Dense Open-Vocabulary Image Recording With Pseudo-Label CLIP Training
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Solution Overview
Problem
Existing fixed pre-trained open-vocabulary techniques perform poorly on target datasets and supervised open-vocabulary techniques lose open-vocabulary capabilities as capacity grows, leading to inefficiencies in image recording systems.
Innovation Solution
A multi-objective dense open-vocabulary system combining a summarization CLIP head trained on supervised and pseudo-label losses, leveraging unlabeled data to maintain open-vocabulary capabilities, includes an image encoder and classifier for improved image embedding and classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If supervised open-vocabulary techniques are used to improve retrieval accuracy on target datasets, then retrieval accuracy on trained categories is improved, but open-vocabulary capabilities are lost as capacity grows
Solution Approach 1:
The system segments the embedding process into two distinct stages: a pre-trained open-vocabulary embedding stage that maintains general adaptability, and a supervised fine-tuning stage that optimizes for target dataset accuracy. This segmentation allows each stage to specialize in one function without compromising the other.
Solution Approach 2:
The patent embeds the supervised fine-tuned model within the broader open-vocabulary framework. The fine-tuned model operates as a specialized component that works in conjunction with the pre-trained model, allowing the system to maintain open-vocabulary capabilities while achieving high accuracy on target categories.
2Adaptability or versatility
If fixed pre-trained open-vocabulary techniques are used to maintain open-vocabulary capabilities, then adaptability to new categories is preserved, but retrieval accuracy on target datasets deteriorates
Solution Approach 1:
The system performs preliminary open-vocabulary pre-training to establish strong general capabilities before applying supervised fine-tuning for target dataset optimization. This preliminary action ensures that the base model possesses robust open-vocabulary understanding that can be later specialized without complete retraining.
Solution Approach 2:
The patent applies local quality by maintaining different optimization characteristics in different parts of the model architecture. The early layers preserve general open-vocabulary semantics while later layers are fine-tuned for target dataset specificity, allowing each part to have optimized properties for its function.
3Adaptability or versatility
If two-stage embedding processes are used to achieve both open-vocabulary and supervised performance, then both capabilities are achieved, but system complexity and inference time increase
Solution Approach 1:
The patent merges the two-stage process into a unified architecture where the pre-trained and fine-tuned components work together seamlessly. By combining the strengths of both approaches in a single integrated system, the complexity of managing separate systems is reduced while maintaining the benefits of both open-vocabulary and supervised performance.
Solution Approach 2:
The system achieves multi-functionality by designing an embedding process that can handle both open-vocabulary queries and target dataset-specific retrieval tasks through a single unified framework, eliminating the need for separate processing pipelines and reducing overall system complexity.
4Measurement precision
If supervised fine-tuning is applied to improve retrieval accuracy, then performance on annotated categories is improved, but inference speed decreases
Solution Approach 1:
The patent extracts only the essential supervised learning components needed for target dataset optimization, separating them from the full two-stage process. By taking out only the critical fine-tuning elements and integrating them efficiently, the system achieves high accuracy without the complete computational overhead of full supervised training during inference.
Data Source
AI summary
A multi-objective dense open-vocabulary system includes an image encoder and a classifier. The image encoder includes a summarization contrastive language image pre-training (CLIP) head trained on supervised losses from unlabeled and labeled image data. The summarization CLIP head loses open-vocabulary capabilities as capacity grows, and offsets the loss with pseudo-labels generated by a dense CLIP head. The summarization CLIP head is operational to receive captured images from a source device, and generate image embeddings based on current images. The classifier is operational to receive one or more targets from a text encoder, receive the plurality of image embeddings from the summarization CLIP, classify the plurality of image embeddings to identify one or more output images that contain the one or more targets, and present the one or more output images to the destination device.