Multimodal annotation
Optimize your data for multimodal models combining text, image, audio, and video. Our multimodal annotation services ensure accurate and consistent structuring, ensuring high-quality datasets to train and refine your advanced AI models.
🧠 Multimodal data
Optimize your AI models with data sets annotated on several modalities - images, texts, videos, audio, sensor data, etc. We structure your complex data according to your specific use cases and formats.
🧩 Cross-expertise
Our annotators master the interaction between multiple sources — text, image, video, sensors — to ensure coherent, accurate and perfectly synchronized annotation.
🌍 For all sectors
Transport, health, retail, industry, education, etc. We adapt our workflows to the specific needs of your field and to the diversity of your data to provide rich, aligned and ready-to-train datasets.
Annotation techniques
Text-image alignment
Associate textual elements (captions, descriptions, dialogues) to specific areas in an image. This cross-annotation makes it possible to train models to visually relate the content of an image to natural or informative expressions.
Identify the relevant visual elements in the image (objects, scenes, actions)
Delimit areas (bounding box, segment, etc.)
Associate each area with a text segment or descriptive tag
Validate the semantic and visual consistency of links
Visual search — Allow the search of images by text captions
E-commerce — Associate produced texts with visually identified objects
Generating captioned images — Train automatic description models
Audio-video transcription
Text annotation of audio or video content, generally synchronized with time markers. It is used in subtitling, indexing, or automated voice analysis.
Segment audio or video content into logical units (sentences, scenes...)
Transcribe words or sounds accurately
Add accurate timecodes for each segment
Check fluidity and synchronization
Automatic subtitling — Create synchronized subtitles for movies or videos
Content indexing — Allow long videos to be searched
Conversational analysis — Study the tone and vocabulary in customer calls
Detection of visual-auditory events
Annotate events that produce both a visual and an audio signal. This allows models to recognize synchronized multisensory stimuli.
Watch the audio-visual excerpts
Identify visible and audible trigger events
Annotate the objects or areas concerned
Link events to corresponding sound segments
Smart surveillance — Detect suspicious noises combined with movements
Audiovisual scene analysis — Understand interactions in complex videos
Robotics — Locate obstacles in volume for smart navigation
Cross referencing (Cross-modal Grounding)
Link entities or concepts expressed in text to their visual representations in an image or video. This improves the intermodal understanding by the models.
Identify named entities or referential expressions in text
Annotate their correspondence in the image (object, person, place...)
Establishing explicit links (anchors, cross IDs)
Validate the accuracy of the semantic mapping
Visual Question Answering (VQA) — Link question text to visual objects
Accessibility — Generate visual descriptions for visually impaired people
Rich translation — Improve contextual translation with visual support
Multimodal emotion annotation
Capture and annotate emotions expressed through multiple channels: voice, facial expressions, and verbal content. This annotation makes it possible to train AIs that are sensitive to emotional signals.
Identify emotionally charged multimodal sequences
Annotate vocal (intonation, rhythm), visual (expressions), and verbal (word choice) expressions
Classify according to a taxonomy of emotions (joy, anger, stress...)
Mark the temporal or visual areas concerned
Call centers — Detect frustration or satisfaction in customer exchanges
UX studies — Analyze emotional reactions to a product or interface
Voice assistants and robots — Enabling empathetic interactions in real time
Multimodal question and answer
Create or annotate question-answer pairs on visual or audiovisual content. The objective is to allow an AI to answer questions about images or videos.
Present a media (image, video, audio-visual scene)
Generate or collect a relevant content-related question
Provide a correct and clear answer
Annotate the type of question (open, boolean, multiple choice,...)
Visual education systems — Ask questions about illustrated content
Rich chatbots — Integrate the understanding of images or videos into interactions
AI assistants — Answer questions by analyzing what is seen
Use cases
Our expertise covers a wide range of AI use cases, regardless of the domain or the complexity of the data. Here are a few examples:
Why choose Innovatiana?
Our added value
Extensive technical expertise in data annotation
Specialized teams by sector of activity
Customized solutions according to your needs
Rigorous and documented quality process
State-of-the-art annotation technologies
Measurable results
Boost your model’s accuracy with quality data, for model training and custom fine-tuning
Reduced processing times
Optimizing annotation costs
Increased performance of AI systems
Demonstrable ROI on your projects
Customer engagement
Dedicated support throughout the project
Transparent and regular communication
Continuous adaptation to your needs
Personalized strategic support
Training and technical support
Compatible with
your stack
We use all the data annotation platforms of the market to adapt us to your needs and your most specific requests!
Secure data
We pay particular attention to data security and confidentiality. We assess the criticality of the data you want to entrust to us and deploy best information security practices to protect it.
No stack? No prob.
Regardless of your tools, your constraints or your starting point: our mission is to deliver a quality dataset. We choose, integrate or adapt the best annotation software solution to meet your challenges, without technological bias.
Feed your AI models with high-quality, expertly crafted training data!
