Language-Conditioned Feature Pyramids for Visual Selection Tasks (2020.findings-emnlp)
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| Challenge: | Referring expression comprehension is the ability to locate language to an object in an image. |
| Approach: | They propose a neural network architecture that modulates visual features with varying sizes of receptive field by linguistic features and a visual dialogue game to generate referring expression comprehension. |
| Outcome: | The proposed architecture is able to modulate visual features with varying sizes of receptive field by linguistic features on two visual dialogue games. |
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| Challenge: | Neural relation extraction models capture linguistic and semantic properties of the input, a recent study shows. |
| Approach: | They introduce 14 probing tasks targeting linguistic properties relevant to RE . they add contextualized word representations to enhance probing performance . |
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Visual Referring Expression Recognition: What Do Systems Actually Learn? (N18-2)
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| Challenge: | Existing systems for referring expression recognition ignore linguistic structure, instead relying on shallow correlations introduced by unintended biases in the data selection and annotation process. |
| Approach: | They propose to use a system trained on the input image without the input referring expression to achieve a precision of 71.2% in top-2 predictions. |
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FineCops-Ref: A new Dataset and Task for Fine-Grained Compositional Referring Expression Comprehension (2024.emnlp-main)
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| 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. |
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RECANTFormer: Referring Expression Comprehension with Varying Numbers of Targets (2024.emnlp-main)
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| Challenge: | Existing methods for REC assume that a single referring expression always refers to a one instance in the image. |
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On Efficient Language and Vision Assistants for Visually-Situated Natural Language Understanding: What Matters in Reading and Reasoning (2024.emnlp-main)
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| Challenge: | Recent advances in language and vision assistants have showcased impressive capabilities but suffer from a lack of transparency, limiting broader research and reproducibility. |
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Interpreting Recurrent and Attention-Based Neural Models: a Case Study on Natural Language Inference (D18-1)
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| Challenge: | In this paper, we examine the behavior of deep learning models in their intermediate layers . saliency determines what is critical for the final decision of a deep model . |
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Visual Interrogation of Attention-Based Models for Natural Language Inference and Machine Comprehension (D18-2)
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| Challenge: | Neural networks models have gained popularity due to their state-of-the-art performance but lack of interpretability hinders their deployment and refinement. |
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Learning Explainable Linguistic Expressions with Neural Inductive Logic Programming for Sentence Classification (2020.emnlp-main)
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Prithviraj Sen, Marina Danilevsky, Yunyao Li, Siddhartha Brahma, Matthias Boehm, Laura Chiticariu, Rajasekar Krishnamurthy
| Challenge: | Existing approaches to explain models are difficult to interpret and have undesirable biases. |
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iParaphrasing: Extracting Visually Grounded Paraphrases via an Image (C18-1)
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| Challenge: | iParaphrasing extracts visually grounded paraphrases, which are different phrasal expressions describing the same visual concept in an image. |
| Approach: | They propose a task to extract visually grounded paraphrases from images . they propose to model the similarity between the extracted VGPs using existing methods . |
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Prompting Vision-Language Models For Aspect-Controlled Generation of Referring Expressions (2024.findings-naacl)
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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. |
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| Outcome: | The proposed model beats all prior works in the CIDEr score and achieves comparable performance to training with 100% of real data. |