(Featured Report**: Phantom Film and Television Industry).
sora: The epoch-making Wensheng ** large model
Text Generation**:Sora is capable of generating up to 60 seconds of ** based on user-provided text descriptions.
Deepen language comprehension:GPT technology is used to convert short user prompts into longer detailed translations and send them to the model.
**Spawn Ability:Sora can generate variable-sized images up to an astonishing 2048 2048 resolution, depending on the user's needs.
NewSimulation capabilities:SORA has 3D consistency, long-term coherence and object continuity, and can interact with the world, simulating the digital world.
Multimodal downstream applications are in full bloom
Multimodal+**Creation: Improve the efficiency of creators
Lumière: One of the core features of Lumière is its ability to support text-to-and image-to-conversion capabilities. This is made possible by the Spatiotemporal U-Net (Stunet) architecture, which is designed with a focus on increasing the realism of motion in AI-generated**. Lumière is able to generate complete sequences in a single pass, rather than simply combining static frames. This technology can simultaneously process the spatial (i.e., objects in the object) and temporal (i.e., motion in the object) aspect of the user, bringing users a more natural and fluid motion perception experience.
Bilibili: In the field of generative AI, large language models have shown great potential, whether it is writing articles, writing**, or open-ended Q&A. Based on the strong comprehension ability of the large language model, by processing the ** subtitle into formatted text, input it to the model, so that it can combine the context and select the most exciting parts. Through prompt engineering, large language models also have high accuracy in the selection of high-energy points. Bilibili is also actively exploring the application scenarios of related technologies in other business forms, such as: chapter splitting and live streaming outlines to improve the efficiency of creators.
Multimodal+ Autonomous driving: Revolutionizing human-vehicle interaction
LIMSIM++: A closed-loop platform for deploying multimodal LLMS in autonomous driving. LIMSIM++ provides a closed-loop system that includes road topology, dynamic traffic flow, navigation, traffic control, and other essential information. Prompts are the basis of the (M)LLM-supported Physiognomy System, which contains real-time scene information presented through image or text descriptions. The LLM-enabled intellectual system has functions such as information processing, tool use, policy development, and self-assessment.
V2VFormer++: The first multimodal V2V framework. For each vehicle, a dual-stream network with a modal-specific backbone is used for camera-LiDAR feature extraction in the BEV plane (camera-view transformation using the sparse cross-attention SCA module), and dynamic channel fusion (DCF) is designed to achieve fine-grained pixel aggregation. Given a multimodal BEV map, data compression and sharing are performed to generate a set of feature maps FC** at self-vehicle-coordinates. Subsequently, a global-local Transformer co-strategy is proposed for channel semantic exploration and spatial correlation modeling between adjacent C**s. Finally, the multi-vehicle fusion diagram fjoint was input into the ** header for target classification and positioning regression.
Multimodal+ Autonomous driving: Revolutionizing human-vehicle interaction
SenseTime proposes the DriveMLM model, which aligns with the decision state in the existing autonomous driving system behavior planning module, and enables the vehicle to be operated in closed-loop testing, surpassing the previous end-to-end and rule-based approach to autonomous driving systems.
First of all, it aligns the language decision output of the LLM with the decision state of the regulatory part in the mature modular scheme, so that the language signal output of the LLM can be converted into a vehicle control signal.
Secondly, the MLLM Planner module of DriveMLM consists of two parts: a multi-modal tokenizer and a MLLM decoder.
The former is responsible for converting various inputs such as cameras, lidars, user language requirements, and traffic rules into unified token embedding; The latter, i.e., the MLLM decoder, is based on the token generated here, and then generates the ** description, driving decision, and decision interpretation and so on.
On the TOWN05Long benchmark, which is widely used in CARLA, its driving score and route completion are significantly higher than those of non-large model methods such as Apollo.
Multimodal+ Advertising (e-commerce): Create a variety of marketing selling points
Use AI technology to further enhance the diversity of digital humans, such as face replacement, background replacement, accent voice replacement to adapt to our prompt, and finally script, digital human lip replacement, background replacement, face replacement, **After suppression, you can get an oral broadcast**. Customers can use digital humans to introduce some of the marketing selling points corresponding to the product. In this way, you can do a good job of a digital human in 3 minutes, which greatly improves the ability of advertisers to be digital humans.
Large models can also help businesses generate marketing posters and replace product backgrounds. After being trained on big data, the customer also wants something particularly personalized, and some fine-tuning methods need to be added in the future.
Multimodal+ Education: Improve teaching efficiency and strengthen human-computer interaction
Teaching resources are automatically generated:In terms of automatic generation of teaching resources, the current multimodal large model in the general field has shown certain capabilities. Image generation models such as stable diffusion can input the text description of the subject and its details according to the teaching needs, and quickly and automatically generate a variety of styles, high-definition realistic, and aesthetic aesthetic teaching resources, and the generated teaching resources not only have significant cross-modality, but also have novelty and uniqueness.
Human-robot collaborative process support:At present, multimodal large models in the general domain have also shown good potential. In terms of knowledge question answering, the proposed ERNIE large model can enhance the knowledge of domain entities and professional terms, and use the question and answer matching task for model training, so as to deeply understand domain knowledge and its internal connections.
Teacher's Teaching Intelligence Assist:In terms of using large models to carry out intelligent teaching assistance for teachers, the current industry and academia have also begun to actively explore. Based on about 20 million pieces of educational text data generated by teachers' online teaching voice transcription, Good Future has built a first-class teaching model tal-edubert.
Multimodal+ Medical: Smarter and more efficient solutions for clinical medical tasks
The RADFM has tremendous clinical implications:
Support for 3D data: CT and MRI are widely used in real-world clinical settings, and the diagnosis of most diseases relies heavily on them. RADFM's models are designed to handle real-world clinical imaging data.
Multi-image input: Diagnosis often requires the input of multiple images from various modalities, and sometimes even historical radiological images, so supporting multi-image input RADFM can well meet such clinical needs.
Interleaved data format: In clinical practice, image analysis often requires understanding the patient's medical history or context. The interleaved data format allows users to freely input additional image background information, ensuring that the model can be combined with multi-source information to complete complex clinical decision-making tasks.
Multimodal+ Security: AI + Security accelerates evolution
Algorithm accuracy and effect improvement: For example, in the ** monitoring scene, these technologies can achieve functions such as target behavior recognition and anomaly detection through the analysis of images and sounds.
Multimodal algorithm fusion application: In the field of security, multimodal technology can fuse data such as images, voice and text, so as to achieve more comprehensive and accurate intelligence analysis and early warning.
The inclination of AI algorithms from edge intelligence to central intelligence: security AI algorithms were mainly processed by central intelligence algorithms at the beginning, and later began to rise edge intelligent devices to integrate algorithms into terminals; With the promotion of large models, the necessity of central intelligence will increase, and the intelligent algorithm center of AI will play a new core role.
Algorithmic adaptive learning: In the field of security, this technology can achieve rapid response and processing of unknown events through the analysis and learning of historical data.
Intelligent decision support: In the field of security, this technology can achieve intelligent decision support and emergency response through the classification and improvement of events.
Personalized service: In the field of security, this technology can provide specific security concepts and risk assessments for different customers.
Summary:
Multimodal + ** Creation: Improve the efficiency of creators. With the exception of Sora and Runway, Lumière is able to generate complete sequences in a single process, rather than simply combining static frames. This technology can simultaneously process the spatial (i.e., objects in the object) and temporal (i.e., motion in the object) aspect of the user, bringing users a more natural and fluid motion perception experience.
Multimodal + Autonomous Driving: Revolutionizing Human-Vehicle Interaction. SenseTime proposes the DriveMLM model, which enables the operation of vehicles in closed-loop testing, surpassing the previous end-to-end and rule-based approach to autonomous driving systems.
Multimodal + Advertising (E-commerce): Create diversified marketing selling points. Use AI technology to further enhance the diversity of digital humans, such as face replacement, background replacement, accent voice replacement to adapt to our prompt, and finally script, digital face replacement, background replacement, etc., **After suppression, you can get an oral broadcast**; It can also help businesses achieve marketing poster generation.
Multimodal + Education: Improve teaching efficiency and strengthen human-computer interaction. Image generation models such as stable diffusion can input the text description of the subject and its details according to the teaching needs, and quickly and automatically generate a variety of styles, high-definition realistic, and aesthetic aesthetic teaching resources, and the generated teaching resources not only have significant cross-modality, but also have novelty and uniqueness.
Multimodal + Medical: Provide more intelligent and efficient solutions for clinical medical tasks. A large amount of data generated by clinical medical services is stored in the database in different modalities, sorted and cleaned, and then preprocessed for multi-modal fusion. Multimodal fusion can organically integrate different information, which is more comprehensive than single-modal information.
Multimodal + Security: AI + Security Accelerates Evolution. According to the global government and enterprise solutions, the current three application directions of AI technology in the field of "AI + security" in China are biometric technology, first-class structuring and object recognition system. Among them, biometric identification technology has been applied for the earliest time, involving a wide range of applications, and it is the most advanced technology for portrait recognition.
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