pwc-archive/papers-with-abstracts huggingface dataset:
A new way of video compression via forward-referencing using deep learning
Authors: S. M. A. K. Rajin, M. Murshed, M. Paul, S. W. Teng, J. Ma
Date: 2022-08-13 00:00:00
Tasks: Generative Adversarial Network, Motion Estimation, Video Compression

To exploit high temporal correlations in video frames of the same scene, the current frame is predicted from the already-encoded reference frames using block-based motion estimation and compensation techniques. While this approach can efficiently exploit the translation motion of the moving objects, it is susceptible to other types of affine motion and object occlusion/deocclusion. Recently, deep learning has been used to model the high-level structure of human pose in specific actions from short videos and then generate virtual frames in future time by predicting the pose using a generative adversarial network (GAN). Therefore, modelling the high-level structure of human pose is able to exploit semantic correlation by predicting human actions and determining its trajectory. Video surveillance applications will benefit as stored big surveillance data can be compressed by estimating human pose trajectories and generating future frames through semantic correlation. This paper explores a new way of video coding by modelling human pose from the already-encoded frames and using the generated frame at the current time as an additional forward-referencing frame. It is expected that the proposed approach can overcome the limitations of the traditional backward-referencing frames by predicting the blocks containing the moving objects with lower residuals. Experimental results show that the proposed approach can achieve on average up to 2.83 dB PSNR gain and 25.93\% bitrate savings for high motion video sequences
DS-MVSNet: Unsupervised Multi-view Stereo via Depth Synthesis
Authors: Jingliang Li, Zhengda Lu, Yiqun Wang, Ying Wang, Jun Xiao
Date: 2022-08-13 00:00:00

In recent years, supervised or unsupervised learning-based MVS methods achieved excellent performance compared with traditional methods. However, these methods only use the probability volume computed by cost volume regularization to predict reference depths and this manner cannot mine enough information from the probability volume. Furthermore, the unsupervised methods usually try to use two-step or additional inputs for training which make the procedure more complicated. In this paper, we propose the DS-MVSNet, an end-to-end unsupervised MVS structure with the source depths synthesis. To mine the information in probability volume, we creatively synthesize the source depths by splattering the probability volume and depth hypotheses to source views. Meanwhile, we propose the adaptive Gaussian sampling and improved adaptive bins sampling approach that improve the depths hypotheses accuracy. On the other hand, we utilize the source depths to render the reference images and propose depth consistency loss and depth smoothness loss. These can provide additional guidance according to photometric and geometric consistency in different views without additional inputs. Finally, we conduct a series of experiments on the DTU dataset and Tanks & Temples dataset that demonstrate the efficiency and robustness of our DS-MVSNet compared with the state-of-the-art methods.
Bidirectional Feature Globalization for Few-shot Semantic Segmentation of 3D Point Cloud Scenes
Authors: Yongqiang Mao, Zonghao Guo, Xiaonan Lu, Zhiqiang Yuan, Haowen Guo
Date: 2022-08-13 00:00:00
Tasks: Few-Shot Semantic Segmentation, Metric Learning, Segmentation, Semantic Segmentation

Few-shot segmentation of point cloud remains a challenging task, as there is no effective way to convert local point cloud information to global representation, which hinders the generalization ability of point features. In this study, we propose a bidirectional feature globalization (BFG) approach, which leverages the similarity measurement between point features and prototype vectors to embed global perception to local point features in a bidirectional fashion. With point-to-prototype globalization (Po2PrG), BFG aggregates local point features to prototypes according to similarity weights from dense point features to sparse prototypes. With prototype-to-point globalization (Pr2PoG), the global perception is embedded to local point features based on similarity weights from sparse prototypes to dense point features. The sparse prototypes of each class embedded with global perception are summarized to a single prototype for few-shot 3D segmentation based on the metric learning framework. Extensive experiments on S3DIS and ScanNet demonstrate that BFG significantly outperforms the state-of-the-art methods.
Self-Contained Entity Discovery from Captioned Videos
Authors: Melika Ayoughi, Pascal Mettes, Paul Groth
Date: 2022-08-13 00:00:00

This paper introduces the task of visual named entity discovery in videos without the need for task-specific supervision or task-specific external knowledge sources. Assigning specific names to entities (e.g. faces, scenes, or objects) in video frames is a long-standing challenge. Commonly, this problem is addressed as a supervised learning objective by manually annotating faces with entity labels. To bypass the annotation burden of this setup, several works have investigated the problem by utilizing external knowledge sources such as movie databases. While effective, such approaches do not work when task-specific knowledge sources are not provided and can only be applied to movies and TV series. In this work, we take the problem a step further and propose to discover entities in videos from videos and corresponding captions or subtitles. We introduce a three-stage method where we (i) create bipartite entity-name graphs from frame-caption pairs, (ii) find visual entity agreements, and (iii) refine the entity assignment through entity-level prototype construction. To tackle this new problem, we outline two new benchmarks SC-Friends and SC-BBT based on the Friends and Big Bang Theory TV series. Experiments on the benchmarks demonstrate the ability of our approach to discover which named entity belongs to which face or scene, with an accuracy close to a supervised oracle, just from the multimodal information present in videos. Additionally, our qualitative examples show the potential challenges of self-contained discovery of any visual entity for future work. The code and the data are available on GitHub.
SSP-Pose: Symmetry-Aware Shape Prior Deformation for Direct Category-Level Object Pose Estimation
Authors: Ruida Zhang, Yan Di, Fabian Manhardt, Federico Tombari, Xiangyang Ji
Date: 2022-08-13 00:00:00
Tasks: Pose Estimation, regression

Category-level pose estimation is a challenging problem due to intra-class shape variations. Recent methods deform pre-computed shape priors to map the observed point cloud into the normalized object coordinate space and then retrieve the pose via post-processing, i.e., Umeyama's Algorithm. The shortcomings of this two-stage strategy lie in two aspects: 1) The surrogate supervision on the intermediate results can not directly guide the learning of pose, resulting in large pose error after post-processing. 2) The inference speed is limited by the post-processing step. In this paper, to handle these shortcomings, we propose an end-to-end trainable network SSP-Pose for category-level pose estimation, which integrates shape priors into a direct pose regression network. SSP-Pose stacks four individual branches on a shared feature extractor, where two branches are designed to deform and match the prior model with the observed instance, and the other two branches are applied for directly regressing the totally 9 degrees-of-freedom pose and performing symmetry reconstruction and point-wise inlier mask prediction respectively. Consistency loss terms are then naturally exploited to align the outputs of different branches and promote the performance. During inference, only the direct pose regression branch is needed. In this manner, SSP-Pose not only learns category-level pose-sensitive characteristics to boost performance but also keeps a real-time inference speed. Moreover, we utilize the symmetry information of each category to guide the shape prior deformation, and propose a novel symmetry-aware loss to mitigate the matching ambiguity. Extensive experiments on public datasets demonstrate that SSP-Pose produces superior performance compared with competitors with a real-time inference speed at about 25Hz.
Entropy Induced Pruning Framework for Convolutional Neural Networks
Authors: Yiheng Lu, Ziyu Guan, Yaming Yang, Maoguo Gong, Wei Zhao, Kaiyuan Feng
Date: 2022-08-13 00:00:00
Tasks: image-classification, Image Classification

Structured pruning techniques have achieved great compression performance on convolutional neural networks for image classification task. However, the majority of existing methods are weight-oriented, and their pruning results may be unsatisfactory when the original model is trained poorly. That is, a fully-trained model is required to provide useful weight information. This may be time-consuming, and the pruning results are sensitive to the updating process of model parameters. In this paper, we propose a metric named Average Filter Information Entropy (AFIE) to measure the importance of each filter. It is calculated by three major steps, i.e., low-rank decomposition of the "input-output" matrix of each convolutional layer, normalization of the obtained eigenvalues, and calculation of filter importance based on information entropy. By leveraging the proposed AFIE, the proposed framework is able to yield a stable importance evaluation of each filter no matter whether the original model is trained fully. We implement our AFIE based on AlexNet, VGG-16, and ResNet-50, and test them on MNIST, CIFAR-10, and ImageNet, respectively. The experimental results are encouraging. We surprisingly observe that for our methods, even when the original model is only trained with one epoch, the importance evaluation of each filter keeps identical to the results when the model is fully-trained. This indicates that the proposed pruning strategy can perform effectively at the beginning stage of the training process for the original model.
Recent Progress in Transformer-based Medical Image Analysis
Authors: Zhaoshan Liu, Qiujie Lv, Ziduo Yang, YiFan Li, Chau Hung Lee, Lei Shen
Date: 2022-08-13 00:00:00
Tasks: Denoising, Medical Image Analysis

The transformer is primarily used in the field of natural language processing. Recently, it has been adopted and shows promise in the computer vision (CV) field. Medical image analysis (MIA), as a critical branch of CV, also greatly benefits from this state-of-the-art technique. In this review, we first recap the core component of the transformer, the attention mechanism, and the detailed structures of the transformer. After that, we depict the recent progress of the transformer in the field of MIA. We organize the applications in a sequence of different tasks, including classification, segmentation, captioning, registration, detection, enhancement, localization, and synthesis. The mainstream classification and segmentation tasks are further divided into eleven medical image modalities. A large number of experiments studied in this review illustrate that the transformer-based method outperforms existing methods through comparisons with multiple evaluation metrics. Finally, we discuss the open challenges and future opportunities in this field. This task-modality review with the latest contents, detailed information, and comprehensive comparison may greatly benefit the broad MIA community.
Online Refinement of a Scene Recognition Model for Mobile Robots by Observing Human's Interaction with Environments
Authors: Shigemichi Matsuzaki, Hiroaki Masuzawa, Jun Miura
Date: 2022-08-13 00:00:00
Tasks: Robot Navigation, Scene Recognition, Semantic Segmentation

This paper describes a method of online refinement of a scene recognition model for robot navigation considering traversable plants, flexible plant parts which a robot can push aside while moving. In scene recognition systems that consider traversable plants growing out to the paths, misclassification may lead the robot to getting stuck due to the traversable plants recognized as obstacles. Yet, misclassification is inevitable in any estimation methods. In this work, we propose a framework that allows for refining a semantic segmentation model on the fly during the robot's operation. We introduce a few-shot segmentation based on weight imprinting for online model refinement without fine-tuning. Training data are collected via observation of a human's interaction with the plant parts. We propose novel robust weight imprinting to mitigate the effect of noise included in the masks generated by the interaction. The proposed method was evaluated through experiments using real-world data and shown to outperform an ordinary weight imprinting and provide competitive results to fine-tuning with model distillation while requiring less computational cost.
A Unified Two-Stage Group Semantics Propagation and Contrastive Learning Network for Co-Saliency Detection
Authors: Zhenshan Tan, Cheng Chen, Keyu Wen, Yuzhuo Qin, Xiaodong Gu
Date: 2022-08-13 00:00:00
Tasks: Contrastive Learning, Saliency Detection

Co-saliency detection (CoSOD) aims at discovering the repetitive salient objects from multiple images. Two primary challenges are group semantics extraction and noise object suppression. In this paper, we present a unified Two-stage grOup semantics PropagatIon and Contrastive learning NETwork (TopicNet) for CoSOD. TopicNet can be decomposed into two substructures, including a two-stage group semantics propagation module (TGSP) to address the first challenge and a contrastive learning module (CLM) to address the second challenge. Concretely, for TGSP, we design an image-to-group propagation module (IGP) to capture the consensus representation of intra-group similar features and a group-to-pixel propagation module (GPP) to build the relevancy of consensus representation. For CLM, with the design of positive samples, the semantic consistency is enhanced. With the design of negative samples, the noise objects are suppressed. Experimental results on three prevailing benchmarks reveal that TopicNet outperforms other competitors in terms of various evaluation metrics.
A Study of Demographic Bias in CNN-based Brain MR Segmentation
Authors: Stefanos Ioannou, Hana Chockler, Alexander Hammers, Andrew P. King
Date: 2022-08-13 00:00:00
Tasks: Segmentation

Convolutional neural networks (CNNs) are increasingly being used to automate the segmentation of brain structures in magnetic resonance (MR) images for research studies. In other applications, CNN models have been shown to exhibit bias against certain demographic groups when they are under-represented in the training sets. In this work, we investigate whether CNN models for brain MR segmentation have the potential to contain sex or race bias when trained with imbalanced training sets. We train multiple instances of the FastSurferCNN model using different levels of sex imbalance in white subjects. We evaluate the performance of these models separately for white male and white female test sets to assess sex bias, and furthermore evaluate them on black male and black female test sets to assess potential racial bias. We find significant sex and race bias effects in segmentation model performance. The biases have a strong spatial component, with some brain regions exhibiting much stronger bias than others. Overall, our results suggest that race bias is more significant than sex bias. Our study demonstrates the importance of considering race and sex balance when forming training sets for CNN-based brain MR segmentation, to avoid maintaining or even exacerbating existing health inequalities through biased research study findings.
Confidence Matters: Inspecting Backdoors in Deep Neural Networks via Distribution Transfer
Authors: Tong Wang, Yuan YAO, Feng Xu, Miao Xu, Shengwei An, Ting Wang
Date: 2022-08-13 00:00:00
Tasks: Backdoor Attack, backdoor defense

Backdoor attacks have been shown to be a serious security threat against deep learning models, and detecting whether a given model has been backdoored becomes a crucial task. Existing defenses are mainly built upon the observation that the backdoor trigger is usually of small size or affects the activation of only a few neurons. However, the above observations are violated in many cases especially for advanced backdoor attacks, hindering the performance and applicability of the existing defenses. In this paper, we propose a backdoor defense DTInspector built upon a new observation. That is, an effective backdoor attack usually requires high prediction confidence on the poisoned training samples, so as to ensure that the trained model exhibits the targeted behavior with a high probability. Based on this observation, DTInspector first learns a patch that could change the predictions of most high-confidence data, and then decides the existence of backdoor by checking the ratio of prediction changes after applying the learned patch on the low-confidence data. Extensive evaluations on five backdoor attacks, four datasets, and three advanced attacking types demonstrate the effectiveness of the proposed defense.
Enhanced Vehicle Re-identification for ITS: A Feature Fusion approach using Deep Learning
Authors: Ashutosh Holla B, Manohara Pai M. M, Ujjwal Verma, Radhika M. Pai
Date: 2022-08-13 00:00:00
Tasks: Vehicle Re-Identification

In recent years, the development of robust Intelligent transportation systems (ITS) is tackled across the globe to provide better traffic efficiency by reducing frequent traffic problems. As an application of ITS, vehicle re-identification has gained ample interest in the domain of computer vision and robotics. Convolutional neural network (CNN) based methods are developed to perform vehicle re-identification to address key challenges such as occlusion, illumination change, scale, etc. The advancement of transformers in computer vision has opened an opportunity to explore the re-identification process further to enhance performance. In this paper, a framework is developed to perform the re-identification of vehicles across CCTV cameras. To perform re-identification, the proposed framework fuses the vehicle representation learned using a CNN and a transformer model. The framework is tested on a dataset that contains 81 unique vehicle identities observed across 20 CCTV cameras. From the experiments, the fused vehicle re-identification framework yields an mAP of 61.73% which is significantly better when compared with the standalone CNN or transformer model.
Drone Referring Localization: An Efficient Heterogeneous Spatial Feature Interaction Method For UAV Self-Localization
Authors: Ming Dai, Enhui Zheng, Jiahao Chen, Lei Qi, ZhenHua Feng, Wankou Yang
Date: 2022-08-13 00:00:00
Tasks: Image Retrieval, Retrieval, Visual Localization

Image retrieval (IR) has emerged as a promising approach for self-localization in unmanned aerial vehicles (UAVs). However, IR-based methods face several challenges: 1) Pre- and post-processing incur significant computational and storage overhead; 2) The lack of interaction between dual-source features impairs precise spatial perception. In this paper, we propose an efficient heterogeneous spatial feature interaction method, termed Drone Referring Localization (DRL), which aims to localize UAV-view images within satellite imagery. Unlike conventional methods that treat different data sources in isolation, followed by cosine similarity computations, DRL facilitates the learnable interaction of heterogeneous features. To implement the proposed DRL, we design two transformer-based frameworks, Post-Fusion and Mix-Fusion, enabling end-to-end training and inference. Furthermore, we introduce random scale cropping and weight balance loss techniques to augment paired data and optimize the balance between positive and negative sample weights. Additionally, we construct a new dataset, UL14, and establish a benchmark tailored to the DRL framework. Compared to traditional IR methods, DRL achieves superior localization accuracy (MA@20 +9.4\%) while significantly reducing computational time (1/7) and storage overhead (1/3). The dataset and code will be made publicly available. The dataset and code are available at \url{https://github.com/Dmmm1997/DRL} .
Memory Efficient Temporal & Visual Graph Model for Unsupervised Video Domain Adaptation
Authors: Xinyue Hu, Lin Gu, Liangchen Liu, Ruijiang Li, Chang Su, Tatsuya Harada, Yingying Zhu
Date: 2022-08-13 00:00:00
Tasks: Domain Adaptation, Graph Attention

Existing video domain adaption (DA) methods need to store all temporal combinations of video frames or pair the source and target videos, which are memory cost expensive and can't scale up to long videos. To address these limitations, we propose a memory-efficient graph-based video DA approach as follows. At first our method models each source or target video by a graph: nodes represent video frames and edges represent the temporal or visual similarity relationship between frames. We use a graph attention network to learn the weight of individual frames and simultaneously align the source and target video into a domain-invariant graph feature space. Instead of storing a large number of sub-videos, our method only constructs one graph with a graph attention mechanism for one video, reducing the memory cost substantially. The extensive experiments show that, compared with the state-of-art methods, we achieved superior performance while reducing the memory cost significantly.
Exploiting Multiple Sequence Lengths in Fast End to End Training for Image Captioning
Authors: Jia Cheng Hu, Roberto Cavicchioli, Alessandro Capotondi
Date: 2022-08-13 00:00:00
Tasks: Image Captioning

We introduce a method called the Expansion mechanism that processes the input unconstrained by the number of elements in the sequence. By doing so, the model can learn more effectively compared to traditional attention-based approaches. To support this claim, we design a novel architecture ExpansionNet v2 that achieved strong results on the MS COCO 2014 Image Captioning challenge and the State of the Art in its respective category, with a score of 143.7 CIDErD in the offline test split, 140.8 CIDErD in the online evaluation server and 72.9 AllCIDEr on the nocaps validation set. Additionally, we introduce an End to End training algorithm up to 2.8 times faster than established alternatives. Source code available at: https://github.com/jchenghu/ExpansionNet_v2
Transferable Adversarial Examples with Bayes Approach
Authors: Mingyuan Fan, Cen Chen, Wenmeng Zhou, Yinggui Wang
Date: 2022-08-13 00:00:00

The vulnerability of deep neural networks (DNNs) to black-box adversarial attacks is one of the most heated topics in trustworthy AI. In such attacks, the attackers operate without any insider knowledge of the model, making the cross-model transferability of adversarial examples critical. Despite the potential for adversarial examples to be effective across various models, it has been observed that adversarial examples that are specifically crafted for a specific model often exhibit poor transferability. In this paper, we explore the transferability of adversarial examples via the lens of Bayesian approach. Specifically, we leverage Bayesian approach to probe the transferability and then study what constitutes a transferability-promoting prior. Following this, we design two concrete transferability-promoting priors, along with an adaptive dynamic weighting strategy for instances sampled from these priors. Employing these techniques, we present BayAtk. Extensive experiments illustrate the significant effectiveness of BayAtk in crafting more transferable adversarial examples against both undefended and defended black-box models compared to existing state-of-the-art attacks.