AI Driven Soccer Analysis Using Computer Vision
arXiv:2604.08722v1 Announce Type: cross Abstract: Sport analysis is crucial for team performance since it provides actionable data that can inform coaching decisions, improve player performance, and e
Knowledge catalogue
arXiv:2604.08722v1 Announce Type: cross Abstract: Sport analysis is crucial for team performance since it provides actionable data that can inform coaching decisions, improve player performance, and e
arXiv:2604.08866v1 Announce Type: cross Abstract: As organizations increasingly deploy AI as a teammate rather than a standalone tool, morally consequential mistakes often arise from joint human-AI wo
arXiv:2604.08963v1 Announce Type: cross Abstract: While Multi-Agent Systems (MAS) are increasingly deployed for complex workflows, their emergent properties-particularly the accumulation of bias-remai
arXiv:2510.26641v4 Announce Type: replace Abstract: Autonomous Vehicles (AVs) are transforming the future of transportation through advances in intelligent perception, decision-making, and control sys
arXiv:2604.08802v1 Announce Type: new Abstract: During disasters, cascading failures across power grids, communication networks, and social behavior amplify community fear and undermine cooperation. E
arXiv:2412.16107v4 Announce Type: replace Abstract: Tilt-rotor aerial robots are more dynamic and versatile than fixed-rotor platforms, since the thrust vector and body orientation are decoupled. Howe
arXiv:2603.21180v3 Announce Type: replace Abstract: Sequential experimental design under expensive, gradient-free objectives is a central challenge in computational statistics: evaluation budgets are
arXiv:2511.08947v5 Announce Type: replace Abstract: Time series forecasting plays a crucial role in decision-making across many real-world applications. Despite substantial progress, most existing met
arXiv:2604.08590v1 Announce Type: cross Abstract: We present AlphaLab, an autonomous research harness that leverages frontier LLM agentic capabilities to automate the full experimental cycle in quanti
arXiv:2604.05426v2 Announce Type: replace-cross Abstract: Low-Rank Adaptation (LoRA) is now the dominant method for parameter-efficient fine-tuning of large language models, but achieving a high-quali
arXiv:2604.09280v1 Announce Type: cross Abstract: Extranodal extension (ENE) is an emerging prognostic factor in human papillomavirus (HPV)-associated oropharyngeal cancer (OPC), although it is curren
arXiv:2602.13939v3 Announce Type: replace-cross Abstract: Business environments characterized by intermittent demand, high variability, and multi-step planning horizons require forecasting policies th
arXiv:2604.08669v1 Announce Type: cross Abstract: It is widely believed that tens of thousands of physical qubits are needed to build a practically useful quantum computer. Atom arrays formed by optic
arXiv:2604.09451v1 Announce Type: cross Abstract: The accelerometer has become an almost ubiquitous device, providing enormous opportunities in healthcare monitoring beyond step counting or other aver
arXiv:2604.09066v1 Announce Type: new Abstract: Linguistic steganography based on language models typically assumes that steganographic texts are transmitted without alteration, making them fragile to
arXiv:2604.08746v1 Announce Type: cross Abstract: Animatable 3D assets, defined as geometry equipped with an articulated skeleton and skinning weights, are fundamental to interactive graphics, embodie
arXiv:2511.05168v2 Announce Type: replace Abstract: Vision foundation models achieve strong performance on both global and locally dense downstream tasks. Pretrained on large images, the recent DINOv3
arXiv:2604.09543v1 Announce Type: new Abstract: The persistent storage requirements for high-resolution, spatiotemporally evolving fields governed by large-scale and high-dimensional partial different
arXiv:2509.02967v3 Announce Type: replace-cross Abstract: Traditional neural networks struggle to capture the spectral structure of complex signals. Fourier neural networks (FNNs) attempt to address t
arXiv:2604.09364v1 Announce Type: cross Abstract: When a Vision-Language Model (VLM) sees a blue banana and answers 'yellow', is the problem of perception or arbitration? We explore the question in te
arXiv:2604.09288v1 Announce Type: new Abstract: Trusted multi-view classification typically relies on a view-wise evidential fusion process: each view independently produces class evidence and uncerta
arXiv:2604.08756v1 Announce Type: new Abstract: The situated view of cognition holds that intelligent behavior depends not only on internal memory, but on an agent's active use of environmental resour
arXiv:2604.09200v1 Announce Type: cross Abstract: There is substantial concern about the ability of advanced artificial intelligence to influence people's behaviour. A rapidly growing body of research
arXiv:2604.08355v2 Announce Type: replace Abstract: Reinforcement Learning (RL) agents often struggle to generalize knowledge to new tasks, even those structurally similar to ones they have mastered.
arXiv:2604.08983v1 Announce Type: new Abstract: Spatial reasoning is a fundamental capability for embodied intelligence, especially for fine-grained manipulation tasks such as robotic assembly. While
arXiv:2604.08999v1 Announce Type: cross Abstract: Table serialization remains a critical bottleneck for Large Language Models (LLMs) in complex table question answering, hindered by challenges such as
arXiv:2604.09445v1 Announce Type: new Abstract: Precise and real-time visual localization is critical for applications like AR/VR and robotics, especially on resource-constrained edge devices such as
arXiv:2604.08564v1 Announce Type: new Abstract: Auto-regressive models (ARMs) have established a dominant paradigm in language modeling. However, their strictly sequential decoding paradigm imposes fu
arXiv:2604.08867v1 Announce Type: cross Abstract: Audio has rapidly become a primary interface for foundation models, powering real-time voice assistants. Ensuring safety in audio systems is inherentl
arXiv:2604.09166v1 Announce Type: new Abstract: Anomaly detection (AD) in chemical processes based on deep learning offers significant opportunities but requires large, diverse, and well-annotated tra
arXiv:2604.09418v1 Announce Type: new Abstract: This paper studies Automated Instruction Revision (AIR), a rule-induction-based method for adapting large language models (LLMs) to downstream tasks usi
arXiv:2604.08552v1 Announce Type: cross Abstract: Scientific metadata are often incomplete and noncompliant with community standards, limiting dataset findability, interoperability, and reuse. When re
arXiv:2412.18735v3 Announce Type: replace-cross Abstract: In recent years, researchers have leveraged social relations to enhance recommendation performance. However, most existing social recommendati
arXiv:2511.18960v3 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models have shown remarkable progress in embodied tasks recently, but most methods process visual observations in
arXiv:2603.24245v3 Announce Type: replace Abstract: Micro-actions, fleeting and low-amplitude motions, such as glances, nods, or minor posture shifts, carry rich social meaning but remain difficult fo
arXiv:2604.09378v1 Announce Type: cross Abstract: Agent ecosystems increasingly rely on installable skills to extend functionality, and some skills bundle learned model artifacts as part of their exec
arXiv:2405.16772v2 Announce Type: replace-cross Abstract: Social recommendation models weave social interactions into their design to provide uniquely personalized recommendation results for users. Ho
arXiv:2512.11179v3 Announce Type: replace Abstract: Graph-based multi-agent reinforcement learning (MARL) enables coordinated behavior under partial observability by modeling agents as nodes and commu
arXiv:2510.18075v2 Announce Type: replace Abstract: Machine learning (ML) holds great potential to advance anomaly detection (AD) in chemical processes. However, the development of ML-based methods is
arXiv:2509.16606v5 Announce Type: replace-cross Abstract: In networked multi-agent reinforcement learning (Networked-MARL), decentralized agents must act under local observability and constrained comm
arXiv:2506.17788v2 Announce Type: replace Abstract: Social reasoning - inferring unobservable beliefs and intentions from partial observations of other agents - remains a challenging task for large la
arXiv:2509.05215v3 Announce Type: replace Abstract: Recent works propose complex multi-modal models that handle both time series and language, ultimately claiming high performance on complex tasks lik
arXiv:2604.09151v1 Announce Type: new Abstract: Accurate segmentation of surgical instruments in robotic-assisted surgery is critical for enabling context-aware computer-assisted interventions, such a
arXiv:2604.09497v1 Announce Type: cross Abstract: Accurate evaluation is central to the large language model (LLM) ecosystem, guiding model selection and downstream adoption across diverse use cases.
arXiv:2603.06665v2 Announce Type: replace-cross Abstract: Large vision-language models (VLMs) often benefit from chain-of-thought (CoT) prompting in general domains, yet its efficacy in medical vision
arXiv:2604.09414v1 Announce Type: cross Abstract: Learning-to-Defer routes each input to the expert that minimizes expected cost, but it assumes that the information available to every expert is fixed
arXiv:2512.04175v2 Announce Type: replace Abstract: Generalizing deepfake detection to unseen manipulations remains a key challenge. A recent approach to tackle this issue is to train a network with p
arXiv:2604.09085v1 Announce Type: cross Abstract: Large-scale digital platforms generate billions of timestamped user-item interactions (events) that are crucial for predicting user attributes in, e.g
arXiv:2604.08920v1 Announce Type: cross Abstract: Information retrieval systems have traditionally optimized for topical relevance-the degree to which retrieved documents match a query. However, relev
arXiv:2604.09260v1 Announce Type: new Abstract: Standard object detectors typically treat architectural elements independently, often resulting in facade parsings that lack the structural coherence re
arXiv:2509.14875v2 Announce Type: replace-cross Abstract: Characterizing the geometry of an object orbiting around a star from its transit light curve is a powerful tool to uncover various complex phe
arXiv:2511.23071v2 Announce Type: replace-cross Abstract: Reading scene text, that is, text appearing in images, has numerous application areas, including assistive technology, search, and e-commerce.
arXiv:2604.08858v1 Announce Type: new Abstract: We present BIAS, a fast, biologically inspired model for dynamic visual saliency detection in continuous video streams. Building on the Itti--Koch frame
arXiv:2604.08357v2 Announce Type: replace Abstract: Conditional Diffusion Models are powerful surrogates for emulating complex spatiotemporal dynamics, yet they often fail to match the accuracy of det
arXiv:2604.09369v1 Announce Type: cross Abstract: Generative models can now propose thousands of de novo antibody sequences, yet translating these designs into viable therapeutics remains constrained
arXiv:2604.07361v2 Announce Type: replace Abstract: Graph Neural Networks (GNNs) have been widely used in diverse brain network analysis tasks based on preprocessed functional magnetic resonance imagi
arXiv:2604.09022v1 Announce Type: new Abstract: With the rapid adoption of diffusion models, synthetic data generation has emerged as a promising approach for addressing the growing demand for large-s
arXiv:2604.04334v2 Announce Type: replace-cross Abstract: Researchers and practitioners are increasingly considering reinforcement learning to optimize decisions in complex domains like robotics and h
arXiv:2511.17687v2 Announce Type: replace Abstract: The brain's Path Integration (PI) mechanism offers substantial guidance and inspiration for Brain-Inspired Navigation (BIN). However, the PI capabil
arXiv:2604.08964v1 Announce Type: new Abstract: Diffusion Large Language Models (dLLMs) have recently become a promising alternative to autoregressive large language models (ARMs). Semi-autoregressive