pwc-archive/papers-with-abstracts huggingface dataset:
OAMatcher: An Overlapping Areas-based Network for Accurate Local Feature Matching
Authors: Kun Dai, Tao Xie, Ke Wang, Zhiqiang Jiang, Ruifeng Li, Lijun Zhao
Date: 2023-02-12 00:00:00

Local feature matching is an essential component in many visual applications. In this work, we propose OAMatcher, a Tranformer-based detector-free method that imitates humans behavior to generate dense and accurate matches. Firstly, OAMatcher predicts overlapping areas to promote effective and clean global context aggregation, with the key insight that humans focus on the overlapping areas instead of the entire images after multiple observations when matching keypoints in image pairs. Technically, we first perform global information integration across all keypoints to imitate the humans behavior of observing the entire images at the beginning of feature matching. Then, we propose Overlapping Areas Prediction Module (OAPM) to capture the keypoints in co-visible regions and conduct feature enhancement among them to simulate that humans transit the focus regions from the entire images to overlapping regions, hence realizeing effective information exchange without the interference coming from the keypoints in non overlapping areas. Besides, since humans tend to leverage probability to determine whether the match labels are correct or not, we propose a Match Labels Weight Strategy (MLWS) to generate the coefficients used to appraise the reliability of the ground-truth match labels, while alleviating the influence of measurement noise coming from the data. Moreover, we integrate depth-wise convolution into Tranformer encoder layers to ensure OAMatcher extracts local and global feature representation concurrently. Comprehensive experiments demonstrate that OAMatcher outperforms the state-of-the-art methods on several benchmarks, while exhibiting excellent robustness to extreme appearance variants. The source code is available at https://github.com/DK-HU/OAMatcher.
Graph Matching Optimization Network for Point Cloud Registration
Authors: Qianliang Wu, Yaqi Shen, Haobo Jiang, Guofeng Mei, Yaqing Ding, Lei Luo, Jin Xie, Jian Yang
Date: 2023-02-12 00:00:00
Tasks: Graph Matching, Point Cloud Registration

Point Cloud Registration is a fundamental and challenging problem in 3D computer vision. Recent works often utilize the geometric structure information in point feature embedding or outlier rejection for registration while neglecting to consider explicitly isometry-preserving constraint ($e.g.,$ point pair linked edge's length preserving after transformation) in training. We claim that the explicit isometry-preserving constraint is also important for improving feature representation abilities in the feature training stage. To this end, we propose a \underline{G}raph \underline{M}atching \underline{O}ptimization based \underline{Net}work (GMONet for short), which utilizes the graph-matching optimizer to explicitly exert the isometry preserving constraints in the point feature training to improve the point feature representation. Specifically, we exploit a partial graph-matching optimizer to optimize the super point ($i.e.,$ down-sampled key points) features and a full graph-matching optimizer to optimize fine-level point features in the overlap region. Meanwhile, we leverage the inexact proximal point method and the mini-batch sampling technique to accelerate these two graph-matching optimizers. Given high discriminative point features in the evaluation stage, we utilize the RANSAC approach to estimate the transformation between the scanned pairs. The proposed method has been evaluated on the 3DMatch/3DLoMatch benchmarks and the KITTI benchmark. The experimental results show that our method performs competitively compared to state-of-the-art baselines.
Fairness-aware Multi-view Clustering
Authors: Lecheng Zheng, Yada Zhu, Jingrui He
Date: 2023-02-11 00:00:00
Tasks: Clustering, Contrastive Learning, Fairness, Fraud Detection

In the era of big data, we are often facing the challenge of data heterogeneity and the lack of label information simultaneously. In the financial domain (e.g., fraud detection), the heterogeneous data may include not only numerical data (e.g., total debt and yearly income), but also text and images (e.g., financial statement and invoice images). At the same time, the label information (e.g., fraud transactions) may be missing for building predictive models. To address these challenges, many state-of-the-art multi-view clustering methods have been proposed and achieved outstanding performance. However, these methods typically do not take into consideration the fairness aspect and are likely to generate biased results using sensitive information such as race and gender. Therefore, in this paper, we propose a fairness-aware multi-view clustering method named FairMVC. It incorporates the group fairness constraint into the soft membership assignment for each cluster to ensure that the fraction of different groups in each cluster is approximately identical to the entire data set. Meanwhile, we adopt the idea of both contrastive learning and non-contrastive learning and propose novel regularizers to handle heterogeneous data in complex scenarios with missing data or noisy features. Experimental results on real-world data sets demonstrate the effectiveness and efficiency of the proposed framework. We also derive insights regarding the relative performance of the proposed regularizers in various scenarios.
Stochastic Surprisal: An inferential measurement of Free Energy in Neural Networks
Authors: Mohit Prabhushankar, Ghassan AlRegib
Date: 2023-02-11 00:00:00
Tasks: Image Quality Assessment

This paper conjectures and validates a framework that allows for action during inference in supervised neural networks. Supervised neural networks are constructed with the objective to maximize their performance metric in any given task. This is done by reducing free energy and its associated surprisal during training. However, the bottom-up inference nature of supervised networks is a passive process that renders them fallible to noise. In this paper, we provide a thorough background of supervised neural networks, both generative and discriminative, and discuss their functionality from the perspective of free energy principle. We then provide a framework for introducing action during inference. We introduce a new measurement called stochastic surprisal that is a function of the network, the input, and any possible action. This action can be any one of the outputs that the neural network has learnt, thereby lending stochasticity to the measurement. Stochastic surprisal is validated on two applications: Image Quality Assessment and Recognition under noisy conditions. We show that, while noise characteristics are ignored to make robust recognition, they are analyzed to estimate image quality scores. We apply stochastic surprisal on two applications, three datasets, and as a plug-in on twelve networks. In all, it provides a statistically significant increase among all measures. We conclude by discussing the implications of the proposed stochastic surprisal in other areas of cognitive psychology including expectancy-mismatch and abductive reasoning.
Improving Sign Recognition with Phonology
Authors: Lee Kezar, Jesse Thomason, Zed Sevcikova Sehyr
Date: 2023-02-11 00:00:00
Tasks: Sign Language Recognition

We use insights from research on American Sign Language (ASL) phonology to train models for isolated sign language recognition (ISLR), a step towards automatic sign language understanding. Our key insight is to explicitly recognize the role of phonology in sign production to achieve more accurate ISLR than existing work which does not consider sign language phonology. We train ISLR models that take in pose estimations of a signer producing a single sign to predict not only the sign but additionally its phonological characteristics, such as the handshape. These auxiliary predictions lead to a nearly 9% absolute gain in sign recognition accuracy on the WLASL benchmark, with consistent improvements in ISLR regardless of the underlying prediction model architecture. This work has the potential to accelerate linguistic research in the domain of signed languages and reduce communication barriers between deaf and hearing people.
DaliID: Distortion-Adaptive Learned Invariance for Identification Models
Authors: Wes Robbins, Gabriel Bertocco, Terrance E. Boult
Date: 2023-02-11 00:00:00
Tasks: Face Recognition, Person Re-Identification

In unconstrained scenarios, face recognition and person re-identification are subject to distortions such as motion blur, atmospheric turbulence, or upsampling artifacts. To improve robustness in these scenarios, we propose a methodology called Distortion-Adaptive Learned Invariance for Identification (DaliID) models. We contend that distortion augmentations, which degrade image quality, can be successfully leveraged to a greater degree than has been shown in the literature. Aided by an adaptive weighting schedule, a novel distortion augmentation is applied at severe levels during training. This training strategy increases feature-level invariance to distortions and decreases domain shift to unconstrained scenarios. At inference, we use a magnitude-weighted fusion of features from parallel models to retain robustness across the range of images. DaliID models achieve state-of-the-art (SOTA) for both face recognition and person re-identification on seven benchmark datasets, including IJB-S, TinyFace, DeepChange, and MSMT17. Additionally, we provide recaptured evaluation data at a distance of 750+ meters and further validate on real long-distance face imagery.
Removing Image Artifacts From Scratched Lens Protectors
Authors: YuFei Wang, Renjie Wan, Wenhan Yang, Bihan Wen, Lap-Pui Chau, Alex C. Kot
Date: 2023-02-11 00:00:00
Tasks: JPEG Artifact Removal

A protector is placed in front of the camera lens for mobile devices to avoid damage, while the protector itself can be easily scratched accidentally, especially for plastic ones. The artifacts appear in a wide variety of patterns, making it difficult to see through them clearly. Removing image artifacts from the scratched lens protector is inherently challenging due to the occasional flare artifacts and the co-occurring interference within mixed artifacts. Though different methods have been proposed for some specific distortions, they seldom consider such inherent challenges. In our work, we consider the inherent challenges in a unified framework with two cooperative modules, which facilitate the performance boost of each other. We also collect a new dataset from the real world to facilitate training and evaluation purposes. The experimental results demonstrate that our method outperforms the baselines qualitatively and quantitatively. The code and datasets will be released after acceptance.
Rethinking Vision Transformer and Masked Autoencoder in Multimodal Face Anti-Spoofing
Authors: Zitong Yu, Rizhao Cai, Yawen Cui, Xin Liu, Yongjian Hu, Alex Kot
Date: 2023-02-11 00:00:00
Tasks: Face Anti-Spoofing

Recently, vision transformer (ViT) based multimodal learning methods have been proposed to improve the robustness of face anti-spoofing (FAS) systems. However, there are still no works to explore the fundamental natures (\textit{e.g.}, modality-aware inputs, suitable multimodal pre-training, and efficient finetuning) in vanilla ViT for multimodal FAS. In this paper, we investigate three key factors (i.e., inputs, pre-training, and finetuning) in ViT for multimodal FAS with RGB, Infrared (IR), and Depth. First, in terms of the ViT inputs, we find that leveraging local feature descriptors benefits the ViT on IR modality but not RGB or Depth modalities. Second, in observation of the inefficiency on direct finetuning the whole or partial ViT, we design an adaptive multimodal adapter (AMA), which can efficiently aggregate local multimodal features while freezing majority of ViT parameters. Finally, in consideration of the task (FAS vs. generic object classification) and modality (multimodal vs. unimodal) gaps, ImageNet pre-trained models might be sub-optimal for the multimodal FAS task. To bridge these gaps, we propose the modality-asymmetric masked autoencoder (M$^{2}$A$^{2}$E) for multimodal FAS self-supervised pre-training without costly annotated labels. Compared with the previous modality-symmetric autoencoder, the proposed M$^{2}$A$^{2}$E is able to learn more intrinsic task-aware representation and compatible with modality-agnostic (e.g., unimodal, bimodal, and trimodal) downstream settings. Extensive experiments with both unimodal (RGB, Depth, IR) and multimodal (RGB+Depth, RGB+IR, Depth+IR, RGB+Depth+IR) settings conducted on multimodal FAS benchmarks demonstrate the superior performance of the proposed methods. We hope these findings and solutions can facilitate the future research for ViT-based multimodal FAS.
Flexible-modal Deception Detection with Audio-Visual Adapter
Authors: Zhaoxu Li, Zitong Yu, Nithish Muthuchamy Selvaraj, Xiaobao Guo, Bingquan Shen, Adams Wai-Kin Kong, Alex Kot
Date: 2023-02-11 00:00:00
Tasks: Deception Detection

Detecting deception by human behaviors is vital in many fields such as custom security and multimedia anti-fraud. Recently, audio-visual deception detection attracts more attention due to its better performance than using only a single modality. However, in real-world multi-modal settings, the integrity of data can be an issue (e.g., sometimes only partial modalities are available). The missing modality might lead to a decrease in performance, but the model still learns the features of the missed modality. In this paper, to further improve the performance and overcome the missing modality problem, we propose a novel Transformer-based framework with an Audio-Visual Adapter (AVA) to fuse temporal features across two modalities efficiently. Extensive experiments conducted on two benchmark datasets demonstrate that the proposed method can achieve superior performance compared with other multi-modal fusion methods under flexible-modal (multiple and missing modalities) settings.
ConMAE: Contour Guided MAE for Unsupervised Vehicle Re-Identification
Authors: Jing Yang, Jianwu Fang, Hongke Xu
Date: 2023-02-11 00:00:00
Tasks: Self-Supervised Learning, Unsupervised Vehicle Re-Identification, Vehicle Re-Identification

Vehicle re-identification is a cross-view search task by matching the same target vehicle from different perspectives. It serves an important role in road-vehicle collaboration and intelligent road control. With the large-scale and dynamic road environment, the paradigm of supervised vehicle re-identification shows limited scalability because of the heavy reliance on large-scale annotated datasets. Therefore, the unsupervised vehicle re-identification with stronger cross-scene generalization ability has attracted more attention. Considering that Masked Autoencoder (MAE) has shown excellent performance in self-supervised learning, this work designs a Contour Guided Masked Autoencoder for Unsupervised Vehicle Re-Identification (ConMAE), which is inspired by extracting the informative contour clue to highlight the key regions for cross-view correlation. ConMAE is implemented by preserving the image blocks with contour pixels and randomly masking the blocks with smooth textures. In addition, to improve the quality of pseudo labels of vehicles for unsupervised re-identification, we design a label softening strategy and adaptively update the label with the increase of training steps. We carry out experiments on VeRi-776 and VehicleID datasets, and a significant performance improvement is obtained by the comparison with the state-of-the-art unsupervised vehicle re-identification methods. The code is available on the website of https://github.com/2020132075/ConMAE.
Dual Relation Knowledge Distillation for Object Detection
Authors: ZhenLiang Ni, Fukui Yang, Shengzhao Wen, Gang Zhang
Date: 2023-02-11 00:00:00
Tasks: Knowledge Distillation, Model Compression, Object, object-detection, Object Detection, Relation

Knowledge distillation is an effective method for model compression. However, it is still a challenging topic to apply knowledge distillation to detection tasks. There are two key points resulting in poor distillation performance for detection tasks. One is the serious imbalance between foreground and background features, another one is that small object lacks enough feature representation. To solve the above issues, we propose a new distillation method named dual relation knowledge distillation (DRKD), including pixel-wise relation distillation and instance-wise relation distillation. The pixel-wise relation distillation embeds pixel-wise features in the graph space and applies graph convolution to capture the global pixel relation. By distilling the global pixel relation, the student detector can learn the relation between foreground and background features, and avoid the difficulty of distilling features directly for the feature imbalance issue. Besides, we find that instance-wise relation supplements valuable knowledge beyond independent features for small objects. Thus, the instance-wise relation distillation is designed, which calculates the similarity of different instances to obtain a relation matrix. More importantly, a relation filter module is designed to highlight valuable instance relations. The proposed dual relation knowledge distillation is general and can be easily applied for both one-stage and two-stage detectors. Our method achieves state-of-the-art performance, which improves Faster R-CNN based on ResNet50 from 38.4% to 41.6% mAP and improves RetinaNet based on ResNet50 from 37.4% to 40.3% mAP on COCO 2017.
Dive into the Resolution Augmentations and Metrics in Low Resolution Face Recognition: A Plain yet Effective New Baseline
Authors: Xu Ling, Yichen Lu, Wenqi Xu, Weihong Deng, Yingjie Zhang, Xingchen Cui, Hongzhi Shi, Dongchao Wen
Date: 2023-02-11 00:00:00
Tasks: Face Recognition, General Knowledge

Although deep learning has significantly improved Face Recognition (FR), dramatic performance deterioration may occur when processing Low Resolution (LR) faces. To alleviate this, approaches based on unified feature space are proposed with the sacrifice under High Resolution (HR) circumstances. To deal with the huge domain gap between HR and LR domains and achieve the best on both domains, we first took a closer look at the impacts of several resolution augmentations and then analyzed the difficulty of LR samples from the perspective of the model gradient produced by different resolution samples. Besides, we also find that the introduction of some resolutions could help the learning of lower resolutions. Based on these, we divide the LR samples into three difficulties according to the resolution and propose a more effective Multi-Resolution Augmentation. Then, due to the rapidly increasing domain gap as the resolution decreases, we carefully design a novel and effective metric loss based on a LogExp distance function that provides decent gradients to prevent oscillation near the convergence point or tolerance to small distance errors; it could also dynamically adjust the penalty for errors in different dimensions, allowing for more optimization of dimensions with large errors. Combining these two insights, our model could learn more general knowledge in a wide resolution range of images and balanced results can be achieved by our extremely simple framework. Moreover, the augmentations and metrics are the cornerstones of LRFR, so our method could be considered a new baseline for the LRFR task. Experiments on the LRFR datasets: SCface, XQLFW, and large-scale LRFR dataset: TinyFace demonstrate the effectiveness of our methods, while the degradation on HRFR datasets is significantly reduced.
Differentiable Outlier Detection Enable Robust Deep Multimodal Analysis
Authors: Zhu Wang, Sourav Medya, Sathya N. Ravi
Date: 2023-02-11 00:00:00
Tasks: Image-text Retrieval, Knowledge Graphs, Language Modeling, Language Modelling, Outlier Detection, Question Answering, Retrieval, Text Retrieval, Visual Question Answering, Visual Question Answering (VQA), Visual Reasoning

Often, deep network models are purely inductive during training and while performing inference on unseen data. Thus, when such models are used for predictions, it is well known that they often fail to capture the semantic information and implicit dependencies that exist among objects (or concepts) on a population level. Moreover, it is still unclear how domain or prior modal knowledge can be specified in a backpropagation friendly manner, especially in large-scale and noisy settings. In this work, we propose an end-to-end vision and language model incorporating explicit knowledge graphs. We also introduce an interactive out-of-distribution (OOD) layer using implicit network operator. The layer is used to filter noise that is brought by external knowledge base. In practice, we apply our model on several vision and language downstream tasks including visual question answering, visual reasoning, and image-text retrieval on different datasets. Our experiments show that it is possible to design models that perform similarly to state-of-art results but with significantly fewer samples and training time.
Multi-class Brain Tumor Segmentation using Graph Attention Network
Authors: Dhrumil Patel, Dhruv Patel, Rudra Saxena, Thangarajah Akilan
Date: 2023-02-11 00:00:00
Tasks: Brain Tumor Segmentation, BraTS2021, Diagnostic, Graph Attention, RAG, Segmentation, Tumor Segmentation

Brain tumor segmentation from magnetic resonance imaging (MRI) plays an important role in diagnostic radiology. To overcome the practical issues in manual approaches, there is a huge demand for building automatic tumor segmentation algorithms. This work introduces an efficient brain tumor summation model by exploiting the advancement in MRI and graph neural networks (GNNs). The model represents the volumetric MRI as a region adjacency graph (RAG) and learns to identify the type of tumors through a graph attention network (GAT) -- a variant of GNNs. The ablation analysis conducted on two benchmark datasets proves that the proposed model can produce competitive results compared to the leading-edge solutions. It achieves mean dice scores of 0.91, 0.86, 0.79, and mean Hausdorff distances in the 95th percentile (HD95) of 5.91, 6.08, and 9.52 mm, respectively, for whole tumor, core tumor, and enhancing tumor segmentation on BraTS2021 validation dataset. On average, these performances are >6\% and >50%, compared to a GNN-based baseline model, respectively, on dice score and HD95 evaluation metrics.