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BGT-Net: Bidirectional GRU Transformer Network for Scene Graph Generation

BGT-Net: Bidirectional GRU Transformer Network for Scene Gra...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Dhingra, Naina Ritter, Florian Kunz, Andreas Swiss Fed Inst Technol Innovat Ctr Virtual Real Zurich Switzerland

ISBN: (纸本)9781665448994

Scene graphs are nodes and edges consisting of objects and object-object relationships, respectively. Scene graph generation (SGG) aims to identify the objects and their relationships. We propose a bidirectional GRU (BiGRU) transformer network (BGT-Net) for the scene graph generation for images. This model implements novel object-object communication to enhance the object information using a BiGRU layer. Thus, the information of all objects in the image is available for the other objects, which can be leveraged later in the object prediction step. This object information is used in a transformer encoder to predict the object class as well as to create object-specific edge information via the use of another transformer encoder. To handle the dataset bias induced by the long-tailed relationship distribution, softening with a log-softmax function and adding a bias adaptation term to regulate the bias for every relation prediction individually showed to be an effective approach. We conducted an elaborate study on experiments and ablations using open-source datasets, i.e., Visual Genome, Open-Images, and Visual Relationship Detection datasets, demonstrating the effectiveness of the proposed model over state of the art.

关键词： Visualization computer vision Image edge detection conferences Genomics pattern recognition Softening

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S2F2: Single-Stage Flow Forecasting for Future Multiple Trajectories Prediction

S2F2: Single-Stage Flow Forecasting for Future Multiple Traj...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Chen, Yu-Wen Yang, Hsuan-Kung Chiu, Chu-Chi Lee, Chun-Yi Natl Tsing Hua Univ Dept Comp Sci Elsa Lab Hsinchu Taiwan

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In this work, we present a single-stage framework, named S2F2, for forecasting multiple human trajectories from raw video images by predicting future optical flows. S2F2 differs from the previous two-stage approaches in that it performs detection, Re-ID, and forecasting of multiple pedestrians at the same time. Unlike the prior approaches, the computational burden of S2F2 remains consistent even as the number of pedestrians grows. The experimental results demonstrate that S2F2 is able to outperform two conventional forecasting algorithms and a recent learning-based two-stage model [1], while maintaining its tracking performance on par with the contemporary MOT models.

关键词： computer vision Computational modeling conferences Predictive models Prediction algorithms Trajectory pattern recognition

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SkipPLUS: Skip the First Few Layers to Better Explain vision Transformers

SkipPLUS: Skip the First Few Layers to Better Explain Vision...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Mehri, Faridoun Fayyaz, Mohsen Baghshah, Mahdieh Soleymani Pilehvar, Mohammad Taher Sharif Univ Technol Tehran Iran Univ Tehran Tehran Iran Cardiff Univ Cardiff Wales

ISBN: (纸本)9798350365474

Despite their remarkable performance, the explainability of vision Transformers (ViTs) remains a challenge. While forward attention-based token attribution techniques have become popular in text processing, their suitability for ViTs hasn't been extensively explored. In this paper, we compare these methods against state-of-the-art input attribution methods from the vision literature, revealing their limitations due to improper aggregation of information across layers. To address this, we introduce two general techniques, PLUS and SkipPLUS, that can be composed with any input attribution method to more effectively aggregate information across layers while handling noisy layers. Through comprehensive and quantitative evaluations of faithfulness and human interpretability on a variety of ViT architectures and datasets, we demonstrate the effectiveness of PLUS and SkipPLUS, establishing a new state-of-the-art in white-box token attribution. We conclude with a comparative analysis highlighting the strengths and weaknesses of the best versions of all the studied methods. The code used in this paper is freely available at https://***/NightMachinery/SkipPLUS-CVPR-2024.

关键词： Explainable AI Forward Attention-Based Token Attribution Interpretability Neural Network Visualization vision Transformers White-Box Input Attribution Methods xAI

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Disguised Faces in the Wild 31

Disguised Faces in the Wild

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Kushwaha, Vineet Singh, Maneet Singh, Richa Vatsa, Mayank Ratha, Nalini Chellappa, Rama IIIT Delhi Delhi India IBM TJ Watson Res Ctr Ossining NY USA Univ Maryland College Pk MD 20742 USA

ISBN: (纸本)9781538661000

Existing research in the field of face recognition with variations due to disguises focuses primarily on images captured in controlled settings. Limited research has been performed on images captured in unconstrained environments, primarily due to the lack of corresponding disguised face datasets. In order to overcome this limitation, this work presents a novel Disguised Faces in the Wild (DFW) dataset, consisting of over 11,000 images for understanding and pushing the current state-of-the-art for disguised face recognition. To the best of our knowledge, DFW is a first-of-a-kind dataset containing images pertaining to both obfuscation and impersonation for understanding the effect of disguise variations. A major portion of the dataset has been collected from the Internet, thereby encompassing a wide variety of disguise accessories and variations across other covariates. As part of CVPR2018, a competition and workshop are organized to facilitate research in this direction. This paper presents a description of the dataset, the baseline protocols and performance, along with the phase-I results of the competition.

关键词： computer vision pattern recognition conferences

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Searching for Efficient Neural Architectures for On-Device ML on Edge TPUs

Searching for Efficient Neural Architectures for On-Device M...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Akin, Berkin Gupta, Suyog Long, Yun Spiridonov, Anton Wang, Zhuo White, Marie Xu, Hao Zhou, Ping Zhou, Yanqi

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

On-device ML accelerators are becoming a standard in modern mobile system-on-chips (SoC). Neural architecture search (NAS) comes to the rescue for efficiently utilizing the high compute throughput offered by these accelerators. However, existing NAS frameworks have several practical limitations in scaling to multiple tasks and different target platforms. In this work, we provide a two-pronged approach to this challenge: (i) a NAS-enabling infrastructure that decouples model cost evaluation, search space design, and the NAS algorithm to rapidly target various on-device ML tasks, and (ii) search spaces crafted from group convolution based inverted bottleneck (IBN) variants that provide flexible quality/performance trade-offs on ML accelerators, complementing the existing full and depthwise convolution based IBNs. Using this approach we target a state-of-the-art mobile platform, Google Tensor SoC, and demonstrate neural architectures1 that improve the quality-performance pareto frontier for various computer vision (classification, detection, segmentation) as well as natural language processing tasks.

关键词： computer vision Tensors Costs Convolution Image edge detection computer architecture Throughput

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Improving In-field Cassava Whitefly Pest Surveillance with Machine Learning

Improving In-field Cassava Whitefly Pest Surveillance with M...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Tusubira, Jeremy Francis Nsumba, Solomon Ninsiima, Flavia Akera, Benjamin Acellam, Guy Nakatumba, Joyce Mwebaze, Ernest Quinn, John Oyana, Tonny Makerere Univ Artificial Intelligence Lab Kampala Uganda Google Res Mountain View CA USA Makerere Univ Geospatial Data & Computat Intelligence Lab Kampala Uganda

ISBN: (纸本)9781728193601

Whiteflies are the major vector responsible for the transmission of cassava related diseases in tropical environments, and knowing the numbers of whiteflies is key in detecting and identifying their spread and prevention. However, the current approach for counting whiteflies is a simple visual inspection, where a cassava leaf is turned upside down to reveal the underside where the whiteflies reside to enable a manual count. Repeated across many cassava farms, this task is quite tedious and time-consuming. In this paper, we propose a method to automatically count whiteflies using computer vision techniques. To implement this approach, we collected images of infested cassava leaves and trained a computer vision detector using Haar Cascade and Deep Learning techniques. The two techniques were used to identify the pest in images and return a count. Our results show that this novel method produces a white-fly count with high precision. This method could be applied to similar object detection scenarios similar to the whitefly problem with minor adjustments.

关键词： computer vision

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M²SGD: Learning to Learn ImportantWeights

M<SUP>2</SUP>SGD: Learning to Learn ImportantWeights

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Kuo, Nicholas I-Hsien Harandi, Mehrtash Fourrier, Nicolas Walder, Christian Ferraro, Gabriela Suominen, Hanna Australian Natl Univ RSCS Canberra ACT Australia Monash Univ ECSE Clayton Vic Australia CSIRO Data61 Canberra ACT Australia Vole Univ Leonard de Vinci Paris France Univ Turku Dept Future Technol Turku Finland

ISBN: (纸本)9781728193601

Meta-learning concerns rapid knowledge acquisition. One popular approach cast optimisation as a learning problem and it has been shown that learnt neural optimisers updated base learners more quickly than their hand-crafted counterparts. In this paper, we learn an optimisation rule that sparsely updates the learner parameters and removes redundant weights. We present Masked Meta-SGD (M2SGD), a neural optimiser which is not only capable of updating learners quickly, but also capable of removing 83.71% weights for ResNet20s. We release our codes at https://***/Nic5472K/CLvision2020_CVPR_M2SGD.

关键词： computer vision

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ADNet: Attention-guided Deformable Convolutional Network for High Dynamic Range Imaging

ADNet: Attention-guided Deformable Convolutional Network for...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Liu, Zhen Lin, Wenjie Li, Xinpeng Rao, Qing Jiang, Ting Han, Mingyan Fan, Haoqiang Sun, Jian Liu, Shuaicheng Megvii Technol Beijing Peoples R China Sichuan Univ Chengdu Peoples R China Univ Elect Sci & Technol China Chengdu Peoples R China

ISBN: (纸本)9781665448994

In this paper, we present an attention-guided deformable convolutional network for hand-held multi frame high dynamic range (HDR) imaging, namely ADNet. This problem comprises two intractable challenges of how to handle saturation and noise properly and how to tackle misalignments caused by object motion or camera jittering. To address the former, we adopt a spatial attention module to adaptively select the most appropriate regions of various exposure low dynamic range (LDR) images for fusion. For the latter one, we propose to align the gamma-corrected images in the feature-level with a Pyramid, Cascading and Deformable (PCD) alignment module. The proposed ADNet shows state-of-the-art performance compared with previous methods, achieving a PSNR-l of 39.4471 and a PSNR-mu of 37.6359 in NTIRE 2021 Multi-Frame HDR Challenge.

关键词： Convolutional codes computer vision conferences Pipelines Dynamic range Cameras pattern recognition

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Deep Fusion of Appearance and Frame Differencing for Motion Segmentation

Deep Fusion of Appearance and Frame Differencing for Motion ...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Ellenfeld, Marc Moosbauer, Sebastian Cardenes, Ruben Klauck, Ulrich Teutsch, Michael Hensoldt Optron GmbH Oberkochen Germany Hensoldt Analyt GmbH Oberkochen Germany Aalen Univ Appl Sci Aalen Germany Univ Western Cape Cape Town South Africa

ISBN: (纸本)9781665448994

Motion segmentation is a technique to detect and localize class-agnostic motion in videos. This motion is assumed to be relative to a stationary background and usually originates from objects such as vehicles or humans. When the camera moves, too, frame differencing approaches that do not have to model the stationary background over minutes, hours, or even days are more promising compared to background subtraction methods. In this paper, we propose a Deep Convolutional Neural Network (DCNN) for multi-modal motion segmentation: the current image contributes with appearance information to distinguish between relevant and irrelevant motion and frame differencing captures the temporal information, which is the scene's motion independent of the camera motion. We fuse this information to receive an effective and efficient approach for robust motion segmentation. The effectiveness is demonstrated using the multi-spectral CDNet-2014 dataset that we re-labeled for motion segmentation. We specifically show that we can detect tiny moving objects significantly better compared to methods based on optical flow.

关键词： computer vision Image segmentation Three-dimensional displays Fuses Motion segmentation Cameras pattern recognition

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Semantic Binary Segmentation using Convolutional Networks without Decoders 31

Semantic Binary Segmentation using Convolutional Networks wi...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Aich, Shubhra van der Kamp, William Stavness, Ian Univ Saskatchewan Dept Comp Sci Saskatoon SK Canada

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

In this paper, we propose an efficient architecture for semantic image segmentation using the depth-to-space (D2S) operation. Our D2S model is comprised of a standard CNN encoder followed by a depth-to-space reordering of the final convolutional feature maps. Our approach eliminates the decoder portion of traditional encoder-decoder segmentation models and reduces the amount of computation almost by half. As a participant of the DeepGlobe Road Extraction competition, we evaluate our models on the corresponding road segmentation dataset. Our highly efficient D2S models exhibit comparable performance to standard segmentation models with much lower computational cost.

关键词： Image segmentation Decoding Convolution computer architecture Computational modeling Roads Semantics

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