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Detecting Stable Keypoints from Events through Image Gradient Prediction

Detecting Stable Keypoints from Events through Image Gradien...

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

作者： Chiberre, Philippe Perot, Etienne Sironi, Amos Lepetit, Vincent PROPHESEE Paris France Univ Gustave Eiffel CNRS Ecole Ponts LIGM Marne La Vallee France

ISBN: (纸本)9781665448994

We present a method that detects stable keypoints from an event stream at high speed with a low memory footprint. Our key observation connects two points: It should be easier to reconstruct the image gradients rather than the image itself from the events, and the Harris corner detector, one of the most reliable keypoint detectors for short baseline regular images, depends on the image gradients, not the image. We therefore introduce a recurrent convolutional neural network to predict image gradients from events. As image gradients and events are correlated, this prediction task is relatively easy and we can keep this network very small. We train our network solely on synthetic data. Extracting Harris corners from these gradients is then very efficient. Moreover, in contrast to learned methods, we can change the hyperparameters of the detector without retraining. Our experiments confirm that predicting image gradients rather than images is much more efficient, and that our approach predicts stable corner points which are easier to track for a longer time compared to state-of-the-art event-based methods.

关键词： Image analysis Detectors computer architecture Streaming media Cameras pattern recognition Reliability

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On the Application of Binary Neural Networks in Oblivious Inference

On the Application of Binary Neural Networks in Oblivious In...

引用

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

作者： Samragh, Mohammad Hussain, Siam Zhang, Xinqiao Huang, Ke Koushanfar, Farinaz Univ Calif San Diego La Jolla CA 92093 USA San Diego State Univ San Diego CA 92182 USA

ISBN: (纸本)9781665448994

This paper explores the application of Binary Neural Networks (BNN) in oblivious inference, a service provided by a server to mistrusting clients. Using this service, a client can obtain the inference result on her data by a trained model held by the server without disclosing the data or leaning the model parameters. We make two contributions to this field. First, we devise light-weight cryptographic protocols designed specifically to exploit the unique characteristics of BNNs. Second, we present dynamic exploration of the runtime -accuracy tradeoff of BNNs in a single -shot training process. While previous works trained multiple BNNs with different computational complexities (which is cumbersome due to the slow convergence of BNNs), we train a single BNN that can perform inference under different computational budgets. Compared to CryptFlow2, the state-of-the-art in oblivious inference of non binary DNNs, our approach reaches 2x faster inference at the same accuracy. Compared to XONN, the state-of-the-art in oblivious inference of binary networks, we achieve 2x to llx faster inference while obtaining higher accuracy.

关键词： Training computer vision Data analysis Computational modeling Face recognition Neural networks Data models

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BasisNet: Two-stage Model Synthesis for Efficient Inference

BasisNet: Two-stage Model Synthesis for Efficient Inference

引用

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

作者： Zhang, Mingda Chu, Chun-Te Zhmoginov, Andrey Howard, Andrew Jou, Brendan Zhu, Yukun Zhang, Li Hwa, Rebecca Kovashka, Adriana Univ Pittsburgh Dept Comp Sci Pittsburgh PA 15260 USA Google Res Mountain View CA 94043 USA

ISBN: (纸本)9781665448994

In this work, we present BasisNet which combines recent advancements in efficient neural network architectures, conditional computation, and early termination in a simple new form. Our approach incorporates a lightweight model to preview the input and generate input-dependent combination coefficients, which later controls the synthesis of a more accurate specialist model to make final prediction. The two-stage model synthesis strategy can be applied to any network architectures and both stages are jointly trained. We also show that proper training recipes are critical for increasing generalizability for such high capacity neural networks. On ImageNet classification benchmark, our BasisNet with MobileNets as backbone demonstrated clear advantage on accuracy-efficiency trade-off over several strong baselines. Specifically, BasisNet-MobileNetV3 obtained 80.3% top-1 accuracy with only 290M MultiplyAdd operations, halving the computational cost of previous state-of-the-art without sacrificing accuracy. With early termination, the average cost can be further reduced to 198M MAdds while maintaining accuracy of 80.0% on ImageNet.

关键词： Training computer vision Computational modeling conferences Neural networks computer architecture Predictive models

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Fine-Grained Visual Attribute Extraction from Fashion Wear

Fine-Grained Visual Attribute Extraction from Fashion Wear

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

作者： Parekh, Viral Shaik, Karimulla Biswas, Soma Chelliah, Muthusamy Flipkart Internet Private Ltd Bangalore Karnataka India Indian Inst Sci Bangalore Karnataka India

ISBN: (纸本)9781665448994

Automatically extracting visual attributes for e-commerce data has widespread applications in cataloging, catalogue qualification and enrichment, visual search, etc. Here, we address the task of visual attribute extraction for a highly challenging real-world fashion data from Flipkart catalogue (an Indian e-commerce platform), which is collected from seller uploaded product images. This data not only contains widely varying categories (e.g., shirt, sari, shoes), but also has both coarse-grained (e.g., occasion, top type, sari type) and fine-grained (e.g., neck type, print type) attributes. Training examples available for different attributes are highly imbalanced, making this task even more challenging. To this end, we propose an end-to-end framework which integrates multi-task learning with transformer as an attention module, in addition to handling the data imbalance. The proposed architecture supports multiple attributes across various product categories in a scalable manner. Extensive experiments on the in-house dataset shows effectiveness of the proposed framework in improving performance of the fine-grained attributes by 13% on the baseline across the attributes.

关键词： Visualization computer vision conferences Footwear computer architecture Neck Data mining

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InstaFormer: Instance-Aware Image-to-Image Translation with Transformer

InstaFormer: Instance-Aware Image-to-Image Translation with ...

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

作者： Kim, Soohyun Baek, Jongbeom Park, Jihye Kim, Gyeongnyeon Kim, Seungryong Korea Univ Seoul South Korea

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We present a novel Transformer-based network architecture for instance-aware image-to-image translation, dubbed InstaFormer, to effectively integrate global- and instance-level information. By considering extracted content features from an image as tokens, our networks discover global consensus of content features by considering context information through a self-attention module in Transformers. By augmenting such tokens with an instance-level feature extracted from the content feature with respect to bounding box information, our framework is capable of learning an interaction between object instances and the global image, thus boosting the instance-awareness. We replace layer normalization (LayerNorm) in standard Transformers with adaptive instance normalization (AdaIN) to enable a multi-modal translation with style codes. In addition, to improve the instance-awareness and translation quality at object regions, we present an instance-level content contrastive loss defined between input and translated image. We conduct experiments to demonstrate the effectiveness of our InstaFormer over the latest methods and provide extensive ablation studies.

关键词： Measurement computer vision Codes Network architecture Transformers Feature extraction Boosting

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Determining Dendrometry Using Drone Scouting, Convolutional Neural Networks and Point Clouds

Determining Dendrometry Using Drone Scouting, Convolutional ...

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

作者： Jensen, Kim Krogh, Oskar Kondrup Jorgensen, Marius Willemoes Lehotsky, Daniel Andersen, Anton Bock Porqueras, Ernest Sondergaard, Jens Aksel S. Gade, Rikke Aalborg Univ Dept Elect Syst Aalborg Denmark TeeJet Technol Aabybro Denmark Aalborg Univ Sect Media Technol Aalborg Denmark

ISBN: (纸本)9781665448994

This paper presents a solution for mapping the location of trees in an orchard and estimating the dendrometric data of the trees. The combined solution consists of a mapping and navigation algorithm, which allows for autonomous data collection at an orchard with a regular rectangular layout, and data processing for tree detection and dendrometric data estimation. The data collection is done using an Intel RealSense D435i camera, which can obtain both RGB and depth data. The paper presents a comparison between the performance of point cloud processing (PCP) and convolutional neural networks (CNNs) on RGB data for tree detection and dendrometric data estimation. The YOLOv3 CNN achieved a mAP50 of 63.53% with 65.5 FPS and a mean error of 20.6 cm in height estimation. Point cloud processing achieved a precision of 76.72% with 2.1 FPS and a mean error of 20.4 cm in height estimation. In conclusion, this work shows that point cloud processing shows comparable results to convolutional neural networks for height estimation, but trades off processing time for better precision in detection.

关键词： Three-dimensional displays Navigation Shape Estimation Vegetation Data collection pattern recognition

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Weighted Multi-Kernel Prediction Network for Burst Image Super-Resolution

Weighted Multi-Kernel Prediction Network for Burst Image Sup...

引用

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

作者： Cho, Wooyeong Son, Sanghyeok Kim, Dae-Shik Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (纸本)9781665448994

Burst image super-resolution is an ill-posed problem tha' aims to restore a high-resolution (HR) image from a sequence of low-resolution (LR) burst images. To restore a photo-realistic HR image using their abundant information, it is essential to align each burst of frames containing random hand-held motion. Some kernel prediction networks (KPNs) that are operated without external motion compensation such as optical flow estimation have been applied to burst image processing as implicit image alignment modules. However, the existing methods do not consider the interdependencies among the kernels of different sizes that have a significant effect on each pixel. In this paper, we propose a novel weighted multi-kernel prediction network (WMKPN) that can learn the discriminative features on each pixel for burst image super-resolution. Our experimental results demonstrate that WMKPN improves the visual quality of super-resolved images. To the best of our knowledge, it outperforms the state-of-the-art within kernel prediction methods and multiple frame super-resolution (MFSR) on both the Zurich RAW to RGB and BurstSR datasets.

关键词： Visualization Computational modeling Superresolution Prediction methods Motion compensation Image restoration pattern recognition

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What is Point Supervision Worth in Video Instance Segmentation?

What is Point Supervision Worth in Video Instance Segmentati...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Shuaiyi Huang De-An Huang Zhiding Yu Shiyi Lan Subhashree Radhakrishnan Jose M. Alvarez Abhinav Shrivastava Anima Anandkumar University of Maryland College Park NVIDIA Caltech

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Video instance segmentation (VIS) is a challenging vision task that aims to detect, segment, and track objects in videos. Conventional VIS methods rely on densely-annotated object masks which are expensive. We reduce the human annotations to only one point for each object in a video frame during training, and obtain high-quality mask predictions close to fully supervised models. Our proposed training method consists of a class-agnostic proposal generation module to provide rich negative samples and a spatio-temporal point-based matcher to match the object queries with the provided point annotations. Comprehensive experiments on three VIS benchmarks demonstrate competitive performance of the proposed framework, nearly matching fully supervised methods.

关键词： Instance segmentation Training computer vision Annotations Shape Object segmentation Object detection

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Dynamic Distinction Learning: Adaptive Pseudo Anomalies for Video Anomaly Detection

Dynamic Distinction Learning: Adaptive Pseudo Anomalies for ...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Demetris Lappas Vasileios Argyriou Dimitrios Makris School of Computer Science and Mathematics Kingston University London UK

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We introduce Dynamic Distinction Learning (DDL) for Video Anomaly Detection, a novel video anomaly detection methodology that combines pseudo-anomalies, dynamic anomaly weighting, and a distinction loss function to improve detection accuracy. By training on pseudo-anomalies, our approach adapts to the variability of normal and anomalous behaviors without fixed anomaly thresholds. Our model showcases superior performance on the Ped2, Avenue and ShanghaiTech datasets, where individual models are tailored for each scene. These achievements highlight DDL’s effectiveness in advancing anomaly detection, offering a scalable and adaptable solution for video surveillance challenges. Our work can be found on: https://***/demetrislappas/***

关键词： Training Adaptation models Technological innovation computer vision Accuracy conferences Refining

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Learning A Cascaded Non-Local Residual Network for Super-resolving Blurry Images

Learning A Cascaded Non-Local Residual Network for Super-res...

引用

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

作者： Bai, Haoran Cheng, Songsheng Tang, Jinhui Pan, Jinshan Nanjing Univ Sci & Technol Nanjing Peoples R China

ISBN: (纸本)9781665448994

Deblurring low-resolution images is quite challenging as blur exists in the images and the resolution of the images is low. Existing deblurring methods usually require high-resolution input while the super-resolution methods usually assume that the blur is known or small. Simply applying the deblurring and super-resolution does not solve this problem well. In this paper, we develop an effective cascaded non-local residual network which cascades the deblurring module and super-resolution module to estimate latent high-resolution images from blurry low-resolution ones. The network first uses the deblurring module to generate intermediate clear features and then develops a non-local residual network (NLRN) as the super-resolution module to generate clear high-resolution images from the intermediate clear features. To better constrain the network and reduce the training difficulty, we develop an effective constraint based on image gradients for edge preservation and adopt the progressive upsampling mechanism. We train the proposed network in an end-to-end manner. Both quantitative and qualitative results on the benchmarks demonstrate the effectiveness of the proposed method. Moreover, the proposed method achieves top-3 performance on the low-resolution track of the NTIRE 2021 Image Deblurring Challenge.

关键词： Training computer vision Image edge detection conferences Superresolution Benchmark testing Image restoration

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