Introducing Hybrid AI by Binat.us

Combining computer vision, natural language processing, machine learning, traditional programming, to analyze and enhance video and audio sources with unmatched precision and speed

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A subway station scene at Bedford Avenue with people waiting or walking

Output of an AI analysis on a subway station scene: individuals highlighted with annotations for demographics, clothing details, and actions.

Output of AI clothing analysis: annotated silhouette showcasing details like material, pattern, style, and potential brands for items such as a coat, jeans, and backpack

AI-driven location recognition: annotated subway station scene with details about the environment and identified location, including station name and context

Data We Process

We deal with diverse datasets. Sometimes they are unstructured, unlabeled, incomplete, broken, or excessive. This is how we proceed with different kinds of datasets.

Detection and tracking

Detection and tracking of objects of specified types (as requested by the client) in video

General annotation

Descriptions of ongoing actions, annotation of main events

Advanced annotation

Integration with other types of datasets to create a complete picture of the events and extract new details

Semantic tagging

Discover semantic tags, build semantic trees connected to the moments

Extra processing

Enhancing (upscaling up to 16k, SDR to HDR processing)

Collection

Getting the best images from a video source or online

Classification

Typing by kinds of images (e.g., landscape, portrait, action, documentary, abstract, macro)

Semantic annotation

Detecting signs/text, locations, people, clothing, accessories, and other objects

Structuring

Organize text and focus on key segments

Semantic tagging

Identify patterns and discover semantic tags, build semantic trees

Recognition

Detect entities and connect other types of datasets

Transcription

Audio to text transcription, speaker diarization

Advanced analysis

Matching speakers with other datasets sources, identifying speakers by context

Soundtracks analysis

Recognize background soundtracks with the music library

Extra processing

Enhancing (5.1/7.1 sound transformation, AI de-noice, AI translation)

Gathering

Gathering legally available information from the Internet and social media tailored to customer-specific criteria

Connection

Integration with other types of datasets to create a complete picture of the events and extract new details (e.g. connecting online resources with certain moments on video)

Case studies

Explore our cutting-edge technology transforming industries. See AI in action, enhancing customer service and streamlining operations. Witness how we drive efficiency, innovation, and growth. Join us on an innovation journey and discover AI's endless possibilities.

View All cases

Media Content Labeling

Categorize and enrich a media library with advanced labeling and metadata enrichment.

Sensitivity Detection

Identify sensitive content through robust media analysis for emotional and thematic elements.

Automated Shopping

Integrate automated shopping within video content to enhance viewer interaction.

Landmark Identification

Identify landmarks and locations from video footage when no specific GPS data provided

Object Counting

Accurately count people, vehicles, and objects within video frames.

Scene Segmentation

Segment video into discrete scenes or activities for detailed analysis.

Multilingual Translation

Automatically translate audio and text data into 30+ languages.

Audio De-noise

Clear unwanted background noise from corporate communications.

Speaker Identification

Identify and transcribe speech by individual speakers in conferences.

Want to implement these use cases yourself or customize them for your project?

Our solutions power these projects

Highlight demo

Multimedia analysis is an example usage of Binat's Hybrid AI System. It can identify what is generally and specifically on a video - be it a movie clip or a recorded meeting.

We have used this historic video for results demonstration

Swipe right or left to explore different aspects of the analysis

1. Gather textual information within the frame

This analysis extracts text from video frames in various languages and scripts. For example handwritten slogans on posters are identified at the 109th second of this video.

180
frames analysed

3681
words extracted

1380
phrases extracted

Download Processed Output

2. Discover depicted locations and retrieve details about them

This processor analyzes video locations to maximize geographic connections by performing a coarse frames analysis (e.g., street, metro, outside/inside, people, landscapes) and then selecting the best frames for detailed analysis.

68
detected locations

23
hotels

7
selected locations

Download Processed Output

3. Identify clothing in the frame

“The clothing selection works through describing outfits using an NLP model. It searches online (US only) for tagged information on specific websites. Results are structured by similarity of images and descriptors (styles, fabric structure, etc.).

129
people

228
clothing items

64
accessories items

Download Processed Output

4. Recognize people and their activities

The people and activities processor collects the maximum available information on characters from the video. It is a high-level processor that uses results from other processes to carefully identify and verify each person.

Cecilia Moy Yep
Name

Herself
Role

8
Total speakers

Download Processed Output

5. Summarize the video into a comprehensive description

The summary considers video, dialogues, captions, and online information about the source to create the most accurate video summary

Download Processed Output

6. Create a tag cloud from the extracted information

Tags are semantic elements that represent the core meaning of dialogues and events in the video. They carry weight, indicating the importance of specific video segments to particular themes.

7. Uncover video details and search for related information online

We gather and structure information available online linked to specific moments. We take into account:
- Images and scene dynamics;
- Dialogue;
- Captions

Download Processed Output

view source

8. Generate subtitles

Subtitles are generated through speech transcribing, initially identifying speakers as Speaker 0, Speaker 1, etc. Through context, these speakers are then matched to the corresponding actors

Download Processed Output

9. Determine what people are talking about

This section provides data on the positions of each person, aligning dialogues with ongoing events. It also includes timelines of key points

Download Processed Output

10. Build ratings and classifications for content, marking scenes

Our processor analyzes video content to score segments across various categories:
‍
Content Restrictions: Age Restrictions, Violence, Abuse, Profanity, Substance Use, Horror

11. Analyze sounds and music soundtracks

The audio processor identifies soundtracks, even if incomplete or poorly audible, and links them to specific video moments.This results in precise knowledge of the background soundtrack and the exact moments where each part plays.

3
total soundtracks

198 sec
total played duration

Download Processed Output

view source

12. Extras

The maximum amount of structured information in the form of contacts, URLs, emails, phone numbers, and other connections from videos. This includes information that was on screen, discussed in any language, or implied somehow.
The processor can gather and link these details to video participants and moments. The example includes lists of contacts and URLs.

DOWNLOAD THE FULL RESULT

Try it for free

If you want to see how our annotation technology works on your content, send it to us, and we will provide the annotations
(video: up to 100MB, mp4, up to 10 minutes long)

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About us

Binat.us (Binat, Inc), founded by professionals in the video annotation and processing industry, has been a leader in automatic content annotation since 2020. Our USA headquarters opened in Miami, FL in February 2023.

Binat’s founders are members of ACM and IEEE and hold U.S. and foreign patents in commercial video stream management. They are key developers of our system and bring significant know-how to the company.

We provide advanced and fast-processing services for media service providers and custom-solutions platforms, utilizing cutting-edge technology to solve complex challenges with precision and speed.

Our commitment

is to deliver high-quality data annotation services whether you’re a small startup or a large enterprise.

‍

We specialize

in deep indexing and tagging of video content, as well as assisting in the training and fine-tuning of client neural networks, primarily for video processing and recognition.

Our approach

begins with a deep understanding of your specific priorities and business objectives.

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We're here to help with your video processing, fine-tuning ML models, and dataset creation needs - contact us for expert assistance and tailored solutions

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AdDress

Binat, Inc

333 SE 2nd Ave

Miami, FL, 33131

Email

services@binat.us