Frequently Asked Questions
1. How is annotation quality ensured?
Quality often conflicts with efficiency. How does the platform ensure high accuracy while delivering efficiently?
AIO has been operating data services for over a year, processing more than 300TB of annotated data and accumulating valuable operational experience.
- Efficiency improvement: For similar data, the platform leverages previous correct annotation history to provide suggestions for the next annotation, greatly improving the efficiency of large-scale data annotation.
- Quality improvement: For incorrect annotations, algorithms compare them with the majority of similar cases. If an annotation is an outlier, the system prompts for focused review.
Are there automated pre-annotation or AI-assisted annotation technologies (such as active learning) in use?
We support using large language models to check and validate annotation semantics in context (by integrating Tencent Cloud's Deepseek API). In the future, we plan to integrate image recognition engines to track and detect target objects, enabling more automated annotation.
2. How is the annotation team managed?
How do you quantify annotator efficiency and quality (such as error rate statistics)?
Issues found by reviewers are recorded in the database. Project details allow statistics on each person's number of errors and correct annotations.
Is dynamic task allocation used (e.g., assigning complex tasks to more experienced annotators)?
Project managers can adjust and distribute annotation and review tasks. In the future, a task pool may be introduced to allow high-efficiency annotators to claim tasks themselves.
For highly subjective annotation tasks (such as action intent recognition), how does the review mechanism reduce ambiguity?
By reviewing annotation and audit results daily, and notifying annotators via meetings or messages, standards can be aligned among the team.
How are annotation standards for complex scenarios established?
- Each project's annotation rule library can be independent and customized per project (shared by default).
- Additionally, project managers or experienced annotators can pre-label one or two data samples as templates for subsequent algorithmic suggestions and annotator reference.
3. How is data quality ensured?
How do you address temporal alignment and consistency checks for multimodal data?
- Alignment is completed during the preprocessing and packaging stage. For AIO's data collection devices, the process is seamless and requires no additional development. For customer-defined data, some adaptation may be needed, as timestamp alignment depends on the parameters of the collection device.
- Consistency checks are also performed during preprocessing before data enters the platform, such as frame drop detection and time difference checks, with visualization available.
4. How is data security and compliance ensured?
The platform emphasizes storing only access links and relies on third-party cloud storage (such as Tencent Cloud). If customers need to process sensitive data, how can "IP whitelist + permission control + encryption keys" achieve end-to-end data isolation?
Data access protection:
- All cloud storage providers support IP whitelisting and server-side encryption. Customers can specify which IPs or devices can access the data.
- The AIO data platform supports project-level permission control, role-based access control, and access auditing for multiple layers of isolation.
Data transmission encryption: In addition to basic HTTPS transmission encryption, data can be encrypted with a public key during preprocessing before uploading to the cloud. Each time data is accessed from the cloud, it is decrypted using a private key stored locally in the browser (this private key is never uploaded to the cloud, ensuring only specific customers can access the real data).
5. Customization capabilities
Does the platform have experience serving clients?
We primarily deliver and deploy in clients' private offline environments, fully meeting their annotation needs. Delivered cases include major domestic enterprises, university robotics research teams, and humanoid robot model teams.
Does the platform provide APIs and documentation for secondary development?
Yes, we offer comprehensive API documentation and tutorials for secondary development. Currently, all platform data is accessed via API using login keys. Clients can develop third-party plugins themselves or entrust AIO for custom development.
Can the platform support large-scale data?
Yes, the platform was designed from the outset to store data in professional object storage services.
Our internal private object storage (MinIO + NAS) already holds over 300TB of data, and storage space can be expanded without limitation.
The platform only needs to store data access links and keys, greatly reducing storage complexity. It also supports distributed cluster deployment. On mature cloud providers such as Tencent Cloud, server resources can be dynamically adjusted based on request load.