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1. Improving Visual Question Answering by Referring to Generated Paragraph Captions

   Kim, Hyounghun; Bansal, Mohit (July 2019, Proceedings of the conference - Association for Computational Linguistics. Meeting)

   Paragraph-style image captions describe diverse aspects of an image as opposed to the more common single-sentence captions that only provide an abstract description of the image. These paragraph captions can hence contain substantial information of the image for tasks such as visual question answering. Moreover, this textual information is complementary with visual information present in the image because it can discuss both more abstract concepts and more explicit, intermediate symbolic information about objects, events, and scenes that can directly be matched with the textual question and copied into the textual answer (i.e., via easier modality match). Hence, we propose a combined Visual and Textual Question Answering (VTQA) model which takes as input a paragraph caption as well as the corresponding image, and answers the given question based on both inputs. In our model, the inputs are fused to extract related information by cross-attention (early fusion), then fused again in the form of consensus (late fusion), and finally expected answers are given an extra score to enhance the chance of selection (later fusion). Empirical results show that paragraph captions, even when automatically generated (via an RL-based encoder-decoder model), help correctly answer more visual questions. Overall, our joint model, when trained on the Visual Genome dataset, significantly improves the VQA performance over a strong baseline model. 

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2. Dense-Caption Matching and Frame-Selection Gating for Temporal Localization in VideoQA

   https://doi.org/10.18653/v1/2020.acl-main.435  

   Kim, Hyounghun; Tang, Zineng; Bansal, Mohit (July 2020, Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics)

   Videos convey rich information. Dynamic spatio-temporal relationships between people/objects, and diverse multimodal events are present in a video clip. Hence, it is important to develop automated models that can accurately extract such information from videos. Answering questions on videos is one of the tasks which can evaluate such AI abilities. In this paper, we propose a video question answering model which effectively integrates multi-modal input sources and finds the temporally relevant information to answer questions. Specifically, we first employ dense image captions to help identify objects and their detailed salient regions and actions, and hence give the model useful extra information (in explicit textual format to allow easier matching) for answering questions. Moreover, our model is also comprised of dual-level attention (word/object and frame level), multi-head self/cross-integration for different sources (video and dense captions), and gates which pass more relevant information to the classifier. Finally, we also cast the frame selection problem as a multi-label classification task and introduce two loss functions, In-andOut Frame Score Margin (IOFSM) and Balanced Binary Cross-Entropy (BBCE), to better supervise the model with human importance annotations. We evaluate our model on the challenging TVQA dataset, where each of our model components provides significant gains, and our overall model outperforms the state-of-the-art by a large margin (74.09% versus 70.52%). We also present several word, object, and frame level visualization studies. 

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3. Image and Text Feature Based Multimodal Learning for Multi-Label Classification of Radiology Images in Biomedical Literature [Image and Text Feature Based Multimodal Learning for Multi-Label Classification of Radiology Images in Biomedical Literature]

   https://doi.org/10.5220/0012438400003657  

   Hasan, Md; Jani, Md; Rahman, Md (January 2024, SCITEPRESS - Science and Technology Publications)

   Biomedical images are crucial for diagnosing and planning treatments, as well as advancing scientific understanding of various ailments. To effectively highlight regions of interest (RoIs) and convey medical concepts, annotation markers like arrows, letters, or symbols are employed. However, annotating these images with appropriate medical labels poses a significant challenge. In this study, we propose a framework that leverages multimodal input features, including text/label features and visual features, to facilitate accurate annotation of biomedical images with multiple labels. Our approach integrates state-of-the-art models such as ResNet50 and Vision Transformers (ViT) to extract informative features from the images. Additionally, we employ Generative Pre-trained Distilled-GPT2 (Transformer based Natural Language Processing architecture) to extract textual features, leveraging their natural language understanding capabilities. This combination of image and text modalities allows for a more comprehensive representation of the biomedical data, leading to improved annotation accuracy. By combining the features extracted from both image and text modalities, we trained a simplified Convolutional Neural Network (CNN) based multi-classifier to learn the image-text relations and predict multi-labels for multi-modal radiology images. We used ImageCLEFmedical 2022 and 2023 datasets to demonstrate the effectiveness of our framework. This dataset likely contains a diverse range of biomedical images, enabling the evaluation of the framework’s performance under realistic conditions. We have achieved promising results with the F1 score of 0.508. Our proposed framework exhibits potential performance in annotating biomedical images with multiple labels, contributing to improved image understanding and analysis in the medical image processing domain. 

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4. Twitter Sentiment Analysis via Bi-sense Emoji Embedding and Attention-based LSTM

   Chen, Yuxiao; Yuan, Jianbo; You, Quanzeng; Luo, Jiebo (January 2018, Proceedings of ACM Multimedia Conference)

   Sentiment analysis on large-scale social media data is important to bridge the gaps between social media contents and real world activities including political election prediction, individual and public emotional status monitoring and analysis, and so on. Although textual sentiment analysis has been well studied based on platforms such as Twitter and Instagram, analysis of the role of extensive emoji uses in sentiment analysis remains light. In this paper, we propose a novel scheme for Twitter sentiment analysis with extra attention on emojis.We first learn bi-sense emoji embeddings under positive and negative sentimental tweets individually, and then train a sentiment classifier by attending on these bi-sense emoji embeddings with an attention-based long short-term memory network (LSTM). Our experiments show that the bi-sense embedding is effective for extracting sentiment-aware embeddings of emojis and outperforms the state-of-the-art models. We also visualize the attentions to show that the bi-sense emoji embedding provides better guidance on the attention mechanism to obtain a more robust understanding of the semantics and sentiments. 

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5. Effects of Feature Scaling and Fusion on Sign Language Translation

   https://doi.org/10.21437/Interspeech.2021-1863  

   Ananthanarayana, Tejaswini; Chaudhary, Lipisha; Nwogu, Ifeoma (August 2021, Proceedings of Interspeech 2021)

   Sign language translation without transcription has only recently started to gain attention. In our work, we focus on improving the state-of-the-art translation by introducing a multi-feature fusion architecture with enhanced input features. As sign language is challenging to segment, we obtain the input features by extracting overlapping scaled segments across the video and obtaining their 3D CNN representations. We exploit the attention mechanism in the fusion architecture by initially learning dependencies between different frames of the same video and later fusing them to learn the relations between different features from the same video. In addition to 3D CNN features, we also analyze pose-based features. Our robust methodology outperforms the state-of-the-art sign language translation model by achieving higher BLEU 3 – BLEU 4 scores and also outperforms the state-of-the-art sequence attention models by achieving a 43.54% increase in BLEU 4 score. We conclude that the combined effects of feature scaling and feature fusion make our model more robust in predicting longer n-grams which are crucial in continuous sign language translation. 

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