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Diagnosing Rarity in Human-object Interaction Detection

Diagnosing Rarity in Human-object Interaction Detection

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Kilickaya, Mert Smeulders, Arnold QUvA Deep Vis Lab Amsterdam Netherlands

ISBN: (纸本)9781728193601

Human-object interaction (HOI) detection is a core task in computer vision. The goal is to localize all human-object pairs and recognize their interactions. An interaction defined by a tuple leads to a long-tailed visual recognition challenge since many combinations are rarely represented. The performance of the proposed models is limited especially for the tail categories, but little has been done to understand the reason. To that end, in this paper, we propose to diagnose rarity in HOI detection. We propose a three-step strategy, namely Detection, Identification and recognition where we carefully analyse the limiting factors by studying state-of-the-art models. Our findings indicate that detection and identification steps are altered by the interaction signals like occlusion and relative location, as a result limiting the recognition accuracy.

关键词： computer vision

来源：评论

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Generalized Focal Loss V2: Learning Reliable Localization Quality Estimation for Dense Object Detection

Generalized Focal Loss V2: Learning Reliable Localization Qu...

引用

2021 ieee/CVF conference on computer vision and pattern recognition, cvpr 2021

作者： Li, Xiang Wang, Wenhai Hu, Xiaolin Li, Jun Tang, Jinhui Yang, Jian Nanjing University of Science and Technology Momenta Nanjing University Tsinghua University

Localization Quality Estimation (LQE) is crucial and popular in the recent advancement of dense object detectors since it can provide accurate ranking scores that benefit the Non-Maximum Suppression processing and improve detection performance. As a common practice, most existing methods predict LQE scores through vanilla convolutional features shared with object classification or bounding box regression. In this paper, we explore a completely novel and different perspective to perform LQE - based on the learned distributions of the four parameters of the bounding box. The bounding box distributions are inspired and introduced as “General Distribution” in GFLV1, which describes the uncertainty of the predicted bounding boxes well. Such a property makes the distribution statistics of a bounding box highly correlated to its real localization quality. Specifically, a bounding box distribution with a sharp peak usually corresponds to high localization quality, and vice versa. By leveraging the close correlation between distribution statistics and the real localization quality, we develop a considerably lightweight Distribution-Guided Quality Predictor (DGQP) for reliable LQE based on GFLV1, thus producing GFLV2. To our best knowledge, it is the first attempt in object detection to use a highly relevant, statistical representation to facilitate LQE. Extensive experiments demonstrate the effectiveness of our method. Notably, GFLV2 (ResNet-101) achieves 46.2 AP at 14.6 FPS, surpassing the previous state-of-the-art ATSS baseline (43.6 AP at 14.6 FPS) by absolute 2.6 AP on COCO test-dev, without sacrificing the efficiency both in training and inference. © 2021 ieee

关键词： Location awareness Training computer vision Uncertainty Correlation Estimation Object detection

来源：评论

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As Seen on TV: Automatic Basketball Video Production using Gaussian-based Actionness and Game States recognition

As Seen on TV: Automatic Basketball Video Production using G...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Quiroga, Julian Carrillo, Henry Maldonado, Edisson Ruiz, John Zapata, Luis M. Genius Sports Comp Vis Medellin Colombia

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193601

Automatic video production of sports aims at producing an aesthetic broadcast of sporting events. We present a new video system able to automatically produce a smooth and pleasant broadcast of Basketball games using a single fixed 4K camera. The system automatically detects and localizes players, ball and referees, to recognize main action coordinates and game states yielding to a professional cameraman-like production of the basketball event. We also release a fully annotated dataset consisting of single 4K camera and twelve-camera videos of basketball games.

关键词： Cameras Games Production Streaming media Robot vision systems Computational modeling

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Intelligent Scene Caching to Improve Accuracy for Energy-Constrained Embedded vision

Intelligent Scene Caching to Improve Accuracy for Energy-Con...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Simpson, Benjamin Lubana, Ekdeep Liu, Yuchen Dick, Robert Univ Michigan Ann Arbor MI 48109 USA

ISBN: (纸本)9781728193601

We describe an efficient method of improving the performance of vision algorithms operating on video streams by reducing the amount of data captured and transferred from image sensors to analysis servers in a data-aware manner. The key concept is to combine guided, highly heterogeneous sampling with an intelligent Scene Cache. This enables the system to adapt to spatial and temporal patterns in the scene, thus reducing redundant data capture and processing. A software prototype of our framework running on a general-purpose embedded processor enables superior object detection accuracy (by 56%) at similar energy consumption (slight improvement of 4%) compared to an H.264 hardware accelerator.

关键词： Image enhancement

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Photometric Stereo via Discrete Hypothesis-and-Test Search

Photometric Stereo via Discrete Hypothesis-and-Test Search

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Enomoto, Kenji Waechter, Michael Kutulakos, Kiriakos N. Matsushita, Yasuyuki Osaka Univ Osaka Japan Univ Toronto Toronto ON Canada

ISBN: (纸本)9781728171685

In this paper, we consider the problem of estimating surface normals of a scene with spatially varying, general BRDFs observed by a static camera under varying, known, distant illumination. Unlike previous approaches that are mostly based on continuous local optimization, we cast the problem as a discrete hypothesis-and-test search problem over the discretized space of surface normals. While a naive search requires a significant amount of time, we show that the expensive computation block can be precomputed in a scene-independent manner, resulting in accelerated inference for new scenes. It allows us to perform a MI search over the finely discretized space of surface normals to determine the globally optimal surface normal for each scene point. We show that our method can accurately estimate surface normals of scenes with spatially varying different reflectances in a reasonable amount of time.

关键词： computer vision

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Deep Learning for Automatic Pneumonia Detection

Deep Learning for Automatic Pneumonia Detection

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Gabruseva, Tatiana Poplavskiy, Dmytro Kalinin, Alexandr Topcon Positioning Syst Brisbane Qld Australia Univ Michigan Ann Arbor MI 48109 USA Shenzhen Res Inst Big Data Shenzhen 518172 Guangdong Peoples R China

ISBN: (纸本)9781728193601

Pneumonia is the leading cause of death among young children and one of the top mortality causes worldwide. The pneumonia detection is usually performed through examine of chest X-Ray radiograph by highly-trained specialists. This process is tedious and often leads to a disagreement between radiologists. computer-aided diagnosis systems showed the potential for improving diagnostic accuracy. In this work, we develop the computational approach for pneumonia regions detection based on single-shot detectors, squeeze-and-extinction deep convolution neural networks, augmentations and multi-task learning. The proposed approach was evaluated in the context of the Radiological society of North America Pneumonia Detection Challenge, achieving one of the best results in the challenge.

关键词： computer aided diagnosis

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Curvature: A signature for Action recognition in Video Sequences

Curvature: A signature for Action Recognition in Video Seque...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Chen, He Chirikjian, Gregory S. Johns Hopkins Univ Baltimore MD 21218 USA Natl Univ Singapore Singapore Singapore

ISBN: (纸本)9781728193601

In this paper, a novel signature of human action recognition, namely the curvature of a video sequence, is introduced. In this way, the distribution of sequential data is modeled, which enables few-shot learning. Instead of depending on recognizing features within images, our algorithm views actions as sequences on the universal time scale across a whole sequence of images. The video sequence, viewed as a curve in pixel space, is aligned by reparameterization using the arclength of the curve in pixel space. Once such curvatures are obtained, statistical indexes are extracted and fed into a learning-based classifier. Overall, our method is simple but powerful. Preliminary experimental results show that our method is effective and achieves state-of-the-art performance in video-based human action recognition.

关键词： Pixels

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TAL EmotioNet Challenge 2020 Rethinking the Model Chosen Problem in Multi-Task Learning

TAL EmotioNet Challenge 2020 Rethinking the Model Chosen Pro...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Wang, Pengcheng Wang, Zihao Ji, Zhilong Liu, Xiao Yang, Songfan Wu, Zhongqin TAL Educ Grp Beijing Peoples R China

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193601

This paper introduces our approach to the EmotioNet Challenge 2020. We pose the AU recognition problem as a multi-task learning problem, where the non-rigid facial muscle motion (mainly the first 17 AUs) and the rigid head motion (the last 6 AUs) are modeled separately. The co-occurrence of the expression features and the head pose features are explored. We observe that different AUs converge at various speed. By choosing the optimal checkpoint for each AU, the recognition results are improved. We are able to obtain a final score of 0.746 in validation set and 0.7306 in the test set of the challenge.

关键词： Gold Training Noise measurement Face recognition Face Data models Optimization

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Face recognition: Too Bias, or Not Too Bias?

Face Recognition: Too Bias, or Not Too Bias?

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Robinson, Joseph P. Livitz, Gennady Henon, Yann Qin, Can Fu, Yun Timoner, Samson Northeastern Univ Boston MA 02115 USA ISM Connect Doylestown PA USA

ISBN: (纸本)9781728193601

We reveal critical insights into problems of bias in state-of-the-art facial recognition (FR) systems using a novel Balanced Faces In the Wild (BFW) dataset: data balanced for gender and ethnic groups. We show variations in the optimal scoring threshold for face-pairs across different subgroups. Thus, the conventional approach of learning a global threshold for all pairs results in performance gaps between subgroups. By learning subgroup-specific thresholds, we reduce performance gaps, and also show a notable boost in overall performance. Furthermore, we do a human evaluation to measure bias in humans, which supports the hypothesis that an analogous bias exists in human perception. For the BFW database, source code, and more, visit https://***/visionjo/facerec-bias-bfw.

关键词： Face recognition

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SomethingFinder: Localizing undefined regions using referring expressions

SomethingFinder: Localizing undefined regions using referrin...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Eum, Sungmin Han, David Briggs, Gordon US Army Res Lab Adelphi MD 20783 USA Booz Allen Hamilton Mclean VA 22102 USA US Naval Res Lab Washington DC USA

ISBN: (纸本)9781728193601

Previous research on localizing a target region in an image referred to by a natural language expression has occurred within an object-centric paradigm. However, in practice, there may not be any easily named or identifiable objects near a target location. Instead, references may need to rely on basic visual attributes, such as color or geometric clues. An expression like "a red something beside a blue vertical line" could still pinpoint a target location. As such, we begin to explore the open challenge of computational object-agnostic reference by constructing a novel dataset and by devising a new set of algorithms that can identify a target region in an image when given a referring expression containing only basic conceptual features.

关键词： computer vision

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