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1. Deep Boosting Multi-Modal Ensemble Face Recognition with Sample-Level Weighting

   https://doi.org/10.1109/IJCB57857.2023.10448893  

   Malakshan, Sahar Rahimi ; Saadabadi, Mohammad Saeed ; Najafzadeh, Nima ; Nasrabadi, Nasser M. ( September 2023 , IEEE International Joint Conference on Biometrics (IJCB))

   We introduce caption-guided face recognition (CGFR) as a new framework to improve the performance of commercial-off-the-shelf (COTS) face recognition (FR) systems. In contrast to combining soft biometrics (e.g., facial marks, gender, and age) with face images, in this work, we use facial descriptions provided by face examiners as a piece of auxiliary information. However, due to the heterogeneity of the modalities, improving the performance by directly fusing the textual and facial features is very challenging, as both lie in different embedding spaces. In this paper, we propose a contextual feature aggregation module (CFAM) that addresses this issue by effectively exploiting the fine-grained word-region interaction and global image-caption association. Specifically, CFAM adopts a self-attention and a cross-attention scheme for improving the intra-modality and inter-modality relationship between the image and textual features. Additionally, we design a textual feature refinement module (TFRM) that refines the textual features of the pre-trained BERT encoder by updating the contextual embeddings. This module enhances the discriminative power of textual features with a crossmodal projection loss and realigns the word and caption embeddings with visual features by incorporating a visualsemantic alignment loss. We implemented the proposed CGFR framework on two face recognition models (Arc- Face and AdaFace) and evaluated its performance on the Multimodal CelebA-HQ dataset. Our framework improves the performance of ArcFace from 16.75% to 66.83% on TPR@FPR=1e-4 in the 1:1 verification protocol. 

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2. USING DEEP CROSS MODAL HASHING AND ERROR CORRECTING CODES FOR IMPROVING THE EFFICIENCY OF ATTRIBUTE GUIDED FACIAL IMAGE RETRIEVAL

   https://doi.org/10.1109/GlobalSIP.2018.8646467  

   Talreja, Veeru ; Taherkhani, Fariborz ; Valenti, Matthew C. ; Nasrabadi, Nasser M. ( November 2018 , IEEE Global Conference on Signal and Information Processing (GlobalSIP))

   With benefits of fast query speed and low storage cost, hashing-based image retrieval approaches have garnered considerable attention from the research community. In this paper, we propose a novel Error-Corrected Deep Cross Modal Hashing (CMH-ECC) method which uses a bitmap specifying the presence of certain facial attributes as an input query to retrieve relevant face images from the database. In this architecture, we generate compact hash codes using an end-to-end deep learning module, which effectively captures the inherent relationships between the face and attribute modality. We also integrate our deep learning module with forward error correction codes to further reduce the distance between different modalities of the same subject. Specifically, the properties of deep hashing and forward error correction codes are exploited to design a cross modal hashing framework with high retrieval performance. Experimental results using two standard datasets with facial attributes-image modalities indicate that our CMH-ECC face image retrieval model outperforms most of the current attribute-based face image retrieval approaches. 

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3. HUMAN-CENTERED EMOTION RECOGNITION IN ANIMATED GIFS

   Yang, Zhengyuan ; Zhang, Yixuan ; Luo, Jiebo ( January 2019 , Proceedings of IEEE International Conference on Multimedia and Expo)

   As an intuitive way of expression emotion, the animated Graphical Interchange Format (GIF) images have been widely used on social media. Most previous studies on automated GIF emotion recognition fail to effectively utilize GIF’s unique properties, and this potentially limits the recognition performance. In this study, we demonstrate the importance of human related information in GIFs and conduct humancentered GIF emotion recognition with a proposed Keypoint Attended Visual Attention Network (KAVAN). The framework consists of a facial attention module and a hierarchical segment temporal module. The facial attention module exploits the strong relationship between GIF contents and human characters, and extracts frame-level visual feature with a focus on human faces. The Hierarchical Segment LSTM (HSLSTM) module is then proposed to better learn global GIF representations. Our proposed framework outperforms the state-of-the-art on the MIT GIFGIF dataset. Furthermore, the facial attention module provides reliable facial region mask predictions, which improves the model’s interpretability. 

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4. 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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5. CATFace: Cross-Attribute-Guided Transformer With Self-Attention Distillation for Low-Quality Face Recognition

   https://doi.org/10.1109/TBIOM.2023.3349218  

   Alipour Talemi, Niloufar ; Kashiani, Hossein ; Nasrabadi, Nasser M. ( January 2024 , IEEE Transactions on Biometrics, Behavior, and Identity Science)

   Although face recognition (FR) has achieved great success in recent years, it is still challenging to accurately recognize faces in low-quality images due to the obscured facial details. Nevertheless, it is often feasible to make predictions about specific soft biometric (SB) attributes, such as gender, age, and baldness even in dealing with low-quality images. In this paper, we propose a novel multi-branch neural network that leverages SB attribute information to boost the performance of FR. To this ed, we propose a cross-attribute-guided transformer fusion (CATF) module that effectively captures the long-range dependencies and relationships between FR and SB feature representations. The synergy created by the reciprocal flow of information in the dual cross-attention operations of the proposed CATF module enhances the performance of FR. Furthermore, we introduce a novel self-attention distillation framework that effectively highlights crucial facial regions, such as landmarks by aligning low-quality images with those of their high-quality counterparts in the feature space. The proposed self-attention distillation regularizes our network. to learn a unified quality-invariant feature representation in unconstrained environments. We conduct extensive experiments on various real-world FR benchmarks varying in quality. Experimental results demonstrate the superiority of our FR method compared to state-of-the-art FR studies. 

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