Papers with VL tasks
MetaVL: Transferring In-Context Learning Ability From Language Models to Vision-Language Models (2023.acl-short)
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| Challenge: | Large-scale pre-trained vision-language models do not possess the ability to conduct in-context learning. |
| Approach: | They propose to meta-train a language model to perform in-context learning on NLP tasks and then transfer this model to VL tasks by attaching a visual encoder. |
| Outcome: | The proposed model outperforms the baseline model on VQA, OK-VQA, and GQA while having 20 times fewer parameters. |
CoLLaVO: Crayon Large Language and Vision mOdel (2024.findings-acl)
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| Challenge: | Existing Large Language Models (LLMs) and instruction tuning have been used to drive the evolution of Vision Language Model (VLM) towards a versatile general-purpose model. |
| Approach: | They propose a learning strategy of Dual QLoRA to preserve object-level image understanding without forgetting it during visual instruction tuning, thereby achieving a significant leap in numerous VL benchmarks in a zero-shot setting. |
| Outcome: | The proposed model outperforms closed-source models on vision language tasks and achieves a significant leap in numerous benchmarks. |
“Let’s not Quote out of Context”: Unified Vision-Language Pretraining for Context Assisted Image Captioning (2023.acl-industry)
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| Challenge: | Large enterprises have several teams to create their content for the purpose of marketing, campaigning, or even maintaining a brand presence. |
| Approach: | They propose a new unified Vision-Language (VL) model with a focus on context-assisted image captioning where the caption is generated based on both the image and its context. |
| Outcome: | The proposed model achieves state-of-the-art with an improvement of up to 8.34 CIDEr score on the benchmark news image captioning datasets. |
Understanding Attention for Vision-and-Language Tasks (2022.coling-1)
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| Challenge: | Attention mechanism has been used in Vision-and-Language (VL) tasks to bridge the semantic gap between visual and textual clues. |
| Approach: | They conduct a comprehensive analysis on understanding the role of attention alignment by looking into attention score calculation methods and checking how it represents the visual region’s and textual token’s significance for the global assessment. |
| Outcome: | The attention score calculation methods represent visual region’s and textual token’s significance for the global assessment. |
Dataset Bias Mitigation in Multiple-Choice Visual Question Answering and Beyond (2023.findings-emnlp)
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Zhecan Wang, Long Chen, Haoxuan You, Keyang Xu, Yicheng He, Wenhao Li, Noel Codella, Kai-Wei Chang, Shih-Fu Chang
| Challenge: | Existing studies have examined dataset biases in VQA benchmarks with short-phrase answers Multiple-choice Question with the LONG Answers (VCR, VLEP, etc.) |
| Approach: | They propose to use Adversarial Data Synthesis (ADS) to generate synthetic training and debiased evaluation data and introduce Intra-sample Counterfactual Training (ICT) to assist models in utilizing synthesized training data. |
| Outcome: | The proposed approach improves model performance even in domain-shifted scenarios. |