First, we use the scale invariant heat kernel signature (SIHKS) to describe the vertex of the shape. The locality-constrained linear coding (LLC) is employed to encode each vertex of the shape to form the global
At train stage, a nonlinear regression was learned on train data to infer class “template” in representation space using semantic embeddings as inputs. At test stage, as one test sample arrives, we first reduce its dimension to representation space, we calculate its similarities to unseen ...
DeepFirearm: Learning Discriminative Feature Representation for Fine-grained Firearm Retrieval INTRODUCTION 这是最近看的一篇文章,论文并没有太多方法上的创新点,但是将神经网络用于现代武器的检索,方向挺新奇的。所以记录一下。论文之初作者就指出现在CNN发展得很好,网络上像Facebook这一类...A...
In this paper, it is proved that dictionary learning and sparse representation is invariant to a linear transformation. It subsumes the special case of transforming/projecting the data into a discriminative space. This is important because recently, supervised dictionary learning algorithms have been ...
The DDC method [5] used MMD in addition to the regular clas- sification loss on the source to learn a representation that is both discriminative and domain invariant. The Deep Adapta- tion Network (DAN) [6] applied MMD to layers embedded in a reproducing kernel Hilbert space, effectively ...
2020c). We roughly divide existing target representation approaches into three categories: Region of Interest (ROI-) based features, histogram-based features and multi-channel features. Typical examples of ROI-based features include Scale-Invariant Feature Transform (SIFT) (Lowe 1999) and Speeded Up...
Our clustered representation performs similarly while being faster to evaluate. Secondly, observe that on average the performance of the LDA model is very similar to the performance of a linear SVM, and is also highly correlated with 468 B. Hariharan, J. Malik, and D. Ramanan (a) horse (...
A Gaussian Process (GP) model is proposed for sparse representation to optimize the dictionary objective function. The sparse coding property allows a kernel with a compact support in GP to realize a very efficient dictionary learning process. Hence, an action video can be described by a set of...
Designing an appropriate feature representation and an effective matching framework for age invariant face recognition remains an open problem. In this paper, we propose a discriminative model to address face matching in the presence of age variation. In this framework, we first represent each face ...
Finally, temporal-discriminative features are learnt by minimizing the distance between each anchor and its augmented positive, while the distance between each anchor and its augmented negative as well as other videos saved in the memory bank is maximized to enrich the representation diversity. In ...