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272880939 | 2409.16604 | 2024-09-25 | Semi-LLIE: Semi-supervised Contrastive Learning with Mamba-based Low-light Image Enhancement | Despite the impressive advancements made in recent low-light image enhancement techniques, the scarcity of paired data has emerged as a significant obstacle to further advancements. This work proposes a mean-teacher-based semi-supervised low-light enhancement (Semi-LLIE) framework that integrates the unpaired data into... | [
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272910550 | 2409.17564 | 2024-09-25 | General Compression Framework for Efficient Transformer Object Tracking | Previous works have attempted to improve tracking efficiency through lightweight architecture design or knowledge distillation from teacher models to compact student trackers. However, these solutions often sacrifice accuracy for speed to a great extent, and also have the problems of complex training process and struct... | [
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272880746 | 2409.16978 | 2024-09-25 | Towards User-Focused Research in Training Data Attribution for Human-Centered Explainable AI | Explainable AI (XAI) aims to make AI systems more transparent, yet many practices emphasise mathematical rigour over practical user needs. We propose an alternative to this model-centric approach by following a design thinking process for the emerging XAI field of training data attribution (TDA), which risks repeating ... | [
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272911075 | 2409.17403 | 2024-09-25 | Transient Adversarial 3D Projection Attacks on Object Detection in Autonomous Driving | Object detection is a crucial task in autonomous driving. While existing research has proposed various attacks on object detection, such as those using adversarial patches or stickers, the exploration of projection attacks on 3D surfaces remains largely unexplored. Compared to adversarial patches or stickers, which hav... | [
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272880764 | 2409.16611 | 2024-09-25 | Achieving Stable High-Speed Locomotion for Humanoid Robots with Deep Reinforcement Learning | Humanoid robots offer significant versatility for performing a wide range of tasks, yet their basic ability to walk and run, especially at high velocities, remains a challenge. This letter presents a novel method that combines deep reinforcement learning with kinodynamic priors to achieve stable locomotion control (KSL... | [
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272881006 | 2409.16727 | 2024-09-25 | RoleBreak: Character Hallucination as a Jailbreak Attack in Role-Playing Systems | Role-playing systems powered by large language models (LLMs) have become increasingly influential in emotional communication applications. However, these systems are susceptible to character hallucinations, where the model deviates from predefined character roles and generates responses that are inconsistent with the i... | [
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272911418 | 2409.17221 | 2024-09-25 | Walker: Self-supervised Multiple Object Tracking by Walking on Temporal Appearance Graphs | The supervision of state-of-the-art multiple object tracking (MOT) methods requires enormous annotation efforts to provide bounding boxes for all frames of all videos, and instance IDs to associate them through time. To this end, we introduce Walker, the first self-supervised tracker that learns from videos with sparse... | [
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272880709 | 2409.16751 | 2024-09-25 | E-SQL: Direct Schema Linking via Question Enrichment in Text-to-SQL | Translating Natural Language Queries into Structured Query Language (Text-to-SQL or NLQ-to-SQL) is a critical task extensively studied by both the natural language processing and database communities, aimed at providing a natural language interface to databases (NLIDB) and lowering the barrier for non-experts. Despite ... | [
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272880816 | 2409.17095 | 2024-09-25 | General Detection-based Text Line Recognition | We introduce a general detection-based approach to text line recognition, be it printed (OCR) or handwritten (HTR), with Latin, Chinese, or ciphered characters. Detection-based approaches have until now been largely discarded for HTR because reading characters separately is often challenging, and character-level annota... | [
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272911172 | 2409.17433 | 2024-09-25 | HDFlow: Enhancing LLM Complex Problem-Solving with Hybrid Thinking and Dynamic Workflows | Despite recent advancements in large language models (LLMs), their performance on complex reasoning problems requiring multi-step thinking and combining various skills is still limited. To address this, we propose a novel framework HDFlow for complex reasoning with LLMs that combines fast and slow thinking modes in an ... | [
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272910742 | 2409.17417 | 2024-09-25 | Enhancing Investment Opinion Ranking through Argument-Based Sentiment Analysis | In the era of rapid Internet and social media platform development, individuals readily share their viewpoints online. The overwhelming quantity of these posts renders comprehensive analysis impractical. This necessitates an efficient recommendation system to filter and present significant, relevant opinions. Our resea... | [
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272881278 | 2409.17125 | 2024-09-25 | On-orbit Servicing for Spacecraft Collision Avoidance With Autonomous Decision Making | This study develops an AI-based implementation of autonomous On-Orbit Servicing (OOS) mission to assist with spacecraft collision avoidance maneuvers (CAMs). We propose an autonomous `servicer' trained with Reinforcement Learning (RL) to autonomously detect potential collisions between a target satellite and space debr... | [
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272880879 | 2409.16680 | 2024-09-25 | Online 6DoF Global Localisation in Forests using Semantically-Guided Re-Localisation and Cross-View Factor-Graph Optimisation | This paper presents FGLoc6D, a novel approach for robust global localisation and online 6DoF pose estimation of ground robots in forest environments by leveraging deep semantically-guided re-localisation and cross-view factor graph optimisation. The proposed method addresses the challenges of aligning aerial and ground... | [
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272880814 | 2409.16834 | 2024-09-25 | Conditional Generative Denoiser for Nighttime UAV Tracking | State-of-the-art (SOTA) visual object tracking methods have significantly enhanced the autonomy of unmanned aerial vehicles (UAVs). However, in low-light conditions, the presence of irregular real noise from the environments severely degrades the performance of these SOTA methods. Moreover, existing SOTA denoising tech... | [
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272910533 | 2409.17294 | 2024-09-25 | Schr\"odinger bridge based deep conditional generative learning | Conditional generative models represent a significant advancement in the field of machine learning, allowing for the controlled synthesis of data by incorporating additional information into the generation process. In this work we introduce a novel Schr\"odinger bridge based deep generative method for learning conditio... | [
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272911083 | 2409.17419 | 2024-09-25 | Pre-Finetuning with Impact Duration Awareness for Stock Movement Prediction | Understanding the duration of news events' impact on the stock market is crucial for effective time-series forecasting, yet this facet is largely overlooked in current research. This paper addresses this research gap by introducing a novel dataset, the Impact Duration Estimation Dataset (IDED), specifically designed to... | [
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272880785 | 2409.16666 | 2024-09-25 | TalkinNeRF: Animatable Neural Fields for Full-Body Talking Humans | We introduce a novel framework that learns a dynamic neural radiance field (NeRF) for full-body talking humans from monocular videos. Prior work represents only the body pose or the face. However, humans communicate with their full body, combining body pose, hand gestures, as well as facial expressions. In this work, w... | [
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272911374 | 2409.17345 | 2024-09-25 | SeaSplat: Representing Underwater Scenes with 3D Gaussian Splatting and a Physically Grounded Image Formation Model | We introduce SeaSplat, a method to enable real-time rendering of underwater scenes leveraging recent advances in 3D radiance fields. Underwater scenes are challenging visual environments, as rendering through a medium such as water introduces both range and color dependent effects on image capture. We constrain 3D Gaus... | [
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272911390 | 2409.17896 | 2024-09-26 | Model-Free versus Model-Based Reinforcement Learning for Fixed-Wing UAV Attitude Control Under Varying Wind Conditions | This paper evaluates and compares the performance of model-free and model-based reinforcement learning for the attitude control of fixed-wing unmanned aerial vehicles using PID as a reference point. The comparison focuses on their ability to handle varying flight dynamics and wind disturbances in a simulated environmen... | [
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272911023 | 2409.18051 | 2024-09-26 | Inverse Reinforcement Learning with Multiple Planning Horizons | In this work, we study an inverse reinforcement learning (IRL) problem where the experts are planning under a shared reward function but with different, unknown planning horizons. Without the knowledge of discount factors, the reward function has a larger feasible solution set, which makes it harder for existing IRL ap... | [
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272910893 | 2409.17605 | 2024-09-26 | Good Data Is All Imitation Learning Needs | In this paper, we address the limitations of traditional teacher-student models, imitation learning, and behaviour cloning in the context of Autonomous/Automated Driving Systems (ADS), where these methods often struggle with incomplete coverage of real-world scenarios. To enhance the robustness of such models, we intro... | [
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272910689 | 2409.17457 | 2024-09-26 | CadVLM: Bridging Language and Vision in the Generation of Parametric CAD Sketches | Parametric Computer-Aided Design (CAD) is central to contemporary mechanical design. However, it encounters challenges in achieving precise parametric sketch modeling and lacks practical evaluation metrics suitable for mechanical design. We harness the capabilities of pre-trained foundation models, renowned for their s... | [
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272910737 | 2409.18055 | 2024-09-26 | Visual Data Diagnosis and Debiasing with Concept Graphs | The widespread success of deep learning models today is owed to the curation of extensive datasets significant in size and complexity. However, such models frequently pick up inherent biases in the data during the training process, leading to unreliable predictions. Diagnosing and debiasing datasets is thus a necessity... | [
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272910665 | 2409.17825 | 2024-09-26 | Physics-aligned Schr\"{o}dinger bridge | The reconstruction of physical fields from sparse measurements is pivotal in both scientific research and engineering applications. Traditional methods are increasingly supplemented by deep learning models due to their efficacy in extracting features from data. However, except for the low accuracy on complex physical s... | [
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275836103 | 2409.17606 | 2024-09-26 | FlooNoC: A 645 Gbps/link 0.15 pJ/B/hop Open-Source NoC with Wide Physical Links and End-to-End AXI4 Parallel Multi-Stream Support | The new generation of domain-specific AI accelerators is characterized by rapidly increasing demands for bulk data transfers, as opposed to small, latency-critical cache line transfers typical of traditional cache-coherent systems. In this paper, we address this critical need by introducing the FlooNoC Network-on-Chip ... | [
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272968739 | 2409.18313 | 2024-09-26 | Embodied-RAG: General Non-parametric Embodied Memory for Retrieval and Generation | There is no limit to how much a robot might explore and learn, but all of that knowledge needs to be searchable and actionable. Within language research, retrieval augmented generation (RAG) has become the workhorse of large-scale non-parametric knowledge; however, existing techniques do not directly transfer to the em... | [
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272910863 | 2409.17763 | 2024-09-26 | Confidence intervals uncovered: Are we ready for real-world medical imaging AI? | Medical imaging is spearheading the AI transformation of healthcare. Performance reporting is key to determine which methods should be translated into clinical practice. Frequently, broad conclusions are simply derived from mean performance values. In this paper, we argue that this common practice is often a misleading... | [
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272910747 | 2409.18099 | 2024-09-26 | EfficientCrackNet: A Lightweight Model for Crack Segmentation | Crack detection, particularly from pavement images, presents a formidable challenge in the domain of computer vision due to several inherent complexities such as intensity inhomogeneity, intricate topologies, low contrast, and noisy backgrounds. Automated crack detection is crucial for maintaining the structural integr... | [
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272968772 | 2409.18261 | 2024-09-26 | Omni6D: Large-Vocabulary 3D Object Dataset for Category-Level 6D Object Pose Estimation | 6D object pose estimation aims at determining an object's translation, rotation, and scale, typically from a single RGBD image. Recent advancements have expanded this estimation from instance-level to category-level, allowing models to generalize across unseen instances within the same category. However, this generaliz... | [
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272910736 | 2409.17691 | 2024-09-26 | Efficient Bias Mitigation Without Privileged Information | Deep neural networks trained via empirical risk minimisation often exhibit significant performance disparities across groups, particularly when group and task labels are spuriously correlated (e.g., "grassy background" and "cows"). Existing bias mitigation methods that aim to address this issue often either rely on gro... | [
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272969427 | 2409.18733 | 2024-09-26 | Search and Detect: Training-Free Long Tail Object Detection via Web-Image Retrieval | In this paper, we introduce SearchDet, a training-free long-tail object detection framework that significantly enhances open-vocabulary object detection performance. SearchDet retrieves a set of positive and negative images of an object to ground, embeds these images, and computes an input image-weighted query which is... | [
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272910935 | 2409.17547 | 2024-09-26 | Triple Point Masking | Existing 3D mask learning methods encounter performance bottlenecks under limited data, and our objective is to overcome this limitation. In this paper, we introduce a triple point masking scheme, named TPM, which serves as a scalable framework for pre-training of masked autoencoders to achieve multi-mask learning for ... | [
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272969322 | 2409.18335 | 2024-09-26 | A Fairness-Driven Method for Learning Human-Compatible Negotiation Strategies | Despite recent advancements in AI and NLP, negotiation remains a difficult domain for AI agents. Traditional game theoretic approaches that have worked well for two-player zero-sum games struggle in the context of negotiation due to their inability to learn human-compatible strategies. On the other hand, approaches tha... | [
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272911268 | 2409.18073 | 2024-09-26 | Infer Human's Intentions Before Following Natural Language Instructions | For AI agents to be helpful to humans, they should be able to follow natural language instructions to complete everyday cooperative tasks in human environments. However, real human instructions inherently possess ambiguity, because the human speakers assume sufficient prior knowledge about their hidden goals and intent... | [
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272911420 | 2409.17985 | 2024-09-26 | Hypergame Theory for Decentralized Resource Allocation in Multi-user Semantic Communications | Semantic communications (SC) is an emerging communication paradigm in which wireless devices can send only relevant information from a source of data while relying on computing resources to regenerate missing data points. However, the design of a multi-user SC system becomes more challenging because of the computing an... | [
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272968901 | 2409.18205 | 2024-09-26 | Bridging OOD Detection and Generalization: A Graph-Theoretic View | In the context of modern machine learning, models deployed in real-world scenarios often encounter diverse data shifts like covariate and semantic shifts, leading to challenges in both out-of-distribution (OOD) generalization and detection. Despite considerable attention to these issues separately, a unified framework ... | [
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272911000 | 2409.17954 | 2024-09-26 | Enhancing elusive clues in knowledge learning by contrasting attention of language models | Causal language models acquire vast amount of knowledge from general text corpus during pretraining, but the efficiency of knowledge learning is known to be unsatisfactory, especially when learning from knowledge-dense and small-sized corpora. The deficiency can come from long-distance dependencies which are hard to ca... | [
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272968799 | 2409.18260 | 2024-09-26 | PCEvE: Part Contribution Evaluation Based Model Explanation for Human Figure Drawing Assessment and Beyond | For automatic human figure drawing (HFD) assessment tasks, such as diagnosing autism spectrum disorder (ASD) using HFD images, the clarity and explainability of a model decision are crucial. Existing pixel-level attribution-based explainable AI (XAI) approaches demand considerable effort from users to interpret the sem... | [
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272911037 | 2409.17480 | 2024-09-26 | What Would Happen Next? Predicting Consequences from An Event Causality Graph | Existing script event prediction task forcasts the subsequent event based on an event script chain. However, the evolution of historical events are more complicated in real world scenarios and the limited information provided by the event script chain also make it difficult to accurately predict subsequent events. This... | [
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272911121 | 2409.17446 | 2024-09-26 | Efficient Federated Learning against Heterogeneous and Non-stationary Client Unavailability | Addressing intermittent client availability is critical for the real-world deployment of federated learning algorithms. Most prior work either overlooks the potential non-stationarity in the dynamics of client unavailability or requires substantial memory/computation overhead. We study federated learning in the presenc... | [
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272969239 | 2409.18329 | 2024-09-26 | Harnessing and modulating chaos to sample from neural generative models | Chaos is generic in strongly-coupled recurrent networks of model neurons, and thought to be an easily accessible dynamical regime in the brain. While neural chaos is typically seen as an impediment to robust computation, we show how such chaos might play a functional role in allowing the brain to learn and sample from ... | [
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272911106 | 2409.17523 | 2024-09-26 | EAGLE: Egocentric AGgregated Language-video Engine | The rapid evolution of egocentric video analysis brings new insights into understanding human activities and intentions from a first-person perspective. Despite this progress, the fragmentation in tasks like action recognition, procedure learning, and moment retrieval, \etc, coupled with inconsistent annotations and is... | [
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272910781 | 2409.17684 | 2024-09-26 | Preserving logical and functional dependencies in synthetic tabular data | Dependencies among attributes are a common aspect of tabular data. However, whether existing tabular data generation algorithms preserve these dependencies while generating synthetic data is yet to be explored. In addition to the existing notion of functional dependencies, we introduce the notion of logical dependencie... | [
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272969463 | 2409.18228 | 2024-09-26 | Analysis of Spatial augmentation in Self-supervised models in the purview of training and test distributions | In this paper, we present an empirical study of typical spatial augmentation techniques used in self-supervised representation learning methods (both contrastive and non-contrastive), namely random crop and cutout. Our contributions are: (a) we dissociate random cropping into two separate augmentations, overlap and pat... | [
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272969446 | 2409.18336 | 2024-09-26 | DeBaRA: Denoising-Based 3D Room Arrangement Generation | Generating realistic and diverse layouts of furnished indoor 3D scenes unlocks multiple interactive applications impacting a wide range of industries. The inherent complexity of object interactions, the limited amount of available data and the requirement to fulfill spatial constraints all make generative modeling for ... | [
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272910545 | 2409.17899 | 2024-09-26 | Exploring Acoustic Similarity in Emotional Speech and Music via Self-Supervised Representations | Emotion recognition from speech and music shares similarities due to their acoustic overlap, which has led to interest in transferring knowledge between these domains. However, the shared acoustic cues between speech and music, particularly those encoded by Self-Supervised Learning (SSL) models, remain largely unexplor... | [
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272950484 | 2409.18204 | 2024-09-26 | Toward Efficient Deep Blind RAW Image Restoration | Multiple low-vision tasks such as denoising, deblurring and super-resolution depart from RGB images and further reduce the degradations, improving the quality. However, modeling the degradations in the sRGB domain is complicated because of the Image Signal Processor (ISP) transformations. Despite of this known issue, v... | [
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272910746 | 2409.18125 | 2024-09-26 | LLaVA-3D: A Simple yet Effective Pathway to Empowering LMMs with 3D-awareness | Recent advancements in Large Multimodal Models (LMMs) have greatly enhanced their proficiency in 2D visual understanding tasks, enabling them to effectively process and understand images and videos. However, the development of LMMs with 3D scene understanding capabilities has been hindered by the lack of large-scale 3D... | [
"cs.CV"
] | [
"Instruction tuning",
"Multimodality and language grounding",
"Open-vocabulary / open-task vision-language models",
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"Point cloud and 3D geometric learning",
"3D foundation models",
"Embodied question answering / embodied vision-language",
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272910544 | 2409.17728 | 2024-09-26 | AlterMOMA: Fusion Redundancy Pruning for Camera-LiDAR Fusion Models with Alternative Modality Masking | Camera-LiDAR fusion models significantly enhance perception performance in autonomous driving. The fusion mechanism leverages the strengths of each modality while minimizing their weaknesses. Moreover, in practice, camera-LiDAR fusion models utilize pre-trained backbones for efficient training. However, we argue that d... | [
"cs.CV",
"cs.AI"
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"Autonomous driving perception / prediction / planning",
"Efficient and scalable vision models",
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272910861 | 2409.17730 | 2024-09-26 | Autoregressive Generation Strategies for Top-K Sequential Recommendations | The goal of modern sequential recommender systems is often formulated in terms of next-item prediction. In this paper, we explore the applicability of generative transformer-based models for the Top-K sequential recommendation task, where the goal is to predict items a user is likely to interact with in the "near futur... | [
"cs.IR",
"cs.LG"
] | [
"Deep learning for recommender systems",
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] | [
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... | [
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