TagAlign: Improving Vision-Language Alignment with Multi-Tag Classification
- URL: http://arxiv.org/abs/2312.14149v4
- Date: Tue, 26 Mar 2024 12:47:12 GMT
- Title: TagAlign: Improving Vision-Language Alignment with Multi-Tag Classification
- Authors: Qinying Liu, Wei Wu, Kecheng Zheng, Zhan Tong, Jiawei Liu, Yu Liu, Wei Chen, Zilei Wang, Yujun Shen,
- Abstract summary: We propose an embarrassingly simple approach to better align image and text features with no need of additional data formats other than image-text pairs.
We parse objects and attributes from the description, which are highly likely to exist in the image.
Experiments substantiate the average 5.2% improvement of our framework over existing alternatives.
- Score: 59.779532652634295
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The crux of learning vision-language models is to extract semantically aligned information from visual and linguistic data. Existing attempts usually face the problem of coarse alignment, e.g., the vision encoder struggles in localizing an attribute-specified object. In this work, we propose an embarrassingly simple approach to better align image and text features with no need of additional data formats other than image-text pairs. Concretely, given an image and its paired text, we manage to parse objects (e.g., cat) and attributes (e.g., black) from the description, which are highly likely to exist in the image. It is noteworthy that the parsing pipeline is fully automatic and thus enjoys good scalability. With these parsed semantics as supervision signals, we can complement the commonly used image-text contrastive loss with the multi-tag classification loss. Extensive experimental results on a broad suite of semantic segmentation datasets substantiate the average 5.2\% improvement of our framework over existing alternatives. Furthermore, the visualization results indicate that attribute supervision makes vision-language models accurately localize attribute-specified objects. Project page can be found at https://qinying-liu.github.io/Tag-Align.
Related papers
- Improving fine-grained understanding in image-text pre-training [37.163228122323865]
We introduce SPARse Fine-grained Contrastive Alignment (SPARC), a simple method for pretraining more fine-grained multimodal representations from image-text pairs.
We show improved performance over competing approaches over both image-level tasks relying on coarse-grained information.
arXiv Detail & Related papers (2024-01-18T10:28:45Z) - Tag2Text: Guiding Vision-Language Model via Image Tagging [32.30893277821682]
This paper presents Tag2Text, a vision language pre-training framework, which introduces image tagging into vision-language models.
In contrast to prior works which utilize object tags either manually labeled or automatically detected with an off-the-shelf detector with limited performance, our approach explicitly learns an image tagger using tags parsed from image-paired text.
arXiv Detail & Related papers (2023-03-10T02:16:35Z) - Learning Object-Language Alignments for Open-Vocabulary Object Detection [83.09560814244524]
We propose a novel open-vocabulary object detection framework directly learning from image-text pair data.
It enables us to train an open-vocabulary object detector on image-text pairs in a much simple and effective way.
arXiv Detail & Related papers (2022-11-27T14:47:31Z) - I2DFormer: Learning Image to Document Attention for Zero-Shot Image
Classification [123.90912800376039]
Online textual documents, e.g., Wikipedia, contain rich visual descriptions about object classes.
We propose I2DFormer, a novel transformer-based ZSL framework that jointly learns to encode images and documents.
Our method leads to highly interpretable results where document words can be grounded in the image regions.
arXiv Detail & Related papers (2022-09-21T12:18:31Z) - LANIT: Language-Driven Image-to-Image Translation for Unlabeled Data [39.421312439022316]
We present a LANguage-driven Image-to-image Translation model, dubbed LANIT.
We leverage easy-to-obtain candidate attributes given in texts for a dataset: the similarity between images and attributes indicates per-sample domain labels.
Experiments on several standard benchmarks demonstrate that LANIT achieves comparable or superior performance to existing models.
arXiv Detail & Related papers (2022-08-31T14:30:00Z) - Revising Image-Text Retrieval via Multi-Modal Entailment [25.988058843564335]
Many-to-many matching phenomenon is quite common in the widely-used image-text retrieval datasets.
We propose a multi-modal entailment classifier to determine whether a sentence is entailed by an image plus its linked captions.
arXiv Detail & Related papers (2022-08-22T07:58:54Z) - Integrating Visuospatial, Linguistic and Commonsense Structure into
Story Visualization [81.26077816854449]
We first explore the use of constituency parse trees for encoding structured input.
Second, we augment the structured input with commonsense information and study the impact of this external knowledge on the generation of visual story.
Third, we incorporate visual structure via bounding boxes and dense captioning to provide feedback about the characters/objects in generated images.
arXiv Detail & Related papers (2021-10-21T00:16:02Z) - NewsCLIPpings: Automatic Generation of Out-of-Context Multimodal Media [93.51739200834837]
We propose a dataset where both image and text are unmanipulated but mismatched.
We introduce several strategies for automatic retrieval of suitable images for the given captions.
Our large-scale automatically generated NewsCLIPpings dataset requires models to jointly analyze both modalities.
arXiv Detail & Related papers (2021-04-13T01:53:26Z) - Scaling Up Visual and Vision-Language Representation Learning With Noisy
Text Supervision [57.031588264841]
We leverage a noisy dataset of over one billion image alt-text pairs, obtained without expensive filtering or post-processing steps.
A simple dual-encoder architecture learns to align visual and language representations of the image and text pairs using a contrastive loss.
We show that the scale of our corpus can make up for its noise and leads to state-of-the-art representations even with such a simple learning scheme.
arXiv Detail & Related papers (2021-02-11T10:08:12Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.