Vision-Language Representation Learning via Intermediate Contrastive Loss
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Solution Overview
Problem
Existing vision-and-language pretraining (VLP) frameworks face challenges such as poor modeling efficiency, high annotation and computation overheads, and overfitting to noise, due to difficulties in aligning image and text features and requiring extensive annotations.
Innovation Solution
The proposed VLP systems and methods employ an intermediate image-text contrastive (ITC) loss to align image and text features, and utilize momentum distillation (MoD) to generate pseudo-targets, improving learning under noisy supervision and enabling the use of larger datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If prior VLP frameworks are used to learn multimodal representations from large-scale image-text pairs, then downstream vision-and-language tasks can be improved, but the framework suffers from poor modeling efficiency and high computation overhead
Solution Approach 1:
The patent divides the training process into two distinct stages: (1) an intermediate contrastive stage that aligns image and text features using contrastive loss, and (2) a downstream task training stage. This segmentation allows each stage to focus on specific objectives, improving overall modeling efficiency while maintaining performance.
Solution Approach 2:
The patent performs preliminary feature alignment between image and text modalities before training downstream tasks. By pre-aligning features through contrastive learning and generating pseudo-labels, the system reduces the computational burden during downstream task training, thereby improving productivity without sacrificing reliability.
2Reliability
If prior VLP frameworks require bounding box annotations for pretraining, then feature alignment can be achieved, but annotation overhead becomes excessively high
Solution Approach 1:
The patent employs self-supervised contrastive learning where the model learns to align image and text features without requiring bounding box annotations. The system uses the inherent structure of image-text pairs to generate supervision signals, eliminating the need for manual annotation while maintaining feature alignment quality.
Solution Approach 2:
The patent generates pseudo-labels and pseudo-annotations automatically from the training data itself, creating synthetic supervision signals that replace the need for manual bounding box annotations. This copying approach maintains alignment quality while dramatically reducing annotation overhead.
3Quantity of substance
If large-scale noisy image-text datasets are used for training, then more learning samples are available, but the model overfits to noise and performance decreases
Solution Approach 1:
The patent introduces an intermediate contrastive learning stage that acts as a mediator between raw noisy data and downstream task training. This intermediate stage filters out noise by learning robust feature alignments before the model is applied to downstream tasks, allowing the use of large-scale noisy datasets without overfitting.
Solution Approach 2:
The patent converts the harmful effect of noisy training data into a benefit by using contrastive loss to teach the model to distinguish meaningful correlations from noise. The noise in large-scale datasets becomes a form of regularization that improves generalization when properly processed through the intermediate alignment stage.
Data Source
AI summary
Embodiments described herein provide visual-and-language (V+L) systems and methods for learning vision and language representations. Specifically, a method may comprise receiving a training dataset comprising a plurality of image samples and a plurality of text samples; encoding the plurality of image samples into a plurality of encoded image samples and the plurality of text samples into a plurality of encoded text samples; computing a first loss objective based on the plurality of encoded image samples and the plurality of encoded text samples; encoding a first subset of the plurality of encoded image samples and a second subset of the plurality of encoded text samples into a plurality of encoded image-text samples; computing a second loss objective based on the plurality of encoded image-text samples; and updating the V+L model based at least in part on the first loss objective and the second loss objective.