bluuebunny/crossref_metadata_2025 huggingface dataset:
IMMEDIATE ANALGESIC EFFECT OF 4KHZ AMFS INTERFERENTIAL CURRENT ON CHRONIC LOW BACK PAIN
Authors: ALMEIDA NICOLE , PALADINI LUIS HENRIQUE , DIAS LUCAS VINICIUS , SALES RAMON SCHMIDT DE , MACEDO ANA CAROLINA BRANDT DE
Published: July 2022

ABSTRACT Objective: To analyze the immediate effect of amplitude modulation frequencies (AMFs) of 4kHz interferential current (IFC) on chronic low back pain (CLBP). Method: This is a randomized controlled clinical trial. Sixty-three subjects with CLBP were recruited. The subjects were randomized into 3 groups: the placebo group (PG, n=21) and 2 intervention groups (IG), IG4kHz/2Hz (n=21) and IG4kHz/100Hz (n=21). All groups were submitted to a single session of 30 minutes. Pain was evaluated using a numerical rating scale (NRS), the McGill Pain Questionnaire (MPQ), and pressure algometry. Flexibility was evaluated using the Modified Schober Test (MST), the Sit-and-Reach Test (SRT), the Fingertip-to-Floor Test (FTF), and the Passive Straight-Leg Raise Test (PSLR). Results: Comparing IG4kHz/100Hz with PG, we found a significant difference (p<0.05) in NRS in the total and in the MPQ categories, whereas in the comparison between IG4kHz/2Hz and PG, we found a significant difference only in the sensory and evaluative categories of MPQ. Regarding the flexibility tests, we observed a significant difference of both IG4kHz/100Hz and IG4kHz/2Hz in comparison to PG in MST and PSLR, and of IG4kHz/2Hz in comparison to PG in SRT. The 4kHz IFC was effective in immediately reducing CLBP and, consequently, in increasing the flexibility of the lumbar spine and lower limbs. Conclusion: Conclusion: There was a greater number of significant positive outcomes when the 100Hz AMF was adopted. Level of Evidence I; High quality randomized clinical trial with or without statistically significant differences, but with narrow confidence intervals.
THE INFLUENCE OF PHYSICAL ACTIVITY ON THE PREVALENCE OF LOW BACK PAIN AMONG THE PORTUGUESE POPULATION
Authors: ROMÃO CATARINA ISABEL GUERREIRO , MERCÊ CRISTIANA ISABEL ANDRÉ , BRANCO MARCO ANTÓNIO COLAÇO
Published: July 2022

ABSTRACT Objective: I) To investigate the influence of physical activity (PA) on levels of low back pain, and II) To classify the respondents regarding low back pain. Methods: Collection of responses, through an online questionnaire, from 199 adults aged between 18 and 65 years (36.05 ± 11.90 years). The following inclusion criteria were applied: I) Suffering or have suffered pain in the spine at some point in life; and as an exclusion criterion: I) Being outside the required age range. Results: Investigating the level of pain and the risk of low back pain, there was a significant association (r = 0.481; p≤0.01) between these two factors, indicating that the higher the levels of pain, the higher the risk of low back pain. Those who presented higher levels of chronic pain either did not practice any physical activity (58.8%), or practiced formal PA (42.9%) or practiced informal PA (30.7%). Those who had practiced physical exercise for three months or more mostly did not have chronic pain (70.1%). Conclusions: There was a decrease in chronic low back pain which was associated with increased time and frequency of PA, as well as the practice of postural physical exercises. Level of Evidence I; Prognostic Studies— Investigating the Effect of a Patient Characteristic on the Outcome of Disease.
VALIDATION OF MEASURING PI USING CT AND A COMPARISON WITH WHOLE SPINE AND LUMBOSACRAL X-RAYS
Authors: DAHER MURILO TAVARES , DAHER RENATO TAVARES , DAHER RICARDO TAVARES , MELO LUCAS LODOMIRO ARAÚJO , MOURA WENDER GONÇALVES , NASCIMENTO VINÍCIO NUNES , FELISBINO JR PEDRO , TELES FILHO RICARDO VIEIRA , ORCINO JULIANE LEITE , RABAHI MARCELO FOUAD
Published: July 2022

ABSTRACT Objective: The objective of this study is to describe a new, extremely simple method for measuring pelvic incidence (PI) using computed tomography (CT) and to compare those measurements with measurements derived from whole spine and lumbosacral X-rays in a Brazilian population. Methods: Patients who had whole spine and lumbosacral X-rays and whole abdomen, pelvis, or lumbar spine CT performed within a period of less than three months were selected. Image overlay was used to measure PI from the CT. The PI was calculated by two independent examiners, and the PI for each exam was calculated twice, with an interval of two months between the assessments. The intra- and interexaminer reliability and reproducibility were evaluatedusing the intraclass correlation coefficient (ICC) and the repeatability coefficient, considering a 95% confidence interval. Results: Fifty-five patients of both sexes with a mean age of 58.7 years (±19) were analyzed. The mean PI angles in the analyses of both examiners at both evaluations were 54.85° (±13.73) for the whole spine X-ray, 54.06° (±11.67) for the lumbosacral spine X-ray, and 49.96° (±9.85) for the CT. There was good intra- and interexaminer reliability and reproducibility. There was also high concordancewith the whole spine and lumbosacral X-rays. Conclusion: CT is a reliable and reproducible alternative for measuring PI. Level of Evidence III; Prospective comparative.
The outcomes of polyparasitism in stray cats from Brazilian Midwest assessed by epidemiological, hematological and pathological data
Authors: Silva Alanderson Rodrigues da , Andrade Gisele Braziliano , Carvalho Joyce Katiuccia Medeiros Ramos , Barreto Wanessa Teixeira Gomes , Santos Filipe Martins , Sousa Keyla Carstens Marques de , André Marcos Rogério , Ferreira Luiz Claudio , Menezes Rodrigo Caldas , Herrera Heitor Miraglia
Published: July 2022

Abstract We evaluated the epidemiological, hematological, and pathological data of Leishmania spp., Toxoplasma gondii, Platynosomum illiciens, feline immunodeficiency virus (FIV), and feline leukemia virus (FeLV) infections and the coinfections in stray cats of an endemic area for leishmaniasis. The diagnosis was performed by serological tests and necropsy. We described gross lesions and histopathological findings. We used immunohistochemistry and chromogenic in situ hybridization for L. infantum detection. We found infection in 27 out of 50 sampled cats, among them, 14 presented coinfections. A strong correlation between splenomegaly and lymphadenomegaly with FeLV, and an association between hepatic lesions and cachexia with parasitism due to P. illiciens were observed. Moreover, we found a significant increase in the monocyte count in the FeLV-infected and a decrease in the red blood cell count in the FIV-infected animals. Amastigote forms of Leishmania spp. and tissue changes were detected in lymphoid organs of an animal coinfected with P. illiciens, T. gondii, and FIV. Polyparasitism recorded in stray cats of the Brazilian Midwest should be considered in effective control strategies for public health diseases. Moreover, stray cats of Campo Grande may be a source of infection of FIV, FeLV and P. illiciens for populations of domiciled cats.
Identification of Pentatrichomonas hominis in preputial washes of bulls in Brazil
Authors: Silva Otávia Reis e , Ribeiro Laura , Jesus Vera Lucia Teixeira de , McIntosh Douglas , Silenciato Lara Nogueira , Ferreira Joaquim Esquerdo , Mello Marco Roberto Bourg de
Published: July 2022

Abstract The parabasalid Pentatrichomonas hominis is generally considered to represent a symbiotic component of the gastrointestinal microbiota in a wide variety of vertebrate hosts including humans. Nevertheless, a limited number of studies have raised the possibility that it may act as a pathogen of humans, dogs, and pigs and that some human infections may have a zoonotic origin. Data from North America revealed an association between P. hominis and the bovine urogenital tract, principally in bulls and rarely in cows. The importance of this observation is linked to possible interference in the accurate diagnosis of the economically important venereal pathogen Tritrichomonas foetus. The current study employed culture-based and molecular methods to examine the preputial cavities of four breeding bulls, raised in open pasture in southeastern Brazil, for the presence of trichomonads. Motile protozoa were isolated from three of the bulls and were definitively identified as P. hominis based on nucleotide sequencing of polymerase chain reaction (PCR) amplicons derived from the ribosomal RNA operon (ITS1-5.8S rDNA-ITS2) of the parasite. The potential implications of these findings for bovine and human health are discussed.
Optimized Potential Initialization for Low-Latency Spiking Neural Networks
Authors: Bu Tong , Ding Jianhao , Yu Zhaofei , Huang Tiejun
Published: July 2022

Spiking Neural Networks (SNNs) have been attached great importance due to the distinctive properties of low power consumption, biological plausibility, and adversarial robustness. The most effective way to train deep SNNs is through ANN-to-SNN conversion, which have yielded the best performance in deep network structure and large-scale datasets. However, there is a trade-off between accuracy and latency. In order to achieve high precision as original ANNs, a long simulation time is needed to match the firing rate of a spiking neuron with the activation value of an analog neuron, which impedes the practical application of SNN. In this paper, we aim to achieve high-performance converted SNNs with extremely low latency (fewer than 32 time-steps). We start by theoretically analyzing ANN-to-SNN conversion and show that scaling the thresholds does play a similar role as weight normalization. Instead of introducing constraints that facilitate ANN-to-SNN conversion at the cost of model capacity, we applied a more direct way by optimizing the initial membrane potential to reduce the conversion loss in each layer. Besides, we demonstrate that optimal initialization of membrane potentials can implement expected error-free ANN-to-SNN conversion. We evaluate our algorithm on the CIFAR-10 dataset and CIFAR-100 dataset and achieve state-of-the-art accuracy, using fewer time-steps. For example, we reach top-1 accuracy of 93.38% on CIFAR-10 with 16 time-steps. Moreover, our method can be applied to other ANN-SNN conversion methodologies and remarkably promote performance when the time-steps is small.
ADD: Frequency Attention and Multi-View Based Knowledge Distillation to Detect Low-Quality Compressed Deepfake Images
Authors: Binh Le  Minh , Woo Simon
Published: July 2022

Despite significant advancements of deep learning-based forgery detectors for distinguishing manipulated deepfake images, most detection approaches suffer from moderate to significant performance degradation with low-quality compressed deepfake images. Because of the limited information in low-quality images, detecting low-quality deepfake remains an important challenge. In this work, we apply frequency domain learning and optimal transport theory in knowledge distillation (KD) to specifically improve the detection of low-quality compressed deepfake images. We explore transfer learning capability in KD to enable a student network to learn discriminative features from low-quality images effectively. In particular, we propose the Attention-based Deepfake detection Distiller (ADD), which consists of two novel distillations: 1) frequency attention distillation that effectively retrieves the removed high-frequency components in the student network, and 2) multi-view attention distillation that creates multiple attention vectors by slicing the teacher’s and student’s tensors under different views to transfer the teacher tensor’s distribution to the student more efficiently. Our extensive experimental results demonstrate that our approach outperforms state-of-the-art baselines in detecting low-quality compressed deepfake images.
CF-DETR: Coarse-to-Fine Transformers for End-to-End Object Detection
Authors: Cao Xipeng , Yuan Peng , Feng Bailan , Niu Kun
Published: July 2022

The recently proposed DEtection TRansformer (DETR) achieves promising performance for end-to-end object detection. However, it has relatively lower detection performance on small objects and suffers from slow convergence. This paper observed that DETR performs surprisingly well even on small objects when measuring Average Precision (AP) at decreased Intersection-over-Union (IoU) thresholds. Motivated by this observation, we propose a simple way to improve DETR by refining the coarse features and predicted locations. Specifically, we propose a novel Coarse-to-Fine (CF) decoder layer constituted of a coarse layer and a carefully designed fine layer. Within each CF decoder layer, the extracted local information (region of interest feature) is introduced into the flow of global context information from the coarse layer to refine and enrich the object query features via the fine layer. In the fine layer, the multi-scale information can be fully explored and exploited via the Adaptive Scale Fusion(ASF) module and Local Cross-Attention (LCA) module. The multi-scale information can also be enhanced by another proposed Transformer Enhanced FPN (TEF) module to further improve the performance. With our proposed framework (named CF-DETR), the localization accuracy of objects (especially for small objects) can be largely improved. As a byproduct, the slow convergence issue of DETR can also be addressed. The effectiveness of CF-DETR is validated via extensive experiments on the coco benchmark. CF-DETR achieves state-of-the-art performance among end-to-end detectors, e.g., achieving 47.8 AP using ResNet-50 with 36 epochs in the standard 3x training schedule.
Comprehensive Regularization in a Bi-directional Predictive Network for Video Anomaly Detection
Authors: Chen Chengwei , Xie Yuan , Lin Shaohui , Yao Angela , Jiang Guannan , Zhang Wei , Qu Yanyun , Qiao Ruizhi , Ren Bo , Ma Lizhuang
Published: July 2022

Video anomaly detection aims to automatically identify unusual objects or behaviours by learning from normal videos. Previous methods tend to use simplistic reconstruction or prediction constraints, which leads to the insufficiency of learned representations for normal data. As such, we propose a novel bi-directional architecture with three consistency constraints to comprehensively regularize the prediction task from pixel-wise, cross-modal, and temporal-sequence levels. First, predictive consistency is proposed to consider the symmetry property of motion and appearance in forwards and backwards time, which ensures the highly realistic appearance and motion predictions at the pixel-wise level. Second, association consistency considers the relevance between different modalities and uses one modality to regularize the prediction of another one. Finally, temporal consistency utilizes the relationship of the video sequence and ensures that the predictive network generates temporally consistent frames. During inference, the pattern of abnormal frames is unpredictable and will therefore cause higher prediction errors. Experiments show that our method outperforms advanced anomaly detectors and achieves state-of-the-art results on UCSD Ped2, CUHK Avenue, and ShanghaiTech datasets.
DCAN: Improving Temporal Action Detection via Dual Context Aggregation
Authors: Chen Guo , Zheng Yin-Dong , Wang Limin , Lu Tong
Published: July 2022

Temporal action detection aims to locate the boundaries of action in the video. The current method based on boundary matching enumerates and calculates all possible boundary matchings to generate proposals. However, these methods neglect the long-range context aggregation in boundary prediction. At the same time, due to the similar semantics of adjacent matchings, local semantic aggregation of densely-generated matchings cannot improve semantic richness and discrimination. In this paper, we propose the end-to-end proposal generation method named Dual Context Aggregation Network (DCAN) to aggregate context on two levels, namely, boundary level and proposal level, for generating high-quality action proposals, thereby improving the performance of temporal action detection. Specifically, we design the Multi-Path Temporal Context Aggregation (MTCA) to achieve smooth context aggregation on boundary level and precise evaluation of boundaries. For matching evaluation, Coarse-to-fine Matching (CFM) is designed to aggregate context on the proposal level and refine the matching map from coarse to fine. We conduct extensive experiments on ActivityNet v1.3 and THUMOS-14. DCAN obtains an average mAP of 35.39% on ActivityNet v1.3 and reaches mAP 54.14% at IoU@0.5 on THUMOS-14, which demonstrates DCAN can generate high-quality proposals and achieve state-of-the-art performance. We release the code at https://github.com/cg1177/DCAN.
Explore Inter-contrast between Videos via Composition for Weakly Supervised Temporal Sentence Grounding
Authors: Chen Jiaming , Luo Weixin , Zhang Wei , Ma Lin
Published: July 2022

Weakly supervised temporal sentence grounding aims to temporally localize the target segment corresponding to a given natural language query, where it provides video-query pairs without temporal annotations during training. Most existing methods use the fused visual-linguistic feature to reconstruct the query, where the least reconstruction error determines the target segment. This work introduces a novel approach that explores the inter-contrast between videos in a composed video by selecting components from two different videos and fusing them into a single video. Such a straightforward yet effective composition strategy provides the temporal annotations at multiple composed positions, resulting in numerous videos with temporal ground-truths for training the temporal sentence grounding task. A transformer framework is introduced with multi-tasks training to learn a compact but efficient visual-linguistic space. The experimental results on the public Charades-STA and ActivityNet-Caption dataset demonstrate the effectiveness of the proposed method, where our approach achieves comparable performance over the state-of-the-art weakly-supervised baselines. The code is available at https://github.com/PPjmchen/Composition_WSTG.
Towards High-Fidelity Face Self-Occlusion Recovery via Multi-View Residual-Based GAN Inversion
Authors: Chen Jinsong , Han Hu , Shan Shiguang
Published: July 2022

Face self-occlusions are inevitable due to the 3D nature of the human face and the loss of information in the projection process from 3D to 2D images. While recovering face self-occlusions based on 3D face reconstruction, e.g., 3D Morphable Model (3DMM) and its variants provides an effective solution, most of the existing methods show apparent limitations in expressing high-fidelity, natural, and diverse facial details. To overcome these limitations, we propose in this paper a new generative adversarial network (MvInvert) for natural face self-occlusion recovery without using paired image-texture data. We design a coarse-to-fine generator for photorealistic texture generation. A coarse texture is computed by inpainting the invisible areas in the photorealistic but incomplete texture sampled directly from the 2D image using the unrealistic but complete statistical texture from 3DMM. Then, we design a multi-view Residual-based GAN Inversion, which re-renders and refines multi-view 2D images, which are used for extracting multiple high-fidelity textures. Finally, these high-fidelity textures are fused based on their visibility maps via Poisson blending. To perform adversarial learning to assure the quality of the recovered texture, we design a discriminator consisting of two heads, i.e., one for global and local discrimination between the recovered texture and a small set of real textures in UV space, and the other for discrimination between the input image and the re-rendered 2D face images via pixel-wise, identity, and adversarial losses. Extensive experiments demonstrate that our approach outperforms the state-of-the-art methods in face self-occlusion recovery under unconstrained scenarios.
ProgressiveMotionSeg: Mutually Reinforced Framework for Event-Based Motion Segmentation
Authors: Chen Jinze , Wang Yang , Cao Yang , Wu Feng , Zha Zheng-Jun
Published: July 2022

Dynamic Vision Sensor (DVS) can asynchronously output the events reflecting apparent motion of objects with microsecond resolution, and shows great application potential in monitoring and other fields. However, the output event stream of existing DVS inevitably contains background activity noise (BA noise) due to dark current and junction leakage current, which will affect the temporal correlation of objects, resulting in deteriorated motion estimation performance. Particularly, the existing filter-based denoising methods cannot be directly applied to suppress the noise in event stream, since there is no spatial correlation. To address this issue, this paper presents a novel progressive framework, in which a Motion Estimation (ME) module and an Event Denoising (ED) module are jointly optimized in a mutually reinforced manner. Specifically, based on the maximum sharpness criterion, ME module divides the input event into several segments by adaptive clustering in a motion compensating warp field, and captures the temporal correlation of event stream according to the clustered motion parameters. Taking temporal correlation as guidance, ED module calculates the confidence that each event belongs to real activity events, and transmits it to ME module to update energy function of motion segmentation for noise suppression. The two steps are iteratively updated until stable motion segmentation results are obtained. Extensive experimental results on both synthetic and real datasets demonstrate the superiority of our proposed approaches against the State-Of-The-Art (SOTA) methods.
Attacking Video Recognition Models with Bullet-Screen Comments
Authors: Chen Kai , Wei Zhipeng , Chen Jingjing , Wu Zuxuan , Jiang Yu-Gang
Published: July 2022

Recent research has demonstrated that Deep Neural Networks (DNNs) are vulnerable to adversarial patches which introduce perceptible but localized changes to the input. Nevertheless, existing approaches have focused on generating adversarial patches on images, their counterparts in videos have been less explored. Compared with images, attacking videos is much more challenging as it needs to consider not only spatial cues but also temporal cues. To close this gap, we introduce a novel adversarial attack in this paper, the bullet-screen comment (BSC) attack, which attacks video recognition models with BSCs. Specifically, adversarial BSCs are generated with a Reinforcement Learning (RL) framework, where the environment is set as the target model and the agent plays the role of selecting the position and transparency of each BSC. By continuously querying the target models and receiving feedback, the agent gradually adjusts its selection strategies in order to achieve a high fooling rate with non-overlapping BSCs. As BSCs can be regarded as a kind of meaningful patch, adding it to a clean video will not affect people’s understanding of the video content, nor will arouse people’s suspicion. We conduct extensive experiments to verify the effectiveness of the proposed method. On both UCF-101 and HMDB-51 datasets, our BSC attack method can achieve about 90% fooling rate when attacking three mainstream video recognition models, while only occluding < 8% areas in the video. Our code is available at https://github.com/kay-ck/BSC-attack.
Imagine by Reasoning: A Reasoning-Based Implicit Semantic Data Augmentation for Long-Tailed Classification
Authors: Chen Xiaohua , Zhou Yucan , Wu Dayan , Zhang Wanqian , Zhou Yu , Li Bo , Wang Weiping
Published: July 2022

Real-world data often follows a long-tailed distribution, which makes the performance of existing classification algorithms degrade heavily. A key issue is that the samples in tail categories fail to depict their intra-class diversity. Humans can imagine a sample in new poses, scenes and view angles with their prior knowledge even if it is the first time to see this category. Inspired by this, we propose a novel reasoning-based implicit semantic data augmentation method to borrow transformation directions from other classes. Since the covariance matrix of each category represents the feature transformation directions, we can sample new directions from similar categories to generate definitely different instances. Specifically, the long-tailed distributed data is first adopted to train a backbone and a classifier. Then, a covariance matrix for each category is estimated, and a knowledge graph is constructed to store the relations of any two categories. Finally, tail samples are adaptively enhanced via propagating information from all the similar categories in the knowledge graph. Experimental results on CIFAR-LT-100, ImageNet-LT, and iNaturalist 2018 have demonstrated the effectiveness of our proposed method compared with the state-of-the-art methods.
Guide Local Feature Matching by Overlap Estimation
Authors: Chen Ying , Huang Dihe , Xu Shang , Liu Jianlin , Liu Yong
Published: July 2022

Local image feature matching under large appearance, viewpoint, and distance changes is challenging yet important. Conventional methods detect and match tentative local features across the whole images, with heuristic consistency checks to guarantee reliable matches. In this paper, we introduce a novel Overlap Estimation method conditioned on image pairs with TRansformer, named OETR, to constrain local feature matching in the commonly visible region. OETR performs overlap estimation in a two step process of feature correlation and then overlap regression. As a preprocessing module, OETR can be plugged into any existing local feature detection and matching pipeline, to mitigate potential view angle or scale variance. Intensive experiments show that OETR can boost state of the art local feature matching performance substantially, especially for image pairs with small shared regions. The code will be publicly available at https://github.com/AbyssGaze/OETR.
LCTR: On Awakening the Local Continuity of Transformer for Weakly Supervised Object Localization
Authors: Chen Zhiwei , Wang Changan , Wang Yabiao , Jiang Guannan , Shen Yunhang , Tai Ying , Wang Chengjie , Zhang Wei , Cao Liujuan
Published: July 2022

Weakly supervised object localization (WSOL) aims to learn object localizer solely by using image-level labels. The convolution neural network (CNN) based techniques often result in highlighting the most discriminative part of objects while ignoring the entire object extent. Recently, the transformer architecture has been deployed to WSOL to capture the long-range feature dependencies with self-attention mechanism and multilayer perceptron structure. Nevertheless, transformers lack the locality inductive bias inherent to CNNs and therefore may deteriorate local feature details in WSOL. In this paper, we propose a novel framework built upon the transformer, termed LCTR (Local Continuity TRansformer), which targets at enhancing the local perception capability of global features among long-range feature dependencies. To this end, we propose a relational patch-attention module (RPAM), which considers cross-patch information on a global basis. We further design a cue digging module (CDM), which utilizes local features to guide the learning trend of the model for highlighting the weak local responses. Finally, comprehensive experiments are carried out on two widely used datasets, ie, CUB-200-2011 and ILSVRC, to verify the effectiveness of our method.
Efficient Virtual View Selection for 3D Hand Pose Estimation
Authors: Cheng Jian , Wan Yanguang , Zuo Dexin , Ma Cuixia , Gu Jian , Tan Ping , Wang Hongan , Deng Xiaoming , Zhang Yinda
Published: July 2022

3D hand pose estimation from single depth is a fundamental problem in computer vision, and has wide applications. However, the existing methods still can not achieve satisfactory hand pose estimation results due to view variation and occlusion of human hand. In this paper, we propose a new virtual view selection and fusion module for 3D hand pose estimation from single depth. We propose to automatically select multiple virtual viewpoints for pose estimation and fuse the results of all and find this empirically delivers accurate and robust pose estimation. In order to select most effective virtual views for pose fusion, we evaluate the virtual views based on the confidence of virtual views using a light-weight network via network distillation. Experiments on three main benchmark datasets including NYU, ICVL and Hands2019 demonstrate that our method outperforms the state-of-the-arts on NYU and ICVL, and achieves very competitive performance on Hands2019-Task1, and our proposed virtual view selection and fusion module is both effective for 3D hand pose estimation.
Pose Adaptive Dual Mixup for Few-Shot Single-View 3D Reconstruction
Authors: Cheng Ta-Ying , Yang Hsuan-Ru , Trigoni Niki , Chen Hwann-Tzong , Liu Tyng-Luh
Published: July 2022

We present a pose adaptive few-shot learning procedure and a two-stage data interpolation regularization, termed Pose Adaptive Dual Mixup (PADMix), for single-image 3D reconstruction. While augmentations via interpolating feature-label pairs are effective in classification tasks, they fall short in shape predictions potentially due to inconsistencies between interpolated products of two images and volumes when rendering viewpoints are unknown. PADMix targets this issue with two sets of mixup procedures performed sequentially. We first perform an input mixup which, combined with a pose adaptive learning procedure, is helpful in learning 2D feature extraction and pose adaptive latent encoding. The stagewise training allows us to build upon the pose invariant representations to perform a follow-up latent mixup under one-to-one correspondences between features and ground-truth volumes. PADMix significantly outperforms previous literature on few-shot settings over the ShapeNet dataset and sets new benchmarks on the more challenging real-world Pix3D dataset.
PureGaze: Purifying Gaze Feature for Generalizable Gaze Estimation
Authors: Cheng Yihua , Bao Yiwei , Lu Feng
Published: July 2022

Gaze estimation methods learn eye gaze from facial features. However, among rich information in the facial image, real gaze-relevant features only correspond to subtle changes in eye region, while other gaze-irrelevant features like illumination, personal appearance and even facial expression may affect the learning in an unexpected way. This is a major reason why existing methods show significant performance degradation in cross-domain/dataset evaluation. In this paper, we tackle the cross-domain problem in gaze estimation. Different from common domain adaption methods, we propose a domain generalization method to improve the cross-domain performance without touching target samples. The domain generalization is realized by gaze feature purification. We eliminate gaze-irrelevant factors such as illumination and identity to improve the cross-domain performance. We design a plug-and-play self-adversarial framework for the gaze feature purification. The framework enhances not only our baseline but also existing gaze estimation methods directly and significantly. To the best of our knowledge, we are the first to propose domain generalization methods in gaze estimation. Our method achieves not only state-of-the-art performance among typical gaze estimation methods but also competitive results among domain adaption methods. The code is released in https://github.com/yihuacheng/PureGaze.
(2.5+1)D Spatio-Temporal Scene Graphs for Video Question Answering
Authors: Cherian Anoop , Hori Chiori , Marks Tim K. , Le Roux Jonathan
Published: July 2022

Spatio-temporal scene-graph approaches to video-based reasoning tasks, such as video question-answering (QA), typically construct such graphs for every video frame. These approaches often ignore the fact that videos are essentially sequences of 2D ``views'' of events happening in a 3D space, and that the semantics of the 3D scene can thus be carried over from frame to frame. Leveraging this insight, we propose a (2.5+1)D scene graph representation to better capture the spatio-temporal information flows inside the videos. Specifically, we first create a 2.5D (pseudo-3D) scene graph by transforming every 2D frame to have an inferred 3D structure using an off-the-shelf 2D-to-3D transformation module, following which we register the video frames into a shared (2.5+1)D spatio-temporal space and ground each 2D scene graph within it. Such a (2.5+1)D graph is then segregated into a static sub-graph and a dynamic sub-graph, corresponding to whether the objects within them usually move in the world. The nodes in the dynamic graph are enriched with motion features capturing their interactions with other graph nodes. Next, for the video QA task, we present a novel transformer-based reasoning pipeline that embeds the (2.5+1)D graph into a spatio-temporal hierarchical latent space, where the sub-graphs and their interactions are captured at varied granularity. To demonstrate the effectiveness of our approach, we present experiments on the NExT-QA and AVSD-QA datasets. Our results show that our proposed (2.5+1)D representation leads to faster training and inference, while our hierarchical model showcases superior performance on the video QA task versus the state of the art.
Event-Image Fusion Stereo Using Cross-Modality Feature Propagation
Authors: Cho Hoonhee , Yoon Kuk-Jin
Published: July 2022

Event cameras asynchronously output the polarity values of pixel-level log intensity alterations. They are robust against motion blur and can be adopted in challenging light conditions. Owing to these advantages, event cameras have been employed in various vision tasks such as depth estimation, visual odometry, and object detection. In particular, event cameras are effective in stereo depth estimation to ﬁnd correspondence points between two cameras under challenging illumination conditions and/or fast motion. However, because event cameras provide spatially sparse event stream data, it is difﬁcult to obtain a dense disparity map. Although it is possible to estimate disparity from event data at the edge of a structure where intensity changes are likely to occur, estimating the disparity in a region where event occurs rarely is challenging. In this study, we propose a deep network that combines the features of an image with the features of an event to generate a dense disparity map. The proposed network uses images to obtain spatially dense features that are lacking in events. In addition, we propose a spatial multi-scale correlation between two fused feature maps for an accurate disparity map. To validate our method, we conducted experiments using synthetic and real-world datasets.
MMA: Multi-Camera Based Global Motion Averaging
Authors: Cui Hainan , Shen Shuhan
Published: July 2022

In order to fully perceive the surrounding environment, many intelligent robots and self-driving cars are equipped with a multi-camera system. Based on this system, the structure-from-motion (SfM) technology is used to realize scene reconstruction, but the fixed relative poses between cameras in the multi-camera system are usually not considered. This paper presents a tailor-made multi-camera based motion averaging system, where the fixed relative poses are utilized to improve the accuracy and robustness of SfM. Our approach starts by dividing the images into reference images and non-reference images, and edges in view-graph are divided into four categories accordingly. Then, a multi-camera based rotating averaging problem is formulated and solved in two stages, where an iterative re-weighted least squares scheme is used to deal with outliers. Finally, a multi-camera based translation averaging problem is formulated and a l1-norm based optimization scheme is proposed to compute the relative translations of multi-camera system and reference camera positions simultaneously. Experiments demonstrate that our algorithm achieves superior accuracy and robustness on various data sets compared to the state-of-the-art methods.
Spatio-Temporal Recurrent Networks for Event-Based Optical Flow Estimation
Authors: Ding Ziluo , Zhao Rui , Zhang Jiyuan , Gao Tianxiao , Xiong Ruiqin , Yu Zhaofei , Huang Tiejun
Published: July 2022

Event camera has offered promising alternative for visual perception, especially in high speed and high dynamic range scenes. Recently, many deep learning methods have shown great success in providing model-free solutions to many event-based problems, such as optical flow estimation. However, existing deep learning methods did not address the importance of temporal information well from the perspective of architecture design and cannot effectively extract spatio-temporal features. Another line of research that utilizes Spiking Neural Network suffers from training issues for deeper architecture. To address these points, a novel input representation is proposed that captures the events temporal distribution for signal enhancement. Moreover, we introduce a spatio-temporal recurrent encoding-decoding neural network architecture for event-based optical flow estimation, which utilizes Convolutional Gated Recurrent Units to extract feature maps from a series of event images. Besides, our architecture allows some traditional frame-based core modules, such as correlation layer and iterative residual refine scheme, to be incorporated. The network is end-to-end trained with self-supervised learning on the Multi-Vehicle Stereo Event Camera dataset. We have shown that it outperforms all the existing state-of-the-art methods by a large margin.
Construct Effective Geometry Aware Feature Pyramid Network for Multi-Scale Object Detection
Authors: Dong Jinpeng , Huang Yuhao , Zhang Songyi , Chen Shitao , Zheng Nanning
Published: July 2022

Feature Pyramid Network (FPN) has been widely adopted to exploit multi-scale features for scale variation in object detection. However, intrinsic defects in most of the current methods with FPN make it difficult to adapt to the feature of different geometric objects. To address this issue, we introduce geometric prior into FPN to obtain more discriminative features. In this paper, we propose Geometry-aware Feature Pyramid Network (GaFPN), which mainly consists of the novel Geometry-aware Mapping Module and Geometry-aware Predictor Head.The Geometry-aware Mapping Module is proposed to make full use of all pyramid features to obtain better proposal features by the weight-generation subnetwork. The weights generation subnetwork generates fusion weight for each layer proposal features by using the geometric information of the proposal. The Geometry-aware Predictor Head introduces geometric prior into predictor head by the embedding generation network to strengthen feature representation for classification and regression. Our GaFPN can be easily extended to other two-stage object detectors with feature pyramid and applied to instance segmentation task. The proposed GaFPN significantly improves detection performance compared to baseline detectors with ResNet-50-FPN: +1.9, +2.0, +1.7, +1.3, +0.8 points Average Precision (AP) on Faster-RCNN, Cascade R-CNN, Dynamic R-CNN, SABL, and AugFPN respectively on MS COCO dataset.
SVT-Net: Super Light-Weight Sparse Voxel Transformer for Large Scale Place Recognition
Authors: Fan Zhaoxin , Song Zhenbo , Liu Hongyan , Lu Zhiwu , He Jun , Du Xiaoyong
Published: July 2022

Simultaneous Localization and Mapping (SLAM) and Autonomous Driving are becoming increasingly more important in recent years. Point cloud-based large scale place recognition is the spine of them. While many models have been proposed and have achieved acceptable performance by learning short-range local features, they always skip long-range contextual properties. Moreover, the model size also becomes a serious shackle for their wide applications. To overcome these challenges, we propose a super light-weight network model termed SVT-Net. On top of the highly efficient 3D Sparse Convolution (SP-Conv), an Atom-based Sparse Voxel Transformer (ASVT) and a Cluster-based Sparse Voxel Transformer (CSVT) are proposed respectively to learn both short-range local features and long-range contextual features. Consisting of ASVT and CSVT, SVT-Net can achieve state-of-the-art performance in terms of both recognition accuracy and running speed with a super-light model size (0.9M parameters). Meanwhile, for the purpose of further boosting efficiency, we introduce two simplified versions, which also achieve state-of-the-art performance and further reduce the model size to 0.8M and 0.4M respectively.
Backdoor Attacks on the DNN Interpretation System
Authors: Fang Shihong , Choromanska Anna
Published: July 2022

Interpretability is crucial to understand the inner workings of deep neural networks (DNNs). Many interpretation methods help to understand the decision-making of DNNs by generating saliency maps that highlight parts of the input image that contribute the most to the prediction made by the DNN. In this paper we design a backdoor attack that alters the saliency map produced by the network for an input image with a specific trigger pattern while not losing the prediction performance significantly. The saliency maps are incorporated in the penalty term of the objective function that is used to train a deep model and its influence on model training is conditioned upon the presence of a trigger. We design two types of attacks: a targeted attack that enforces a specific modification of the saliency map and a non-targeted attack when the importance scores of the top pixels from the original saliency map are significantly reduced. We perform empirical evaluations of the proposed backdoor attacks on gradient-based interpretation methods, Grad-CAM and SimpleGrad, and a gradient-free scheme, VisualBackProp, for a variety of deep learning architectures. We show that our attacks constitute a serious security threat to the reliability of the interpretation methods when deploying models developed by untrusted sources. We furthermore show that existing backdoor defense mechanisms are ineffective in detecting our attacks. Finally, we demonstrate that the proposed methodology can be used in an inverted setting, where the correct saliency map can be obtained only in the presence of a trigger (key), effectively making the interpretation system available only to selected users.
Exploiting Fine-Grained Face Forgery Clues via Progressive Enhancement Learning
Authors: Gu Qiqi , Chen Shen , Yao Taiping , Chen Yang , Ding Shouhong , Yi Ran
Published: July 2022

With the rapid development of facial forgery techniques, forgery detection has attracted more and more attention due to security concerns. Existing approaches attempt to use frequency information to mine subtle artifacts under high-quality forged faces. However, the exploitation of frequency information is coarse-grained, and more importantly, their vanilla learning process struggles to extract fine-grained forgery traces. To address this issue, we propose a progressive enhancement learning framework to exploit both the RGB and fine-grained frequency clues. Specifically, we perform a fine-grained decomposition of RGB images to completely decouple the real and fake traces in the frequency space. Subsequently, we propose a progressive enhancement learning framework based on a two-branch network, combined with self-enhancement and mutual-enhancement modules. The self-enhancement module captures the traces in different input spaces based on spatial noise enhancement and channel attention. The Mutual-enhancement module concurrently enhances RGB and frequency features by communicating in the shared spatial dimension. The progressive enhancement process facilitates the learning of discriminative features with fine-grained face forgery clues. Extensive experiments on several datasets show that our method outperforms the state-of-the-art face forgery detection methods.
Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-Supervised Action Recognition
Authors: Guo Tianyu , Liu Hong , Chen Zhan , Liu Mengyuan , Wang Tao , Ding Runwei
Published: July 2022

In recent years, self-supervised representation learning for skeleton-based action recognition has been developed with the advance of contrastive learning methods. The existing contrastive learning methods use normal augmentations to construct similar positive samples, which limits the ability to explore novel movement patterns. In this paper, to make better use of the movement patterns introduced by extreme augmentations, a Contrastive Learning framework utilizing Abundant Information Mining for self-supervised action Representation (AimCLR) is proposed. First, the extreme augmentations and the Energy-based Attention-guided Drop Module (EADM) are proposed to obtain diverse positive samples, which bring novel movement patterns to improve the universality of the learned representations. Second, since directly using extreme augmentations may not be able to boost the performance due to the drastic changes in original identity, the Dual Distributional Divergence Minimization Loss (D3M Loss) is proposed to minimize the distribution divergence in a more gentle way. Third, the Nearest Neighbors Mining (NNM) is proposed to further expand positive samples to make the abundant information mining process more reasonable. Exhaustive experiments on NTU RGB+D 60, PKU-MMD, NTU RGB+D 120 datasets have verified that our AimCLR can significantly perform favorably against state-of-the-art methods under a variety of evaluation protocols with observed higher quality action representations. Our code is available at https://github.com/Levigty/AimCLR.
Meta Faster R-CNN: Towards Accurate Few-Shot Object Detection with Attentive Feature Alignment
Authors: Han Guangxing , Huang Shiyuan , Ma Jiawei , He Yicheng , Chang Shih-Fu
Published: July 2022

Few-shot object detection (FSOD) aims to detect objects using only a few examples. How to adapt state-of-the-art object detectors to the few-shot domain remains challenging. Object proposal is a key ingredient in modern object detectors. However, the quality of proposals generated for few-shot classes using existing methods is far worse than that of many-shot classes, e.g., missing boxes for few-shot classes due to misclassification or inaccurate spatial locations with respect to true objects. To address the noisy proposal problem, we propose a novel meta-learning based FSOD model by jointly optimizing the few-shot proposal generation and fine-grained few-shot proposal classification. To improve proposal generation for few-shot classes, we propose to learn a lightweight metric-learning based prototype matching network, instead of the conventional simple linear object/nonobject classifier, e.g., used in RPN. Our non-linear classifier with the feature fusion network could improve the discriminative prototype matching and the proposal recall for few-shot classes. To improve the fine-grained few-shot proposal classification, we propose a novel attentive feature alignment method to address the spatial misalignment between the noisy proposals and few-shot classes, thus improving the performance of few-shot object detection. Meanwhile we learn a separate Faster R-CNN detection head for many-shot base classes and show strong performance of maintaining base-classes knowledge. Our model achieves state-of-the-art performance on multiple FSOD benchmarks over most of the shots and metrics.