Towards Unifying Reference Expression Generation and Comprehension (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing models for REG and REC have distinct inputs and connections between them . a new model for REg and reprehension is needed to solve these problems . |
| Approach: | They propose a unified model for REG and REC that fuses image, region and text . they propose Vision-conditioned Masked Language Modeling and Text-Conditioned Region Prediction . |
| Outcome: | The proposed model outperforms existing models on REG and REC tasks. |
Similar Papers
Prompting Vision-Language Models For Aspect-Controlled Generation of Referring Expressions (2024.findings-naacl)
Copied to clipboard
Danfeng Guo, Sanchit Agarwal, Arpit Gupta, Jiun-Yu Kao, Emre Barut, Tagyoung Chung, Jing Huang, Mohit Bansal
| Challenge: | Referring Expression Generation (REG) is the task of generating a descriptive caption that uniquely identifies a given target in the scene. |
| Approach: | They propose an Aspect-Controlled REG task which requires generating a referring expression conditioned on the input aspect(s) by changing the input input such as color, location, action etc. |
| Outcome: | The proposed model beats all prior works in the CIDEr score and achieves comparable performance to training with 100% of real data. |
FineCops-Ref: A new Dataset and Task for Fine-Grained Compositional Referring Expression Comprehension (2024.emnlp-main)
Copied to clipboard
| Challenge: | Referring Expression Comprehension (REC) is a cross-modal task that objectively evaluates the capabilities of language understanding, image comprehension, and language-to-image grounding. |
| Approach: | They propose to use a new reference expression comprehension (REC) dataset to evaluate the capabilities of language understanding, image comprehension, and language-to-image grounding. |
| Outcome: | The proposed model is able to reject scenarios where the target object is not visible in the image, a key aspect often overlooked in existing models and approaches. |
Scene Graph Enhanced Pseudo-Labeling for Referring Expression Comprehension (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Referring expression comprehension is a visual-linguistic task that involves localizing objects in images based on textual referring expressions. |
| Approach: | They propose a scene graph-based framework that generates high-quality pseudo region-query pairs . their method captures relationships between objects in images and generates expressions enriched with relation information. |
| Outcome: | The proposed framework outperforms existing methods by 10%, 12%, and 11% on RefCOCO, RefCoCO+, and Ref COCOg datasets. |
ReCLIP: A Strong Zero-Shot Baseline for Referring Expression Comprehension (2022.acl-long)
Copied to clipboard
| 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%. |
DU-VLG: Unifying Vision-and-Language Generation via Dual Sequence-to-Sequence Pre-training (2022.findings-acl)
Copied to clipboard
| Challenge: | Existing vision-and-language generation models cannot utilize pair-wise images and text through bi-directional generation due to the limitations of the model structure and pre-training objectives. |
| Approach: | They propose a framework which unifies vision-and-language generation as sequence generation problems. |
| Outcome: | The proposed framework achieves better performance than variants trained with uni-directional generation objectives or the variant without the commitment loss on image captioning and text-to-image generation datasets. |
CLEVR-Implicit: A Diagnostic Dataset for Implicit Reasoning in Referring Expression Comprehension (2023.emnlp-main)
Copied to clipboard
| 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. |
UnifiedVisual: A Framework for Constructing Unified Vision-Language Datasets (2025.emnlp-main)
Copied to clipboard
Pengyu Wang, Shaojun Zhou, Chenkun Tan, Xinghao Wang, Wei Huang, Zhen Ye, Zhaowei Li, Botian Jiang, Dong Zhang, Xipeng Qiu
| Challenge: | Existing datasets address understanding and generation in isolation, limiting the performance of unified vision large language models. |
| Approach: | They propose a dataset that facilitates mutual enhancement between multimodal understanding and generation. |
| Outcome: | The proposed framework integrates diverse visual and textual inputs and outputs, enabling comprehensive cross-modal reasoning and precise text-to-image alignment. |
UNIMO-2: End-to-End Unified Vision-Language Grounded Learning (2022.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for vision-language pre-training can only learn from aligned image-caption data and rely heavily on expensive regional features. |
| Approach: | They propose an end-to-end unified-modal pre-training framework for joint learning . they propose to conduct grounded learning on both images and texts via a sharing grounded space . |
| Outcome: | The proposed model improves visual and visual semantic alignment on images and texts. |
NeuralREG: An end-to-end approach to referring expression generation (P18-1)
Copied to clipboard
| Challenge: | Referring Expression Generation models typically rely on features such as salience and grammatical function to make decisions about form and content. |
| Approach: | They propose a new approach that makes decisions about form and content in one go . they use a delexicalized version of the WebNLG corpus to test the approach . |
| Outcome: | The proposed approach significantly improves over two strong baselines. |
Referring to what you know and do not know: Making Referring Expression Generation Models Generalize To Unseen Entities (2020.coling-main)
Copied to clipboard
| Challenge: | Data-to-text Natural Language Generation (NLG) is a computational process of generating natural language from non-linguistic data. |
| Approach: | They propose two extensions to a state-of-the-art encoder-decoder REG model that generates referring expressions to unseen entities. |
| Outcome: | The proposed model generates more meaningful referring expressions to unseen entities than the original system and related work. |