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1. AudiFace: Multimodal Deep Learning for Depression Screening

   Ricardo Flores, ML Tlachac (December 2022, Proceedings of Machine Learning Research)

   Depression is a very common mental health disorder with a devastating social and economic impact. It can be costly and difficult to detect, traditionally requiring a significant number of hours by a trained mental health professional. Recently, machine learning and deep learning models have been trained for depression screening using modalities extracted from videos of clinical interviews conducted by a virtual agent. This complex task is challenging for deep learning models because of the multiple modalities and limited number of participants in the dataset. To address these challenges we propose AudiFace, a multimodal deep learning model that inputs temporal facial features, audio, and transcripts to screen for depression. To incorporate all three modalities, AudiFace combines multiple pre-trained transfer learning models and bidirectional LSTM with self-Attention. When compared with the state-of-the-art models, AudiFace achieves the highest F1 scores for thirteen of the fifteen different datasets. AudiFace notably improves the depression screening capabilities of general wellbeing questions. Eye gaze proved to be the most valuable of the temporal facial features, both in the unimodal and multimodal models. Our results can be used to determine the best combination of modalities, temporal facial features, as well as clinical interview questions for future depression screening applications. 

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2. Improving Speech Emotion Recognition Using Self-Supervised Learning with Domain-Specific Audiovisual Tasks

   https://doi.org/10.21437/Interspeech.2022-11012  

   Goncalves, Lucas; Busso, Carlos (September 2022, Interspeech 2022)

   Speech emotion recognition (SER) is a challenging task due to the limited availability of real-world labeled datasets. Since it is easier to find unlabeled data, the use of self-supervised learning (SSL) has become an attractive alternative. This study proposes new pre-text tasks for SSL to improve SER. While our target application is SER, the proposed pre-text tasks include audio-visual formulations, leveraging the relationship between acoustic and facial features. Our proposed approach introduces three new unimodal and multimodal pre-text tasks that are carefully designed to learn better representations for predicting emotional cues from speech. Task 1 predicts energy variations (high or low) from a speech sequence. Task 2 uses speech features to predict facial activation (high or low) based on facial landmark movements. Task 3 performs a multi-class emotion recognition task on emotional labels obtained from combinations of action units (AUs) detected across a video sequence. We pre-train a network with 60.92 hours of unlabeled data, fine-tuning the model for the downstream SER task. The results on the CREMA-D dataset show that the model pre-trained on the proposed domain-specific pre-text tasks significantly improves the precision (up to 5.1%), recall (up to 4.5%), and F1-scores (up to 4.9%) of our SER system. 

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3. Protecting Privacy in Multimodal Large Language Models with MLLMU-Bench

   Liu, Zheyuan; Dou, Guangyao; Jia, Mengzhao; Tan, Zhaoxuan; Zeng, Qingkai; Yuan, Yongle; Jiang, Meng (April 2025, Association for Computational Linguistics)

   Generative models such as Large Language Models (LLM) and Multimodal Large Language models (MLLMs) trained on massive web corpora can memorize and disclose individuals’ confidential and private data, raising legal and ethical concerns. While many previous works have addressed this issue in LLM via machine unlearning, it remains largely unexplored for MLLMs. To tackle this challenge, we introduce Multimodal Large Language Model Unlearning Benchmark (MLLMU-Bench), a novel benchmark aimed at advancing the understanding of multimodal machine unlearning. MLLMU-Bench consists of 500 fictitious profiles and 153 profiles for public celebrities, each profile feature over 14 customized question-answer pairs, evaluated from both multimodal (image+text) and unimodal (text) perspectives. The benchmark is divided into four sets to assess unlearning algorithms in terms of efficacy, generalizability, and model utility. Finally, we provide baseline results using existing generative model unlearning algorithms. Surprisingly, our experiments show that unimodal unlearning algorithms excel in generation tasks, while multimodal unlearning approaches perform better in classification with multimodal inputs. 

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4. Coarse-to-Fine Vision-Language Pre-training with Fusion in the Backbone

   Dou, Zi-Yi; Kamath, Aishwarya; Gan, Zhe; Zhang, Pengchuan; Wang, Jianfeng; Li, Linjie; Liu, Zicheng; Liu, Ce; LeCun, Yann; Peng, Nanyun; et al (October 2022, NeurIPS)

   Vision-language (VL) pre-training has recently received considerable attention. However, most existing end-to-end pre-training approaches either only aim to tackle VL tasks such as image-text retrieval, visual question answering (VQA) and image captioning that test high-level understanding of images, or only target region-level understanding for tasks such as phrase grounding and object detection. We present FIBER (Fusion-In-the-Backbone-based transformER), a new VL model architecture that can seamlessly handle both these types of tasks. Instead of having dedicated transformer layers for fusion after the uni-modal backbones, FIBER pushes multimodal fusion deep into the model by inserting cross-attention into the image and text backbones to better capture multimodal interactions. In addition, unlike previous work that is either only pre-trained on image-text data or on fine-grained data with box-level annotations, we present a two-stage pre-training strategy that uses both these kinds of data efficiently: (i) coarse-grained pre-training based on image-text data; followed by (ii) fine-grained pre-training based on image-text-box data. We conduct comprehensive experiments on a wide range of VL tasks, ranging from VQA, image captioning, and retrieval, to phrase grounding, referring expression comprehension, and object detection. Using deep multimodal fusion coupled with the two-stage pre-training, FIBER provides consistent performance improvements over strong baselines across all tasks, often outperforming methods using magnitudes more data. Code is released at https://github.com/microsoft/FIBER. 

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5. A Multi-Level Optimization Framework for End-to-End Text Augmentation

   https://doi.org/10.1162/tacl_a_00464  

   Somayajula, Sai Ashish; Song, Linfeng; Xie, Pengtao (January 2022, Transactions of the Association for Computational Linguistics)

   Abstract Text augmentation is an effective technique in alleviating overfitting in NLP tasks. In existing methods, text augmentation and downstream tasks are mostly performed separately. As a result, the augmented texts may not be optimal to train the downstream model. To address this problem, we propose a three-level optimization framework to perform text augmentation and the downstream task end-to- end. The augmentation model is trained in a way tailored to the downstream task. Our framework consists of three learning stages. A text summarization model is trained to perform data augmentation at the first stage. Each summarization example is associated with a weight to account for its domain difference with the text classification data. At the second stage, we use the model trained at the first stage to perform text augmentation and train a text classification model on the augmented texts. At the third stage, we evaluate the text classification model trained at the second stage and update weights of summarization examples by minimizing the validation loss. These three stages are performed end-to-end. We evaluate our method on several text classification datasets where the results demonstrate the effectiveness of our method. Code is available at https://github.com/Sai-Ashish/End-to-End-Text-Augmentation. 

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