
"cs.CL"
] | This paper explores the use of Large Language Models (LLMs) for sequential recommendation, which predicts users' future interactions based on their past behavior. We introduce a new concept, "Integrating Recommendation Systems as a New Language in Large Models" (RSLLM), which combines the strengths of traditional recom... | {
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2412.16934 | Efficiently Solving Turn-Taking Stochastic Games with Extensive-Form
Correlation | [
"cs.GT",
"cs.AI",
"cs.DS",
"cs.LG"
] | We study equilibrium computation with extensive-form correlation in two-player turn-taking stochastic games. Our main results are two-fold: (1) We give an algorithm for computing a Stackelberg extensive-form correlated equilibrium (SEFCE), which runs in time polynomial in the size of the game, as well as the number of ... | {
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2412.16935 | Detecting and Classifying Defective Products in Images Using YOLO | [
"cs.CV"
] | With the continuous advancement of industrial automation, product quality inspection has become increasingly important in the manufacturing process. Traditional inspection methods, which often rely on manual checks or simple machine vision techniques, suffer from low efficiency and insufficient accuracy. In recent year... | {
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2412.16936 | Prompting Large Language Models with Rationale Heuristics for
Knowledge-based Visual Question Answering | [
"cs.CL",
"cs.AI"
] | Recently, Large Language Models (LLMs) have been used for knowledge-based Visual Question Answering (VQA). Despite the encouraging results of previous studies, prior methods prompt LLMs to predict answers directly, neglecting intermediate thought processes. We argue that prior methods do not sufficiently activate the c... | {
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2412.16937 | PINN-EMFNet: PINN-based and Enhanced Multi-Scale Feature Fusion Network
for Breast Ultrasound Images Segmentation | [
"cs.CV"
] | With the rapid development of deep learning and computer vision technologies, medical image segmentation plays a crucial role in the early diagnosis of breast cancer. However, due to the characteristics of breast ultrasound images, such as low contrast, speckle noise, and the highly diverse morphology of tumors, existi... | {
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2412.16938 | ImagineMap: Enhanced HD Map Construction with SD Maps | [
"cs.CV"
] | Track Mapless demands models to process multi-view images and Standard-Definition (SD) maps, outputting lane and traffic element perceptions along with their topological relationships. We propose a novel architecture that integrates SD map priors to improve lane line and area detection performance. Inspired by TopoMLP,... | {
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2412.16939 | Image Quality Assessment: Investigating Causal Perceptual Effects with
Abductive Counterfactual Inference | [
"cs.CV"
] | Existing full-reference image quality assessment (FR-IQA) methods often fail to capture the complex causal mechanisms that underlie human perceptual responses to image distortions, limiting their ability to generalize across diverse scenarios. In this paper, we propose an FR-IQA method based on abductive counterfactual... | {
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2412.16942 | BloomCoreset: Fast Coreset Sampling using Bloom Filters for Fine-Grained
Self-Supervised Learning | [
"cs.CV"
] | The success of deep learning in supervised fine-grained recognition for domain-specific tasks relies heavily on expert annotations. The Open-Set for fine-grained Self-Supervised Learning (SSL) problem aims to enhance performance on downstream tasks by strategically sampling a subset of images (the Core-Set) from a larg... | {
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2412.16943 | A Career Interview Dialogue System using Large Language Model-based
Dynamic Slot Generation | [
"cs.CL"
] | This study aims to improve the efficiency and quality of career interviews conducted by nursing managers. To this end, we have been developing a slot-filling dialogue system that engages in pre-interviews to collect information on staff careers as a preparatory step before the actual interviews. Conventional slot-filli... | {
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2412.16944 | Linguistics-Vision Monotonic Consistent Network for Sign Language
Production | [
"cs.CV",
"cs.MM"
] | Sign Language Production (SLP) aims to generate sign videos corresponding to spoken language sentences, where the conversion of sign Glosses to Poses (G2P) is the key step. Due to the cross-modal semantic gap and the lack of word-action correspondence labels for strong supervision alignment, the SLP suffers huge challe... | {
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2412.16946 | Video Domain Incremental Learning for Human Action Recognition in Home
Environments | [
"cs.CV"
] | It is significantly challenging to recognize daily human actions in homes due to the diversity and dynamic changes in unconstrained home environments. It spurs the need to continually adapt to various users and scenes. Fine-tuning current video understanding models on newly encountered domains often leads to catastroph... | {
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2412.16947 | Separating Drone Point Clouds From Complex Backgrounds by Cluster Filter
-- Technical Report for CVPR 2024 UG2 Challenge | [
"cs.CV"
] | The increasing deployment of small drones as tools of conflict and disruption has amplified their threat, highlighting the urgent need for effective anti-drone measures. However, the compact size of most drones presents a significant challenge, as traditional supervised point cloud or image-based object detection metho... | {
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2412.16948 | DTSGAN: Learning Dynamic Textures via Spatiotemporal Generative
Adversarial Network | [
"cs.CV"
] | Dynamic texture synthesis aims to generate sequences that are visually similar to a reference video texture and exhibit specific stationary properties in time. In this paper, we introduce a spatiotemporal generative adversarial network (DTSGAN) that can learn from a single dynamic texture by capturing its motion and co... | {
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2412.16953 | Aristotle: Mastering Logical Reasoning with A Logic-Complete
Decompose-Search-Resolve Framework | [
"cs.CL"
] | In the context of large language models (LLMs), current advanced reasoning methods have made impressive strides in various reasoning tasks. However, when it comes to logical reasoning tasks, major challenges remain in both efficacy and efficiency. This is rooted in the fact that these systems fail to fully leverage the... | {
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2412.16955 | NumbOD: A Spatial-Frequency Fusion Attack Against Object Detectors | [
"cs.CV"
] | With the advancement of deep learning, object detectors (ODs) with various architectures have achieved significant success in complex scenarios like autonomous driving. Previous adversarial attacks against ODs have been focused on designing customized attacks targeting their specific structures (e.g., NMS and RPN), yie... | {
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2412.16956 | Semantic Hierarchical Prompt Tuning for Parameter-Efficient Fine-Tuning | [
"cs.CV"
] | As the scale of vision models continues to grow, Visual Prompt Tuning (VPT) has emerged as a parameter-efficient transfer learning technique, noted for its superior performance compared to full fine-tuning. However, indiscriminately applying prompts to every layer without considering their inherent correlations, can ca... | {
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2412.16958 | Breaking Barriers in Physical-World Adversarial Examples: Improving
Robustness and Transferability via Robust Feature | [
"cs.CV"
] | As deep neural networks (DNNs) are widely applied in the physical world, many researches are focusing on physical-world adversarial examples (PAEs), which introduce perturbations to inputs and cause the model's incorrect outputs. However, existing PAEs face two challenges: unsatisfactory attack performance (i.e., poor ... | {
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2412.16963 | LH-Mix: Local Hierarchy Correlation Guided Mixup over Hierarchical
Prompt Tuning | [
"cs.CL"
] | Hierarchical text classification (HTC) aims to assign one or more labels in the hierarchy for each text. Many methods represent this structure as a global hierarchy, leading to redundant graph structures. To address this, incorporating a text-specific local hierarchy is essential. However, existing approaches often mod... | {
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2412.16964 | System-2 Mathematical Reasoning via Enriched Instruction Tuning | [
"cs.AI",
"cs.CL"
] | Solving complex mathematical problems via system-2 reasoning is a natural human skill, yet it remains a significant challenge for current large language models (LLMs). We identify the scarcity of deliberate multi-step reasoning data as a primary limiting factor. To this end, we introduce Enriched Instruction Tuning (EI... | {
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2412.16968 | FedCross: Intertemporal Federated Learning Under Evolutionary Games | [
"cs.LG",
"cs.DC",
"cs.GT"
] | Federated Learning (FL) mitigates privacy leakage in decentralized machine learning by allowing multiple clients to train collaboratively locally. However, dynamic mobile networks with high mobility, intermittent connectivity, and bandwidth limitation severely hinder model updates to the cloud server. Although previous... | {
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2412.16969 | Multifaceted User Modeling in Recommendation: A Federated Foundation
Models Approach | [
"cs.IR"
] | Multifaceted user modeling aims to uncover fine-grained patterns and learn representations from user data, revealing their diverse interests and characteristics, such as profile, preference, and personality. Recent studies on foundation model-based recommendation have emphasized the Transformer architecture's remarkabl... | {
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2412.16970 | Environment Descriptions for Usability and Generalisation in
Reinforcement Learning | [
"cs.AI",
"stat.ML"
] | The majority of current reinforcement learning (RL) research involves training and deploying agents in environments that are implemented by engineers in general-purpose programming languages and more advanced frameworks such as CUDA or JAX. This makes the application of RL to novel problems of interest inaccessible to ... | {
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2412.16971 | Part-Of-Speech Sensitivity of Routers in Mixture of Experts Models | [
"cs.CL"
] | This study investigates the behavior of model-integrated routers in Mixture of Experts (MoE) models, focusing on how tokens are routed based on their linguistic features, specifically Part-of-Speech (POS) tags. The goal is to explore across different MoE architectures whether experts specialize in processing tokens wit... | {
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2412.16974 | Cannot or Should Not? Automatic Analysis of Refusal Composition in
IFT/RLHF Datasets and Refusal Behavior of Black-Box LLMs | [
"cs.AI",
"cs.CL"
] | Refusals - instances where large language models (LLMs) decline or fail to fully execute user instructions - are crucial for both AI safety and AI capabilities and the reduction of hallucinations in particular. These behaviors are learned during post-training, especially in instruction fine-tuning (IFT) and reinforceme... | {
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2412.16976 | On Fusing ChatGPT and Ensemble Learning in Discon-tinuous Named Entity
Recognition in Health Corpora | [
"cs.CL",
"cs.AI"
] | Named Entity Recognition has traditionally been a key task in natural language processing, aiming to identify and extract important terms from unstructured text data. However, a notable challenge for contemporary deep-learning NER models has been identifying discontinuous entities, which are often fragmented within the... | {
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2412.16978 | PromptDresser: Improving the Quality and Controllability of Virtual
Try-On via Generative Textual Prompt and Prompt-aware Mask | [
"cs.CV",
"cs.AI"
] | Recent virtual try-on approaches have advanced by fine-tuning the pre-trained text-to-image diffusion models to leverage their powerful generative ability. However, the use of text prompts in virtual try-on is still underexplored. This paper tackles a text-editable virtual try-on task that changes the clothing item bas... | {
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2412.16979 | A Conditional Diffusion Model for Electrical Impedance Tomography Image
Reconstruction | [
"cs.CV"
] | Electrical impedance tomography (EIT) is a non-invasive imaging technique, capable of reconstructing images of the electrical conductivity of tissues and materials. It is popular in diverse application areas, from medical imaging to industrial process monitoring and tactile sensing, due to its low cost, real-time capab... | {
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2412.16982 | InterDance:Reactive 3D Dance Generation with Realistic Duet Interactions | [
"cs.CV",
"cs.GR",
"cs.MM",
"cs.SD",
"eess.AS"
] | Humans perform a variety of interactive motions, among which duet dance is one of the most challenging interactions. However, in terms of human motion generative models, existing works are still unable to generate high-quality interactive motions, especially in the field of duet dance. On the one hand, it is due to the... | {
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2412.16984 | LLM-Powered User Simulator for Recommender System | [
"cs.IR",
"cs.AI"
] | User simulators can rapidly generate a large volume of timely user behavior data, providing a testing platform for reinforcement learning-based recommender systems, thus accelerating their iteration and optimization. However, prevalent user simulators generally suffer from significant limitations, including the opacity... | {
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2412.16986 | Pinwheel-shaped Convolution and Scale-based Dynamic Loss for Infrared
Small Target Detection | [
"cs.CV"
] | These recent years have witnessed that convolutional neural network (CNN)-based methods for detecting infrared small targets have achieved outstanding performance. However, these methods typically employ standard convolutions, neglecting to consider the spatial characteristics of the pixel distribution of infrared smal... | {
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2412.16988 | Distributed Target Tracking based on Localization with Linear
Time-Difference-of-Arrival Measurements: A Delay-Tolerant Networked
Estimation Approach | [
"eess.SY",
"cs.SI",
"cs.SY",
"eess.SP",
"math.OC"
] | This paper considers target tracking based on a beacon signal's time-difference-of-arrival (TDOA) to a group of cooperating sensors. The sensors receive a reflected signal from the target where the time-of-arrival (TOA) renders the distance information. The existing approaches include: (i) classic centralized solutions... | {
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2412.16990 | Multi-Scale Foreground-Background Confidence for Out-of-Distribution
Segmentation | [
"cs.CV"
] | Deep neural networks have shown outstanding performance in computer vision tasks such as semantic segmentation and have defined the state-of-the-art. However, these segmentation models are trained on a closed and predefined set of semantic classes, which leads to significant prediction failures in open-world scenarios ... | {
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2412.16995 | Leveraging Neural Networks to Optimize Heliostat Field Aiming Strategies
in Concentrating Solar Power Tower Plants | [
"eess.SY",
"cs.SY",
"math.OC"
] | Concentrating Solar Power Tower (CSPT) plants rely on heliostat fields to focus sunlight onto a central receiver. Although simple aiming strategies, such as directing all heliostats to the receivers equator, can maximize energy collection, they often result in uneven flux distributions that lead to hotspots, thermal st... | {
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2412.17001 | Solving Nonlinear Energy Supply and Demand System Using Physics-Informed
Neural Networks | [
"cs.LG",
"cs.AI",
"cs.NA",
"math.NA"
] | Nonlinear differential equations and systems play a crucial role in modeling systems where time-dependent factors exhibit nonlinear characteristics. Due to their nonlinear nature, solving such systems often presents significant difficulties and challenges. In this study, we propose a method utilizing Physics-Informed N... | {
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2412.17002 | To Travel Quickly or to Park Conveniently: Coupled Resource Allocations
with Multi-Karma Economies | [
"cs.GT",
"cs.SY",
"eess.SY"
] | The large-scale allocation of public resources (e.g., transportation, energy) is among the core challenges of future Cyber-Physical-Human Systems (CPHS). In order to guarantee that these systems are efficient and fair, recent works have investigated non-monetary resource allocation schemes, including schemes that emplo... | {
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} |
2412.17003 | Anonymous Shamir's Secret Sharing via Reed-Solomon Codes Against
Permutations, Insertions, and Deletions | [
"cs.IT",
"cs.CR",
"math.IT"
] | In this work, we study the performance of Reed-Solomon codes against an adversary that first permutes the symbols of the codeword and then performs insertions and deletions. This adversarial model is motivated by the recent interest in fully anonymous secret-sharing schemes [EBG+24],[BGI+24]. A fully anonymous secret-s... | {
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} |
2412.17006 | Resilience Dynamics in Coupled Natural-Industrial Systems: A Surrogate
Modeling Approach for Assessing Climate Change Impacts on Industrial
Ecosystems | [
"eess.SY",
"cs.SY"
] | Industrial ecosystems are coupled with natural systems through utilization of feedstocks and waste disposal. To ensure resilience in production of industrial systems under the threat of climate change scenarios, it is necessary to evaluate the impact of this coupling on productivity and waste generation. In this work, ... | {
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} |
2412.17007 | Where am I? Cross-View Geo-localization with Natural Language
Descriptions | [
"cs.CV"
] | Cross-view geo-localization identifies the locations of street-view images by matching them with geo-tagged satellite images or OSM. However, most studies focus on image-to-image retrieval, with fewer addressing text-guided retrieval, a task vital for applications like pedestrian navigation and emergency response. In t... | {
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} |
2412.17008 | Data value estimation on private gradients | [
"cs.LG",
"cs.AI",
"cs.CR"
] | For gradient-based machine learning (ML) methods commonly adopted in practice such as stochastic gradient descent, the de facto differential privacy (DP) technique is perturbing the gradients with random Gaussian noise. Data valuation attributes the ML performance to the training data and is widely used in privacy-awar... | {
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} |
2412.17009 | Generate to Discriminate: Expert Routing for Continual Learning | [
"cs.LG"
] | In many real-world settings, regulations and economic incentives permit the sharing of models but not data across institutional boundaries. In such scenarios, practitioners might hope to adapt models to new domains, without losing performance on previous domains (so-called catastrophic forgetting). While any single mod... | {
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} |
2412.17011 | Robustness of Large Language Models Against Adversarial Attacks | [
"cs.CL"
] | The increasing deployment of Large Language Models (LLMs) in various applications necessitates a rigorous evaluation of their robustness against adversarial attacks. In this paper, we present a comprehensive study on the robustness of GPT LLM family. We employ two distinct evaluation methods to assess their resilience.... | {
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} |
2412.17012 | Robust Adaptive Data-Driven Control of Positive Systems with Application
to Learning in SSP Problems | [
"math.OC",
"cs.SY",
"eess.SY"
] | An adaptive data-driven controller is proposed and analysed for the class of infinite-horizon optimal control of positive linear system problems presented in [1]. This controller is synthesized from the solution of a "data-driven algebraic equation" constructed from the model-free Bellman equation used in Q-learning. T... | {
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} |
2412.17018 | GAS: Generative Auto-bidding with Post-training Search | [
"cs.AI"
] | Auto-bidding is essential in facilitating online advertising by automatically placing bids on behalf of advertisers. Generative auto-bidding, which generates bids based on an adjustable condition using models like transformers and diffusers, has recently emerged as a new trend due to its potential to learn optimal stra... | {
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} |
2412.17019 | Reversed Attention: On The Gradient Descent Of Attention Layers In GPT | [
"cs.CL"
] | The success of Transformer-based Language Models (LMs) stems from their attention mechanism. While this mechanism has been extensively studied in explainability research, particularly through the attention values obtained during the forward pass of LMs, the backward pass of attention has been largely overlooked. In thi... | {
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} |
2412.17022 | FriendsQA: A New Large-Scale Deep Video Understanding Dataset with
Fine-grained Topic Categorization for Story Videos | [
"cs.CV"
] | Video question answering (VideoQA) aims to answer natural language questions according to the given videos. Although existing models perform well in the factoid VideoQA task, they still face challenges in deep video understanding (DVU) task, which focuses on story videos. Compared to factoid videos, the most significan... | {
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} |
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