Visual Language Grounding via Blended Image Separation Loss
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing visual language grounding models face challenges in localizing objects in images using noisy and non-localized textual queries, particularly when traditional annotation methods are costly and inefficient, and recent detector-based methods require extensive retraining for different domains.
Innovation Solution
A system that generates a blended image using a randomly generated alpha-map and trains a visual language grounding model to separate the image into heatmaps without relying on pre-trained object detectors, utilizing a Bidirectional Encoder Representations from Transformers (BERT) model and additional losses to improve accuracy and reduce overfitting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If detector-based methods are used for weakly supervised grounding, then localization performance is improved, but domain adaptability deteriorates as extensive retraining is required for different domains
Solution Approach 1:
The patent uses pre-trained object detectors to generate pseudo-grounding labels (bounding boxes) that are then converted into heatmap annotations. These synthesized labels serve as training data for the grounding model, allowing the system to leverage existing detector knowledge without requiring domain-specific retraining, thus maintaining both performance and adaptability
Solution Approach 2:
The patent introduces an intermediary process that converts detector bounding boxes into heatmap annotations through blending images with randomly generated alpha-maps. This intermediary representation allows the grounding model to learn from detector outputs without being constrained by detector-specific training requirements, enabling cross-domain applicability
2Measurement precision
If manual bounding box annotations are used for training, then localization accuracy is improved, but annotation cost and time consumption increase significantly
Solution Approach 1:
The system uses pre-trained object detectors to automatically generate pseudo-grounding labels (bounding boxes) that are then converted into heatmap annotations for training the grounding model. This self-service approach eliminates the need for manual annotation while maintaining training quality, as the detectors provide sufficient localization signals for the model to learn effective grounding
Solution Approach 2:
The patent creates synthetic training annotations by copying and transforming detector outputs (bounding boxes) into heatmap format through image blending operations. These synthesized labels serve as effective training data, replacing costly manual annotations while preserving the localization accuracy needed for effective grounding
3Measurement precision
If pre-trained object detectors are used to generate localization regions, then grounding performance is improved, but the approach shifts away from true weakly supervised grounding as detectors require bounding box training
Solution Approach 1:
The patent introduces heatmap annotations as an intermediary representation that bridges detector outputs and grounding model inputs. By converting bounding boxes into heatmaps through blending operations, the system creates a training format that is both compatible with detector outputs and suitable for grounding models, maintaining weakly supervised capabilities while improving performance
Solution Approach 2:
The system copies detector bounding box outputs and transforms them into heatmap annotations through blending images with randomly generated alpha-maps. This copying and transformation process creates synthetic training data that preserves the localization information while adapting it to the grounding task, enabling performance improvement without sacrificing weakly supervised capability
Data Source
AI summary
An example system includes a processor to receive, a randomly generated alpha-map, a pair of training images, and a pair of training texts associated with the pair of training images. The processor is to generate a blended image based on the randomly generated alpha-map and the pair of training images. The processor is to train a visual language grounding model to separate the blended image into a pair of heatmaps identifying portions of the blended image corresponding to each of the training images using a separation loss.