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. |
Similar Papers
E2E-VLP: End-to-End Vision-Language Pre-training Enhanced by Visual Learning (2021.acl-long)
Copied to clipboard
| Challenge: | Existing vision-language pre-training methods use a two-step training procedure to learn visual features from image-text pairs. |
| Approach: | They propose a vision-language pre-trained model for V+L understanding and generation using a unified Transformer framework. |
| Outcome: | The proposed model can learn visual representation and semantic alignments between image and text on visual-text pairs and on visual processing tasks. |
End-to-End Unsupervised Vision-and-Language Pre-training with Referring Expression Matching (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing unsupervised vision-and-language pre-training methods take pre-extracted region-based visual features from external object detectors, which limits flexibility and reduces computational efficiency. |
| Approach: | They propose an unsupervised vision-and-language pre-training task that predicts which patches contain an object referred to in natural language from the encoded visual features. |
| Outcome: | The proposed approach outperforms existing methods and obtains state-of-the-art results on four vision-and-language tasks. |
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. |
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. |
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. |
UNIMO-2: End-to-End Unified Vision-Language Grounded Learning (2022.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for vision-language pre-training can only learn from aligned image-caption data and rely heavily on expensive regional features. |
| Approach: | They propose an end-to-end unified-modal pre-training framework for joint learning . they propose to conduct grounded learning on both images and texts via a sharing grounded space . |
| Outcome: | The proposed model improves visual and visual semantic alignment on images and texts. |
PEVL: Position-enhanced Pre-training and Prompt Tuning for Vision-language Models (2022.emnlp-main)
Copied to clipboard
| Challenge: | Recent advances on self-supervised learning have led to powerful vision-language pre-training models that achieve state-of-the-art performance on a wide range of cross-modal tasks. |
| Approach: | They propose a vision-language pre-training framework that reformulates discretized object positions and language in a unified language modeling framework. |
| Outcome: | The proposed model improves performance on position-sensitive vision-language (VL) tasks and also improves on position insensitive tasks. |
Efficient Vision-Language pre-training via domain-specific learning for human activities (2024.emnlp-main)
Copied to clipboard
| Challenge: | Current vision-language models owe their success to large-scale pretraining on web-collected data. |
| Approach: | They propose a domain-aligned pretraining strategy that aligns the downstream tasks to the downstream domain without additional data collection. |
| Outcome: | The proposed method outperforms existing models on large-scale vision-language training datasets while preserving generalist knowledge. |
LXMERT: Learning Cross-Modality Encoder Representations from Transformers (D19-1)
Copied to clipboard
| Challenge: | Existing models with better representations of visual content and language have been developed for visual-content understanding. |
| Approach: | They propose a framework to learn vision-and-language connections from Transformers models . they pre-train a large-scale Transformer model with large amounts of image-and sentence pairs . |
| Outcome: | The proposed model improves state-of-the-art on two visual-reasoning tasks by 22% . the proposed model is based on a large-scale Transformer model with three encoders . |
Expedited Training of Visual Conditioned Language Generation via Redundancy Reduction (2024.acl-long)
Copied to clipboard
| Challenge: | EVLGen is a framework for visual-language pre-training with high computational demands. |
| Approach: | They propose a streamlined framework for the pre-training of visually conditioned language generation models with high computational demands. |
| Outcome: | The proposed framework accelerates training of vision-language models by a factor of 5 without compromising performance. |