Self-Supervised Cross-Modal Learning for Image-to-Point Cloud Registrationcs.AI updates on arXiv.org
Self-Supervised Cross-Modal Learning for Image-to-Point Cloud Registrationcs.AI updates on arXiv.orgon September 22, 2025 at 4:00 am arXiv:2509.15882v1 Announce Type: cross
Abstract: Bridging 2D and 3D sensor modalities is critical for robust perception in autonomous systems. However, image-to-point cloud (I2P) registration remains challenging due to the semantic-geometric gap between texture-rich but depth-ambiguous images and sparse yet metrically precise point clouds, as well as the tendency of existing methods to converge to local optima. To overcome these limitations, we introduce CrossI2P, a self-supervised framework that unifies cross-modal learning and two-stage registration in a single end-to-end pipeline. First, we learn a geometric-semantic fused embedding space via dual-path contrastive learning, enabling annotation-free, bidirectional alignment of 2D textures and 3D structures. Second, we adopt a coarse-to-fine registration paradigm: a global stage establishes superpoint-superpixel correspondences through joint intra-modal context and cross-modal interaction modeling, followed by a geometry-constrained point-level refinement for precise registration. Third, we employ a dynamic training mechanism with gradient normalization to balance losses for feature alignment, correspondence refinement, and pose estimation. Extensive experiments demonstrate that CrossI2P outperforms state-of-the-art methods by 23.7% on the KITTI Odometry benchmark and by 37.9% on nuScenes, significantly improving both accuracy and robustness.
arXiv:2509.15882v1 Announce Type: cross
Abstract: Bridging 2D and 3D sensor modalities is critical for robust perception in autonomous systems. However, image-to-point cloud (I2P) registration remains challenging due to the semantic-geometric gap between texture-rich but depth-ambiguous images and sparse yet metrically precise point clouds, as well as the tendency of existing methods to converge to local optima. To overcome these limitations, we introduce CrossI2P, a self-supervised framework that unifies cross-modal learning and two-stage registration in a single end-to-end pipeline. First, we learn a geometric-semantic fused embedding space via dual-path contrastive learning, enabling annotation-free, bidirectional alignment of 2D textures and 3D structures. Second, we adopt a coarse-to-fine registration paradigm: a global stage establishes superpoint-superpixel correspondences through joint intra-modal context and cross-modal interaction modeling, followed by a geometry-constrained point-level refinement for precise registration. Third, we employ a dynamic training mechanism with gradient normalization to balance losses for feature alignment, correspondence refinement, and pose estimation. Extensive experiments demonstrate that CrossI2P outperforms state-of-the-art methods by 23.7% on the KITTI Odometry benchmark and by 37.9% on nuScenes, significantly improving both accuracy and robustness. Read More
Data Visualization Explained: What It Is and Why It MattersTowards Data Scienceon September 21, 2025 at 4:00 pm A brief introduction to data visualization and its importance in today’s technological landscape.
The post Data Visualization Explained: What It Is and Why It Matters appeared first on Towards Data Science.
A brief introduction to data visualization and its importance in today’s technological landscape.
The post Data Visualization Explained: What It Is and Why It Matters appeared first on Towards Data Science. Read More
Python Can Now Call MojoTowards Data Scienceon September 21, 2025 at 2:00 pm Boost your runtimes with lightning-fast Mojo code
The post Python Can Now Call Mojo appeared first on Towards Data Science.
Boost your runtimes with lightning-fast Mojo code
The post Python Can Now Call Mojo appeared first on Towards Data Science. Read More
Building LLM Apps That Can See, Think, and Integrate: Using o3 with Multimodal Input and Structured OutputTowards Data Scienceon September 20, 2025 at 4:00 pm A hands-on example of building a time-series anomaly detection system entirely through visualization and prompting
The post Building LLM Apps That Can See, Think, and Integrate: Using o3 with Multimodal Input and Structured Output appeared first on Towards Data Science.
A hands-on example of building a time-series anomaly detection system entirely through visualization and prompting
The post Building LLM Apps That Can See, Think, and Integrate: Using o3 with Multimodal Input and Structured Output appeared first on Towards Data Science. Read More
The SyncNet Research Paper, Clearly ExplainedTowards Data Scienceon September 20, 2025 at 2:00 pm A Deep Dive into “Out of Time: Automated Lip Sync in the Wild”
The post The SyncNet Research Paper, Clearly Explained appeared first on Towards Data Science.
A Deep Dive into “Out of Time: Automated Lip Sync in the Wild”
The post The SyncNet Research Paper, Clearly Explained appeared first on Towards Data Science. Read More
How to Select the 5 Most Relevant Documents for AI SearchTowards Data Scienceon September 19, 2025 at 12:30 pm Improve the document retrieval step of your RAG pipeline
The post How to Select the 5 Most Relevant Documents for AI Search appeared first on Towards Data Science.
Improve the document retrieval step of your RAG pipeline
The post How to Select the 5 Most Relevant Documents for AI Search appeared first on Towards Data Science. Read More
Leveraging Artificial Intelligence as a Strategic Growth Catalyst for Small and Medium-sized Enterprisescs.AI updates on arXiv.orgon September 19, 2025 at 4:00 am arXiv:2509.14532v1 Announce Type: cross
Abstract: Artificial Intelligence (AI) has transitioned from a futuristic concept reserved for large corporations to a present-day, accessible, and essential growth lever for Small and Medium-sized Enterprises (SMEs). For entrepreneurs and business leaders, strategic AI adoption is no longer an option but an imperative for competitiveness, operational efficiency, and long-term survival. This report provides a comprehensive framework for SME leaders to navigate this technological shift, offering the foundational knowledge, business case, practical applications, and strategic guidance necessary to harness the power of AI. The quantitative evidence supporting AI adoption is compelling; 91% of SMEs using AI report that it directly boosts their revenue. Beyond top-line growth, AI drives profound operational efficiencies, with studies showing it can reduce operational costs by up to 30% and save businesses more than 20 hours of valuable time each month. This transformation is occurring within the context of a seismic economic shift; the global AI market is projected to surge from $233.46 Billion in 2024 to an astonishing $1.77 Trillion by 2032. This paper demystifies the core concepts of AI, presents a business case based on market data, details practical applications, and lays out a phased, actionable adoption strategy.
arXiv:2509.14532v1 Announce Type: cross
Abstract: Artificial Intelligence (AI) has transitioned from a futuristic concept reserved for large corporations to a present-day, accessible, and essential growth lever for Small and Medium-sized Enterprises (SMEs). For entrepreneurs and business leaders, strategic AI adoption is no longer an option but an imperative for competitiveness, operational efficiency, and long-term survival. This report provides a comprehensive framework for SME leaders to navigate this technological shift, offering the foundational knowledge, business case, practical applications, and strategic guidance necessary to harness the power of AI. The quantitative evidence supporting AI adoption is compelling; 91% of SMEs using AI report that it directly boosts their revenue. Beyond top-line growth, AI drives profound operational efficiencies, with studies showing it can reduce operational costs by up to 30% and save businesses more than 20 hours of valuable time each month. This transformation is occurring within the context of a seismic economic shift; the global AI market is projected to surge from $233.46 Billion in 2024 to an astonishing $1.77 Trillion by 2032. This paper demystifies the core concepts of AI, presents a business case based on market data, details practical applications, and lays out a phased, actionable adoption strategy. Read More
Trump jokes about AI while US and UK sign new tech dealAI Newson September 19, 2025 at 8:18 am US President Donald Trump said on Thursday that AI was “taking over the world,” and joked that he hoped tech executives understood it better than he did. The comment came as Trump and UK Prime Minister Keir Starmer hosted a gathering of business and technology leaders in London during the president’s second state visit to
The post Trump jokes about AI while US and UK sign new tech deal appeared first on AI News.
US President Donald Trump said on Thursday that AI was “taking over the world,” and joked that he hoped tech executives understood it better than he did. The comment came as Trump and UK Prime Minister Keir Starmer hosted a gathering of business and technology leaders in London during the president’s second state visit to
The post Trump jokes about AI while US and UK sign new tech deal appeared first on AI News. Read More
Can I Trust This Chatbot? Assessing User Privacy in AI-Healthcare Chatbot Applicationscs.AI updates on arXiv.orgon September 19, 2025 at 4:00 am arXiv:2509.14581v1 Announce Type: cross
Abstract: As Conversational Artificial Intelligence (AI) becomes more integrated into everyday life, AI-powered chatbot mobile applications are increasingly adopted across industries, particularly in the healthcare domain. These chatbots offer accessible and 24/7 support, yet their collection and processing of sensitive health data present critical privacy concerns. While prior research has examined chatbot security, privacy issues specific to AI healthcare chatbots have received limited attention. Our study evaluates the privacy practices of 12 widely downloaded AI healthcare chatbot apps available on the App Store and Google Play in the United States. We conducted a three-step assessment analyzing: (1) privacy settings during sign-up, (2) in-app privacy controls, and (3) the content of privacy policies. The analysis identified significant gaps in user data protection. Our findings reveal that half of the examined apps did not present a privacy policy during sign up, and only two provided an option to disable data sharing at that stage. The majority of apps’ privacy policies failed to address data protection measures. Moreover, users had minimal control over their personal data. The study provides key insights for information science researchers, developers, and policymakers to improve privacy protections in AI healthcare chatbot apps.
arXiv:2509.14581v1 Announce Type: cross
Abstract: As Conversational Artificial Intelligence (AI) becomes more integrated into everyday life, AI-powered chatbot mobile applications are increasingly adopted across industries, particularly in the healthcare domain. These chatbots offer accessible and 24/7 support, yet their collection and processing of sensitive health data present critical privacy concerns. While prior research has examined chatbot security, privacy issues specific to AI healthcare chatbots have received limited attention. Our study evaluates the privacy practices of 12 widely downloaded AI healthcare chatbot apps available on the App Store and Google Play in the United States. We conducted a three-step assessment analyzing: (1) privacy settings during sign-up, (2) in-app privacy controls, and (3) the content of privacy policies. The analysis identified significant gaps in user data protection. Our findings reveal that half of the examined apps did not present a privacy policy during sign up, and only two provided an option to disable data sharing at that stage. The majority of apps’ privacy policies failed to address data protection measures. Moreover, users had minimal control over their personal data. The study provides key insights for information science researchers, developers, and policymakers to improve privacy protections in AI healthcare chatbot apps. Read More
Catch Me If You Can? Not Yet: LLMs Still Struggle to Imitate the Implicit Writing Styles of Everyday Authorscs.AI updates on arXiv.orgon September 19, 2025 at 4:00 am arXiv:2509.14543v1 Announce Type: cross
Abstract: As large language models (LLMs) become increasingly integrated into personal writing tools, a critical question arises: can LLMs faithfully imitate an individual’s writing style from just a few examples? Personal style is often subtle and implicit, making it difficult to specify through prompts yet essential for user-aligned generation. This work presents a comprehensive evaluation of state-of-the-art LLMs’ ability to mimic personal writing styles via in-context learning from a small number of user-authored samples. We introduce an ensemble of complementary metrics-including authorship attribution, authorship verification, style matching, and AI detection-to robustly assess style imitation. Our evaluation spans over 40000 generations per model across domains such as news, email, forums, and blogs, covering writing samples from more than 400 real-world authors. Results show that while LLMs can approximate user styles in structured formats like news and email, they struggle with nuanced, informal writing in blogs and forums. Further analysis on various prompting strategies such as number of demonstrations reveal key limitations in effective personalization. Our findings highlight a fundamental gap in personalized LLM adaptation and the need for improved techniques to support implicit, style-consistent generation. To aid future research and for reproducibility, we open-source our data and code.
arXiv:2509.14543v1 Announce Type: cross
Abstract: As large language models (LLMs) become increasingly integrated into personal writing tools, a critical question arises: can LLMs faithfully imitate an individual’s writing style from just a few examples? Personal style is often subtle and implicit, making it difficult to specify through prompts yet essential for user-aligned generation. This work presents a comprehensive evaluation of state-of-the-art LLMs’ ability to mimic personal writing styles via in-context learning from a small number of user-authored samples. We introduce an ensemble of complementary metrics-including authorship attribution, authorship verification, style matching, and AI detection-to robustly assess style imitation. Our evaluation spans over 40000 generations per model across domains such as news, email, forums, and blogs, covering writing samples from more than 400 real-world authors. Results show that while LLMs can approximate user styles in structured formats like news and email, they struggle with nuanced, informal writing in blogs and forums. Further analysis on various prompting strategies such as number of demonstrations reveal key limitations in effective personalization. Our findings highlight a fundamental gap in personalized LLM adaptation and the need for improved techniques to support implicit, style-consistent generation. To aid future research and for reproducibility, we open-source our data and code. Read More