Image Capture Captioning With Selected Non-Recording Frames
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Solution Overview
Problem
Existing image captioning techniques struggle to generate accurate captions due to limited information from a single image, particularly when the content is complex or requires context from surrounding frames.
Innovation Solution
An image capturing apparatus that shoots both recording and non-recording images, utilizing a neural network to generate captions based on the recording image and relevant non-recording images that satisfy specific conditions, such as temporal proximity or priority subject detection, to enhance caption accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If only the recording image is used for caption generation, then the storage requirement is minimized, but the caption accuracy is insufficient due to limited information
Solution Approach 1:
The system performs preliminary actions by capturing non-recording images before the actual recording event. These pre-captured images contain contextual information about the scene, subjects, and actions that occur during the recording period, enabling more accurate caption generation without requiring storage of all captured images.
Solution Approach 2:
The image data is segmented into two categories: recording images (stored in nonvolatile storage) and non-recording images (stored in volatile storage and used for caption generation). This segmentation allows the system to utilize additional contextual information from non-recording images while maintaining efficient storage by not permanently storing all captured images.
2Measurement precision
If non-recording images are stored in nonvolatile storage, then caption accuracy improves, but storage capacity is consumed
Solution Approach 1:
Instead of storing original non-recording images in nonvolatile storage, the system creates a copy mechanism where these images are temporarily stored in volatile memory and processed to generate captions. The caption generation unit processes these temporary copies to extract information needed for accurate descriptions, eliminating the need for permanent storage of all non-recording images.
Solution Approach 2:
The system discards non-recording images after they have been used for caption generation by transferring them from volatile storage to the deletion queue, while recovering and retaining only the essential caption information in nonvolatile storage. This approach maintains storage efficiency while preserving the functional utility of non-recording images for accurate captioning.
3Loss of information
If all captured images are processed for caption generation, then information completeness is maximized, but processing time increases
Solution Approach 1:
The system extracts and processes only the necessary information from non-recording images for caption generation, rather than processing all captured images. The generation unit selectively extracts relevant contextual information from these images to create accurate captions, significantly reducing processing time while maintaining information completeness.
Solution Approach 2:
The system applies partial action by processing only a subset of images (non-recording images) for caption generation rather than all captured images. This partial processing approach provides sufficient contextual information for accurate captions while dramatically reducing the processing time and computational resources required.
Data Source
AI summary
There is provided an image capturing apparatus. A shooting unit shoots at least one first image that is not to be recorded in a nonvolatile storage, and a second image that is to be recorded in the nonvolatile storage. A generation unit generates verbal information that describes content of the second image based on the second image and on one or more first images that satisfy one or more conditions among the at least one first image.