ICC : Quantifying Image Caption Concreteness for Multimodal Dataset Curation (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing methods to curation text-image data are noisy and lack the fine-grained ability to isolate the most concrete samples that provide the strongest signal for learning in a noisy dataset. |
| Approach: | They propose a metric that evaluates caption text without an image reference to measure its concreteness and relevancy. |
| Outcome: | The proposed method detects the concreteness of captions without an image reference and correlates with human evaluation of concreteness in both single-word and caption-level texts. |
Similar Papers
Quantifying the Visual Concreteness of Words and Topics in Multimodal Datasets (N18-1)
Copied to clipboard
| Challenge: | Existing work suggests that concepts with concrete visual manifestations are easier to learn than abstract ones. |
| Approach: | They propose an algorithm for automatically computing the visual concreteness of words and topics within multimodal datasets. |
| Outcome: | The proposed algorithm predicts the capacity of machine learning algorithms to learn textual/visual relationships. |
Grounded Concreteness: Human-Like Concreteness Sensitivity in Vision–Language Models (2026.findings-acl)
Copied to clipboard
| Challenge: | a long tradition in cognitive science treats concreteness as a graded dimension of conceptual representation . concrete words benefit from richer sensory codes and exhibit robust behavioral advantages over abstract words . |
| Approach: | They compare vision-language models with text-only large language models to test their concreteness . they find that VLMs show more human-like sensitivity to concreteness than LLMs . |
| Outcome: | The proposed model-based training improves on the Llama text backbones and Llma Vision counterparts. |
Learning-based Composite Metrics for Improved Caption Evaluation (P18-3)
Copied to clipboard
| Challenge: | Existing image captioning metrics focus on linguistic aspects and do not match human judgements at sentence-level. |
| Approach: | They propose to incorporate lexical and semantic metrics as features to capture adequacy and fluency of captions at different linguistic levels. |
| Outcome: | The proposed framework captures adequacy and fluency of captions at different linguistic levels. |
The Role of Data Curation in Image Captioning (2024.eacl-long)
Copied to clipboard
| 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. |
Crisscrossed Captions: Extended Intramodal and Intermodal Semantic Similarity Judgments for MS-COCO (2021.eacl-main)
Copied to clipboard
| Challenge: | Existing image captioning datasets have limited cross-modal associations, preventing researchers from examining how inter-modal learning impacts intra-modal tasks. |
| Approach: | They propose to use image captioning data to support multi-modal retrieval training and evaluation to assess the impact of inter-modality learning. |
| Outcome: | The proposed model is able to measure the influence of intra- and inter-modality learning. |
Visual Commonsense in Pretrained Unimodal and Multimodal Models (2022.naacl-main)
Copied to clipboard
| Challenge: | Fig. 1 shows how text-only and image-only models can capture commonsense visual attributes, but reporting bias affects their performance. |
| Approach: | They use a Visual Commonsense Tests dataset to validate their findings . they find multimodal models better reconstruct attribute distributions, but are still subject to reporting bias . |
| Outcome: | The proposed model improves on the unimodal and multimodal models, but is still subject to reporting bias. |
RICO: Improving Accuracy and Completeness in Image Recaptioning via Visual Reconstruction (2025.emnlp-main)
Copied to clipboard
Yuchi Wang, Yishuo Cai, Shuhuai Ren, Sihan Yang, Linli Yao, Yuanxin Liu, Yuanxing Zhang, Pengfei Wan, Xu Sun
| Challenge: | Existing recaptioning methods suffer from inaccuracies due to missing fine-grained details. |
| Approach: | They propose a framework that refines captions through visual reconstruction using a text-to-image model and a visual reconstruction framework. |
| Outcome: | The proposed framework outperforms baselines on CapsBench and CompreCap by 10%. |
Uncovering Visual-Semantic Psycholinguistic Properties from the Distributional Structure of Text Embedding Space (2025.acl-long)
Copied to clipboard
| Challenge: | Imageability and concreteness are psycholinguistic properties that link visual and semantic spaces. |
| Approach: | They propose an unsupervised measure that quantifies sharpness of peaks in an image-caption dataset. |
| Outcome: | The proposed method is more robust than existing methods and predicts these properties for classification. |
Multimodal Unlearning Across Vision, Language, Video, and Audio: Survey of Methods, Datasets, and Benchmarks (2026.findings-acl)
Copied to clipboard
| Challenge: | Survey aims to identify challenges of multimodal unlearning for vision, language, audio and video . retraining after deletion requests or policy updates is often impractical, survey finds . |
| Approach: | They propose to enable selective removal across modalities while retaining overall utility. |
| Outcome: | This study compares models with existing models to identify weaknesses and improves performance. |
Caption Enriched Samples for Improving Hateful Memes Detection (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for classifying memes are difficult to perform, with human accuracy only about 85% . recent state-of-the-art models perform considerably less accurately, achieving up to 64.73% accuracy. |
| Approach: | They propose to use an off-the-shelf caption generator to capture the first image and overlayed text. |
| Outcome: | The proposed tool improves classification accuracy for unimodal and multimodal models . the proposed tool can be used to model the contrast between image content and overlayed text . |