Semantics-enhanced Cross-modal Masked Image Modeling for Vision-Language Pre-training (2024.lrec-main)
Copied to clipboard
Haowei Liu, Yaya Shi, Haiyang Xu, Chunfeng Yuan, Qinghao Ye, Chenliang Li, Ming Yan, Ji Zhang, Fei Huang, Bing Li, Weiming Hu
| Challenge: | Existing methods for vision-language pre-training lack high-level semantics and text is not sufficiently involved in masked modeling. |
| Approach: | They propose a semantics-enhanced cross-modal MIM framework for vision-language representation learning that harvests high-level semantics from global image features via self-supervised agreement learning and transfers them to local patch encodings by sharing the encode space. |
| Outcome: | The proposed model achieves state-of-the-art or competitive performance on multiple vision-language tasks. |
Similar Papers
Curriculum Masking in Vision-Language Pretraining to Maximize Cross Modal Interaction (2024.naacl-long)
Copied to clipboard
| Challenge: | masked language modeling is widely used as a pretraining component in Vision and language (V+L) but performance on benchmarks has not received the attention it deserves. |
| Approach: | They propose a curriculum masking scheme that uses a parallel mask selection agent to mask tokens at a frequency proportional to the level of cross modal interaction necessary to reconstruct them. |
| Outcome: | The proposed method improves relational understanding on a wide range of V+L tasks. |
Conditioned Masked Language and Image Modeling for Image-Text Dense Retrieval (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Large-scale two-stream pre-trained models like CLIP have achieved tremendous success in image-text retrieval. |
| Approach: | They propose a cross-modal framework for image-text retrieval using two-stream pre-trained models . they embed images and texts into instance representations with two separate encoders . experimental results on MSCOCO and Flickr30k reveal the effectiveness of their framework . |
| Outcome: | The proposed framework improves image-text retrieval performance on two popular cross-modal retrieval benchmarks. |
Cross-View Language Modeling: Towards Unified Cross-Lingual Cross-Modal Pre-training (2023.acl-long)
Copied to clipboard
| Challenge: | Empirical results show that CCLM significantly outperforms the prior state-of-the-art with an average absolute improvement of over 10%. |
| Approach: | They introduce a pre-training framework that unifies cross-lingual and cross-modal pre-trained models with shared architectures and objectives. |
| Outcome: | The proposed framework outperforms the state-of-the-art in two multi-lingual datasets and two multilingual image-text retrieval datasets. |
KD-VLP: Improving End-to-End Vision-and-Language Pretraining with Object Knowledge Distillation (2022.findings-naacl)
Copied to clipboard
| Challenge: | Existing vision-and-language pretraining approaches rely on external object detectors to encode images in a multi-modal transformer framework. |
| Approach: | They propose an object-aware end-to-end VLP framework which feeds image grid features from CNNs into the Transformer and learns the multi-modal representations jointly. |
| Outcome: | The proposed framework achieves competitive or superior performances on vision-language tasks. |
Data Efficient Masked Language Modeling for Vision and Language (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Masked language modeling (MLM) is one of the key sub-tasks in vision-language pretraining. |
| Approach: | They propose a masking strategy that masks tokens with a 15% probability for text-only data. |
| Outcome: | The proposed masking strategy outperforms the baseline model on a prompt-based probing task designed to elicit image objects. |
Cross-lingual Visual Pre-training for Multimodal Machine Translation (2021.eacl-main)
Copied to clipboard
Ozan Caglayan, Menekse Kuyu, Mustafa Sercan Amac, Pranava Madhyastha, Erkut Erdem, Aykut Erdem, Lucia Specia
| Challenge: | Pre-trained language models have been shown to improve performance in many natural language tasks. |
| Approach: | They propose to combine cross-lingual and visual pre-training to learn visually-grounded cross-linguistic representations using masked region classification and three-way parallel vision & language corpora. |
| Outcome: | The proposed models obtain state-of-the-art performance when fine-tuned for multimodal machine translation. |
Fusion or Defusion? Flexible Vision-and-Language Pre-Training (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to vision-and-language pretraining (VLP) lack effectiveness and efficiency in downstream multimodal tasks. |
| Approach: | They propose a flexible vision-and-language pre-training model by incorporating cross-modal fusions into a dual-encoder architecture and a cross-module knowledge transfer strategy to guide the training process. |
| Outcome: | The proposed model is well-equipped with effectiveness and efficiency compared with other strong VLP models. |
MMCLIP: Cross-Modal Attention Masked Modelling for Medical Language-Image Pre-Training (2026.acl-long)
Copied to clipboard
| Challenge: | Existing vision-and-language pretraining methods face challenges in reconstructing pathological features due to limited data. |
| Approach: | They propose a method that uses masked modeling to enhance visual and linguistic learning. |
| Outcome: | MMCLIP integrates unpaired data through disease-kind prompts to achieve state-of-the-art performance in zero-shot and fine-tuning across five benchmarks. |
Unifying Cross-Lingual and Cross-Modal Modeling Towards Weakly Supervised Multilingual Vision-Language Pre-training (2023.acl-long)
Copied to clipboard
| Challenge: | Existing studies address the problem of translating English data into other languages, but they are limited in form and scale. |
| Approach: | They propose a framework to unify cross-lingual and cross-modal pre-training by using English data. |
| Outcome: | The proposed framework unifies cross-lingual and cross-modal pre-training on different data. |
Probing Multi-modal Machine Translation with Pre-trained Language Model (2021.findings-acl)
Copied to clipboard
| Challenge: | Multi-modal machine translation (MMT) aimed at using images to help disambiguate the target during translation but recent studies showed that visual features are either negligible or incremental. |
| Approach: | They propose to incorporate a visual language model on the source side to improve multi-modal translation quality significantly. |
| Outcome: | The proposed model improves the translation quality significantly on the multi-modal dataset. |