CAGAM is a form of spatial attention mechanism that propagates attention from a known to an unknown context features thereby enhancing the unknown context for relevant pattern discovery. Usually the known context feature is a class activation map ([CAM](https://paperswithcode.com/method/cam)).

**FairMOT** is a model for multi-object tracking which consists of two homogeneous branches to predict pixel-wise objectness scores and re-ID features. The achieved fairness between the tasks is used to achieve high levels of detection and tracking accuracy. The detection branch is implemented in an anchor-free style which estimates object centers and sizes represented as position-aware measurement maps. Similarly, the re-ID branch estimates a re-ID feature for each pixel to characterize the object centered at the pixel. Note that the two branches are completely homogeneous which essentially differs from the previous methods which perform detection and re-ID in a cascaded style. It is also worth noting that FairMOT operates on high-resolution feature maps of strides four while the previous anchor-based methods operate on feature maps of stride 32. The elimination of anchors as well as the use of high-resolution feature maps better aligns re-ID features to object centers which significantly improves the tracking accuracy.

FairMOT

FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking

Class Activation Guided Attention Mechanism

Rendezvous: Attention Mechanisms for the Recognition of Surgical Action Triplets in Endoscopic Videos

**FSAF**, or Feature Selective Anchor-Free, is a building block for single-shot object detectors. It can be plugged into single-shot detectors with feature pyramid structure. The FSAF module addresses two limitations brought up by the conventional anchor-based detection: 1) heuristic-guided feature selection; 2) overlap-based anchor sampling. The general concept of the FSAF module is online feature selection applied to the training of multi-level anchor-free branches. Specifically, an anchor-free branch is attached to each level of the feature pyramid, allowing box encoding and decoding in the anchor-free manner at an arbitrary level. During training, we dynamically assign each instance to the most suitable feature level. At the time of inference, the FSAF module can work jointly with anchor-based branches by outputting predictions in parallel. We instantiate this concept with simple implementations of anchor-free branches and online feature selection strategy

The general concept is presented in the Figure to the right. An anchor-free branch is built per level of feature pyramid, independent to the anchor-based branch. Similar to the anchor-based branch, it consists of a classification subnet and a regression subnet (not shown in figure). An instance can be assigned to arbitrary level of the anchor-free branch. During training, we dynamically select the most suitable level of feature for each instance based on the instance content instead of just the size of instance box. The selected level of feature then learns to detect the assigned instances. At inference, the FSAF module can run independently or jointly with anchor-based branches. The FSAF module is agnostic to the backbone network and can be applied to single-shot detectors with a structure of feature pyramid. Additionally, the instantiation of anchor-free branches and online feature selection can be various.

Source	Rendezvous: Attention Mechanisms for the Recognition of Surgical Action Triplets in Endoscopic Videos
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com