Papers with Flickr30K
Learning to Represent Image and Text with Denotation Graph (2020.emnlp-main)
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| Challenge: | Recent advances in learning representations of visual and language information have been a problem with many applications. |
| Approach: | They propose to extract visual expressions from images aligned with linguistic expressions that describe the images to learn representations from implicit expressions. |
| Outcome: | The proposed representations lead to stronger empirical results on downstream tasks of cross-modal image retrieval, referring expression, and compositional attribute-object recognition. |
The Role of Data Curation in Image Captioning (2024.eacl-long)
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| Challenge: | Existing image captioning models treat all samples equally, neglecting mismatched data . Several other techniques have relied on curriculum learning strategies to adapt learning to the difficulty of the task. |
| Approach: | They propose to actively curate difficult samples in datasets using curriculum learning strategies to improve captioning models. |
| Outcome: | The proposed methods outperform existing models on the Flickr30K and COCO datasets. |
Improving Visual-Semantic Embedding with Adaptive Pooling and Optimization Objective (2023.eacl-main)
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Zijian Zhang, Chang Shu, Ya Xiao, Yuan Shen, Di Zhu, Youxin Chen, Jing Xiao, Jey Han Lau, Qian Zhang, Zheng Lu
| Challenge: | Recent VSE models combine simple pooling methods with hard triplet loss to improve performance. |
| Approach: | They propose an adaptive pooling strategy that allows the model to learn how to aggregate features through a combination of simple pooling methods. |
| Outcome: | The proposed strategy outperforms current state-of-the-art systems on image-to-text and text-toimage retrieval. |
Leaner and Faster: Two-Stage Model Compression for Lightweight Text-Image Retrieval (2022.naacl-main)
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| Challenge: | Existing text-image approaches use pre-trained vision-language representations for text retrieval . however, these models pose non-trivial memory requirements and substantial indexing time . |
| Approach: | They propose a framework to compress large pre-trained dual-encoders for lightweight text-image retrieval. |
| Outcome: | The proposed model performs better on Flickr30K and MSCOCO benchmarks than the original full model on mobile devices. |
An Anchor-based Relative Position Embedding Method for Cross-Modal Tasks (2022.emnlp-main)
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| Challenge: | Position Embedding (PE) is essential for transformer to capture the sequence ordering of input tokens. |
| Approach: | They propose a unified position embedding method that bridges the semantic gap between modalities and embeds the anchor-based distance to guide computation of cross-attention. |
| Outcome: | The proposed method obtains new SOTA results on a wide range of benchmarks. |
Generative Text-to-Image Retrieval via Hierarchical Identifiers and Semantic Internalization (2026.findings-acl)
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| Challenge: | Existing text-to-image retrieval methods suffer from limited semantic discriminability, alignment bias, and closed-set restrictions. |
| Approach: | They propose a framework for semantic internalization for Generative Multimodal Alignment . they construct multi-granularity hierarchical identifiers to ensure unique, semantically consistent image representations . |
| Outcome: | The proposed framework outperforms state-of-the-art frameworks on Flickr30K and MS-COCO datasets . it achieves average Recall@1, Recall @5, and Recall_10 improvements of 10.65%, 8.50%, and 7.00% . |
Visual-Linguistic Dependency Encoding for Image-Text Retrieval (2024.lrec-main)
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| Challenge: | Existing approaches to image-text retrieval ignore semantic discrepancies caused by syntactic structure in natural language expressions and relationships among visual entities. |
| Approach: | They propose a visual-linguistic dependency encoder framework which explicitly models the dependency information among textual words and interaction patterns between image regions. |
| Outcome: | The proposed framework outperforms existing methods on a vision-linguistic compositional structure reasoning dataset. |