Neural networks are used for many real world applications, but often they have problems estimating their own confidence. This is particularly problematic for computervision applications aimed at making high stakes de...
详细信息
ISBN:
(纸本)9781665448994
Neural networks are used for many real world applications, but often they have problems estimating their own confidence. This is particularly problematic for computervision applications aimed at making high stakes decisions with humans and their lives. In this paper we make a meta-analysis of the literature, showing that most if not all computervision applications do not use proper epistemic uncertainty quantification, which means that these models ignore their own limitations. We describe the consequences of using models without proper uncertainty quantification, and motivate the community to adopt versions of the models they use that have proper calibrated epistemic uncertainty, in order to enable out of distribution detection. We close the paper with a summary of challenges on estimating uncertainty for computervision applications and recommendations.
This work analyzes the problem of homography estimation for robust target matching in the context of real-time mobile vision. We present a device-friendly implementation of the Gaussian Elimination algorithm and show ...
详细信息
ISBN:
(纸本)9781479943098
This work analyzes the problem of homography estimation for robust target matching in the context of real-time mobile vision. We present a device-friendly implementation of the Gaussian Elimination algorithm and show that our optimized approach can significantly improve the homography estimation step in a hypothesize-and-verify scheme. Experiments are performed on image sequences in which both speed and accuracy are evaluated and compared with conventional homography estimation schemes.
Several papers addressed ellipse detection as a first step for several computervision applications, but most of the proposed solutions are too slow to be applied in real time on large images or with limited hardware ...
详细信息
ISBN:
(纸本)9780769549903
Several papers addressed ellipse detection as a first step for several computervision applications, but most of the proposed solutions are too slow to be applied in real time on large images or with limited hardware resources, as in the case of mobile devices. This demo is based on a novel algorithm for fast and accurate ellipse detection. The proposed algorithm relies on a careful selection of arcs which are candidate to form ellipses and on the use of Hough transform to estimate parameters in a decomposed space. The demo will show it working on a commercial smart-phone.
Human-object interaction (HOI) detection is a core task in computervision. The goal is to localize all human-object pairs and recognize their interactions. An interaction defined by a tuple leads to a long-tailed vi...
详细信息
ISBN:
(纸本)9781728193601
Human-object interaction (HOI) detection is a core task in computervision. 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.
In this paper we discuss and analyze possible futures for technologies in the field of computervision (CV). Using a method we have coined speculative analysis we take a broad look at research trends in the field to c...
详细信息
ISBN:
(纸本)9781538607336
In this paper we discuss and analyze possible futures for technologies in the field of computervision (CV). Using a method we have coined speculative analysis we take a broad look at research trends in the field to categorize risks, analyze which ones are most threatening and likely, and ultimately summarize conclusions for how the field may attempt to stem future harms caused by CV technologies. We develop narrative case studies to provoke dialogue and deeply explore possible risk scenarios we found to be most probable and severe. We arrive at the position that there are serious potentials for CV to cause discriminatory harm and exacerbate cybersecurity issues.
Laughter detection is an important area of interest in the Affective Computing and Human-computer Interaction fields. In this paper we propose a multi-modal methodology, based on the fusion of audio and visual cues to...
详细信息
ISBN:
(纸本)9781424439942
Laughter detection is an important area of interest in the Affective Computing and Human-computer Interaction fields. In this paper we propose a multi-modal methodology, based on the fusion of audio and visual cues to deal with the laughter recognition problem in face-to-face conversations. The audio features are extracted from the spectogram and the video features are obtained estimating the mouth movement degree and using a smile and laughter classifier Finally, the multi-modal cues are included in a sequential classifier Results over videos from the public discussion blog of the New York Times show that both types of features perform better when considered together by the classifier Moreover the sequential methodology shows to significantly, outperform the results obtained by an Adaboost classifier
Recent interest in developing online computervision algorithms is spurred in part by a growth of applications capable of generating large volumes of images and videos. These applications are rich sources of images an...
详细信息
ISBN:
(纸本)9781479943098
Recent interest in developing online computervision algorithms is spurred in part by a growth of applications capable of generating large volumes of images and videos. These applications are rich sources of images and video streams. Online vision algorithms for managing, processing and analyzing these streams need to rely upon streaming concepts, such as pipelines, to ensure timely and incremental processing of data. This paper is a first attempt at defining a formal stream algebra that provides a mathematical description of vision pipelines and describes the distributed manipulation of image and video streams. We also show how our algebra can effectively describe the vision pipelines of two state of the art techniques.
We develop a deep convolutional neural networks (CNNs) to deal with the blurry artifacts caused by the defocus of the camera using dual-pixel images. Specifically, we develop a double attention network which consists ...
详细信息
ISBN:
(纸本)9781665448994
We develop a deep convolutional neural networks (CNNs) to deal with the blurry artifacts caused by the defocus of the camera using dual-pixel images. Specifically, we develop a double attention network which consists of attentional encoders, triple locals and global local modules to effectively extract useful information from each image in the dual-pixels and select the useful information from each image and synthesize the final output image. We demonstrate the effectiveness of the proposed deblurring algorithm in terms of both qualitative and quantitative aspects by evaluating on the test set in the NTIRE 2021 Defocus Deblurring using Dual-pixel Images Challenge [1] [4].
Lane feature extraction is one of the key computational steps in lane analysis systems. In this paper, we propose a lane feature extraction method, which enables different configurations of embedded solutions that add...
详细信息
ISBN:
(纸本)9780769549903
Lane feature extraction is one of the key computational steps in lane analysis systems. In this paper, we propose a lane feature extraction method, which enables different configurations of embedded solutions that address both accuracy and embedded systems' constraints. The proposed lane feature extraction process is evaluated in detail using real world lane data, to explore its effectiveness for embedded realization and adaptability to varying contextual information like lane types and environmental conditions.
This paper introduces a novel dataset for video enhancement and studies the state-of-the-art methods of the NTIRE 2021 challenge on quality enhancement of compressed video. The challenge is the first NTIRE challenge i...
详细信息
ISBN:
(纸本)9781665448994
This paper introduces a novel dataset for video enhancement and studies the state-of-the-art methods of the NTIRE 2021 challenge on quality enhancement of compressed video. The challenge is the first NTIRE challenge in this direction, with three competitions, hundreds of participants and tens of proposed solutions. Our newly collected Large-scale Diverse Video (LDV) dataset is employed in the challenge. In our study, we analyze the solutions of the challenges and several representative methods from previous literature on the proposed LDV dataset. We find that the NTIRE 2021 challenge advances the state-of-theart of quality enhancement on compressed video.
暂无评论