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NTIRE 2021 Multi-modal Aerial View Object Classification Challenge

NTIRE 2021 Multi-modal Aerial View Object Classification Cha...

引用

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

作者： Liu, Jerrick Inkawhich, Nathan Nina, Oliver Timofte, Radu Duan, Yuru Li, Gongzhe Geng, Xueli Cai, Huanqia Air Force Res Lab Albuquerque NM 87117 USA Univ Illinois Urbana IL 61801 USA Duke Univ Durham NC 27706 USA Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Northwestern Polytech Univ Changan Campus Xian Shaanxi Peoples R China Beihang Univ Beijing Peoples R China Xidian Univ Key Lab Intelligent Percept & Image Understanding Xian Peoples R China Dengzhuang South Rd Beijing Peoples R China

ISBN: (纸本)9781665448994

In this paper, we introduce the first Challenge on Multi-modal Aerial View Object Classification (MAVOC) in conjunction with the NTIRE 2021 workshop at CVPR. This challenge is composed of two different tracks using EO and SAR imagery. Both EO and SAR sensors possess different advantages and drawbacks. The purpose of this competition is to analyze how to use both sets of sensory information in complementary ways. We discuss the top methods submitted for this competition and evaluate their results on our blind test set. Our challenge results show significant improvement of more than 15% accuracy from our current baselines for each track of the competition.

关键词： Image sensors conferences Radar tracking Radar polarimetry Sensors pattern recognition Task analysis

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DFM: A Performance Baseline for Deep Feature Matching

DFM: A Performance Baseline for Deep Feature Matching

引用

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

作者： Efe, Ufuk Ince, Kutalmis Gokalp Alatan, A. Aydin Middle East Tech Univ Dept Elect & Elect Engn Ctr Image Anal OGAM Ankara Turkey

ISBN: (纸本)9781665448994

A novel image matching method is proposed that utilizes learned features extracted by an off-the-shelf deep neural network to obtain a promising performance. The proposed method uses pre-trained VGG architecture as a feature extractor and does not require any additional training specific to improve matching. Inspired by well-established concepts in the psychology area, such as the Mental Rotation paradigm, an initial warping is performed as a result of a preliminary geometric transformation estimate. These estimates are simply based on dense matching of nearest neighbors at the terminal layer of VGG network outputs of the images to be matched. After this initial alignment, the same approach is repeated again between reference and aligned images in a hierarchical manner to reach a good localization and matching performance. Our algorithm achieves 0.57 and 0.80 overall scores in terms of Mean Matching Accuracy (MMA) for 1 pixel and 2 pixels thresholds respectively on Hpatches dataset [4], which indicates a better performance than the state-of-the-art.

关键词： Deep learning Training Location awareness computer vision Image matching conferences Psychology

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

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

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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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Traffic Video Event Retrieval via Text Query using Vehicle Appearance and Motion Attributes

Traffic Video Event Retrieval via Text Query using Vehicle A...

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

作者： Tien-Phat Nguyen Ba-Thinh Tran-Le Xuan-Dang Thai Nguyen, Tam, V Do, Minh N. Minh-Triet Tran Univ Sci Ho Chi Minh City Vietnam Vietnam Natl Univ Ho Chi Minh City Vietnam John von Neumann Inst Ho Chi Minh City Vietnam Univ Dayton Dayton OH 45469 USA Univ Illinois Urbana IL USA

ISBN: (纸本)9781665448994

Traffic event retrieval is one of the important tasks for intelligent traffic system management. To find accurate candidate events in traffic videos corresponding to a specific text query, it is necessary to understand the text query's attributes, represent the visual and motion attributes of vehicles in videos, and measure the similarity between them. Thus we propose a promising method for vehicle event retrieval from a natural-language-based specification. We utilize both appearance and motion attributes of a vehicle and adapt the COOT model to evaluate the semantic relationship between a query and a video track. Experiments with the test dataset of Track 5 in AI City Challenge 2021 show that our method is among the top 6 with a score of 0.1560.

关键词： Visualization computer vision Tracking conferences Urban areas Semantics pattern recognition

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From Evaluation to Verification: Towards Task-oriented Relevance Metrics for Pedestrian Detection in Safety-critical Domains

From Evaluation to Verification: Towards Task-oriented Relev...

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

作者： Lyssenko, Maria Gladisch, Christoph Heinzemann, Christian Woehrle, Matthias Triebel, Rudolph Robert Bosch GmbH Corp Res Robert Bosch Campus 1 D-71272 Renningen Germany Tech Univ Munich Boltzmannstr 3 D-85748 Garching Germany German Aerosp Ctr DLR Munchener Str 20 D-82234 Wessling Germany

ISBN: (纸本)9781665448994

Whenever a visual perception system is employed in safety-critical applications such as automated driving, a thorough, task-oriented experimental evaluation is necessary to guarantee safe system behavior. While most standard evaluation methods in computer vision provide a good comparability on benchmarks, they tend to fall short on assessing the system performance that is actually relevant for the given task. In our work, we consider pedestrian detection as a highly relevant perception task, and we argue that standard measures such as Intersection over Union (IoU) give insufficient results, mainly because they are insensitive to important physical cues including distance, speed, and direction of motion. Therefore, we investigate so-called relevance metrics, where specific domain knowledge is exploited to obtain a task-oriented performance measure focusing on distance in this initial work. Our experimental setup is based on the CARLA simulator and allows a controlled evaluation of the impact of that domain knowledge. Our first results indicate a linear decrease of the IoU related to the pedestrians' distance, leading to the proposal of a first relevance metric that is also conditioned on the distance.

关键词： Measurement Meters computer vision System performance pattern recognition Proposals Motion measurement

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Data-Efficient Language-Supervised Zero-Shot Learning with Self-Distillation

Data-Efficient Language-Supervised Zero-Shot Learning with S...

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

作者： Cheng, Ruizhe Wu, Bichen Zhang, Peizhao Vajda, Peter Gonzalez, Joseph E. Univ Calif Berkeley Berkeley CA 94720 USA Facebook Real Labs Redmond WA USA

ISBN: (纸本)9781665448994

Traditional computer vision models are trained to predict a fixed set of predefined categories. Recently, natural language has been shown to be a broader and richer source of supervision that provides finer descriptions to visual concepts than supervised "gold" labels. Previous works, such as CLIP, use a simple pretraining task of predicting the pairings between images and text captions. CLIP, however, is data hungry and requires more than 400M image text pairs for training. We propose a data-efficient contrastive distillation method that uses soft labels to learn from noisy image-text pairs. Our model transfers knowledge from pre-trained image and sentence encoders and achieves strong performance with only 3M image text pairs, 133x smaller than CLIP. Our method exceeds the previous SoTA of general zero-shot learning on ImageNet 21k+1k by 73% relatively with a ResNet50 image encoder and DeCLUTR text encoder. We also beat CLIP by 10.5% relatively on zeroshot evaluation on Google Open Images (19,958 classes).

关键词： Training computer vision Visualization Natural languages Predictive models pattern recognition Internet

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A case for using rotation invariant features in state of the art feature matchers

A case for using rotation invariant features in state of the...

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

作者： Bokman, Georg Kahl, Fredrik Chalmers Univ Technol Gothenburg Sweden

ISBN: (纸本)9781665487399

The aim of this paper is to demonstrate that a state of the art feature matcher (LoFTR) can be made more robust to rotations by simply replacing the backbone CNN with a steerable CNN which is equivariant to translations and image rotations. It is experimentally shown that this boost is obtained without reducing performance on ordinary illumination and viewpoint matching sequences.

关键词： computer vision conferences Lighting pattern matching

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Cross-modal Speaker Verification and recognition: A Multilingual Perspective

Cross-modal Speaker Verification and Recognition: A Multilin...

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

作者： Nawaz, Shah Saeed, Muhammad Saad Morerio, Pietro Mahmood, Arif Gallo, Ignazio Yousaf, Muhammad Haroon Del Bue, Alessio Ist Italiano Tecnol IIT Pattern Anal & Comp Vis PAVIS Genoa Italy Ist Italiano Tecnol IIT Visual Geometry & Modelling VGM Genoa Italy Univ Insubria Varese VA Italy Univ Engn & Technol Taxila Rawalpindi Punjab India Informat Technol Univ Lahore Pakistan

ISBN: (纸本)9781665448994

Recent years have seen a surge in finding association between faces and voices within a cross-modal biometric application along with speaker recognition. Inspired from this, we introduce a challenging task in establishing association between faces and voices across multiple languages spoken by the same set of persons. The aim of this paper is to answer two closely related questions: "Is face-voice association language independent?" and "Can a speaker be recognized irrespective of the spoken language?". These two questions are important to understand effectiveness and to boost development of multilingual biometric systems. To answer these, we collected a Multilingual Audio-Visual dataset, containing human speech clips of 154 identities with 3 language annotations extracted from various videos uploaded online. Extensive experiments on the two splits of the proposed dataset have been performed to investigate and answer these novel research questions that clearly point out the relevance of the multilingual problem.

关键词： Training Annotations Face recognition Pipelines Speech recognition Speaker recognition Task analysis

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