**DeepIR**, or **Deep InfraRed image processing**, is a thermal image processing framework for recovering high quality images from a very small set of images captured with camera motion. Enhancement is achieved by noting that camera motion, which is usually a hinderance, can be exploited to our advantage to separate a sequence of images into the scene-dependent radiant flux, and a slowly changing scene-independent non-uniformity. DeepIR combines the physics of microbolometer sensors, with powerful regularization capabilities by neural network-based representations. DeepIR relies on the key observation that jittering a camera, while unwanted in visible domain, is highly desirable in the thermal domain as it allows an accurate separation of the sensor-specific non-uniformities from the scene’s radiant flux.

**CenterNet** is a one-stage object detector that detects each object as a triplet, rather than a pair, of keypoints. It utilizes two customized modules named [cascade corner pooling](https://paperswithcode.com/method/cascade-corner-pooling) and [center pooling](https://paperswithcode.com/method/center-pooling), which play the roles of enriching information collected by both top-left and bottom-right corners and providing more recognizable information at the central regions, respectively. The intuition is that, if a predicted bounding box has a high IoU with the ground-truth box, then the probability that the center keypoint in its central region is predicted as the same class is high, and vice versa. Thus, during inference, after a proposal is generated as a pair of corner keypoints, we determine if the proposal is indeed an object by checking if there is a center keypoint of the same class falling within its central region.

CenterNet

CenterNet: Keypoint Triplets for Object Detection

DeepIR

Thermal Image Processing via Physics-Inspired Deep Networks

**GloVe Embeddings** are a type of word embedding that encode the co-occurrence probability ratio between two words as vector differences. GloVe uses a weighted least squares objective $J$ that minimizes the difference between the dot product of the vectors of two words and the logarithm of their number of co-occurrences:

$$ J=\sum\_{i, j=1}^{V}f\left(𝑋\_{i j}\right)(w^{T}\_{i}\tilde{w}_{j} + b\_{i} + \tilde{b}\_{j} - \log{𝑋}\_{ij})^{2} $$

where $w\_{i}$ and $b\_{i}$ are the word vector and bias respectively of word $i$, $\tilde{w}_{j}$ and $b\_{j}$ are the context word vector and bias respectively of word $j$, $X\_{ij}$ is the number of times word $i$ occurs in the context of word $j$, and $f$ is a weighting function that assigns lower weights to rare and frequent co-occurrences.

Source	Thermal Image Processing via Physics-Inspired Deep Networks
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com