Improving Zero-Shot Composed Image Retrieval with Textual Inversion

Concepts de base

A novel zero-shot approach for Composed Image Retrieval (CIR) that maps reference images into pseudo-word tokens and combines them with relative captions to perform text-to-image retrieval.

Résumé

The paper introduces a new task called Zero-Shot Composed Image Retrieval (ZS-CIR) that aims to perform CIR without the need for labeled training data. The proposed approach, named iSEARLE, involves two main steps:

Optimization-based Textual Inversion (OTI): An iterative optimization process that generates pseudo-word tokens to represent the visual information of reference images. This process leverages a GPT-based regularization loss to ensure the pseudo-words can effectively interact with text.

Textual Inversion Network (ϕ) Pre-training: A textual inversion network ϕ is trained on unlabeled images to distill the knowledge from the pseudo-word tokens generated by OTI. This allows ϕ to predict pseudo-words in a single forward pass, making the inference more efficient compared to OTI.

At inference time, iSEARLE uses ϕ to map the reference image to a pseudo-word token, which is then concatenated with the relative caption. The combined text features are then used for standard text-to-image retrieval.
The paper also introduces CIRCO, a new open-domain benchmarking dataset for ZS-CIR with multiple annotated ground truths and semantic categorization of the queries. The experiments show that iSEARLE achieves state-of-the-art performance on three CIR datasets (FashionIQ, CIRR, and CIRCO) and two additional evaluation settings (domain conversion and object composition).

Stats

The average length of the relative captions in CIRCO is 10.4 words.
Approximately 75% of the CIRCO queries are composed of multiple semantic statements, compared to 43% in CIRR.
CIRCO has an estimated total of 4,987 ground truths, with 4,624 (92.7%) annotated.

Citations

"We introduce a new task, Zero-Shot Composed Image Retrieval (ZS-CIR), to eliminate the requirement for costly labeled data for CIR."
"We propose a novel approach, named iSEARLE, that relies on a textual inversion network to address ZS-CIR by mapping images into pseudo-words."
"We introduce CIRCO, an open-domain benchmarking dataset for ZS-CIR with multiple annotated ground truths, reduced false negatives, and a semantic categorization of the queries."

Idées clés tirées de

iSEARLE: Improving Textual Inversion for Zero-Shot Composed Image Retrieval

by Lorenzo Agno... à arxiv.org 05-07-2024

https://arxiv.org/pdf/2405.02951.pdf

Questions plus approfondies

How can the proposed textual inversion approach be extended to other vision-language tasks beyond CIR, such as image captioning or visual question answering

The proposed textual inversion approach can be extended to other vision-language tasks beyond Composed Image Retrieval (CIR) by leveraging the concept of mapping visual information into a pseudo-word token. For image captioning tasks, the pseudo-word token generated from the image can be used as a representation of the visual content, which can then be combined with the generated caption to enhance the multimodal understanding of the image. This approach can help improve the alignment between the visual and textual modalities in image captioning systems, leading to more accurate and descriptive captions.
Similarly, in visual question answering (VQA) tasks, the pseudo-word token can serve as a condensed representation of the image features, which can be integrated with the question to provide a more comprehensive understanding of the visual context. By incorporating the pseudo-word token into the question embedding, the model can better capture the relationship between the image and the query, leading to improved performance in answering visual questions accurately.
Overall, the textual inversion approach can be applied to various vision-language tasks by generating pseudo-word tokens that encode visual information and integrating them with textual inputs to enhance the multimodal understanding and performance of the models.

What are the potential limitations of using a single pseudo-word token to represent the visual information of an image, and how could this be addressed

Using a single pseudo-word token to represent the visual information of an image may have limitations in capturing the complexity and diversity of visual content. Some potential limitations include:

Loss of Fine-Grained Details: A single pseudo-word token may not capture all the intricate details and nuances present in the image, leading to a loss of specific visual features that could be crucial for accurate retrieval or understanding.
Limited Expressiveness: The representation provided by a single pseudo-word token may not be expressive enough to encapsulate the full range of visual information present in the image, potentially limiting the model's ability to differentiate between similar images.
Semantic Gap: There might be a semantic gap between the visual features encoded in the pseudo-word token and the textual descriptions, which could result in mismatches or inaccuracies in the retrieval process.

To address these limitations, one approach could be to explore the use of multiple pseudo-word tokens to represent different aspects or regions of the image. By incorporating a more diverse set of pseudo-word tokens, the model can capture a broader range of visual information and improve the richness and expressiveness of the representation. Additionally, techniques such as attention mechanisms or hierarchical modeling could be employed to focus on different parts of the image and enhance the overall representation.

Given the broad domain of CIRCO, how could the dataset be further expanded to include more diverse and challenging queries, such as those involving complex scene understanding or reasoning

To further expand the CIRCO dataset and include more diverse and challenging queries, such as those involving complex scene understanding or reasoning, several strategies can be considered:

Fine-Grained Semantic Annotations: Introduce more detailed semantic categories that capture complex relationships and attributes in the images, enabling the creation of queries that require advanced scene understanding and reasoning abilities.
Multi-Modal Queries: Develop queries that involve multiple modalities, such as text, images, and audio, to test the model's ability to integrate and reason across different types of information.
Contextual Understanding: Include queries that require contextual understanding and reasoning, such as understanding spatial relationships, temporal sequences, or causal relationships between objects in the scene.
Adversarial Examples: Incorporate adversarial examples or challenging scenarios that test the model's robustness and generalization capabilities in handling ambiguous or misleading information.
Interactive Queries: Design interactive queries where the model needs to interact with the environment or make decisions based on dynamic changes in the scene, simulating real-world scenarios that require adaptive reasoning.

By incorporating these elements into the dataset, CIRCO can provide a more comprehensive and diverse set of queries that challenge models to exhibit advanced scene understanding, reasoning, and multimodal integration capabilities.