Papers with COCO
KG-FLIP: Knowledge-guided Fashion-domain Language-Image Pre-training for E-commerce (2023.acl-industry)
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| Challenge: | Various visionlanguage pre-training (VLP) models learn cross-modal alignment from large-scale well-aligned image-text datasets without leveraging external knowledge. |
| Approach: | They propose a knowledge-guided fashion-domain language-image pre-training framework that learns fine-grained representations in e-commerce domain and utilizes external knowledge to improve the pre-train efficiency. |
| Outcome: | The proposed framework outperforms state-of-the-art models on Amazon and Fashion-Gen datasets by large margins. |
Measuring Social Biases in Grounded Vision and Language Embeddings (2021.naacl-main)
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| Challenge: | Existing methods to measure social biases in word embeddings are limited to visually grounded word embeds . a new study generalizes word embedment associations to visually ground word embeddas . |
| Approach: | They generalize word embeddings' biases to visually grounded word embeds . they propose two generalizations that answer questions about how biase, language, and vision interact . |
| Outcome: | The proposed measures are applied to a new dataset that includes 10,228 images from COCO, Conceptual Captions, and Google Images. |
ECOL-R: Encouraging Copying in Novel Object Captioning with Reinforcement Learning (2021.eacl-main)
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| Challenge: | Novel Object Captioning is a zero-shot Image Caption task requiring describing objects not seen in the training captions, but for which information is available from external object detectors. |
| Approach: | They propose a novel captioning model that encourages copying of object labels with reinforcement learning that encourage a copy-augmented transformer model to accurately describe the object labels. |
| Outcome: | The proposed model sets new state-of-the-art on the nocaps and held-out COCO benchmarks. |
MLLM-I2W: Harnessing Multimodal Large Language Model for Zero-Shot Composed Image Retrieval (2025.coling-main)
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| Challenge: | Existing methods for combining image retrieval are supervised and zero-shot . however, the challenge of mapping pseudo-words to images within the joint image-text embedding space is still a challenge. |
| Approach: | They propose a novel image-text mapping network which converts description-related image information into pseudo-word markers for precise ZS-CIR. |
| Outcome: | The proposed model improves on COCO, CIRR, and Fashion-IQ benchmarks. |
Beyond Language: Learning Commonsense from Images for Reasoning (2020.findings-emnlp)
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| Challenge: | Existing commonsense reasoning methods use raw texts to perform data representation and answer prediction tasks. |
| Approach: | They propose a novel approach to learn commonsense from images instead of limited raw texts or costly knowledge bases. |
| Outcome: | The proposed approach outperforms language-based methods on commonsense reasoning problems on two commonsence reasoning problems. |
Cross-Modal Similarity-Based Curriculum Learning for Image Captioning (2022.emnlp-main)
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| Challenge: | Existing image captioning approaches treat image-caption pairs indistinctly without considering the differences in their learning difficulties. |
| Approach: | They propose a pretrained vision–language model that measures cross-modal similarity and a model that uses cross-module similarity to measure the difficulty of captioning. |
| Outcome: | The proposed model achieves superior performance and competitive convergence speed to baselines without incurring additional training costs. |
Visual Hallucinations of Multi-modal Large Language Models (2024.findings-acl)
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| Challenge: | Existing studies find VH instances only in existing image datasets, which results in biased understanding of MLLMs’ performance under VH. |
| Approach: | They propose a tool called VHTest to generate a diverse set of VH instances from existing image datasets and a text-to-image generative model to generate VH images based on the text descriptions. |
| Outcome: | The proposed tool finds VH instances in existing image datasets and generates images based on the text descriptions. |
Vision-Free Retrieval: Rethinking Multimodal Search with Textual Scene Descriptions (2025.emnlp-main)
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| Challenge: | Contrastively trained Vision-Language Models exhibit shallow language understanding, manifesting bag-of-words behaviour. |
| Approach: | They propose a vision-free, single-encoder retrieval pipeline to replace traditional text-to-image retrieval paradigm with structured image descriptions. |
| Outcome: | The proposed approach reduces the modality gap and improves compositionality and performance on short and long caption queries. |
VHASR: A Multimodal Speech Recognition System With Vision Hotwords (2024.emnlp-main)
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| Challenge: | Existing models that incorporate audio-related image information do not improve speech recognition performance. |
| Approach: | They propose a novel approach utilizing audio-related image information and set up a multimodal speech recognition system that uses vision as hotwords to enhance the model’s speech recognition capability. |
| Outcome: | The proposed model outperforms unimodal ASR model and achieves SOTA among existing image-based multimodal ASL models. |
CLIP-UP: A Simple and Efficient Mixture-of-Experts CLIP Training Recipe with Sparse Upcycling (2025.findings-emnlp)
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Xinze Wang, Chen Chen, Yinfei Yang, Hong-You Chen, Bowen Zhang, Aditya Pal, Xiangxin Zhu, Xianzhi Du
| Challenge: | Mixture-of-Experts (MoE) models are crucial for scaling model capacity while controlling inference costs. |
| Approach: | They propose an alternative training strategy that converts a dense CLIP model into a sparse MoE architecture. |
| Outcome: | The proposed training strategy outperforms dense models on COCO and Flickr30k benchmarks. |
No Culture Left Behind: ArtELingo-28, a Benchmark of WikiArt with Captions in 28 Languages (2024.emnlp-main)
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Youssef Mohamed, Runjia Li, Ibrahim Ahmad, Kilichbek Haydarov, Philip Torr, Kenneth Church, Mohamed Elhoseiny
| Challenge: | Traditionally, vision research focused on unambiguous class labels, whereas ArtELingo emphasizes diversity of opinions over languages and cultures. |
| Approach: | They propose a vision-language benchmark that spans 28 languages and encompasses approximately 200,000 annotations. |
| Outcome: | The proposed benchmark spans 28 languages and encompasses approximately 200,000 annotations . the challenge is to build machine learning systems that assign emotional captions to images . |
Tiny Scales, Great Challenges: The Limits of Multimodal LLMs in Scale Recognition (2026.acl-long)
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| Challenge: | Existing benchmarks focus on a single type of quantity or a specific format, lacking a comprehensive evaluation of scale recognition capabilities. |
| Approach: | They propose a visual scale recognition benchmark built using images from COCO, Open Images, and Flickr to evaluate scale recognition capabilities of multimodal large language models. |
| Outcome: | The proposed model achieves 42.60% accuracy, lower than the 97.40% of humans. |