arxiv: https://arxiv.org/abs/2003.12294
this approach will try to take global context into account by adopting transformer, and also use similar structure to reason semantic content.
(more…)arxiv: https://arxiv.org/pdf/1704.04861.pdf key points focus on optimizing for latency, small networks. use depthwise separable convolutions, to reduce computation as much as possible further reduce size of models based on width/resolution multiplier, but at the cost of accuracy depthwise separable convolution This is a combination of depthwise convonlution + pointwise convolution. Read more…
arxiv: https://arxiv.org/abs/1903.03273
arxiv link: https://arxiv.org/abs/1709.08429
arxiv: https://arxiv.org/abs/2005.10821
arxiv link: https://arxiv.org/abs/1911.09070 key points multi scale with weighted bi-directional fpn. model scaling. compound scaling method, which jointly scales up resolution/depth/width for all backbone, feature network, box/class prediction network. use efficientnet backbone Bi-directional FPN Here are the key points of bi-directional FPN enhancement from PANet with some modifications remove nodes Read more…
arxiv link: https://arxiv.org/abs/1902.09630 key points using l1/l2 norm losses may not always align with the objective of improving IOU IOU is a good metric but they cannot be directly used as loss because it cannot backpropagate when there is no overlap at all GIOU is a modified IOU formula that Read more…
arxiv link key points introduces transformer into the domain of object detection compared to existing approaches, no need for anchor priors and post processing such as NMS to handle with multiple box predictions matching to single gt. this is possible thanks to matching stage matching stage the transformer will make Read more…
link: https://towardsdatascience.com/different-normalization-layers-in-deep-learning-1a7214ff71d6 Batch Normalization Weight Normalization Layer Normalization Group Normalization Weight Standarization Recently, Siyun Qiao et al. introduced Weight Standardization in their paper “Micro-Batch Training with Batch-Channel Normalization and Weight Standardization” and found that group normalization when mixed with weight standardization, could outperform or perform equally well as BN even Read more…
nice summary: https://towardsdatascience.com/what-is-teacher-forcing-3da6217fed1c