{"id":6593,"date":"2025-09-12T17:20:32","date_gmt":"2025-09-12T17:20:32","guid":{"rendered":"https:\/\/techtrendfeed.com\/?p=6593"},"modified":"2025-09-12T17:20:32","modified_gmt":"2025-09-12T17:20:32","slug":"improve-video-understanding-with-amazon-bedrock-information-automation-and-open-set-object-detection","status":"publish","type":"post","link":"https:\/\/techtrendfeed.com\/?p=6593","title":{"rendered":"Improve video understanding with Amazon Bedrock Information Automation and open-set object detection"},"content":{"rendered":"


\n<\/p>\n

\n

In real-world video and picture evaluation, companies typically face the problem of detecting objects that weren\u2019t a part of a mannequin\u2019s authentic coaching set. This turns into particularly tough in dynamic environments the place new, unknown, or user-defined objects often seem. For instance, media publishers may need to observe rising manufacturers or merchandise in user-generated content material; advertisers want to research product appearances in influencer movies regardless of visible variations; retail suppliers intention to help versatile, descriptive search; self-driving vehicles should determine surprising highway particles; and manufacturing methods have to catch novel or delicate defects with out prior labeling.In all these circumstances, conventional closed-set object detection (CSOD) fashions\u2014which solely acknowledge a hard and fast listing of predefined classes\u2014fail to ship. They both misclassify the unknown objects or ignore them totally, limiting their usefulness for real-world functions.Open-set object detection (OSOD) is an strategy that allows fashions to detect each identified and beforehand unseen objects, together with these not encountered throughout coaching. It helps versatile enter prompts, starting from particular object names to open-ended descriptions, and may adapt to user-defined targets in actual time with out requiring retraining. By combining visible recognition with semantic understanding\u2014typically via vision-language fashions\u2014OSOD helps customers question the system broadly, even when it\u2019s unfamiliar, ambiguous, or totally new.<\/p>\n

On this put up, we discover how Amazon Bedrock Information Automation<\/a> makes use of OSOD to reinforce video understanding.<\/p>\n

Amazon Bedrock Information Automation and video blueprints with OSOD<\/h2>\n

Amazon Bedrock Information Automation is a cloud-based service that extracts insights from unstructured content material like paperwork, photographs, video and audio. Particularly, for video content material, Amazon Bedrock Information Automation helps functionalities reminiscent of chapter segmentation, frame-level textual content detection, chapter-level classification Interactive Promoting Bureau (IAB) taxonomies, and frame-level OSOD. For extra details about Amazon Bedrock Information Automation, see Automate video insights for contextual promoting utilizing Amazon Bedrock Information Automation<\/a>.<\/p>\n

Amazon Bedrock Information Automation video blueprints help OSOD on the body degree. You may enter a video together with a textual content immediate specifying the specified objects to detect. For every body, the mannequin outputs a dictionary containing bounding packing containers in XYWH format (the x and y coordinates of the top-left nook, adopted by the width and top of the field), together with corresponding labels and confidence scores. You may additional customise the output based mostly on their wants\u2014as an example, filtering by high-confidence detections when precision is prioritized.<\/p>\n

The enter textual content is extremely versatile, so you may outline dynamic fields within the Amazon Bedrock Information Automation video blueprints powered by OSOD.<\/p>\n

Instance use circumstances<\/h2>\n

On this part, we discover some examples of various use circumstances for Amazon Bedrock Information Automation video blueprints utilizing OSOD. The next desk summarizes the performance of this characteristic.<\/p>\n\n\n\n\n\n\n\n
Performance<\/strong><\/td>\nSub-functionality<\/strong><\/td>\nExamples<\/strong><\/td>\n<\/tr>\n
Multi-granular visible comprehension<\/td>\nObject detection from fine-grained object reference<\/td>\n\"Detect the apple within the video.\"<\/code><\/td>\n<\/tr>\n
\nObject detection from cross-granularity object reference<\/td>\n\"Detect all of the fruit gadgets within the picture.\"<\/code><\/td>\n<\/tr>\n
\nObject detection from open questions<\/td>\n\"Discover and detect probably the most visually essential parts within the picture.\"<\/code><\/td>\n<\/tr>\n
Visible hallucination detection<\/td>\nEstablish and flag object mentionings within the enter textual content that don’t correspond to precise content material within the given picture.<\/td>\n\"Detect if apples seem within the picture.\"<\/code><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Advertisements evaluation<\/h3>\n

Advertisers can use this characteristic to check the effectiveness of varied advert placement methods throughout completely different places and conduct A\/B testing to determine probably the most optimum promoting strategy. For instance, the next picture is the output in response to the immediate \u201cDetect the places of echo units.\u201d<\/p>\n

\"\"<\/p>\n

Sensible resizing<\/h3>\n

By detecting key parts within the video, you may select applicable resizing methods for units with completely different resolutions and facet ratios, ensuring essential visible data is preserved. For instance, the next picture is the output in response to the immediate \u201cDetect the important thing parts within the video.\u201d<\/p>\n

\"\"<\/p>\n

Surveillance with clever monitoring<\/h3>\n

In dwelling safety methods, producers or customers can reap the benefits of the mannequin\u2019s high-level understanding and localization capabilities to take care of security, with out the necessity to manually enumerate all doable eventualities. For instance, the next picture is the output in response to the immediate \u201cTest harmful parts within the video.\u201d<\/p>\n

\"\"<\/p>\n

Customized labels<\/h3>\n

You may outline your individual labels and search via movies to retrieve particular, desired outcomes. For instance, the next picture is the output in response to the immediate \u201cDetect the white automobile with crimson wheels within the video.\u201d<\/p>\n

\"\"<\/p>\n

Picture and video enhancing<\/h3>\n

With versatile text-based object detection, you may precisely take away or exchange objects in picture enhancing software program, minimizing the necessity for imprecise, hand-drawn masks that always require a number of makes an attempt to realize the specified consequence. For instance, the next picture is the output in response to the immediate \u201cDetect the individuals driving bikes within the video.\u201d<\/p>\n

Pattern video blueprint enter and output<\/h2>\n

The next instance demonstrates find out how to outline an Amazon Bedrock Information Automation video blueprint to detect visually distinguished objects on the chapter degree, with pattern output together with objects and their bounding packing containers.<\/p>\n

The next code is our instance blueprint schema:<\/p>\n

\n
blueprint\u00a0=\u00a0{\n\u00a0\u00a0\"$schema\": \"http:\/\/json-schema.org\/draft-07\/schema#\",\n\u00a0\u00a0\"description\": \"This blueprint enhances the searchability and discoverability of video content material by offering complete object detection and scene evaluation.\",\n\u00a0\u00a0\"class\": \"media_search_video_analysis\",\n\u00a0\u00a0\"kind\": \"object\",\n\u00a0\u00a0\"properties\": {\n\u00a0\u00a0 \u00a0# Focused Object Detection: Identifies visually distinguished objects within the video\n\u00a0\u00a0 \u00a0# Set granularity to chapter degree for extra exact object detection\n\u00a0\u00a0 \u00a0\"targeted-object-detection\": {\n\u00a0\u00a0 \u00a0 \u00a0\"kind\": \"array\",\n\u00a0\u00a0 \u00a0 \u00a0\"instruction\": \"Please detect all of the visually distinguished objects within the video\",\n\u00a0\u00a0 \u00a0 \u00a0\"gadgets\": {\n\u00a0\u00a0 \u00a0 \u00a0 \u00a0\"$ref\": \"bedrock-data-automation#\/definitions\/Entity\"\n\u00a0\u00a0 \u00a0 \u00a0},\n\u00a0\u00a0 \u00a0 \u00a0\"granularity\": [\"chapter\"] \u00a0# Chapter-level granularity gives per-scene object detection\n\u00a0\u00a0 \u00a0},\u00a0\u00a0\n\u00a0\u00a0}\n}<\/code><\/pre>\n<\/p><\/div>\n
The next code is out instance video customized output:<\/p>\n
\n
\"chapters\": [\n\u00a0 \u00a0 \u00a0 \u00a0 .....,\n\u00a0\u00a0 \u00a0 \u00a0 \u00a0{\n\u00a0\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\"inference_result\": {\n\u00a0\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\"emotional-tone\": \"Tension and suspense\"\n\u00a0\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0},\n\u00a0\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\"frames\": [\n\u00a0\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0{\n\u00a0\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\"frame_index\": 10289,\n\u00a0\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\"inference_result\": {\n\u00a0\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\"targeted-object-detection\": [\n\u00a0\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0{\n\u00a0\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\"label\": \"man\",\n\u00a0\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\"bounding_box\": {\n\u00a0\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\"left\": 0.6198254823684692,\n\u00a0\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\"top\": 0.10746771097183228,\n\u00a0\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\"width\": 0.16384708881378174,\n\u00a0\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\"height\": 0.7655990719795227\n\u00a0\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0},\n\u00a0\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\"confidence\": 0.9174646443068981\n\u00a0\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0},\n\u00a0\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0{\n\u00a0\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\"label\": \"ocean\",\n\u00a0\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\"bounding_box\": {\n\u00a0\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\"left\": 0.0027531087398529053,\n\u00a0\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\"top\": 0.026655912399291992,\n\u00a0\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\"width\": 0.9967235922813416,\n\u00a0\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\"height\": 0.7752640247344971\n\u00a0\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0},\n\u00a0\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\"confidence\": 0.7712276351034641\n\u00a0\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0},\n\u00a0\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0{\n\u00a0\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\"label\": \"cliff\",\n\u00a0\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\"bounding_box\": {\n\u00a0\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\"left\": 0.4687306359410286,\n\u00a0\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\"top\": 0.5707792937755585,\n\u00a0\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\"width\": 0.168929323554039,\n\u00a0\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\"height\": 0.20445972681045532\n\u00a0\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0},\n\u00a0\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\"confidence\": 0.719932173293829\n\u00a0\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0}\n\u00a0\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0],\n\u00a0\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0},\n\u00a0\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\"timecode_smpte\": \"00:05:43;08\",\n\u00a0\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\"timestamp_millis\": 343276\n\u00a0\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0}\n\u00a0\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0],\n\u00a0\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\"chapter_index\": 11,\n\u00a0\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\"start_timecode_smpte\": \"00:05:36;16\",\n\u00a0\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\"end_timecode_smpte\": \"00:09:27;14\",\n\u00a0\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\"start_timestamp_millis\": 336503,\n\u00a0\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\"end_timestamp_millis\": 567400,\n\u00a0\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\"start_frame_index\": 10086,\n\u00a0\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\"end_frame_index\": 17006,\n\u00a0\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\"duration_smpte\": \"00:03:50;26\",\n\u00a0\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\"duration_millis\": 230897,\n\u00a0\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\"duration_frames\": 6921\n\u00a0\u00a0 \u00a0 \u00a0 \u00a0},\n\u00a0 \u00a0 \u00a0 \u00a0 ..........\n]<\/code><\/pre>\n<\/p><\/div>\n
For the complete instance, consult with the next GitHub repo<\/a>.<\/p>\n
Conclusion<\/h2>\n
The OSOD functionality inside Amazon Bedrock Information Automation considerably enhances the power to extract actionable insights from video content material. By combining versatile text-driven queries with frame-level object localization, OSOD helps customers throughout industries implement clever video evaluation workflows\u2014starting from focused advert analysis and safety monitoring to customized object monitoring. Built-in seamlessly into the broader suite of video evaluation instruments obtainable in Amazon Bedrock Information Automation, OSOD not solely streamlines content material understanding but in addition assist cut back the necessity for handbook intervention and inflexible pre-defined schemas, making it a strong asset for scalable, real-world functions.<\/p>\n
To be taught extra about Amazon Bedrock Information Automation video and audio evaluation, see New Amazon Bedrock Information Automation capabilities streamline video and audio evaluation<\/a>.<\/p>\n\n
Concerning the authors<\/h3>\n
\"\"Dongsheng An<\/strong> is an Utilized Scientist at AWS AI, specializing in face recognition, open-set object detection, and vision-language fashions. He obtained his Ph.D. in Pc Science from Stony Brook College, specializing in optimum transport and generative modeling.<\/p>\n
\"\"Lana Zhang<\/strong> is a Senior Options Architect within the AWS World Vast Specialist Group AI Companies crew, specializing in AI and generative AI with a deal with use circumstances together with content material moderation and media evaluation. She\u2019s devoted to selling AWS AI and generative AI options, demonstrating how generative AI can rework basic use circumstances by including enterprise worth. She assists prospects in remodeling their enterprise options throughout numerous industries, together with social media, gaming, ecommerce, media, promoting, and advertising.<\/p>\n
\"\"Raj Jayaraman<\/strong> is a Senior Generative AI Options Architect at AWS, bringing over a decade of expertise in serving to prospects extract precious insights from information. Specializing in AWS AI and generative AI options, Raj\u2019s experience lies in remodeling enterprise options via the strategic software of AWS\u2019s AI capabilities, making certain prospects can harness the complete potential of generative AI of their distinctive contexts. With a robust background in guiding prospects throughout industries in adopting AWS Analytics and Enterprise Intelligence providers, Raj now focuses on aiding organizations of their generative AI journey\u2014from preliminary demonstrations to proof of ideas and finally to manufacturing implementations.<\/p>\n
       \n      <\/div>\n\n","protected":false},"excerpt":{"rendered":"
In real-world video and picture evaluation, companies typically face the problem of detecting objects that weren\u2019t a part of a mannequin\u2019s authentic coaching set. This turns into particularly tough in dynamic environments the place new, unknown, or user-defined objects often seem. For instance, media publishers may need to observe rising manufacturers or merchandise in user-generated […]<\/p>\n","protected":false},"author":2,"featured_media":6595,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[55],"tags":[387,988,1289,157,703,1094,5308,5307,2742,180],"class_list":["post-6593","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-machine-learning","tag-amazon","tag-automation","tag-bedrock","tag-data","tag-detection","tag-enhance","tag-object","tag-openset","tag-understanding","tag-video"],"_links":{"self":[{"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=\/wp\/v2\/posts\/6593","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=6593"}],"version-history":[{"count":1,"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=\/wp\/v2\/posts\/6593\/revisions"}],"predecessor-version":[{"id":6594,"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=\/wp\/v2\/posts\/6593\/revisions\/6594"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=\/wp\/v2\/media\/6595"}],"wp:attachment":[{"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=6593"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=6593"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=6593"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}