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.
Recent research has shown that faces can be obfuscated in large-scale datasets with a minimal performance impact on image classification and downstream tasks like object recognition. In this paper, we investigate the ...
详细信息
ISBN:
(纸本)9781665448994
Recent research has shown that faces can be obfuscated in large-scale datasets with a minimal performance impact on image classification and downstream tasks like object recognition. In this paper, we investigate the role of face obfuscation in video classification datasets and quantify a more significant reduction in performance caused by face blurring. To reduce such performance effects, we propose a generalized distillation approach in which a privacy-preserving action recognition network is trained with privileged information given by face identities. We show, through experiments performed on Kinetics-400, that the proposed approach can fully close the performance gap caused by face anonymization.
We present an approach to perform supervised action recognition in the dark. In this work, we present our results on the ARID dataset[60]. Most previous works only evaluate performance on large, well illuminated datas...
详细信息
ISBN:
(纸本)9781665448994
We present an approach to perform supervised action recognition in the dark. In this work, we present our results on the ARID dataset[60]. Most previous works only evaluate performance on large, well illuminated datasets like Kinetics and HMDB51. We demonstrate that our work is able to achieve a very low error rate while being trained on a much smaller dataset of dark videos. We also explore a variety of training and inference strategies including domain transfer methodologies and also propose a simple but useful frame selection strategy. Our empirical results demonstrate that we beat previously published baseline models by 11%.
暂无评论