ChatGPT brings the first hot outlet in 2023. ChatGPT is a natural language processing tool driven by artificial intelligence technology, with language understanding and text generation capabilities. Whether it is powerful content generation capabilities such as video scripts, copywriting, emails, translations, codes, etc., or dialogue capabilities such as semantic reasoning and sentiment analysis, they all catch the public's eyes and bring unlimited imagination to the application of AIGC technology represented by ChatGPT .
ChatGPT4.0 has added the function of image input and output image, music, and video answer in addition to the original text-level interaction, thus opening a new era of human-computer interaction. The "multimodal AI generalization technology" involved behind this has become a research hotspot in the artificial intelligence industry in recent years.
What is Multimodal AI Generalization ?
The generalization of multimodal AI refers to the unification of multiple perception modalities (such as sound, image, text, etc.) and their respective languages and formats, so as to realize cross-modal information expression and interaction. Through generalized multi-modal AI technology, people can use more natural and intuitive multiple input methods to interact with machines, make full use of limited perception resources and information flow, and improve interaction efficiency and intelligent driving force.
The core algorithms required for generalized multimodal AI technology include multimodal semantic understanding, cross-modal reasoning, multimodal generation, etc., which require deep learning and knowledge graph modeling for the language and structural characteristics of different modalities. Construction and labeling of cross-modal datasets is required. At present, many AI companies and research institutions are conducting research and practice related to the generalization of multi-modal AI, and have achieved initial success in many fields.
The importance of data annotation for the generalization of multimodal AI
The importance of data annotation to the generalization of multimodal AI cannot be ignored. In a multimodal scenario, data comes from different modalities, such as image, voice, text, etc. To generalize multimodal AI, this data needs to be labeled so that machine learning models can understand and process it. Data annotation can provide meaningful training data for machine learning models, thereby improving the accuracy and performance of the models.
At the same time, data annotation helps to solve the problem of data scarcity. In multimodal scenarios, data sources are distributed in different modalities, so the amount of data is often limited. Through data annotation, a high-performance multimodal AI model can be trained with limited data sets.
Data annotation can also promote the intersection between different fields, thereby promoting the development of multimodal AI. By labeling data from different fields, cross-applications in multiple fields can be promoted, and the development of multi-modal AI technology can be further promoted.
JLW Technology provides multi-modal finished product data sets
JLW Technology provides multi-modal finished data sets, including image, video, audio, text and other types of data, and provides rich scenarios and application scenarios. Segment and filter specific video content. The data set contains emotional labels such as calm, joy, surprise, sadness, anger, fear, etc., including dialogue text content, character gender, character ID information, character age information, dialogue scenes (office, Residential, Hospital, Restaurant, Telephone Conversation, Outdoor, Other) and other information.
High-quality multi-modal finished product data sets can better optimize the model, making the model understand and process tasks more comprehensively and accurately. It can better cope with complex application scenarios and diverse needs, thereby promoting technological progress in the fields of deep learning, computer vision, and natural language processing.
Jinglianwen Technology has a rich data resource collection network, which supports face collection, gesture collection, gait collection, palm print collection, emotional expression collection, 3D face collection, object detection object collection, handwriting collection, speech recognition ASR collection, Speech synthesis TTS collection, wake-up word collection, multi-person dialogue collection, Mandarin collection, dialect collection, English collection, minor language collection, voice VAD collection, knowledge base, chat conversation collection, etc. It has successively established Hangzhou data headquarters, data processing branches in different provinces and cities such as Wuhan, Jinhua, Hengyang, etc., self-developed data labeling platform and full-category labeling tools, self-built data labeling platform, and supports computer vision (drawing frame labeling, semantic segmentation, 3D point Cloud labeling, key point labeling, line labeling, 2D/3D fusion labeling, target tracking, image classification, etc.), voice engineering (voice cutting, ASR voice transcription, voice emotion judgment, voiceprint recognition labeling, etc.), natural language processing ( OCR transcription, text information extraction, NLU sentence generalization) multi-type data annotation. It can meet all kinds of data labeling needs of partners in an all-round way, and the labeling accuracy reaches 99%. It supports AI algorithm preprocessing, supports localized deployment and SAAS services, and can provide enterprises with an integrated data collection and labeling solution.
The products provided by Jinglianwen Technology are full-chain AI data services, from data collection, cleaning, labeling, to the whole process of on-site, one-stop AI data services for vertical field data solutions, which meet the needs of various application scenarios. To meet the needs of data collection and labeling business, assist artificial intelligence companies to solve the corresponding problems in the data collection and labeling link in the entire artificial intelligence chain, promote the application of artificial intelligence in more scenarios, and build a complete AI data ecology.
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