Fine Grained Image Classification with Bilinear-CNN's

Rajesh Bhat (~rajesh35)


Fine-Grained Image Classification(FGIC) is an area of expertise in image recognition where we get to differentiate minor categories such as dog breeds, bird species, airplanes, product images, etc. There are two main challenges associated with such fine-grained tasks. First of all, the visual differences within each category are so small that they can be influenced by factors such as pose, location of the object, and even one small section of the object that can make a big impact on the classification. Secondly, it is very complex to identify the minute discriminating parts within the images. Various methodologies have been implemented to identify and locate those parts within the images using orderless descriptors such as Fisher vectors, VLAD, and Neural models like Bi-linear Convolutions(B-CNN), etc.

In this talk, to start with we will mainly focus on Bilinear-CNN’s which have proven to be one of the most successful techniques in obtaining the best results in Fine-Grained Image Classification tasks, and later talk about reducing the parameters/complexity of B-CNN models using Attention Techniques.

30 mins talk breakdown:

  • Introducing the FGIC problem statement: 3 mins
  • CNN's and receptive fields: 4 mins
  • Explaining B-CNN's: 5 mins
  • Attention Mechanism: 5 mins
  • Code Walkthrough, just the B-CNN part: 5 mins
  • Results & Experiments: 5 mins


Basic understanding of

  • Neural Nets.
  • Convolutional neural networks.
  • Receptive Fields.
  • Attention techniques.

Content URLs:

WIP blog:

Speaker Info:

"Rajesh Shreedhar Bhat is working as a Sr. Data Scientist at Walmart, Bangalore. His work is primarily focused on building reusable machine/deep learning solutions that can be used across various business domains at Walmart. He completed his Bachelor’s degree from PESIT, Bangalore. He has a couple of research publications in the field of NLP and vision, which are published at top-tier conferences such as CoNLL, ASONAM, etc. He is a Kaggle Expert(World Rank 966/122431) with 3 silver and 2 bronze medals and has been a regular speaker at various International and National conferences which includes Data & Spark AI Summit, ODSC, Seoul & Silicon Valley AI Summit, Kaggle Days Meetups, Data Hack Summit, etc. Apart from this, Rajesh is a mentor for Udacity Deep learning & Data Scientist Nanodegree for the past 3 years and has conducted ML & DL workshops in GE Healthcare, IIIT Kancheepuram, and many other places. "

Speaker Links:

All details can be found on this website:

Section: Data Science, Machine Learning and AI
Type: Talks
Target Audience: Advanced
Last Updated: