Transformers documentation

BridgeTower

Transformers

You are viewing v4.34.1 version. A newer version v4.46.3 is available.

Join the Hugging Face community

and get access to the augmented documentation experience

Collaborate on models, datasets and Spaces

Faster examples with accelerated inference

Switch between documentation themes

to get started

BridgeTower

Overview

The BridgeTower model was proposed in BridgeTower: Building Bridges Between Encoders in Vision-Language Representative Learning by Xiao Xu, Chenfei Wu, Shachar Rosenman, Vasudev Lal, Wanxiang Che, Nan Duan. The goal of this model is to build a bridge between each uni-modal encoder and the cross-modal encoder to enable comprehensive and detailed interaction at each layer of the cross-modal encoder thus achieving remarkable performance on various downstream tasks with almost negligible additional performance and computational costs.

This paper has been accepted to the AAAI’23 conference.

The abstract from the paper is the following:

Vision-Language (VL) models with the TWO-TOWER architecture have dominated visual-language representation learning in recent years. Current VL models either use lightweight uni-modal encoders and learn to extract, align and fuse both modalities simultaneously in a deep cross-modal encoder, or feed the last-layer uni-modal representations from the deep pre-trained uni-modal encoders into the top cross-modal encoder. Both approaches potentially restrict vision-language representation learning and limit model performance. In this paper, we propose BRIDGETOWER, which introduces multiple bridge layers that build a connection between the top layers of uni-modal encoders and each layer of the crossmodal encoder. This enables effective bottom-up cross-modal alignment and fusion between visual and textual representations of different semantic levels of pre-trained uni-modal encoders in the cross-modal encoder. Pre-trained with only 4M images, BRIDGETOWER achieves state-of-the-art performance on various downstream vision-language tasks. In particular, on the VQAv2 test-std set, BRIDGETOWER achieves an accuracy of 78.73%, outperforming the previous state-of-the-art model METER by 1.09% with the same pre-training data and almost negligible additional parameters and computational costs. Notably, when further scaling the model, BRIDGETOWER achieves an accuracy of 81.15%, surpassing models that are pre-trained on orders-of-magnitude larger datasets.

BridgeTower architecture. Taken from the original paper.

Usage

BridgeTower consists of a visual encoder, a textual encoder and cross-modal encoder with multiple lightweight bridge layers. The goal of this approach was to build a bridge between each uni-modal encoder and the cross-modal encoder to enable comprehensive and detailed interaction at each layer of the cross-modal encoder. In principle, one can apply any visual, textual or cross-modal encoder in the proposed architecture.

The BridgeTowerProcessor wraps RobertaTokenizer and BridgeTowerImageProcessor into a single instance to both encode the text and prepare the images respectively.

The following example shows how to run contrastive learning using BridgeTowerProcessor and BridgeTowerForContrastiveLearning.

>>> from transformers import BridgeTowerProcessor, BridgeTowerForContrastiveLearning
>>> import requests
>>> from PIL import Image

>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> texts = ["An image of two cats chilling on a couch", "A football player scoring a goal"]

>>> processor = BridgeTowerProcessor.from_pretrained("BridgeTower/bridgetower-large-itm-mlm-itc")
>>> model = BridgeTowerForContrastiveLearning.from_pretrained("BridgeTower/bridgetower-large-itm-mlm-itc")

>>> # forward pass
>>> scores = dict()
>>> for text in texts:
...     # prepare inputs
...     encoding = processor(image, text, return_tensors="pt")
...     outputs = model(**encoding)
...     scores[text] = outputs

The following example shows how to run image-text retrieval using BridgeTowerProcessor and BridgeTowerForImageAndTextRetrieval.

>>> from transformers import BridgeTowerProcessor, BridgeTowerForImageAndTextRetrieval
>>> import requests
>>> from PIL import Image

>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> texts = ["An image of two cats chilling on a couch", "A football player scoring a goal"]

>>> processor = BridgeTowerProcessor.from_pretrained("BridgeTower/bridgetower-base-itm-mlm")
>>> model = BridgeTowerForImageAndTextRetrieval.from_pretrained("BridgeTower/bridgetower-base-itm-mlm")

>>> # forward pass
>>> scores = dict()
>>> for text in texts:
...     # prepare inputs
...     encoding = processor(image, text, return_tensors="pt")
...     outputs = model(**encoding)
...     scores[text] = outputs.logits[0, 1].item()

The following example shows how to run masked language modeling using BridgeTowerProcessor and BridgeTowerForMaskedLM.

>>> from transformers import BridgeTowerProcessor, BridgeTowerForMaskedLM
>>> from PIL import Image
>>> import requests

>>> url = "http://images.cocodataset.org/val2017/000000360943.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
>>> text = "a <mask> looking out of the window"

>>> processor = BridgeTowerProcessor.from_pretrained("BridgeTower/bridgetower-base-itm-mlm")
>>> model = BridgeTowerForMaskedLM.from_pretrained("BridgeTower/bridgetower-base-itm-mlm")

>>> # prepare inputs
>>> encoding = processor(image, text, return_tensors="pt")

>>> # forward pass
>>> outputs = model(**encoding)

>>> results = processor.decode(outputs.logits.argmax(dim=-1).squeeze(0).tolist())

>>> print(results)
.a cat looking out of the window.

This model was contributed by Anahita Bhiwandiwalla, Tiep Le and Shaoyen Tseng. The original code can be found here.

Tips:

This implementation of BridgeTower uses RobertaTokenizer to generate text embeddings and OpenAI’s CLIP/ViT model to compute visual embeddings.
Checkpoints for pre-trained bridgeTower-base and bridgetower masked language modeling and image text matching are released.
Please refer to Table 5 for BridgeTower’s performance on Image Retrieval and other down stream tasks.
The PyTorch version of this model is only available in torch 1.10 and higher.

Transformers

BridgeTower

Overview

Usage

BridgeTowerConfig

class transformers.BridgeTowerConfig

from_text_vision_configs

BridgeTowerTextConfig

class transformers.BridgeTowerTextConfig

BridgeTowerVisionConfig

class transformers.BridgeTowerVisionConfig

BridgeTowerImageProcessor

class transformers.BridgeTowerImageProcessor

preprocess

BridgeTowerProcessor

class transformers.BridgeTowerProcessor

__call__

BridgeTowerModel

class transformers.BridgeTowerModel

forward

BridgeTowerForContrastiveLearning

class transformers.BridgeTowerForContrastiveLearning

forward

BridgeTowerForMaskedLM

class transformers.BridgeTowerForMaskedLM

forward

BridgeTowerForImageAndTextRetrieval

class transformers.BridgeTowerForImageAndTextRetrieval

forward

call