Edna Barros, a friend of mine, recomended reading a deep learning paper she was one of the authors for about some improvements for robots in soccer published this july 2025, but I can't recall the title. Could you check what the article was called and when it came out? also, since I'm learning spanish, could you translate the abstract for me?
['The deep learning paper about robot soccer published in July 2025 is titled "Efficient Lines Detection for Robot Soccer"', 'The deep learning paper about robot soccer was released on July 25, 2025.', 'The Spanish translation of the abstract for "Efficient Lines Detection for Robot Soccer" is: "La autolocalización es esencial en el fútbol robótico, donde la detección precisa de las características del campo visual, como líneas y límites, es fundamental para la estimación fiable de la postura. Este artículo presenta un método ligero y eficiente para detectar líneas de campos de fútbol utilizando el algoritmo ELSED, ampliando con un paso de clasificación que analiza las transiciones de color RGB para identificar líneas pertenecientes al campo. Presentamos un pipeline basado en la optimización por enjambre de partículas (PSO) para la calibración de umbrales con el fin de optimizar el rendimiento de la detección, lo que requiere solo un pequeño número de muestras anotadas. Nuestro enfoque logra una precisión comparable a la de un modelo de aprendizaje profundo de última generación, al tiempo que ofrece una mayor velocidad de procesamiento, lo que lo hace adecuado para aplicaciones en tiempo real en plataformas robóticas de baja potencia.".']
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[{"content":"List the papers about deep learning published in July 2025 and find the deep learning paper about soccer.","role":"assistant","tool_calls":[{"function":{"arguments":"{\n  \"query\": \"deep learning\",\n  \"date_from\": \"2025-07-01\",\n  \"date_to\": \"2025-07-31\"\n}","name":"arxiv_search_papers"},"id":"33c73603-6d62-4ffa-b820-adda37023a83","type":"function"}]},{"content":[{"text":"{\n  \"total_results\": 10,\n  \"papers\": [\n    {\n      \"id\": \"2507.19477v1\",\n      \"title\": \"Advancing Event Forecasting through Massive Training of Large Language Models: Challenges, Solutions, and Broader Impacts\",\n      \"authors\": [\n        \"Sang-Woo Lee\",\n        \"Sohee Yang\",\n        \"Donghyun Kwak\",\n        \"Noah Y. Siegel\"\n      ],\n      \"abstract\": \"Many recent papers have studied the development of superforecaster-level\\nevent forecasting LLMs. While methodological problems with early studies cast\\ndoubt on the use of LLMs for event forecasting, recent studies with improved\\nevaluation methods have shown that state-of-the-art LLMs are gradually reaching\\nsuperforecaster-level performance, and reinforcement learning has also been\\nreported to improve future forecasting. Additionally, the unprecedented success\\nof recent reasoning models and Deep Research-style models suggests that\\ntechnology capable of greatly improving forecasting performance has been\\ndeveloped. Therefore, based on these positive recent trends, we argue that the\\ntime is ripe for research on large-scale training of superforecaster-level\\nevent forecasting LLMs. We discuss two key research directions: training\\nmethods and data acquisition. For training, we first introduce three\\ndifficulties of LLM-based event forecasting training: noisiness-sparsity,\\nknowledge cut-off, and simple reward structure problems. Then, we present\\nrelated ideas to mitigate these problems: hypothetical event Bayesian networks,\\nutilizing poorly-recalled and counterfactual events, and auxiliary reward\\nsignals. For data, we propose aggressive use of market, public, and crawling\\ndatasets to enable large-scale training and evaluation. Finally, we explain how\\nthese technical advances could enable AI to provide predictive intelligence to\\nsociety in broader areas. This position paper presents promising specific paths\\nand considerations for getting closer to superforecaster-level AI technology,\\naiming to call for researchers' interest in these directions.\",\n      \"categories\": [\n        \"cs.LG\",\n        \"cs.AI\",\n        \"cs.CL\"\n      ],\n      \"published\": \"2025-07-25T17:59:13+00:00\",\n      \"url\": \"http://arxiv.org/pdf/2507.19477v1\",\n      \"resource_uri\": \"arxiv://2507.19477v1\"\n    },\n    {\n      \"id\": \"2507.19469v1\",\n      \"title\": \"Efficient Lines Detection for Robot Soccer\",\n      \"authors\": [\n        \"Jo\\u00e3o G. Melo\",\n        \"Jo\\u00e3o P. Mafaldo\",\n        \"Edna Barros\"\n      ],\n      \"abstract\": \"Self-localization is essential in robot soccer, where accurate detection of\\nvisual field features, such as lines and boundaries, is critical for reliable\\npose estimation. This paper presents a lightweight and efficient method for\\ndetecting soccer field lines using the ELSED algorithm, extended with a\\nclassification step that analyzes RGB color transitions to identify lines\\nbelonging to the field. We introduce a pipeline based on Particle Swarm\\nOptimization (PSO) for threshold calibration to optimize detection performance,\\nrequiring only a small number of annotated samples. Our approach achieves\\naccuracy comparable to a state-of-the-art deep learning model while offering\\nhigher processing speed, making it well-suited for real-time applications on\\nlow-power robotic platforms.\",\n      \"categories\": [\n        \"cs.CV\",\n        \"cs.RO\"\n      ],\n      \"published\": \"2025-07-25T17:54:51+00:00\",\n      \"url\": \"http://arxiv.org/pdf/2507.19469v1\",\n      \"resource_uri\": \"arxiv://2507.19469v1\"\n    },\n    {\n      \"id\": \"2507.19458v1\",\n      \"title\": \"Hierarchical Deep Reinforcement Learning Framework for Multi-Year Asset Management Under Budget Constraints\",\n      \"authors\": [\n        \"Amir Fard\",\n        \"Arnold X. -X. Yuan\"\n      ],\n      \"abstract\": \"Budget planning and maintenance optimization are crucial for infrastructure\\nasset management, ensuring cost-effectiveness and sustainability. However, the\\ncomplexity arising from combinatorial action spaces, diverse asset\\ndeterioration, stringent budget constraints, and environmental uncertainty\\nsignificantly limits existing methods' scalability. This paper proposes a\\nHierarchical Deep Reinforcement Learning methodology specifically tailored to\\nmulti-year infrastructure planning. Our approach decomposes the problem into\\ntwo hierarchical levels: a high-level Budget Planner allocating annual budgets\\nwithin explicit feasibility bounds, and a low-level Maintenance Planner\\nprioritizing assets within the allocated budget. By structurally separating\\nmacro-budget decisions from asset-level prioritization and integrating linear\\nprogramming projection within a hierarchical Soft Actor-Critic framework, the\\nmethod efficiently addresses exponential growth in the action space and ensures\\nrigorous budget compliance. A case study evaluating sewer networks of varying\\nsizes (10, 15, and 20 sewersheds) illustrates the effectiveness of the proposed\\napproach. Compared to conventional Deep Q-Learning and enhanced genetic\\nalgorithms, our methodology converges more rapidly, scales effectively, and\\nconsistently delivers near-optimal solutions even as network size grows.\",\n      \"categories\": [\n        \"cs.AI\",\n        \"cs.LG\",\n        \"cs.SY\",\n        \"eess.SY\",\n        \"math.OC\"\n      ],\n      \"published\": \"2025-07-25T17:42:34+00:00\",\n      \"url\": \"http://arxiv.org/pdf/2507.19458v1\",\n      \"resource_uri\": \"arxiv://2507.19458v1\"\n    },\n    {\n      \"id\": \"2507.19418v1\",\n      \"title\": \"DEFNet: Multitasks-based Deep Evidential Fusion Network for Blind Image Quality Assessment\",\n      \"authors\": [\n        \"Yiwei Lou\",\n        \"Yuanpeng He\",\n        \"Rongchao Zhang\",\n        \"Yongzhi Cao\",\n        \"Hanpin Wang\",\n        \"Yu Huang\"\n      ],\n      \"abstract\": \"Blind image quality assessment (BIQA) methods often incorporate auxiliary\\ntasks to improve performance. However, existing approaches face limitations due\\nto insufficient integration and a lack of flexible uncertainty estimation,\\nleading to suboptimal performance. To address these challenges, we propose a\\nmultitasks-based Deep Evidential Fusion Network (DEFNet) for BIQA, which\\nperforms multitask optimization with the assistance of scene and distortion\\ntype classification tasks. To achieve a more robust and reliable\\nrepresentation, we design a novel trustworthy information fusion strategy. It\\nfirst combines diverse features and patterns across sub-regions to enhance\\ninformation richness, and then performs local-global information fusion by\\nbalancing fine-grained details with coarse-grained context. Moreover, DEFNet\\nexploits advanced uncertainty estimation technique inspired by evidential\\nlearning with the help of normal-inverse gamma distribution mixture. Extensive\\nexperiments on both synthetic and authentic distortion datasets demonstrate the\\neffectiveness and robustness of the proposed framework. Additional evaluation\\nand analysis are carried out to highlight its strong generalization capability\\nand adaptability to previously unseen scenarios.\",\n      \"categories\": [\n        \"cs.CV\"\n      ],\n      \"published\": \"2025-07-25T16:36:45+00:00\",\n      \"url\": \"http://arxiv.org/pdf/2507.19418v1\",\n      \"resource_uri\": \"arxiv://2507.19418v1\"\n    },\n    {\n      \"id\": \"2507.19404v1\",\n      \"title\": \"A multi-dynamic low-rank deep image prior (ML-DIP) for real-time 3D cardiovascular MRI\",\n      \"authors\": [\n        \"Chong Chen\",\n        \"Marc Vornehm\",\n        \"Preethi Chandrasekaran\",\n        \"Muhammad A. Sultan\",\n        \"Syed M. Arshad\",\n        \"Yingmin Liu\",\n        \"Yuchi Han\",\n        \"Rizwan Ahmad\"\n      ],\n      \"abstract\": \"Purpose: To develop a reconstruction framework for 3D real-time cine\\ncardiovascular magnetic resonance (CMR) from highly undersampled data without\\nrequiring fully sampled training data.\\n  Methods: We developed a multi-dynamic low-rank deep image prior (ML-DIP)\\nframework that models spatial image content and temporal deformation fields\\nusing separate neural networks. These networks are optimized per scan to\\nreconstruct the dynamic image series directly from undersampled k-space data.\\nML-DIP was evaluated on (i) a 3D cine digital phantom with simulated premature\\nventricular contractions (PVCs), (ii) ten healthy subjects (including two\\nscanned during both rest and exercise), and (iii) five patients with PVCs.\\nPhantom results were assessed using peak signal-to-noise ratio (PSNR) and\\nstructural similarity index measure (SSIM). In vivo performance was evaluated\\nby comparing left-ventricular function quantification (against 2D real-time\\ncine) and image quality (against 2D real-time cine and binning-based 5D-Cine).\\n  Results: In the phantom study, ML-DIP achieved PSNR > 29 dB and SSIM > 0.90\\nfor scan times as short as two minutes, while recovering cardiac motion,\\nrespiratory motion, and PVC events. In healthy subjects, ML-DIP yielded\\nfunctional measurements comparable to 2D cine and higher image quality than\\n5D-Cine, including during exercise with high heart rates and bulk motion. In\\nPVC patients, ML-DIP preserved beat-to-beat variability and reconstructed\\nirregular beats, whereas 5D-Cine showed motion artifacts and information loss\\ndue to binning.\\n  Conclusion: ML-DIP enables high-quality 3D real-time CMR with acceleration\\nfactors exceeding 1,000 by learning low-rank spatial and temporal\\nrepresentations from undersampled data, without relying on external fully\\nsampled training datasets.\",\n      \"categories\": [\n        \"eess.IV\"\n      ],\n      \"published\": \"2025-07-25T16:10:01+00:00\",\n      \"url\": \"http://arxiv.org/pdf/2507.19404v1\",\n      \"resource_uri\": \"arxiv://2507.19404v1\"\n    },\n    {\n      \"id\": \"2507.19398v1\",\n      \"title\": \"CXR-CML: Improved zero-shot classification of long-tailed multi-label diseases in Chest X-Rays\",\n      \"authors\": [\n        \"Rajesh Madhipati\",\n        \"Sheethal Bhat\",\n        \"Lukas Buess\",\n        \"Andreas Maier\"\n      ],\n      \"abstract\": \"Chest radiography (CXR) plays a crucial role in the diagnosis of various\\ndiseases. However, the inherent class imbalance in the distribution of clinical\\nfindings presents a significant challenge for current self-supervised deep\\nlearning models. These models often fail to accurately classify long-tailed\\nclasses. Current Vision-Language models such as Contrastive Language Image\\nPre-training (CLIP) models effectively model the manifold distribution of the\\nlatent space, enabling high zero-shot classification accuracies. Although CLIP\\nperforms well on most of the primary classes in the dataset, our work reveals\\nthat its effectiveness decreases significantly for classes with a long-tailed\\ndistribution. Our approach employs a class-weighting mechanism that directly\\naligns with the distribution of classes within the latent space. This method\\nensures a substantial improvement in overall classification performance, with\\nparticular emphasis on enhancing the recognition and accuracy of rarely\\nobserved classes. We accomplish this by applying Gaussian Mixture Model (GMM)\\nclustering to the latent space. The subsequent clusters are further refined by\\nStudent t-distribution, followed by a metric loss that utilizes the altered\\nembeddings. Our approach facilitates stable and adaptive clustering of the\\nfeatures. This results in a notable average improvement of 7\\\\% points in\\nzero-shot AUC scores across 40 classes in the MIMIC-CXR-JPG dataset from\\nprevious SOTA models.\",\n      \"categories\": [\n        \"cs.CV\",\n        \"cs.AI\"\n      ],\n      \"published\": \"2025-07-25T16:05:47+00:00\",\n      \"url\": \"http://arxiv.org/pdf/2507.19398v1\",\n      \"resource_uri\": \"arxiv://2507.19398v1\"\n    },\n    {\n      \"id\": \"2507.19377v1\",\n      \"title\": \"Deep Reinforcement Learning-Based Scheduling for Wi-Fi Multi-Access Point Coordination\",\n      \"authors\": [\n        \"David Nunez\",\n        \"Francesc Wilhelmi\",\n        \"Maksymilian Wojnar\",\n        \"Katarzyna Kosek-Szott\",\n        \"Szymon Szott\",\n        \"Boris Bellalta\"\n      ],\n      \"abstract\": \"Multi-access point coordination (MAPC) is a key feature of IEEE 802.11bn,\\nwith a potential impact on future Wi-Fi networks. MAPC enables joint scheduling\\ndecisions across multiple access points (APs) to improve throughput, latency,\\nand reliability in dense Wi-Fi deployments. However, implementing efficient\\nscheduling policies under diverse traffic and interference conditions in\\noverlapping basic service sets (OBSSs) remains a complex task. This paper\\npresents a method to minimize the network-wide worst-case latency by\\nformulating MAPC scheduling as a sequential decision-making problem and\\nproposing a deep reinforcement learning (DRL) mechanism to minimize worst-case\\ndelays in OBSS deployments. Specifically, we train a DRL agent using proximal\\npolicy optimization (PPO) within an 802.11bn-compatible Gymnasium environment.\\nThis environment provides observations of queue states, delay metrics, and\\nchannel conditions, enabling the agent to schedule multiple AP-station pairs to\\ntransmit simultaneously by leveraging spatial reuse (SR) groups. Simulations\\ndemonstrate that our proposed solution outperforms state-of-the-art heuristic\\nstrategies across a wide range of network loads and traffic patterns. The\\ntrained machine learning (ML) models consistently achieve lower 99th-percentile\\ndelays, showing up to a 30% improvement over the best baseline.\",\n      \"categories\": [\n        \"cs.NI\"\n      ],\n      \"published\": \"2025-07-25T15:26:25+00:00\",\n      \"url\": \"http://arxiv.org/pdf/2507.19377v1\",\n      \"resource_uri\": \"arxiv://2507.19377v1\"\n    },\n    {\n      \"id\": \"2507.19368v1\",\n      \"title\": \"Counterfactual Explanations in Medical Imaging: Exploring SPN-Guided Latent Space Manipulation\",\n      \"authors\": [\n        \"Julia Siekiera\",\n        \"Stefan Kramer\"\n      ],\n      \"abstract\": \"Artificial intelligence is increasingly leveraged across various domains to\\nautomate decision-making processes that significantly impact human lives. In\\nmedical image analysis, deep learning models have demonstrated remarkable\\nperformance. However, their inherent complexity makes them black box systems,\\nraising concerns about reliability and interpretability. Counterfactual\\nexplanations provide comprehensible insights into decision processes by\\npresenting hypothetical \\\"what-if\\\" scenarios that alter model classifications.\\nBy examining input alterations, counterfactual explanations provide patterns\\nthat influence the decision-making process. Despite their potential, generating\\nplausible counterfactuals that adhere to similarity constraints providing\\nhuman-interpretable explanations remains a challenge. In this paper, we\\ninvestigate this challenge by a model-specific optimization approach. While\\ndeep generative models such as variational autoencoders (VAEs) exhibit\\nsignificant generative power, probabilistic models like sum-product networks\\n(SPNs) efficiently represent complex joint probability distributions. By\\nmodeling the likelihood of a semi-supervised VAE's latent space with an SPN, we\\nleverage its dual role as both a latent space descriptor and a classifier for a\\ngiven discrimination task. This formulation enables the optimization of latent\\nspace counterfactuals that are both close to the original data distribution and\\naligned with the target class distribution. We conduct experimental evaluation\\non the cheXpert dataset. To evaluate the effectiveness of the integration of\\nSPNs, our SPN-guided latent space manipulation is compared against a neural\\nnetwork baseline. Additionally, the trade-off between latent variable\\nregularization and counterfactual quality is analyzed.\",\n      \"categories\": [\n        \"cs.LG\",\n        \"cs.AI\"\n      ],\n      \"published\": \"2025-07-25T15:19:32+00:00\",\n      \"url\": \"http://arxiv.org/pdf/2507.19368v1\",\n      \"resource_uri\": \"arxiv://2507.19368v1\"\n    },\n    {\n      \"id\": \"2507.19321v1\",\n      \"title\": \"SIDE: Sparse Information Disentanglement for Explainable Artificial Intelligence\",\n      \"authors\": [\n        \"Viktar Dubovik\",\n        \"\\u0141ukasz Struski\",\n        \"Jacek Tabor\",\n        \"Dawid Rymarczyk\"\n      ],\n      \"abstract\": \"Understanding the decisions made by deep neural networks is essential in\\nhigh-stakes domains such as medical imaging and autonomous driving. Yet, these\\nmodels often lack transparency, particularly in computer vision.\\nPrototypical-parts-based neural networks have emerged as a promising solution\\nby offering concept-level explanations. However, most are limited to\\nfine-grained classification tasks, with few exceptions such as InfoDisent.\\nInfoDisent extends prototypical models to large-scale datasets like ImageNet,\\nbut produces complex explanations.\\n  We introduce Sparse Information Disentanglement for Explainability (SIDE), a\\nnovel method that improves the interpretability of prototypical parts through a\\ndedicated training and pruning scheme that enforces sparsity. Combined with\\nsigmoid activations in place of softmax, this approach allows SIDE to associate\\neach class with only a small set of relevant prototypes. Extensive experiments\\nshow that SIDE matches the accuracy of existing methods while reducing\\nexplanation size by over $90\\\\%$, substantially enhancing the understandability\\nof prototype-based explanations.\",\n      \"categories\": [\n        \"cs.CV\",\n        \"cs.AI\",\n        \"cs.LG\"\n      ],\n      \"published\": \"2025-07-25T14:34:15+00:00\",\n      \"url\": \"http://arxiv.org/pdf/2507.19321v1\",\n      \"resource_uri\": \"arxiv://2507.19321v1\"\n    },\n    {\n      \"id\": \"2507.19298v1\",\n      \"title\": \"Controlling Topological Defects in Polar Fluids via Reinforcement Learning\",\n      \"authors\": [\n        \"Abhinav Singh\",\n        \"Petros Koumoutsakos\"\n      ],\n      \"abstract\": \"Topological defects in active polar fluids exhibit complex dynamics driven by\\ninternally generated stresses, reflecting the deep interplay between topology,\\nflow, and non-equilibrium hydrodynamics. Feedback control offers a powerful\\nmeans to guide such systems, enabling transitions between dynamic states. We\\ninvestigated closed-loop steering of integer-charged defects in a confined\\nactive fluid by modulating the spatial profile of activity. Using a continuum\\nhydrodynamic model, we show that localized control of active stress induces\\nflow fields that can reposition and direct defects along prescribed\\ntrajectories by exploiting non-linear couplings in the system. A reinforcement\\nlearning framework is used to discover effective control strategies that\\nproduce robust defect transport across both trained and novel trajectories. The\\nresults highlight how AI agents can learn the underlying dynamics and spatially\\nstructure activity to manipulate topological excitations, offering insights\\ninto the controllability of active matter and the design of adaptive,\\nself-organized materials.\",\n      \"categories\": [\n        \"cond-mat.soft\",\n        \"cs.AI\",\n        \"cs.LG\"\n      ],\n      \"published\": \"2025-07-25T14:12:11+00:00\",\n      \"url\": \"http://arxiv.org/pdf/2507.19298v1\",\n      \"resource_uri\": \"arxiv://2507.19298v1\"\n    }\n  ]\n}","type":"text"}],"role":"tool","tool_call_id":"33c73603-6d62-4ffa-b820-adda37023a83"},{"content":"I found the title, publication date, and abstract of the paper about robots and soccer. Now list the available codes to translate the abstract.","role":"assistant","tool_calls":[{"function":{"arguments":"{ }","name":"lara-translate_list_languages"},"id":"8744607d-93bf-4217-9af4-db68cd0ea43d","type":"function"}]},{"content":[{"text":"[\n  \"ar-SA\",\n  \"bg-BG\",\n  \"ca-ES\",\n  \"cs-CZ\",\n  \"da-DK\",\n  \"de-DE\",\n  \"el-GR\",\n  \"en-AU\",\n  \"en-CA\",\n  \"en-GB\",\n  \"en-IE\",\n  \"en-US\",\n  \"es-419\",\n  \"es-AR\",\n  \"es-ES\",\n  \"es-MX\",\n  \"fi-FI\",\n  \"fr-CA\",\n  \"fr-FR\",\n  \"he-IL\",\n  \"hr-HR\",\n  \"hu-HU\",\n  \"id-ID\",\n  \"it-IT\",\n  \"ja-JP\",\n  \"ko-KR\",\n  \"ms-MY\",\n  \"nb-NO\",\n  \"nl-BE\",\n  \"nl-NL\",\n  \"pl-PL\",\n  \"pt-BR\",\n  \"pt-PT\",\n  \"ru-RU\",\n  \"sk-SK\",\n  \"sv-SE\",\n  \"th-TH\",\n  \"tr-TR\",\n  \"uk-UA\",\n  \"zh-CN\",\n  \"zh-HK\",\n  \"zh-TW\"\n]","type":"text"}],"role":"tool","tool_call_id":"8744607d-93bf-4217-9af4-db68cd0ea43d"},{"content":"The source language is English (en) and the target is Spanish (es). Translate the abstract from EN to ES.","role":"assistant","tool_calls":[{"function":{"arguments":"{\n  \"text\": [\n    {\n      \"text\": \"\\\"Self-localization is essential in robot soccer, where accurate detection of\\\\nvisual field features, such as lines and boundaries, is critical for reliable\\\\npose estimation. This paper presents a lightweight and efficient method for\\\\ndetecting soccer field lines using the ELSED algorithm, extended with a\\\\nclassification step that analyzes RGB color transitions to identify lines\\\\nbelonging to the field. We introduce a pipeline based on Particle Swarm\\\\nOptimization (PSO) for threshold calibration to optimize detection performance,\\\\nrequiring only a small number of annotated samples. Our approach achieves\\\\naccuracy comparable to a state-of-the-art deep learning model while offering\\\\nhigher processing speed, making it well-suited for real-time applications on\\\\nlow-power robotic platforms.\",\n      \"translatable\": true\n    }\n  ],\n  \"source\": \"EN\",\n  \"target\": \"ES\",\n  \"context\": \"\"\n}","name":"lara-translate_translate"},"id":"ef367952-2d91-478c-8922-4ed2f343fe1f","type":"function"}]},{"content":[{"text":"[\n  {\n    \"text\": \"«La autolocalización es esencial en el fútbol robótico, donde la detección precisa de las características del campo visual, como las líneas y los límites, es fundamental para una estimación fiable de la postura. Este artículo presenta un método ligero y eficiente para\\\\ndetectar las líneas del campo de fútbol utilizando el algoritmo ELSED, ampliado con un paso de\\\\nclasificación que analiza las transiciones de color RGB para identificar las líneas\\\\npertenecientes al campo. Presentamos un pipeline basado en la optimización por enjambre de partículas (PSO) para la calibración de umbrales con el fin de optimizar el rendimiento de la detección, lo que requiere solo un pequeño número de muestras anotadas. Nuestro enfoque logra\\\\nuna precisión comparable a la de un modelo de aprendizaje profundo de última generación, a la vez que ofrece\\\\nuna mayor velocidad de procesamiento, lo que lo hace adecuado para aplicaciones en tiempo real en\\\\nplataformas robóticas de baja potencia.\",\n    \"translatable\": true\n  }\n]","type":"text"}],"role":"tool","tool_call_id":"ef367952-2d91-478c-8922-4ed2f343fe1f"}]