Image Retrieval

GPT-4 is a transformer based model pre-trained to predict the next token in a document.

DINO (self-distillation with no labels) is a self-supervised learning method that directly predicts the output of a teacher network - built with a momentum encoder - using a standard cross-entropy loss. In the example to the right, DINO is illustrated in the case of one single pair of views $\left(x\_{1}, x\_{2}\right)$ for simplicity. The model passes two different random transformations of an input image to the student and teacher networks. Both networks have the same architecture but other parameters. The output of the teacher network is centered with a mean computed over the batch. Each network outputs a $K$ dimensional feature normalized with a temperature softmax over the feature dimension. Their similarity is then measured with a cross-entropy loss. A stop-gradient (sg) operator is applied to the teacher to propagate gradients only through the student. The teacher parameters are updated with the student parameters' exponential moving average (ema).

OSCAR is a new learning method that uses object tags detected in images as anchor points to ease the learning of image-text alignment. The model take a triple as input (word-tag-region) and pre-trained with two losses (masked token loss over words and tags, and a contrastive loss between tags and others). OSCAR represents an image-text pair into semantic space via dictionary lookup. Object tags are used as anchor points to align image regions with word embeddings of pre-trained language models. The model is then fine-tuned for understanding and generation tasks.

Vision-and-Language BERT (ViLBERT) is a BERT-based model for learning task-agnostic joint representations of image content and natural language. ViLBERT extend the popular BERT architecture to a multi-modal two-stream model, processing both visual and textual inputs in separate streams that interact through co-attentional transformer layers.

FLAVA aims at building a single holistic universal model that targets all modalities at once. FLAVA is a language vision alignment model that learns strong representations from multimodal data (image-text pairs) and unimodal data (unpaired images and text). The model consists of an image encode transformer to capture unimodal image representations, a text encoder transformer to process unimodal text information, and a multimodal encode transformer that takes as input the encoded unimodal image and text and integrates their representations for multimodal reasoning. During pretraining, masked image modeling (MIM) and mask language modeling (MLM) losses are applied onto the image and text encoders over a single image or a text piece, respectively, while contrastive, masked multimodal modeling (MMM), and image-text matching (ITM) loss are used over paired image-text data. For downstream tasks, classification heads are applied on the outputs from the image, text, and multimodal encoders respectively for visual recognition, language understanding, and multimodal reasoning tasks It can be applied to broad scope of tasks from three domains (visual recognition, language understanding, and multimodal reasoning) under a common transformer model architecture.

ViLT is a minimal vision-and-language pre-training transformer model where processing of visual inputs is simplified to just the same convolution-free manner that text inputs are processed. The model-specific components of ViLT require less computation than the transformer component for multimodal interactions. ViLTThe model is pre-trained on the following objectives: image text matching, masked language modeling, and word patch alignment.

In this work, we present a conceptually simple and effective method to train a strong bilingual multimodal representation model. Starting from the pretrained multimodal representation model CLIP released by OpenAI, we switched its text encoder with a pretrained multilingual text encoder XLM-R, and aligned both languages and image representations by a two-stage training schema consisting of teacher learning and contrastive learning. We validate our method through evaluations of a wide range of tasks. We set new state-of-the-art performances on a bunch of tasks including ImageNet-CN, Flicker30k- CN, and COCO-CN. Further, we obtain very close performances with CLIP on almost all tasks, suggesting that one can simply alter the text encoder in CLIP for extended capabilities such as multilingual understanding. Our models and code are available at https://github.com/FlagAI-Open/FlagAI.

The composed video retrieval (CoVR) task is a new task, where the goal is to find a video that matches both a query image and a query text. The query image represents a visual concept that the user is interested in, and the query text specifies how the concept should be modified or refined. For example, given an image of a fountain and the text during show at night, the CoVR task is to retrieve a video that shows the fountain at night with a show.

Proposes a two-tower pre-training model called BriVL within the cross-modal contrastive learning framework. A cross-modal pre-training model is defined based on the image-text retrieval task. The main goal is thus to learn two encoders that can embed image and text samples into the same space for effective image-text retrieval. To enforce such cross-modal embedding learning, we introduce contrastive learning with the InfoNCE loss into the BriVL model. Given text embedding, the learning objective aims to find the best image embedding from a batch of image embeddings. Similarly, for a given image embedding, the learning objective is to find the best text embedding from a batch of text embeddings. The pre-training model learns a cross-modal embedding space by jointly training the image and text encoders to maximize the cosine similarity of the image and text embeddings of the true pair for each sample in the batch while minimizing the cosine similarity of the embeddings of the other incorrect pairs.

In this work, we present a conceptually simple and effective method to train a strong bilingual multimodal representation model. Starting from the pretrained multimodal representation model CLIP released by OpenAI, we switched its text encoder with a pretrained multilingual text encoder XLM-R, and aligned both languages and image representations by a two-stage training schema consisting of teacher learning and contrastive learning. We validate our method through evaluations of a wide range of tasks. We set new state-of-the-art performances on a bunch of tasks including ImageNet-CN, Flicker30k- CN, and COCO-CN. Further, we obtain very close performances with CLIP on almost all tasks, suggesting that one can simply alter the text encoder in CLIP for extended capabilities such as multilingual understanding. Our models and code are available at https://github.com/FlagAI-Open/FlagAI.

DELG is a convolutional neural network for image retrieval that combines generalized mean pooling for global features and attentive selection for local features. The entire network can be learned end-to-end by carefully balancing the gradient flow between two heads – requiring only image-level labels. This allows for efficient inference by extracting an image’s global feature, detected keypoints and local descriptors within a single model. The model is enabled by leveraging hierarchical image representations that arise in CNNs, which are coupled to generalized mean pooling and attentive local feature detection. Secondly, a convolutional autoencoder module is adopted that can successfully learn low-dimensional local descriptors. This can be readily integrated into the unified model, and avoids the need of post-processing learning steps, such as PCA, that are commonly used. Finally, a procedure is used that enables end-to-end training of the proposed model using only image-level supervision. This requires carefully controlling the gradient flow between the global and local network heads during backpropagation, to avoid disrupting the desired representations.

MultiGrain is a type of image model that learns a single embedding for classes, instances and copies. In other words, it is a convolutional neural network that is suitable for both image classification and instance retrieval. We learn MultiGrain by jointly training an image embedding for multiple tasks. The resulting representation is compact and can outperform narrowly-trained embeddings. The learned embedding output incorporates different levels of granularity.

Image Retrieval is a fundamental task of obtaining images similar to the query one from a database. A common image retrieval practice is to firstly retrieve candidate images via similarity search using global image features and then re-rank the candidates by leveraging their local features. Previous learning-based studies mainly focus on either global or local image representation learning to tackle the retrieval task. In this paper, we abandon the two-stage paradigm and seek to design an effective singlestage solution by integrating local and global information inside images into compact image representations. Specifically, we propose a Deep Orthogonal Local and Global (DOLG) information fusion framework for end-to-end image retrieval. It attentively extracts representative local information with multi-atrous convolutions and self-attention at first. Components orthogonal to the global image representation are then extracted from the local information. At last, the orthogonal components are concatenated with the global representation as a complementary, and then aggregation is performed to generate the final representation. The whole framework is end-to-end differentiable and can be trained with image-level labels. Extensive experimental results validate the effectiveness of our solution and show that our model achieves state-of-the-art image retrieval performances on Revisited Oxford and Paris datasets.

InterBERT aims to model interaction between information flows pertaining to different modalities. This new architecture builds multi-modal interaction and preserves the independence of single modal representation. InterBERT is built with an image embedding layer, a text embedding layer, a single-stream interaction module, and a two stream extraction module. The model is pre-trained with three tasks: 1) masked segment modeling, 2) masked region modeling, and 3) image-text matching.

VLMo is a unified vision-language pre-trained model that jointly learns a dual encoder and a fusion encoder with a modular Transformer network. A Mixture-of-Modality-Experts (MOME) transformer is introduced to encode different modalities which helps it to capture modality-specific information by modality experts, and align content of different modalities by the self-attention module shared across modalities. The model parameters are shared across image-text contrastive learning, masked language modeling, and image-text matching tasks. During fine-tuning, the flexible modeling allows for VLMO to be used as either a dual encoder (i.e., separately encode images and text for retrieval tasks) or a fusion encoder (i.e., jointly encode image-text pairs for better interaction across modalities) Stage-wise pretraining on image-only and text-only data improved the vision-language pre-trained model. The model can be used for classification tasks and fine-tuned as a dual encoder for retrieval tasks.

XGPT is a method of cross-modal generative pre-training for image captioning designed to pre-train text-to-image caption generators through three novel generation tasks, including image-conditioned masked language modeling (IMLM), image-conditioned denoising autoencoding (IDA), and text-conditioned image feature generation (TIGF). The pre-trained XGPT can be fine-tuned without any task-specific architecture modifications and build strong image captioning models.