This article compiles ten high‑quality medical datasets—including imaging, clinical records, single‑cell transcriptomics, fMRI/MEG/EEG, health‑lifestyle surveys, and medical QA—each with online access, and explains how their multimodal nature drives current AI research in healthcare.
This survey reviews state‑of‑the‑art research up to Q1 2026 on integrating tactile sensing with vision and language for embodied AI, presenting a four‑stage fusion pipeline, a hierarchical taxonomy of datasets, methods, sensors, and highlighting current evaluation challenges and future directions.
This survey maps the 2021‑2025 progress of imitation learning for dexterous manipulation, detailing theoretical foundations, datasets, algorithms, hardware platforms, and evaluation protocols, and highlights challenges such as data quality, hardware dependence, and the need for standardized benchmarks to advance embodied AI.
This guide explains the end‑to‑end workflow for constructing a distributed scalar index in Lance by orchestrating validation, fragment sharding, worker‑level indexing via Ray, and final metadata merging, complete with code snippets and detailed step‑by‑step instructions.
This article introduces four high‑impact open‑source projects—a curated public dataset collection, the Kronos financial K‑line analysis model, WhisperLiveKit for real‑time speech transcription, and Youtu‑agent for building versatile AI agents—each with descriptions, key features, and GitHub links.
This article compiles seven prominent math reasoning datasets—including We‑Math2.0‑Standard, NuminaMath‑LEAN, T‑Wix, Nemotron‑Math‑HumanReasoning, Open‑Omega‑Atom‑1.5M, GSM8K, and VCBench—detailing their sizes, sources, associated papers, and unique features to support high‑quality AI research on mathematical problem solving.
This article presents a curated list of eight widely used remote sensing object detection datasets covering indoor scenes, landslides, drone imagery, crop diseases, safety vests, human fractures, urban issues, and plant diseases, each with size estimates and direct download links for researchers.
This systematic PRISMA review analyzes 128 highly‑cited RAG papers, covering five major databases, 343 datasets, a detailed technical roadmap, evaluation metrics from EM to LLM‑as‑Judge, and future research directions, showing that RAG has evolved into a complex, programmable, and auditable distributed system.
This article introduces four high‑impact open‑source AI projects—a curated high‑quality dataset collection, the Kronos financial K‑line model, WhisperLiveKit for real‑time speech transcription, and Youtu‑agent for building versatile AI agents—highlighting their features, usage, and GitHub links.
This article compiles a comprehensive list of high‑quality, publicly available datasets across domains such as general platforms, education, finance, health, text, and vision, providing URLs, key features, and practical usage tips to help researchers and practitioners quickly find the right data for their AI and data‑science projects.
This guide explains how to use the TianJi Large Model (TLM) platform to create evaluation tasks, choose effectiveness or performance modes, work with built‑in datasets, interpret detailed reports, and understand the underlying metrics and judge‑model techniques for large‑model assessment.
This article compiles a curated list of widely used multimodal datasets—including CLEVR, Visual Genome, Pangea, Touch‑Vision‑Language, WIT, and more—providing download URLs, key statistics, and brief descriptions to help researchers quickly locate the right data for vision‑language and multimodal model training.
This comprehensive survey reviews the rapid progress of multimodal understanding and text‑to‑image generation models, categorises existing unified architectures into diffusion‑based, autoregressive, and hybrid paradigms, analyses their tokenisation strategies, datasets and benchmarks, and highlights current challenges and future research directions.
Ant Group’s AIGC security detection system was evaluated by the China Information and Communication Research Institute, achieving the highest "Excellent" rating with a 0.99 F1 score across image, video, and audio modalities, while also releasing large‑scale detection datasets for the research community.
This article surveys the rapidly evolving field of Neural Code Intelligence, outlining its historical paradigms, key code models from RNNs to large language models, essential datasets and benchmarks, cross‑domain collaborations, practical applications, and promising research directions.
The CCIR 2023 conference in Beijing, sponsored by Kuaishou, hosted a short‑video recommendation algorithm frontier research forum where over 100 experts and students shared the latest AI‑driven recommendation technologies, open datasets, and interdisciplinary challenges in short‑video platforms.
This article reviews the fundamentals, feature extraction methods, evaluation metrics, and common datasets of content‑based image retrieval (CBIR), discussing traditional low‑level features, local descriptors, unsupervised and supervised learning approaches, and recent deep‑learning models for improving retrieval performance.
This article provides a comprehensive overview of Optical Character Recognition (OCR), covering its definition, historical development, classification, real‑world applications, technical pipeline, common challenges, mitigation strategies, popular datasets, model performance comparisons, and leading open‑source platforms.
This article reviews the evolution of face detection, covering the Widely‑Face dataset, major research directions such as feature fusion, label assignment, auxiliary supervision, anchor‑free methods, NAS‑based designs, summarizes key papers from S3FD to MogFace, introduces ModelScope implementations, and outlines future challenges and opportunities.
This article presents 23 curated machine learning project ideas—ranging from beginner-friendly tasks like Iris classification to advanced challenges such as sentiment analysis—complete with dataset links, brief project descriptions, and source code references to help learners build practical AI skills.
This article presents a comprehensive overview of several publicly available autonomous driving datasets, focusing on Series Six of the Integer Intelligence collection, which includes StreetLearn, UTBM RoboCar, Multi‑Vehicle Stereo Event Camera, comma2k19, the Annotated Laser Dataset, Ford, and Oxford RobotCar, detailing their sources, download links, publication years, key features, and research relevance.
This article introduces EasyCV, an open‑source all‑in‑one visual algorithm platform that abstracts diverse data sources, provides SOTA self‑supervised models, and offers ready‑to‑download datasets for image classification, object detection, segmentation, and pose estimation, complete with configuration examples.
This article surveys automatic text summarization, detailing system classifications, extractive, abstractive and hybrid techniques, notable recent research, multi‑document and cross‑lingual challenges, major datasets, evaluation metrics, and promising future research avenues in the field.
This article summarizes four CIKM 2022 papers that present a client‑side short‑video recommender, the fully‑observed KuaiRec dataset, the unbiased KuaiRand sequential recommendation dataset, and an industrial‑scale solution for billion‑user lifetime value prediction, highlighting their motivations, methods, and reported impacts.
This article teaches how to use Python's pandas library to create various exploratory visualizations—line, scatter, histogram, bar, pie, and box charts—on real datasets such as Iris, the 2013 American Community Survey, and the Boston Housing data, including code examples and interpretation of results.
The article surveys deep‑learning‑based face‑recognition systems, detailing detection, preprocessing, and recognition pipelines, describing evaluation metrics such as TAR, FAR, and Rank‑K, reviewing major datasets like LFW, MS‑Celeb‑1M and VGGFace2, and comparing leading architectures—including FaceNet, CenterLoss, SphereFace and InsightFace—while highlighting their strengths, limitations, real‑world applications, and seminal research references.
This article provides a comprehensive overview of Optical Character Recognition, covering its traditional applications, the evolution to deep learning methods, key datasets, popular tools, and practical strategies for tackling diverse OCR challenges in real-world scenarios.
This article provides a curated list of publicly available data query websites, simple universal datasets, large-scale collections, and specialized datasets for machine learning, image classification, text classification, and recommendation systems, offering valuable resources for AI research and data-driven projects.
NL2SQL converts natural language questions into executable SQL, bridging the gap between users and databases; the article reviews its value, historical roots, academic positioning, major datasets, current models, challenges, and future directions, highlighting its potential to reshape data interaction across industries.
iQIYI leverages multimodal AI—combining audio, visual, and textual cues—to advance video understanding, releasing the world’s largest celebrity dataset (iQIYI‑VID), powering applications such as actor‑focused playback, AI Radar, emoji generation, and rapid automated editing, while pursuing future research in emoji captioning, cross‑modal retrieval, visual question answering, and broader health‑care and education uses.
This article surveys the evolution of face recognition from early statistical methods to modern deep‑learning approaches, outlines key researchers, open‑source projects, popular APIs, core processing steps, the DeepFace architecture, datasets, and experimental results, providing a comprehensive guide for practitioners and researchers.
This article surveys the evolution of face recognition from traditional image‑based methods to real‑time video processing, highlights key researchers and open‑source projects, explains the four‑stage pipeline, details DeepFace's deep‑learning architecture, and provides practical installation and usage instructions for Python developers.
