Scaling Positive and Negative Examples to Improve Composed Image Retrieval via Contrastive Learning

Kernkonzepte

Scaling the number of positive and negative examples in contrastive learning can effectively improve the performance of composed image retrieval models.

Zusammenfassung

The paper proposes a method to efficiently scale the number of positive and negative examples in the Composed Image Retrieval (CIR) task, which aims to retrieve target images using a composed query consisting of a reference image and a modified text.
To address the lack of positive examples in CIR datasets, the authors propose a data generation method that leverages a multi-modal large language model to automatically construct triplets (reference image, modified text, target image) for CIR. This method can scale the number of positive examples from around 20k to 100k without using external datasets.
To introduce more negative examples during fine-tuning, the authors design a two-stage fine-tuning framework. In the first stage, the model is fine-tuned with in-batch negative sampling as in previous works. In the second stage, the target image encoder is frozen, and only the query encoder is fine-tuned. This allows the introduction of a large number of static negative representations from the entire candidate set, guiding the query encoder to optimize the representation space rapidly.
The authors extensively evaluate their method on two popular CIR datasets, FashionIQ and CIRR, and demonstrate that their approach effectively scales positive and negative examples, leading to state-of-the-art performance. They also show that their method can be applied to the zero-shot CIR setting, where the model is built without requiring human-labeled triplets for training, by automatically constructing positive and negative examples from image datasets.

Statistiken

The average token length of the modified text is 16.5 for FashionIQ and 20.9 for CIRR.
The number of triplets (reference image, modified text, target image) is scaled from 18k to 96k for FashionIQ and from 28k to 128k for CIRR.

Zitate

"To address the problem of lack of positives, we propose a data generation method by leveraging a multi-modal large language model to construct triplets for CIR."
"To introduce more negatives during fine-tuning, we design a two-stage fine-tuning framework for CIR, whose second stage introduces plenty of static representations of negatives to optimize the representation space rapidly."

Wichtige Erkenntnisse aus

Improving Composed Image Retrieval via Contrastive Learning with Scaling Positives and Negatives

by Zhangchi Fen... um arxiv.org 04-18-2024

https://arxiv.org/pdf/2404.11317.pdf

Tiefere Fragen

How can the proposed data generation method be extended to other multi-modal tasks beyond CIR

The proposed data generation method can be extended to other multi-modal tasks beyond Composed Image Retrieval (CIR) by adapting the process to suit the specific requirements of different tasks. Here are some ways in which the method can be applied to other tasks:

Image Captioning: The multi-modal large language model (MLLM) used for generating captions in the CIR task can be leveraged for image captioning tasks. By providing image prompts to the MLLM, it can generate descriptive captions for images, which can be used for training image captioning models.

Visual Question Answering (VQA): In VQA tasks, where models are required to answer questions about images, the MLLM can be used to generate relevant questions based on the images. These questions can then be paired with the images to create training data for VQA models.

Visual Dialog: For tasks involving visual dialog, where models engage in a conversation about images, the MLLM can be used to generate dialogues or responses based on the images. This can help in creating diverse and engaging conversations for training visual dialog models.

Image Generation: The MLLM can also be used to generate textual descriptions for images in tasks related to image generation. By providing image prompts to the MLLM, it can generate detailed descriptions that can be used as input for image generation models.

By adapting the prompt templates and the data generation pipeline to suit the specific requirements of these tasks, the proposed method can be effectively extended to a wide range of multi-modal tasks beyond CIR.

What are the potential limitations or drawbacks of the two-stage fine-tuning framework, and how can they be addressed

The two-stage fine-tuning framework proposed in the context has several potential limitations and drawbacks that need to be considered:

Overfitting: One potential limitation of the two-stage fine-tuning framework is the risk of overfitting, especially in the second stage where the target image encoder is frozen. If the model is fine-tuned too aggressively in the second stage, it may memorize the training data and perform poorly on unseen data.

Computational Resources: The second stage of fine-tuning, which involves pre-computing representations for all images, can be computationally intensive, especially for large datasets. This may limit the scalability of the framework to larger datasets or models.

Hyperparameter Sensitivity: The performance of the two-stage fine-tuning framework may be sensitive to hyperparameters such as the number of epochs, learning rate, and temperature hyperparameter in the contrastive loss function. Finding the optimal hyperparameters for both stages can be challenging.

To address these limitations, the following strategies can be implemented:

Regularization: Implement regularization techniques such as dropout or weight decay to prevent overfitting during fine-tuning, especially in the second stage where the target image encoder is frozen.

Early Stopping: Monitor the model's performance on a validation set during training and implement early stopping to prevent overfitting and ensure optimal performance.

Hyperparameter Tuning: Conduct thorough hyperparameter tuning to find the optimal settings for both stages of fine-tuning. This can involve grid search or random search over a range of hyperparameters to identify the best configuration.

By addressing these limitations and implementing appropriate strategies, the two-stage fine-tuning framework can be optimized for improved performance and generalization.

How might the insights from this work on scaling positive and negative examples be applied to improve contrastive learning in other computer vision tasks, such as image classification or object detection

The insights from this work on scaling positive and negative examples can be applied to improve contrastive learning in other computer vision tasks such as image classification or object detection in the following ways:

Data Augmentation: By generating additional positive and negative examples using similar techniques, contrastive learning models for image classification can benefit from a more diverse and balanced training dataset. This can help improve the model's ability to learn robust representations.

Hard Negative Mining: The concept of scaling negatives can be applied to object detection tasks by introducing a larger pool of hard negative examples during training. This can help the model focus on challenging instances and improve its ability to distinguish between different object classes.

Fine-tuning Strategies: Similar to the two-stage fine-tuning framework proposed in the context, contrastive learning models for image classification or object detection can benefit from a multi-stage fine-tuning approach. By introducing more negatives and positives in subsequent stages, the model can refine its representations and enhance performance.

By incorporating these insights into contrastive learning frameworks for image classification and object detection tasks, it is possible to improve the models' performance and robustness in various computer vision applications.