**SSD** is a single-stage object detection method that discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location. At prediction time, the network generates scores for the presence of each object category in each default box and produces adjustments to the box to better match the object shape. Additionally, the network combines predictions from multiple feature maps with different resolutions to naturally handle objects of various sizes. 

The fundamental improvement in speed comes from eliminating bounding box proposals and the subsequent pixel or feature resampling stage. Improvements over competing single-stage methods include using a small convolutional filter to predict object categories and offsets in bounding box locations, using separate predictors (filters) for different aspect ratio detections, and applying these filters to multiple feature maps from the later stages of a network in order to perform detection at multiple scales.

Image Retrieval is a fundamental task of obtaining images similar to the query one from a database. A common image retrieval practice is to firstly retrieve candidate images via similarity search using global image features and then re-rank the candidates by leveraging their
local features. Previous learning-based studies mainly focus on either global or local image representation learning
to tackle the retrieval task. In this paper, we abandon the
two-stage paradigm and seek to design an effective singlestage solution by integrating local and global information
inside images into compact image representations. Specifically, we propose a Deep Orthogonal Local and Global
(DOLG) information fusion framework for end-to-end image retrieval. It attentively extracts representative local information with multi-atrous convolutions and self-attention
at first. Components orthogonal to the global image representation are then extracted from the local information.
At last, the orthogonal components are concatenated with
the global representation as a complementary, and then aggregation is performed to generate the final representation.
The whole framework is end-to-end differentiable and can
be trained with image-level labels. Extensive experimental
results validate the effectiveness of our solution and show
that our model achieves state-of-the-art image retrieval performances on Revisited Oxford and Paris datasets.

DOLG

DOLG: Single-Stage Image Retrieval with Deep Orthogonal Fusion of Local and Global Features

SSD: Single Shot MultiBox Detector

**Hierarchical Softmax** is a is an alternative to [softmax](https://paperswithcode.com/method/softmax) that is faster to evaluate: it is $O\left(\log{n}\right)$ time to evaluate compared to $O\left(n\right)$ for softmax. It utilises a multi-layer binary tree, where the probability of a word is calculated through the product of probabilities on each edge on the path to that node. See the Figure to the right for an example of where the product calculation would occur for the word "I'm".

(Introduced by Morin and Bengio)

Image Credit: [Steven Schmatz](https://www.quora.com/profile/Steven-Schmatz)

Source	SSD: Single Shot MultiBox Detector
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com