Challenge: Existing Video-and-Language models do not take into account the different characteristics of video and text representations.
Approach: They propose a method that exploits Centered Kernel Alignment (CKA) to enhance cross-modality attention by combining multiple modalities.
Outcome: The proposed method outperforms conventional multi-modal methods significantly on video QA tasks with +3.57% accuracy increment compared to the baseline in a popular benchmark dataset.

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Watch, Listen, and Describe: Globally and Locally Aligned Cross-Modal Attentions for Video Captioning (N18-2)

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Challenge: Existing multi-modal fusion methods have shown encouraging results in video understanding, but how to selectively fuse the multi-dimensional representations at different levels of details remains unexplored.
Approach: They propose a hierarchically aligned cross-modal attention framework to fuse audio and visual cues at different levels of detail.
Outcome: The proposed framework outperforms the previous best systems on the video captioning task.
Cross-Align: Modeling Deep Cross-lingual Interactions for Word Alignment (2022.emnlp-main)

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Challenge: Existing word alignment models capture few interactions between input sentence pairs, which severely degrades the word alignment quality.
Approach: They propose to model deep interactions between input and target sentences using a two-stage training framework to train the model.
Outcome: The proposed model achieves the state-of-the-art (SOTA) performance on four out of five language pairs.
DELAN: Dual-Level Alignment for Vision-and-Language Navigation by Cross-Modal Contrastive Learning (2024.lrec-main)

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Challenge: Existing studies focus on cross-modal attention at the fusion stage, but modality features generated by disparate uni-encoders reside in their own spaces, leading to a decline in the quality of cross-modulation and decision-making.
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DALR: Dual-level Alignment Learning for Multimodal Sentence Representation Learning (2025.findings-acl)

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Challenge: Existing multimodal sentence representation learning methods focus on aligning images and text at a coarse level, resulting in cross-modal misalignment bias and intra-modal semantic divergence.
Approach: They propose a dual-level alignment learning framework for multimodal sentence representation learning that promotes cross-modal and intra-modal alignment.
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Video-LLaVA: Learning United Visual Representation by Alignment Before Projection (2024.emnlp-main)

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Challenge: Existing approaches to visual-language understanding lack unified tokenization for images and videos . lack of unified visual representations makes it difficult to learn multi-modal interactions from poor projection layers.
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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.
Approach: They propose a modality-dominance score to measure and leverage modality gaps . they propose automatic interpretability metrics to evaluate these features in a scalable manner .
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Deep Generative Model for Joint Alignment and Word Representation (N18-1)

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Challenge: EmbedAlign model embeds words in their complete observed context and learns by marginalisation of latent lexical alignments.
Approach: They exploit translation as a distributional context and embed words as posterior probability densities, rather than point estimates, which allows them to compare words in context using a measure of overlap between distributions.
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Cross-Modality Relevance for Reasoning on Language and Vision (2020.acl-main)

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Challenge: Existing approaches to learn and reason over language and vision data for downstream tasks such as visual question answering (VQA) and natural language for visual reasoning (NLVR)
Approach: They propose a cross-modality relevance module that is used in an end-to-end framework to learn the relevance representation between components of various input modalities under supervision of a target task.
Outcome: The proposed approach shows competitive performance on two different language and vision tasks using public benchmarks and improves the state-of-the-art published results.
Cross-modal Attention Congruence Regularization for Vision-Language Relation Alignment (2023.acl-long)

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Challenge: Despite recent progress towards scaling up multimodal vision-language models, these models struggle on compositional generalization benchmarks such as Winoground.
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Low-resource Neural Machine Translation with Cross-modal Alignment (2022.emnlp-main)

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Challenge: Existing neural machine translation techniques rely on large monolingual corpus, which is costly for some low-resource languages.
Approach: They propose a cross-modal contrastive learning method to learn a shared space for all languages by additional visual modality.
Outcome: The proposed method can learn cross-modal and cross-lingual alignment with small amount of image-text pairs and achieves significant improvements over the text-only baseline.

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