Pic2Word: Advancing Zero-Shot Composed Image Retrieval

Introduction: mage retrieval plays a vital role in search engines, where users often rely on either images or text as queries to find their desired target images. However, describing images accurately using words can be challenging, leading to limitations in text-based retrieval. To address this, composed image retrieval (CIR) combines both images and text samples to provide instructions on how to modify images and precisely retrieve the intended target image. In a recent research paper titled "Pic2Word: Mapping Pictures to Words for Zero-shot Composed Image Retrieval," a novel approach called zero-shot CIR (ZS-CIR) is proposed to overcome the limitations of traditional CIR methods.

Challenges and Proposed Solution: Traditional CIR methods require large amounts of labeled data, making them expensive and limited to specific use cases. In contrast, ZS-CIR aims to build a single CIR model capable of performing a variety of tasks, such as object composition, attribute editing, or domain conversion, without relying on labeled triplet data. The authors propose leveraging the language capabilities of the contrastive language-image pre-trained model (CLIP) to generate semantically meaningful language embeddings. By mapping images to word tokens using CLIP, the model enables flexible composition of image features and text descriptions, facilitating precise retrieval of target images.

Method Overview: The authors utilize a lightweight mapping sub-module in CLIP to map input images to word tokens. The entire network is optimized with a vision-language contrastive loss to ensure close alignment between visual and text embedding spaces. By treating the query image as a word token, Pic2Word enables seamless composition of image and text descriptions. The training process involves reconstructing the image embedding in the language embedding using the contrastive loss proposed in CLIP. The figure provided in the article illustrates the training process.

Evaluation's evaluate the performance of Pic2Word, several experiments are conducted on various CIR tasks. The first task involves domain conversion, where images are transformed into different desired domains or styles. The proposed method is compared with approaches that don't require supervised training data, showcasing the importance of composing image and text using a language encoder. Additionally, fashion attribute composition is evaluated using the Fashion-IQ dataset. Pic2Word outperforms supervised baselines with smaller backbones, demonstrating its effectiveness even in zero-shot scenarios.

Qualitative Results: The article presents qualitative results comparing Pic2Word with a baseline method that doesn't require supervised training data. Pic2Word demonstrates better accuracy in retrieving the target images, further highlighting its capabilities in zero-shot composed image retrieval.

Conclusion and Future Work: In conclusion, Pic2Word presents a powerful method for zero-shot composed image retrieval by mapping pictures to words. The study demonstrates that training on image-caption datasets can lead to highly effective CIR models without relying on annotated triplets. Future research directions may explore incorporating caption data into the training of the mapping network, further enhancing the capabilities of zero-shot CIR models.

By addressing the limitations of traditional CIR methods, Pic2Word opens new avenues for precise image retrieval and paves the way for advancements in the field of zero-shot composed image retrieval.

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