CNNs for Face Detection and Recognition

deep learning approach using neural network has achieved significant success in tackling face detection as a subclass of object classification, localization, and detection. Appar-ently, the evolve of face detection correlates closely with the development of object classification, localization and detec-tion techniques. 2.1. Sliding Window

  Using, Into, Faces, Recognition, Object, Detection, Detec, Localization, Detec tion, For face detection and recognition

Improved Multiscale Vision Transformers for Classification ...

Vision transformers for object detection tasks [12,55,79, 90] address the challenge of detection typically requiring high-resolution inputs and feature maps for accurate object localization. This significantly increases computation com-plexity due to the quadratic complexity of self-attention oper-ators in transformers [77].

  Object, Detection, Localization, Object detection, Object localization

Multi-scale Patch Aggregation (MPA) for Simultaneous ...

Object Detection Object detection has a long history in computer vision. Before DCNN shows its great abil-ity for image classification [21, 33], part-based models [9, 37] were popular. Recent object detection framework-s [11, 12, 17, 37, 29, 34, 23, 32, 10] are based on DCNN [21, 33] to classify object proposals. These methods either

  Patch, Aggregation, Object, Detection, Simultaneous, Object detection, Patch aggregation, For simultaneous, Object detection object detection

1 Object Detection in 20 Years: A Survey - arXiv

1 Object Detection in 20 Years: A Survey Zhengxia Zou, Zhenwei Shi, Member, IEEE, Yuhong Guo, and Jieping Ye, Senior Member, IEEE Abstract—Object detection, as of one the most fundamental and challenging problems in computer vision, has received great attention in …

  Object, Detection, Object detection

Object Recognition from Local Scale-Invariant Features

Object recognition in cluttered real-world scenes requires local image features that are unaffected by nearby clutter or partial occlusion. The features must be at least partially in-variant to illumination,3D projective transforms, and com …

  Local, Object

AAAI-21 Accepted Paper List.1.29

! 4!! 228:!Rain!Streak!Removal!via!Dual!Graph!Convolutional!Network! Xueyang!Fu,!Qi!Qi,!Yurui!Zhu,!Xinghao!Ding,!Zheng*Jun!Zha!! 233:!RevMan:!Revenue*Aware!Multi*Task ...