Language-Based Object Detection Using Negative Image Augmentation
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Solution Overview
Problem
Existing natural language-based object detection systems face challenges in effectively utilizing negative labels to improve discriminative models, particularly in generating relevant and contradicting object descriptions along with corresponding images.
Innovation Solution
The method involves generating negative descriptions and images using language models and generative image models, respectively, to create semantically contradicting pairs that can be used to train object detection models, thereby improving discriminative loss.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If natural language descriptions are used to expand the label space, then object detection accuracy is improved, but the complexity of training data generation increases
Solution Approach 1:
The system pre-generates negative descriptions and corresponding negative images using language models and generative image models before training the object detection model. This preliminary preparation of training data with both positive and negative examples simplifies the overall training process by having all necessary training materials ready in advance, rather than generating them on-the-fly during training.
Solution Approach 2:
The system creates negative images by copying the structure and style of positive images while replacing the target objects with alternative content generated by the generative image model. This copying approach maintains visual consistency and realism in training data while efficiently generating diverse negative examples without manual intervention.
2Reliability
If negative descriptions and images are generated to improve discriminative loss, then object detection performance is enhanced, but computational resources and time are increased
Solution Approach 1:
Negative descriptions and images are generated in advance before the training process begins. This preliminary generation allows the actual training to proceed more efficiently by having all training data prepared, rather than generating negative examples during the training iterations, thus reducing overall training time despite the initial generation cost.
Solution Approach 2:
The system uses automated language models and generative image models to create negative training data without requiring manual annotation or curation. This self-service approach to training data generation eliminates the time-consuming process of manual negative example creation while maintaining high quality and relevance of the negative training data.
3Loss of information
If semantically contradicting pairs are created for training, then semantic understanding is improved, but the complexity of the training process increases
Solution Approach 1:
Semantically contradicting pairs consisting of negative descriptions and negative images are pre-generated using language models and generative image models before training. This preliminary creation of semantically rich training pairs ensures that the model learns from diverse and contrasting examples, improving semantic understanding while the automated generation process manages the complexity of creating these pairs.
Solution Approach 2:
The system creates semantically contradicting pairs by copying the format and structure of positive training examples but replacing the content with semantically opposite or contrasting information generated by the language model. This copying approach maintains consistency in the training data format while efficiently generating diverse semantic contrasts without manual effort.
Data Source
AI summary
Methods and systems for object detection include generating a negative description for an input image of a road scene, based on a positive description of the input image, using a language model. A negative image is generated based on the input image and the negative description by replacing a portion of the input image that is described by the positive description with content that is described by the negative description using a generative image model. An object detection model is trained with the input image, the positive description, the negative description, and the negative image. An object is identified within a driving scene using the trained object detection model. A driving action is performed in a self-driving vehicle responsive to the identified object.