**EdgeBoxes** is an approach for generating object bounding box proposals directly from edges. Similar to segments, edges provide a simplified but informative representation of an image. In fact, line drawings of an image can accurately convey the high-level information contained in an image
using only a small fraction of the information. 

The main insight behind the method is the observation: the number of contours wholly enclosed by a bounding box is indicative of the likelihood of the box containing an object. We say a contour is wholly enclosed by a box if all edge pixels belonging to the contour lie within the interior of the box. Edges tend to correspond to object boundaries, and as such boxes that tightly enclose a set of edges are likely to contain an object. However, some edges that lie within an object’s bounding box may not be part of the contained object. Specifically, edge pixels that belong to contours straddling the box’s boundaries are likely to correspond to objects or structures that lie outside the box.

Source: [Zitnick and Dollar](https://pdollar.github.io/files/papers/ZitnickDollarECCV14edgeBoxes.pdf)

**TorchBeast** is a platform for reinforcement learning (RL) research in PyTorch. It implements a version of the popular [IMPALA](https://paperswithcode.com/method/impala) algorithm for fast, asynchronous, parallel training of RL agents.

TorchBeast

TorchBeast: A PyTorch Platform for Distributed RL

EdgeBoxes

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.

Year	2000
Data Source	CC BY-SA - https://paperswithcode.com