Papers with VLP model
An Explainable Toolbox for Evaluating Pre-trained Vision-Language Models (2022.emnlp-demos)
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| Challenge: | Existing studies evaluate VLP models by comparing the fine-tuned downstream task performance with the average downstream task accuracy. |
| Approach: | They propose a toolbox for evaluating Vision-Language Pretraining (VLP) models. |
| Outcome: | The proposed toolbox provides the preliminary datasets that deepen the image-texting ability of a VLP model. |
FashionKLIP: Enhancing E-Commerce Image-Text Retrieval with Fashion Multi-Modal Conceptual Knowledge Graph (2023.acl-industry)
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Xiaodan Wang, Chengyu Wang, Lei Li, Zhixu Li, Ben Chen, Linbo Jin, Jun Huang, Yanghua Xiao, Ming Gao
| Challenge: | Recent advances in visual-language pre-trained (VLP) models have greatly improved cross-modal retrieval performance . however, the fine-grained interactions between objects from different modalities are far from well-established . e-commerce domain lacks sufficient training data and fine-granular cross-modulal knowledge . |
| Approach: | They propose a visual-language pre-trained (VLP) image-text retrieval model that integrates cross-modal knowledge into the model to improve performance. |
| Outcome: | The proposed model improves performance on e-commerce image-text retrieval task by a large margin. |
Fusion or Defusion? Flexible Vision-and-Language Pre-Training (2023.findings-acl)
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| 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. |
Probing Cross-modal Semantics Alignment Capability from the Textual Perspective (2022.findings-emnlp)
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| Challenge: | In recent years, vision and language pre-training (VLP) models have advanced the state-of-the-art results in a variety of cross-modal downstream tasks. |
| Approach: | They propose a new probing method that is based on image captioning to first empirically study the cross-modal semantics alignment of VLP models. |
| Outcome: | The proposed method analyzes captions generated by five popular VLP models to reveal how well they align with visual words and how well these align with images. |
Vision Language Pre-training by Contrastive Learning with Cross-Modal Similarity Regulation (2023.acl-long)
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| Challenge: | Large-scale pre-trained vision-language models have recently achieved tremendous success on a wide range of cross-modal tasks. |
| Approach: | They propose a new framework for a semantically-aware contrastive learning that minimizes the MI between false negative and positive samples . |
| Outcome: | The proposed framework minimizes the MI between false negative samples and positive samples even though they share similar semantics. |