Vision-and-Language or Vision-for-Language? On Cross-Modal Influence in Multimodal Transformers (2021.emnlp-main)
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| Challenge: | Pretrained vision-and-language BERTs aim to learn representations that combine information from both modalities. |
| Approach: | They propose a diagnostic method based on cross-modal input ablation to assess the extent to which pretrained models integrate cross-module information. |
| Outcome: | The proposed method evaluates the model's performance on the other modality based on inputs from one or both modality. |
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| Challenge: | Existing audio-language task-specific predictive approaches focus on building complicated late-fusion mechanisms. |
| Approach: | They propose a cross-modal transformer for audio-and-language that learns inter-modal connections between audio and language through two proxy tasks on a large amount of audio- and-language pairs. |
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LXMERT: Learning Cross-Modality Encoder Representations from Transformers (D19-1)
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| Challenge: | Existing models with better representations of visual content and language have been developed for visual-content understanding. |
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Do Vision-and-Language Transformers Learn Grounded Predicate-Noun Dependencies? (2022.emnlp-main)
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| Challenge: | a recent study examines whether vision-and-language models learn syntactic dependencies . a controlled evaluation of the models is crucial for a precise and rigorous test of their knowledge . |
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Beyond Cross-Modal Alignment: Measuring and Leveraging Modality Gap in Vision-Language Models (2026.findings-acl)
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| Challenge: | a recent study shows that vision-language models have modality gaps that persist even in well-aligned models. |
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Attention as Grounding: Exploring Textual and Cross-Modal Attention on Entities and Relations in Language-and-Vision Transformer (2022.findings-acl)
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| Challenge: | Existing work has focused on what is captured by multi-modal architectures. |
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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. |
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Probing Multi-modal Machine Translation with Pre-trained Language Model (2021.findings-acl)
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| 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. |
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Word Representation Learning in Multimodal Pre-Trained Transformers: An Intrinsic Evaluation (2021.tacl-1)
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| Challenge: | Existing models for linguistic representations of words are based on information extracted from large text corpora, and the sensory-motor experiences humans have with the world play an important role in determining word meaning. |
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Cross-lingual Visual Pre-training for Multimodal Machine Translation (2021.eacl-main)
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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. |
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Cross-Modal Taxonomic Generalization in (Vision-) Language Models (2026.acl-long)
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| Challenge: | Existing studies have shown that language models learn from surface form to learn from more grounded evidence. |
| Approach: | They propose to use a vision-language model to learn hypernyms from images . they find that the model can recover this knowledge and generalize even when there is no hypernomia in the image. |
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