Papers with referring expression comprehension
LLMs as Bridges: Reformulating Grounded Multimodal Named Entity Recognition (2024.findings-acl)
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| Challenge: | Existing methods for Grounded Multimodal Named Entity Recognition (GMNER) lack a strong correlation between image-text pairs and is ungroundable. |
| Approach: | They propose a framework that reformulates GMNER into a joint MNER-VE-VG task by leveraging large language models as a connecting bridge. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on the existing GMNER dataset and achieves absolute leads of 10.65%, 6.21%, and 8.83% in all three subtasks. |
Language-Mediated, Object-Centric Representation Learning (2021.findings-acl)
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| Challenge: | Recent work has studied the problem of unsupervised object representation learning, though without language. |
| Approach: | They propose language-mediated, Objectcentric Representation Learning (LORL) a paradigm for learning disentangled, objectcentric scene representations from vision and language. |
| Outcome: | The proposed paradigm improves performance of unsupervised object discovery algorithms on two datasets using language. |
CK-Transformer: Commonsense Knowledge Enhanced Transformers for Referring Expression Comprehension (2023.findings-eacl)
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| Challenge: | Existing frameworks for referring expression comprehension with commonsense knowledge are lacking in the field of multimodal referring . |
| Approach: | They propose a framework for commonsense knowledge Enhanced Transformers which integrates commonsensible knowledge into representations of objects in an image. |
| Outcome: | The proposed framework improves on the existing state of the art in referring expression comprehension with commonsense knowledge (CK-Transformer) it achieves 3.14% accuracy over the existing framework. |
ReCLIP: A Strong Zero-Shot Baseline for Referring Expression Comprehension (2022.acl-long)
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| Challenge: | Visual referring expression comprehension (ReC) models can be trained for a domain, but it remains unclear if they can be applied in a zero-shot manner to more complex tasks like ReC. |
| Approach: | They propose a method that repurposes CLIP, a state-of-the-art large-scale model, for training a referring expression comprehension model for a new visual domain. |
| Outcome: | The proposed model reduces the gap between zero-shot baselines from prior work and supervised models by as much as 29% on RefCOCOg, and on ReFGTA (video game imagery), and its relative improvement over supervised ReC models is 8%. |
PropTest: Automatic Property Testing for Improved Visual Programming (2024.findings-emnlp)
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| Challenge: | Visual Programming is an alternative to end-to-end black-box visual reasoning models. |
| Approach: | They propose a visual programming strategy that leverages Large Language Models to generate the logic of a program in the form of its source code. |
| Outcome: | The proposed method improves ViperGPT on visual question answering and referring expression comprehension with an LLM. |
PEVL: Position-enhanced Pre-training and Prompt Tuning for Vision-language Models (2022.emnlp-main)
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| Challenge: | Recent advances on self-supervised learning have led to powerful vision-language pre-training models that achieve state-of-the-art performance on a wide range of cross-modal tasks. |
| Approach: | They propose a vision-language pre-training framework that reformulates discretized object positions and language in a unified language modeling framework. |
| Outcome: | The proposed model improves performance on position-sensitive vision-language (VL) tasks and also improves on position insensitive tasks. |
CLEVR-Implicit: A Diagnostic Dataset for Implicit Reasoning in Referring Expression Comprehension (2023.emnlp-main)
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| Challenge: | Recent studies show that pre-trained vision-language models perform well in cross-modal tasks, including referring expression comprehension. |
| Approach: | They propose a method that enables VL models to reason with implicit text . they propose to use a dataset to align the text with objects in the images . |
| Outcome: | The proposed method improves performance 37.94% on referring expression comprehension task. |