We propose a novel inference framework for finding maximal cliques in a weighted graph that satisfy hard constraints. The constraints specify the graph nodes that must belong to the solution as well as mutual exclusions of graph nodes, i.e., sets of nodes that cannot belong to the same solution. The proposed inference is based on a novel particle filter algorithm with state permeations. We apply the inference framework to a challenging problem of learning part-based, deformable object models. Two core problems in the learning framework, matching of image patches and finding salient parts, are formulated as two instances of the problem of finding maximal cliques with hard constraints. Our learning framework yields discriminative part based object models that achieve very good detection rate, and outperform other methods on object classes with large deformation.

In Proceedings of the IEEE Workshop on Exploring Opportunities and Challenges for Natural Language Techniques to Support Visual Analysis.

Loss functions play a very important role in the training of modern Deep learning architecture, choosing the right loss function is the key to successful model building. A loss function is a mathematical equation that a deep learning architecture tries to minimize or optimize. Deep learning is an iterative process, in every step, it calculates some metric that tells the system how close its prediction is to the original label. Based on the calculated loss value, the network optimizes its parameters. There are a lot of loss functions and among those, the most popular ones are Mean square error, categorical cross-entropy, Dice loss, etc. Loss functions can be divided into two major categories: specialized loss functions and generalized loss functions.

Readers of this blog already know what loss functions are in AI but for people starting into the field let me define it again. The loss function is a mathematical equation that all the deep learning algorithm tries to minimize or optimize. As we all know that Deep learning takes an iterative process to learn things, in every step, it calculates some metric that tells it how close it is to the original label and based upon that it optimizes its parameters. So the metrics that we minimize or optimize are called loss functions. There are a lot of famous loss functions like Mean square error, categorical cross-entropy, Dice loss, and many more.

We propose a novel inference framework for finding maximal cliques in a weighted graph that satisfy hard constraints. The constraints specify the graph nodes that must belong to the solution as well as mutual exclusions of graph nodes, i.e., sets of nodes that cannot belong to the same solution. The proposed inference is based on a novel particle filter algorithm with state permeations. We apply the inference framework to a challenging problem of learning part-based, deformable object models. Two core problems in the learning framework, matching of image patches and finding salient parts, are formulated as two instances of the problem of finding maximal cliques with hard constraints. Our learning framework yields discriminative part based object models that achieve very good detection rate, and outperform other methods on object classes with large deformation.