The article presents Ola, an open‑source full‑modal LLM that uses progressive modality alignment to jointly process text, images, video, and audio, and demonstrates competitive performance across image, video, and audio benchmarks, surpassing many specialized models.
The paper introduces MILS, a training‑free multimodal iterative LLM solver that lets large language models perceive and generate across image, video, and audio domains, achieving new state‑of‑the‑art results without any task‑specific data or fine‑tuning.
The article analyzes the 2025 AI large‑model landscape, contrasting slowing foundational breakthroughs with fierce application competition, highlighting MiniMax’s low‑cost linear‑attention models, multimodal advances, and the strategic shift from price wars to sustainable, technology‑driven growth.
The article analyzes the US‑China large‑model race, detailing algorithmic and architectural strengths of OpenAI, Google and Microsoft versus Chinese innovations like Doubao 1.5, MiniMax‑01 and Vidu, and projects a timeline from 2025 to 2033 for China to close the gap.
This article presents a comprehensive overview of the RAG2.0 engine design, covering RAG1.0 limitations, effective chunking methods, accurate retrieval techniques, advanced multimodal processing, hybrid search strategies, database indexing choices, and future directions such as agentic RAG and memory‑enhanced models.
MiniMax, a Shanghai‑based AI unicorn, has open‑sourced its MiniMax‑01 series featuring large‑scale linear attention, secured $600 million in funding, launched multimodal products like Talkie and Hailuo AI, and is positioning itself as a competitive force amid rising geopolitical tensions in the global artificial‑intelligence market.
This issue reviews recent AI industry developments, including Lee Kai‑fu’s clarification on Zero‑One’s strategy, Microsoft’s open‑source Phi‑4 model, the multimodal VITA‑1.5 release, and HaiLuo AI’s advanced Chinese voice‑cloning technology, providing technical details and market implications.
In 2025, large language models will see three key trends—agents becoming pervasive in daily life and industry, the emergence of efficient small models for edge and specialized tasks, and the integration of multimodal capabilities that combine text, images, and audio to enable more natural human‑machine interaction.
This article demonstrates how to create an AI‑driven personal expense‑tracking assistant by leveraging Zhipu's GLM‑4V‑Flash multimodal model for receipt OCR, generating SQL statements, and integrating them with MaxKB workflows and a MySQL database, complete with code snippets and deployment steps.
This article examines the unique multimodal and multi‑granular nature of web pages, compares fusion strategies, proposes a cross‑modal attention approach, outlines fine‑ and coarse‑grained pre‑training tasks, and explores low‑cost adaptor methods for adapting large multimodal models to web‑page modeling in the LLM era.
This article explains what AIGC agents are, outlines their four main characteristics, describes the underlying transformer‑based architecture, dual‑stage learning, probabilistic generation and feedback optimization, and explores their current and future commercial use cases across content creation, knowledge bases, customer service, internal operations, and product design.
The article analyses the rapid growth of Retrieval‑Augmented Generation (RAG) in enterprises, explains why self‑built RAG solutions often collapse under cost and maintenance pressures, and demonstrates how Tencent LeXiang AI Assistant addresses these issues through a robust knowledge‑management core, extensive industry experience, scalable resources, and advanced multimodal capabilities.
Physical Intelligence's new Pi-zero model, built on a vision‑language foundation and fine‑tuned with extensive robot data, outperforms prior baselines across multiple tasks, showcasing the promise of large multimodal foundation models for flexible, robust robot control.
EasyAnimate V5, an Alibaba Cloud AI video generation framework, expands model size to 7B/12B, introduces multimodal control, token‑length based training, and inpaint‑based image‑to‑video strategies, while providing easy deployment via PAI, DSW, and local ComfyUI integration.
This article surveys current unified generation-and-understanding multimodal large-model architectures, compares LLM-centric and LLM-plus-diffusion designs, extracts common insights, details large-scale training tricks from models like Emu3, Chameleon and Janus, and outlines open research directions for visual encoders.
The paper introduces a Multimodal Reliable Feedback Network (RFNet) and a consistency‑condition regularization technique that together boost the usable rate of automatically generated advertisement images while preserving visual quality, supported by a new million‑image annotated dataset and extensive ECCV‑2024 experiments.
Bilibili’s AI‑driven game‑recognition system extracts real‑time LoL events through OCR, hero detection and hot‑spot tagging, generating high‑energy timestamps and interactive overlays that let viewers jump to key moments and view detailed statistics, enhancing spectator engagement and analytical capabilities across major esports tournaments.
Alibaba Cloud’s AI platform PAI recently saw two papers accepted at EMNLP2024—VideoCLIP‑XL, which enhances video‑text representation for long descriptions using a large video‑long‑description dataset and novel pre‑training tasks, and TAPIR, a curriculum‑planning framework that distills instruction‑following abilities of large language models—while also releasing associated models, datasets, and integration tools for users.
The article outlines how to obtain the DataFun Summit e‑book by following the public account instructions and provides concise English summaries of twelve technical sessions covering data lineage, integration, AI language models, multimodal content, game AI agents, lake‑warehouse governance, big‑data architecture, and cluster management.
The CNCC 2024 conference featured a series of expert talks on AI large‑model research, covering paradigm shifts in scientific discovery, knowledge enhancement and governance, data‑infrastructure analytics, vertical‑domain inference, diffusion‑model advances, multimodal model progress, and medical AI applications, illustrating the breadth and impact of large‑model technologies across multiple domains.
The article examines how large language models are reshaping JD Retail's e‑commerce search and recommendation pipelines, detailing industry evolution, technical challenges such as knowledge hallucination, intent understanding, personalization, cost, and safety, and presenting JD's end‑to‑end AIGC architecture, data preprocessing, alignment, evaluation, and next‑generation AI search solutions.
The article reviews the evolution of artificial intelligence from early statistical models through deep learning and Transformer architectures, examines current breakthroughs like multimodal models, and discusses the technical, computational, and safety challenges that must be overcome before achieving artificial superintelligence (ASI).
The talk reviews the evolution of data technologies from early database storage to today’s generative AI-driven era, highlighting how massive data, multimodal processing, and advanced analytics are transforming data systems from cost‑centered infrastructures to value‑focused ecosystems that empower intelligent agents, open data ecosystems, and new application paradigms.
The JD Advertising R&D team applies cutting‑edge AI techniques—including query intent models, multimodal representation pipelines, reinforcement‑learning‑based auction mechanisms, generative recommendation with quantized product tokens, and large‑model infrastructure—to boost traffic valuation, ad relevance, revenue, and creative generation across the platform.
JD's advertising research team combines deep learning, multimodal modeling, reinforcement‑learning auctions, and generative recommendation to boost ad relevance, improve long‑tail product exposure, and overcome large‑model inference challenges in a high‑traffic e‑commerce environment.
The article reviews the rapid advances in generative AI and big‑data technologies, traces the historical development of data infrastructure, and argues that modern data systems are shifting from a cost‑efficiency focus to a value‑centric paradigm driven by multimodal, non‑structured data, vector search and machine‑oriented services.
This article presents JD.com’s multimodal recommendation system architecture, covering content understanding, multimodal ranking and recall models, practical deployment pipelines, and future research directions such as large‑model integration and supply‑side generation, all illustrated with detailed diagrams and Q&A.
This article presents eight practical optimization recommendations for the indexing stage of enterprise‑level Retrieval‑Augmented Generation (RAG) applications, covering chunk creation, abbreviation handling, multimodal document processing, semantic enrichment, metadata usage, alternative index types, and embedding model selection.
This article details Tencent Game's end‑to‑end approach to building intelligent NPCs, covering the opportunities brought by AI, the practical implementation of multimodal LLM‑driven dialogue, knowledge‑augmented retrieval, long‑context handling, safety measures, multimodal expression (voice and facial animation), and system‑level performance optimizations for real‑time deployment.
This article presents a comprehensive technical overview of how large AI models are reshaping financial data production, governance, multimodal document understanding, lakehouse storage, private‑domain model deployment, data‑centric engineering methods, and multi‑agent intelligent advisory within the finance sector.
This article analyzes how Xiaohongshu addresses the decentralization challenge in its recommendation system by strengthening side‑information usage, integrating multimodal signals across the full pipeline, and implementing interest exploration and protection mechanisms, while also outlining future research directions such as generative recommendation and large‑model‑driven user profiling.
From August 11‑16, 2024 the ACL conference in Bangkok featured 14 Ant Group papers covering large‑scale information extraction, decomposed LLMs for semantic search, multimodal hallucination detection, long‑context attention mechanisms, concept‑reasoning datasets, knowledge‑graph alignment, and more, highlighting the group's breadth in natural language processing and AI research.
This article reviews why large language models can be applied to graph learning, outlines their capabilities and graph data characteristics, surveys current research across different graph types and LLM roles, and proposes future research directions for unified cross‑domain graph learning.
Exploring Large Language Models, this article surveys their core concepts, evolution through Transformers, GPT and BERT, generation challenges, diverse applications such as QA, multimodal creation, summarization and retrieval‑augmented generation, prompt‑engineering frameworks and tools, LangChain‑based pipelines, AI‑driven agents, and future prospects toward domain‑specific use, multimodality, and AGI.
The article analyzes the rapid evolution of AI large models—from their role in advancing AGI through massive pre‑training and fine‑tuning, to current market dynamics led by GPT and domestic Chinese players, and finally to future multimodal applications, content‑factory capabilities, and emerging AIGC revenue models projected to reach trillion‑yuan scales by 2030.
The article analyzes current challenges in deploying large AI models, covering robot automation, scaling‑law limits, vertical‑domain use cases, multimodal breakthroughs, algorithmic evolution, and the hardware‑software trade‑offs of training and inference infrastructures, while questioning ROI and practical feasibility.
Vitron is a unified pixel‑level visual multimodal large language model that integrates image, video, and region encoders with a text‑centric strategy, delivering precise pixel‑wise perception and a comprehensive suite of vision tasks from understanding to generation and editing.
This article explains how to handle complex multimodal PDFs in RAG systems, outlines extraction, indexing, and multimodal model integration, details four query‑rewriting strategies (HyDE, stepwise, sub‑question, backward), and presents key evaluation metrics and tools for assessing RAG performance.
The 360LayoutAnalysis project from 360 AI Lab releases lightweight, yolov8‑based layout analysis models covering Chinese and English papers, Chinese research reports, and a general document scenario, providing fast inference, paragraph‑level detection, and open‑source code and weights for flexible document‑understanding pipelines.
This article provides a comprehensive introduction to large language models, covering their historical development, core architecture, training process, prompt engineering techniques, Retrieval‑Augmented Generation, agent frameworks, multimodal capabilities, safety challenges, and future research directions.
The IEEE CVPR2024 conference in Seattle accepted 2,719 papers out of 11,532 submissions, and Ant Group contributed 24 papers covering computer vision, deep learning, digital humans, large models, multimodal remote sensing, vision‑language distillation, federated incremental learning, model‑stealing defense, and more, with one highlighted as a highlight.
This article presents Shopee's comprehensive approach to constructing an e‑commerce knowledge graph, detailing the challenges of heterogeneous data, multi‑language handling, entity disambiguation, and the integration of deep learning and large language models to improve product matching, recommendation, and operational efficiency.
This article presents Shopee's comprehensive exploration of building an e‑commerce knowledge graph, detailing its challenges, construction pipeline, AI‑driven extraction and fusion techniques, multilingual and multimodal modeling, and practical applications ranging from search and recommendation to AI assistants and real‑time updates.
This article documents the end‑to‑end workflow of using large multimodal models and specialized agents to automatically generate a storyboard, compose original music and lyrics, produce keyframes, and assemble a complete music video, while highlighting the remaining manual steps and future automation possibilities.
This article reviews the open‑source 9‑billion‑parameter GLM‑4‑9B model, covering installation, quick‑start inference code, quirky Chinese riddles that highlight its strengths over GPT‑4, extensive benchmark tables for dialogue, multilingual, tool‑calling and multimodal tasks, and its broader impact on the Chinese AI ecosystem.
This article details eBay's practical experience integrating multimodal data and graph neural networks into its recommendation pipeline, covering pain‑point analysis, a twin‑tower multimodal embedding model with triplet loss and TransH, engineering design, experimental results, and key takeaways for future AI‑driven product development.
The Mixed‑Modal Expert Model combines ID features with image and text embeddings through optimized representations and conditional output fusion, dramatically improving advertising recall—especially for long‑tail items—and delivering measurable gains in click‑recall, revenue, CTR, and page views in large‑scale online tests.
This article reviews recent advances in generative recommendation, highlighting challenges such as item representation and multimodal fusion, and summarizing four key research papers that propose novel tokenization, collaborative integration, and transformer-based multimodal approaches to improve recommendation performance.
This article presents OPPO's practical research on deploying multi‑modal pre‑training models across mobile devices and cloud, covering edge image‑text retrieval, text‑image generation and understanding optimizations, and lightweight diffusion model techniques, with detailed algorithmic improvements, performance results, and real‑world application cases.
This article examines OpenAI's GPT‑4o, outlining its key performance, speed, accuracy, latency, multimodal, and resource‑efficiency improvements over GPT‑4, and explains why these enhancements broaden the model's applicability across various AI‑driven applications.
The article introduces 360VL, an open‑source multimodal large language model built on Llama‑3‑70B, describes its novel C‑abs bridge architecture for high‑resolution visual understanding, outlines the two‑stage training with bilingual data, and presents benchmark results showing superior performance over prior LMMs.
The article provides a hands‑on review of OpenAI's newly released GPT‑4o model, covering its multimodal capabilities, real‑time voice demo, desktop client rollout, access options for paid and free users, practical usage tips, and early observations on API performance and limitations.
This article presents Baidu's exploration of multimodal content understanding for commercial advertising, detailing the ViCAN pre‑training model, its contrastive and mask‑language learning tasks, integration across recall, ranking and risk‑control pipelines, quantization with MMDict, and future AIGC‑driven generation, all backed by extensive experiments and Q&A.
Meta’s newly open‑source Llama 3 model pushes the frontier of large language models with a larger context window, Mixture‑of‑Experts architecture, multilingual support, and multimodal capabilities, while facing challenges in transparency, bias, and computational resources, and offering diverse applications from NLU to code generation.
Sora, the newly announced text‑to‑video large model, can generate one‑minute high‑fidelity videos from textual prompts or static images, handling complex scenes, expressive characters, and sophisticated camera motions while also supporting video extension and frame‑filling, positioning it at the forefront of multimodal AI research.
This article reviews Tongyi Lab's work on the OFA framework for generative multimodal pretraining and the ONE-PEACE model for unified multimodal representation learning, detailing their architectures, training strategies, experimental results across vision‑language and audio tasks, and future research directions.
The paper presents MuLTI, a multimodal video‑language model that tackles the memory and efficiency challenges of long video‑text sequences by introducing a Text‑Guided MultiWay Sampler and a Multiple Choice Modeling pre‑training task, achieving state‑of‑the‑art results on video QA and retrieval while drastically reducing GPU memory consumption.
The article details how JD.com leverages domain‑specific and generic knowledge graphs to enhance multimodal product information, improve controlled text generation, and boost LLM performance for e‑commerce copywriting, covering model architecture, copy‑only mechanisms, token‑type encoding, experimental results, and practical deployment scenarios.
OpenAI’s Sora model demonstrates large‑scale text‑conditional video generation using a diffusion transformer that operates on spatiotemporal patches, supporting variable durations, resolutions, and aspect ratios while showcasing emergent simulation abilities, flexible sampling, and multimodal editing capabilities, though it still has notable limitations.
Google’s Gemini 1.5, a new multimodal Mixture‑of‑Experts model, supports up to a million‑token context (10 million internally), can understand text, video, audio and code, learns a new language from a single prompt, and is already being used by Samsung, Jasper and Quora, positioning it as a direct challenger to OpenAI’s flagship models.
In this talk, the speaker examines the dual goals of AI agents—being entertaining and useful—while introducing the concepts of fast and slow thinking, multimodal perception, long‑term memory, retrieval‑augmented generation, and tool integration as essential steps toward building truly valuable digital companions.
This article presents Baidu's commercial multimodal understanding and AIGC innovations, detailing rich‑media multimodal perception, a unified large‑scale representation framework, scenario‑specific fine‑tuning, and practical applications such as marketing copy, digital‑human video, and poster generation.
The article examines the security challenges introduced by large‑model AIGC, outlines three technical upgrade paths—richer training data, few‑shot model fine‑tuning, and multimodal fusion—and demonstrates practical implementations that dramatically improve detection efficiency, accuracy, and scalability.
This article introduces a machine‑learning approach for correlating text and image data, covering preprocessing, feature extraction, model training, experimental results, and future directions, and provides complete Python code examples using NLP and deep‑learning libraries.
Google announced Gemini, a suite of multimodal large language models—including Ultra, Pro, and Nano—that achieve state‑of‑the‑art results on dozens of benchmarks, support native multimodal pre‑training, and are being integrated across Google products such as Bard, Search, and upcoming Pixel devices.
This article presents NetEase Cloud Music's multimodal cold‑start solution, detailing the problem background, feature selection using CLIP, two modeling approaches (I2I2U indirect and U2I DSSM direct), contrastive learning enhancements, interest‑boundary modeling, and evaluation results showing significant gains in user engagement.
This article summarizes a collection of newly released open‑source AI projects covering natural‑language processing, multimodal processing, intelligent agents, recommendation systems, and model training acceleration, providing brief descriptions, key capabilities, and links to their repositories.
OpenAI revealed a new data partnership initiative to collect large‑scale public and private datasets across multiple modalities, aiming to improve AI model safety and usefulness by incorporating diverse, hard‑to‑access human‑generated content while respecting privacy and intent.
This article reviews recent research on augmenting language and multimodal models with external knowledge sources and tool‑calling mechanisms, covering three systems—OREO‑LM for knowledge‑graph reasoning, REVEAL for multi‑source visual‑language pretraining, and AVIS for dynamic tool selection—and their experimental results and implications.
The article recounts a personal test of a multimodal AI agent in Newport Beach and expands into a detailed analysis of current multimodal LLM architectures, memory mechanisms, task planning, tool usage, personality modeling, cost constraints, evaluation challenges, and the broader social and reliability implications of deploying such agents.
This article reviews recent research on augmenting language and vision models by incorporating external knowledge sources such as knowledge graphs, multi‑source retrieval, and dynamic tool‑calling frameworks, presenting three systems—OREO‑LM, REVEAL, and AVIS—and their experimental results.
This article presents MiniGPT-4, a multimodal system that combines a frozen visual encoder (Q‑Former + ViT) with an open‑source large language model (Vicuna), describes its motivation, training pipeline, demo capabilities, observed limitations, and includes a brief Q&A session.
This article reviews the technical background of simultaneous speech translation, compares offline and real‑time scenarios, details ASR and MT technologies, describes the system architecture and design strategies, and discusses the major challenges and solutions for deploying robust, low‑latency translation services.
The article surveys how large language models can be integrated into recommendation systems, either as feature extractors or as end‑to‑end recommenders, showing that LLM‑derived semantics improve recall, ranking, diversity, and user experience, and outlining future multimodal, efficiency, and re‑ranking directions.
This article presents Baidu's commercial multimodal understanding framework and AIGC innovations, detailing rich-media multimodal perception, the VICAN‑12B multimodal representation‑generation model, scenario‑specific fine‑tuning, feature quantization for ranking, and practical applications such as marketing content generation, digital‑human video creation, and poster synthesis.
Recent advances in visual language models enable zero-shot multimodal tasks, and this article explores their application to open-set object detection, prompt learning, and promptable surgical instrument segmentation, highlighting methods like CLIP, CoOp, and the DetPro framework with experimental results across multiple benchmarks.
The CSIG‑hosted "Enterprise Visit – Into Qihoo 360" event on June 29, 2023 gathered over a thousand participants to explore multimodal and cross‑modal learning in the large‑model era, featuring keynote speeches from leading university researchers and Qihoo 360 AI experts, a tour of the company's facilities, and discussions on future AI research directions.
Prompt engineering teaches how to craft effective instructions, context, input data, and output formats for large language models, using clear commands, iterative refinement, and advanced methods such as zero‑shot, few‑shot, chain‑of‑thought, Tree of Thoughts, retrieval‑augmented and progressive‑hint prompting to achieve precise, reliable results across diverse tasks.
Amid tightening financial regulations, ICBC's software team proposes a multimodal AI anti‑fraud framework that combines image, video, and structured data to detect deep‑fake, mask, and forged‑document attacks, enriches verification with cross‑modal cues, and outlines future expansion to text and speech modalities.
DataFun will host the DataFun Summit 2023 on June 17‑18, featuring three chairs and eight presenters who will discuss core topics such as large language model research, multimodal generation, reinforcement learning, tool learning, distributed training, and industry applications, with free registration via QR code.
This article provides a detailed, step‑by‑step tutorial on OpenAI’s language models, API endpoints, prompt engineering, embeddings, moderation, fine‑tuning, LangChain workflows, memory management, and multimodal capabilities such as audio transcription and image generation, complete with code examples and practical usage tips.
This article provides a detailed technical review of the evolution of GPT models, the Transformer architecture, large language model training methods, emergent abilities such as in‑context learning and chain‑of‑thought, multimodal extensions, and the challenges of data, scaling, and alignment, offering a holistic view for researchers and practitioners.
This article shares the development of a global search platform that leverages AI technologies such as Chinese word segmentation, part‑of‑speech tagging, text similarity via Simhash and Synonyms, image similarity using histogram, Hamming distance and ResNet‑50, and multimodal CLIP‑based models to improve search efficiency and accuracy.
Microsoft has fully opened Bing Chat to all users, introducing multimodal responses, a multilingual Image Creator, persistent chat history, and upcoming plugin support, while sharing usage statistics and outlining weekly update plans that position Bing as an AI‑driven search copilot competing with ChatGPT.
This presentation introduces the Mengzi lightweight model technology stack, covering large‑scale pre‑training, motivations for lightweight models, detailed techniques such as knowledge and sequence‑relation enhancement, training optimization, model compression, retrieval‑augmented pre‑training, multimodal extensions, open‑source releases, and real‑world applications.
This article reviews Microsoft’s extensive 155‑page work on early experiments with GPT‑4, exploring how the model approaches artificial general intelligence, its testing methodology, multimodal capabilities, programming and mathematical performance, interaction with tools and humans, limitations, societal impact, and future research directions.
In a GTC fireside chat, Nvidia CEO Jensen Huang and OpenAI co‑founder Ilya Sutskever discuss GPT‑4's multimodal advances, the evolution of deep learning from early neural networks to large‑scale models, the pivotal role of GPUs and datasets like ImageNet, and their vision for more reliable, scalable artificial intelligence.
The article reviews Baidu’s newly launched Ernie Bot, a multimodal large language model, comparing its literary, business, mathematical, Chinese comprehension, and multimodal abilities with GPT‑4, while detailing the underlying technologies, knowledge‑enhancement techniques, and deployment strategy behind the model.
Baidu unveiled its new generative AI chatbot ERNIE Bot, showcasing five practical scenarios, multimodal generation, a detailed technical stack based on the ERNIE and PLATO models, and a comparison with ChatGPT and Bing Chat, while also announcing its invitation‑only testing program and API access for enterprises.
The article reviews Baidu's launch of the multimodal large language model Wenxin Yiyan, compares its literary, business, mathematical, Chinese‑understanding and multimodal abilities with GPT‑4, explains the underlying six‑core technologies and hardware stack, and reports the mixed market and netizen response.
OpenAI's GPT‑4, a large multimodal language model, expands token limits, adds image understanding, demonstrates strong reasoning on professional exams, supports many languages, and is already integrated into Microsoft Bing, while offering various access options and improved safety compared to its predecessor.
The paper presents CapOnImage, a novel image‑on‑image captioning task that generates location‑specific decorative text for product images, introduces the 2.1‑million‑image CapOnImage2M dataset, and proposes a mixed‑modality transformer with position‑aware pre‑training and progressive training, achieving superior accuracy and diversity and already deployed in Alibaba’s advertising platforms for measurable business impact.
The paper details Cloud Music’s video‑search pipeline—query understanding, recall, relevance, ranking and re‑ranking—highlighting challenges such as ambiguous content, timeliness and multi‑objective goals, and describes two deployed models (a twin‑tower aspect relevance network and a click‑graph propagator) that together boost click‑through rate by 1.5 % and effective CTR by 2.3 %.
This article presents a comprehensive overview of multimodal pre‑training, describing its motivation, architecture choices, large‑scale Chinese image‑text dataset construction, training optimizations, performance benchmarks, downstream applications, and a Q&A session that highlights practical deployment considerations.
This article explains how neural search enables multimodal data handling, introduces the DocArray data structures (Document and DocumentArray), and demonstrates how Jina’s cloud‑native framework can be used to build, deploy, and scale end‑to‑end multimodal services such as DocsQA.
This paper presents an end‑to‑end approach for extracting relational triples directly from speech signals, bypassing intermediate transcription, and demonstrates its effectiveness on synthesized speech versions of the CoNLL04 and TACRED datasets, highlighting challenges such as length constraints and cross‑modal alignment.
The Intelligent Creative System defines advertising creatives across formats, evaluates image and text quality using reference‑based metrics and models like DeepBIQ, generates multimodal ads via GANs and Transformers, and selects optimal variants through bandit‑based CTR prediction and multimodal fusion, enabling scalable, data‑driven creative production.
This article presents an in‑depth overview of intelligent question answering in QQ Browser search, covering its background, the core KBQA and DeepQA technologies, system architecture, challenges, recent advances such as end‑to‑end, knowledge‑guided and multimodal QA, and practical Q&A for deployment.
The paper describes NetEase Cloud Music’s LOOK live‑streaming recommendation system for the song‑playback page, which combines millisecond‑level real‑time feature pipelines, multi‑target optimization (click, watch, gift, comment) via ESMM+FM and MMoE models, GradNorm‑based loss fusion, and a multimodal avatar‑text‑host ranking model, achieving double‑digit CTR and CTCVR gains while balancing producer and consumer retention.
This article introduces the concept of modality, outlines the five research branches of multimodal models, and presents a concrete case where multimodal deep‑learning techniques are applied to predict and improve game frame rates using both static and temporal features.
