Posted by Kuniaki Saito, Student Researcher, Google Research, Cloud AI Team, and Kihyuk Sohn, Research Scientist, Google Research

Image retrieval plays a crucial role in search engines. Typically, their users rely on either image or text as a query to retrieve a desired target image. However, text-based retrieval has its limitations, as describing the target image accurately using words can be challenging. For instance, when searching for a fashion item, users may want an item whose specific attribute, e.g., the color of a logo or the logo itself, is different from what they find in a website. Yet searching for the item in an existing search engine is not trivial since precisely describing the fashion item by text can be challenging. To address this fact, composed image retrieval (CIR) retrieves images based on a query that combines both an image and a text sample that provides instructions on how to modify the image to fit the intended retrieval target. Thus, CIR allows precise retrieval of the target image by combining image and text.

However, CIR methods require large amounts of labeled data, i.e., triplets of a 1) query image, 2) description, and 3) target image. Collecting such labeled data is costly, and models trained on this data are often tailored to a specific use case, limiting their ability to generalize to different datasets.

To address these challenges, in “Pic2Word: Mapping Pictures to Words for Zero-shot Composed Image Retrieval”, we propose a task called zero-shot CIR (ZS-CIR). In ZS-CIR, we aim to build a single CIR model that performs a variety of CIR tasks, such as object composition, attribute editing, or domain conversion, without requiring labeled triplet data. Instead, we propose to train a retrieval model using large-scale image-caption pairs and unlabeled images, which are considerably easier to collect than supervised CIR datasets at scale. To encourage reproducibility and further advance this space, we also release the code.

Description of existing composed image retrieval model.

We train a composed image retrieval model using image-caption data only. Our model retrieves images aligned with the composition of the query image and text.

Method overview

We propose to leverage the language capabilities of the language encoder in the contrastive language-image pre-trained model (CLIP), which excels at generating semantically meaningful language embeddings for a wide range of textual concepts and attributes. To that end, we use a lightweight mapping sub-module in CLIP that is designed to map an input picture (e.g., a photo of a cat) from the image embedding space to a word token (e.g., “cat”) in the textual input space. The whole network is optimized with the vision-language contrastive loss to again ensure the visual and text embedding spaces are as close as possible given a pair of an image and its textual description. Then, the query image can be treated as if it is a word. This enables the flexible and seamless composition of query image features and text descriptions by the language encoder. We call our method Pic2Word and provide an overview of its training process in the figure below. We want the mapped token s to represent the input image in the form of word token. Then, we train the mapping network to reconstruct the image embedding in the language embedding, p. Specifically, we optimize the contrastive loss proposed in CLIP computed between the visual embedding v and the textual embedding p.

Training of the mapping network (fM) using unlabeled images only. We optimize only the mapping network with a frozen visual and text encoder.

Given the trained mapping network, we can regard an image as a word token and pair it with the text description to flexibly compose the joint image-text query as shown in the figure below.

With the trained mapping network, we regard the image as a word token and pair it with the text description to flexibly compose the joint image-text query.

Evaluation

We conduct a variety of experiments to evaluate Pic2Word’s performance on a variety of CIR tasks.

Domain conversion

We first evaluate the capability of compositionality of the proposed method on domain conversion — given an image and the desired new image domain (e.g., sculpture, origami, cartoon, toy), the output of the system should be an image with the same content but in the new desired image domain or style. As illustrated below, we evaluate the ability to compose the category information and domain description given as an image and text, respectively. We evaluate the conversion from real images to four domains using ImageNet and ImageNet-R.

To compare with approaches that do not require supervised training data, we pick three approaches: (i) image only performs retrieval only with visual embedding, (ii) text only employs only text embedding, and (iii) image + text averages the visual and text embedding to compose the query. The comparison with (iii) shows the importance of composing image and text using a language encoder. We also compare with Combiner, which trains the CIR model on Fashion-IQ or CIRR.

We aim to convert the domain of the input query image into the one described with text, e.g., origami.

As shown in figure below, our proposed approach outperforms baselines by a large margin.

Results (recall@10, i.e., the percentage of relevant instances in the first 10 images retrieved.) on composed image retrieval for domain conversion.

Fashion attribute composition

Next, we evaluate the composition of fashion attributes, such as the color of cloth, logo, and length of sleeve, using the Fashion-IQ dataset. The figure below illustrates the desired output given the query.

Overview of CIR for fashion attributes.

In the figure below, we present a comparison with baselines, including supervised baselines that utilized triplets for training the CIR model: (i) CB uses the same architecture as our approach, (ii) CIRPLANT, ALTEMIS, MAAF use a smaller backbone, such as ResNet50. Comparison to these approaches will give us the understanding on how well our zero-shot approach performs on this task.

Although CB outperforms our approach, our method performs better than supervised baselines with smaller backbones. This result suggests that by utilizing a robust CLIP model, we can train a highly effective CIR model without requiring annotated triplets.

Results (recall@10, i.e., the percentage of relevant instances in the first 10 images retrieved.) on composed image retrieval for Fashion-IQ dataset (higher is better). Light blue bars train the model using triplets. Note that our approach performs on par with these supervised baselines with shallow (smaller) backbones.

Qualitative results

We show several examples in the figure below. Compared to a baseline method that does not require supervised training data (text + image feature averaging), our approach does a better job of correctly retrieving the target image.

Qualitative results on diverse query images and text description.

Conclusion and future work

In this article, we introduce Pic2Word, a method for mapping pictures to words for ZS-CIR. We propose to convert the image into a word token to achieve a CIR model using only an image-caption dataset. Through a variety of experiments, we verify the effectiveness of the trained model on diverse CIR tasks, indicating that training on an image-caption dataset can build a powerful CIR model. One potential future research direction is utilizing caption data to train the mapping network, although we use only image data in the present work.

Acknowledgements

This research was conducted by Kuniaki Saito, Kihyuk Sohn, Xiang Zhang, Chun-Liang Li, Chen-Yu Lee, Kate Saenko, and Tomas Pfister. Also thanks to Zizhao Zhang and Sergey Ioffe for their valuable feedback.

Posted by Kuniaki Saito, Student Researcher, Cloud AI Team, and Kihyuk Sohn, Research Scientist, Perception Team

Pre-training visual language (VL) models on web-scale image-caption datasets has recently emerged as a powerful alternative to traditional pre-training on image classification data. Image-caption datasets are considered to be more “open-domain” because they contain broader scene types and vocabulary words, which result in models with strong performance in few- and zero-shot recognition tasks. However, images with fine-grained class descriptions can be rare, and the class distribution can be imbalanced since image-caption datasets do not go through manual curation. By contrast, large-scale classification datasets, such as ImageNet, are often curated and can thus provide fine-grained categories with a balanced label distribution. While it may sound promising, directly combining caption and classification datasets for pre-training is often unsuccessful as it can result in biased representations that do not generalize well to various downstream tasks.

In “Prefix Conditioning Unifies Language and Label Supervision”, presented at CVPR 2023, we demonstrate a pre-training strategy that uses both classification and caption datasets to provide complementary benefits. First, we show that naïvely unifying the datasets results in sub-optimal performance on downstream zero-shot recognition tasks as the model is affected by dataset bias: the coverage of image domains and vocabulary words is different in each dataset. We address this problem during training through prefix conditioning, a novel simple and effective method that uses prefix tokens to disentangle dataset biases from visual concepts. This approach allows the language encoder to learn from both datasets while also tailoring feature extraction to each dataset. Prefix conditioning is a generic method that can be easily integrated into existing VL pre-training objectives, such as Contrastive Language-Image Pre-training (CLIP) or Unified Contrastive Learning (UniCL).

High-level idea

We note that classification datasets tend to be biased in at least two ways: (1) the images mostly contain single objects from restricted domains, and (2) the vocabulary is limited and lacks the linguistic flexibility required for zero-shot learning. For example, the class embedding of “a photo of a dog” optimized for ImageNet usually results in a photo of one dog in the center of the image pulled from the ImageNet dataset, which does not generalize well to other datasets containing images of multiple dogs in different spatial locations or a dog with other subjects.

By contrast, caption datasets contain a wider variety of scene types and vocabularies. As shown below, if a model simply learns from two datasets, the language embedding can entangle the bias from the image classification and caption dataset, which can decrease the generalization in zero-shot classification. If we can disentangle the bias from two datasets, we can use language embeddings that are tailored for the caption dataset to improve generalization.

Top: Language embedding entangling the bias from image classification and caption dataset. Bottom: Language embeddings disentangles the bias from two datasets.

Prefix conditioning

Prefix conditioning is partially inspired by prompt tuning, which prepends learnable tokens to the input token sequences to instruct a pre-trained model backbone to learn task-specific knowledge that can be used to solve downstream tasks. The prefix conditioning approach differs from prompt tuning in two ways: (1) it is designed to unify image-caption and classification datasets by disentangling the dataset bias, and (2) it is applied to VL pre-training while the standard prompt tuning is used to fine-tune models. Prefix conditioning is an explicit way to specifically steer the behavior of model backbones based on the type of datasets provided by users. This is especially helpful in production when the number of different types of datasets is known ahead of time.

During training, prefix conditioning learns a text token (prefix token) for each dataset type, which absorbs the bias of the dataset and allows the remaining text tokens to focus on learning visual concepts. Specifically, it prepends prefix tokens for each dataset type to the input tokens that inform the language and visual encoder of the input data type (e.g., classification vs. caption). Prefix tokens are trained to learn the dataset-type-specific bias, which enables us to disentangle that bias in language representations and utilize the embedding learned on the image-caption dataset during test time, even without an input caption.

We utilize prefix conditioning for CLIP using a language and visual encoder. During test time, we employ the prefix used for the image-caption dataset since the dataset is supposed to cover broader scene types and vocabulary words, leading to better performance in zero-shot recognition.

Illustration of the Prefix Conditioning.

Experimental results

We apply prefix conditioning to two types of contrastive loss, CLIP and UniCL, and evaluate their performance on zero-shot recognition tasks compared to models trained with ImageNet21K (IN21K) and Conceptual 12M (CC12M). CLIP and UniCL models trained with two datasets using prefix conditioning show large improvements in zero-shot classification accuracy.

Zero-shot classification accuracy of models trained with only IN21K or CC12M compared to CLIP and UniCL models trained with both two datasets using prefix conditioning (“Ours”).

Study on test-time prefix

The table below describes the performance change by the prefix used during test time. We demonstrate that by using the same prefix used for the classification dataset (“Prompt”), the performance on the classification dataset (IN-1K) improves. When using the same prefix used for the image-caption dataset (“Caption”), the performance on other datasets (Zero-shot AVG) improves. This analysis illustrates that if the prefix is tailored for the image-caption dataset, it achieves better generalization of scene types and vocabulary words.

Analysis of the prefix used for test-time.

Study on robustness to image distribution shift

We study the shift in image distribution using ImageNet variants. We see that the “Caption” prefix performs better than “Prompt” in ImageNet-R (IN-R) and ImageNet-Sketch (IN-S), but underperforms in ImageNet-V2 (IN-V2). This indicates that the “Caption” prefix achieves generalization on domains far from the classification dataset. Therefore, the optimal prefix probably differs by how far the test domain is from the classification dataset.

Analysis on the robustness to image-level distribution shift. IN: ImageNet, IN-V2: ImageNet-V2, IN-R: Art, Cartoon style ImageNet, IN-S: ImageNet Sketch.

Conclusion and future work

We introduce prefix conditioning, a technique for unifying image caption and classification datasets for better zero-shot classification. We show that this approach leads to better zero-shot classification accuracy and that the prefix can control the bias in the language embedding. One limitation is that the prefix learned on the caption dataset is not necessarily optimal for the zero-shot classification. Identifying the optimal prefix for each test dataset is an interesting direction for future work.

Acknowledgements

This research was conducted by Kuniaki Saito, Kihyuk Sohn, Xiang Zhang, Chun-Liang Li, Chen-Yu Lee, Kate Saenko, and Tomas Pfister. Thanks to Zizhao Zhang and Sergey Ioffe for their valuable feedback.