Zobrazeno 1 - 10
of 152
pro vyhledávání: '"Scott, Matthew R."'
One-stage object detection is commonly implemented by optimizing two sub-tasks: object classification and localization, using heads with two parallel branches, which might lead to a certain level of spatial misalignment in predictions between the two
Externí odkaz:
http://arxiv.org/abs/2108.07755
Differentiable architecture search is prevalent in the field of NAS because of its simplicity and efficiency, where two paradigms, multi-path algorithms and single-path methods, are dominated. Multi-path framework (e.g. DARTS) is intuitive but suffer
Externí odkaz:
http://arxiv.org/abs/2107.04324
Pair-wise loss functions have been extensively studied and shown to continuously improve the performance of deep metric learning (DML). However, they are primarily designed with intuition based on simple toy examples, and experimentally identifying t
Externí odkaz:
http://arxiv.org/abs/2103.14003
Recent advances in neuroscience have highlighted the effectiveness of multi-modal medical data for investigating certain pathologies and understanding human cognition. However, obtaining full sets of different modalities is limited by various factors
Externí odkaz:
http://arxiv.org/abs/2103.11587
Differentiable architecture search (DAS) has made great progress in searching for high-performance architectures with reduced computational cost. However, DAS-based methods mainly focus on searching for a repeatable cell structure, which is then stac
Externí odkaz:
http://arxiv.org/abs/2101.04028
Region Proposal Network (RPN) provides strong support for handling the scale variation of objects in two-stage object detection. For one-stage detectors which do not have RPN, it is more demanding to have powerful sub-networks capable of directly cap
Externí odkaz:
http://arxiv.org/abs/2007.12075
Siamese-based trackers have achieved excellent performance on visual object tracking. However, the target template is not updated online, and the features of the target template and search image are computed independently in a Siamese architecture. I
Externí odkaz:
http://arxiv.org/abs/2004.06711
Fine-grained image categorization is challenging due to the subtle inter-class differences.We posit that exploiting the rich relationships between channels can help capture such differences since different channels correspond to different semantics.
Externí odkaz:
http://arxiv.org/abs/2003.05235
Training an object detector on a data-rich domain and applying it to a data-poor one with limited performance drop is highly attractive in industry, because it saves huge annotation cost. Recent research on unsupervised domain adaptive object detecti
Externí odkaz:
http://arxiv.org/abs/2003.04132
In this work, we propose Knowledge Integration Networks (referred as KINet) for video action recognition. KINet is capable of aggregating meaningful context features which are of great importance to identifying an action, such as human information an
Externí odkaz:
http://arxiv.org/abs/2002.07471