Indian Institute of Information Technology, Allahabad
Computer Vision and Biometrics Lab (CVBL)
Visual Recognition
Jan-May 2022 Semester
Previous Offerings
Visual Recognition 2021Course Information
Objective of the course: The field of visual recognition has become part of our lives with applications in self-driving cars, satellite monitoring, surveillance, video analytics particularly in scene understanding, crowd behaviour analysis, action recognition etc. It has eased human lives by acquiring, processing, analyzing and understanding digital images and extraction of high-dimensional data from the real world in order to produce numerical or symbolic information. The visual recognition encapsulates image classification, localization and detection. The course on visual recognition will help students understand new tools, techniques and methods which are influencing the visual recognition field.
Outcome of the course: At the end of this course, the students will be able apply the concepts to solve some real problems in recognition. The students will be able to use computational visual recognition for problems ranging from extracting features, classifying images, to detecting and outlining objects and activities in an image or video using machine learning and deep learning concepts. The student will be also being able to invent new methods in visual recognition for various applications.
Course Instructors
Teaching Assistants
- Class meets
- Wednesday: 11.00 AM - 01.00 PM, Thursday: 07.00 - 09.00 PM, Friday: 03.00 - 05.00 PM
- Course Ethics
- Students are strictly advised to avoid the unethical practices in the course including review tests and practice components.
- The project component will be done in team. The team will be formed by the course instructors. The project allotment will be also done by the course instructors.
- Students are not allowed to simply claim the existing solutions available in public domain as your own work in this course.
- If it happens that you have already done the similar projects in any other course or with any other faculty which is allotted to you, you should immediately inform us for the same as it is not allowed to have similar projects in this course which you might have already done previously.
- It is best to try to solve problems on your own, since problem solving is an important component of the course.
- You are not allowed to do or continue same project in any other course and with any other faculty.
- You are allowed to discuss class material, problems, and general solution strategies with your classmates. But, when it comes to formulating or writing solutions you must work/implement by yourself.
- You may use free and publicly available sources, such as books, journal and conference publications, and web pages, as research material for your answers. (You will not lose marks for using external sources.) It is does not mean that you claim these existing resources as your work.
- You may not use any paid service and you must clearly and explicitly cite all outside sources and materials that you made use of.
- Students are not allowed to post the code/report/any other material of course project in public domain or share with any one else without written permission from course instructors.
- We consider the use of uncited external sources as portraying someone else's work as your own, and as such it is a violation of the Institute's policies on academic dishonesty.
- Instances will be dealt with harshly and typically result in a failing course grade.
Schedule
| Date | Topic | Resources |
| L01: Jan 10, 2022 | Course Introduction Slide, Recorded Lecture |
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| L02: Jan 13, 2022 | Local Features: What, Why and How Slide, Recorded Lecture |
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| L03: Jan 19, 2022 | Corner Detection Slide, Recorded Lecture | |
| L04: Jan 21, 2022 | Harris Detector and Invariance Property Slide, Recorded Lecture | |
| L05: Jan 29, 2022 | Blob and Region Detection Slide, Recorded Lecture | |
| L06: Feb 02, 2022 | Region Descriptors Slide, Recorded Lecture | |
| L07: Feb 09, 2022 | Local Descriptors Slide, Recorded Lecture | |
| L08: Feb 09, 2022 | Image Categorization Slide, Recorded Lecture | |
| L09: Feb 16, 2022 | Image Classifiers Slide, Recorded Lecture | |
| L10: Feb 18, 2022 | Neural Networks Slide, Recorded Lecture | |
| L11: March 02, 2022 | Convolutional Neural Networks Slide, Recorded Lecture | |
| L12: March 04, 2022 | CNN Training 1 Slide, Recorded Lecture | |
| L13: March 09, 2022 | CNN Training 2 Slide, Recorded Lecture | |
| L14: March 16, 2022 | CNN Architectures 1 Slide, Recorded Lecture | |
| L15: March 23, 2022 | CNN Architectures 2 Slide, Recorded Lecture 1, Recorded Lecture 2 | |
| L16: March 30, 2022 | Object Detection Slide, Recorded Lecture | |
| L17: April 06, 2022 | Adversarial Attack Slide, Recorded Lecture | |
| L18: April 13, 2022 | Generative Models Slide, Recorded Lecture |
Course Projects
| Project Code | Team Members | Project Title |
| IIITA-VR22-P01 | IIT2019004 Naina Kumari, IIT2019006 Asha Jyothi Donga, IIT2019017 Shruti Nanda, IIT2019023 Utkarsh Gangwar | Face Recognition using Face Super-resolution |
| IIITA-VR22-P02 | IIT2019025 Ritesh Raj, IIT2019027 Vidushi Pathak, IIT2019036 Jyotika Bhatti, IIT2019045 Amit Singh | Face Sketch Recognition using Sketch-to-Face Synthesis |
| IIITA-VR22-P03 | IIT2019077 Gade Srinivas Priyatham Reddy, IIT2019098 Abhinav, IIT2019112 Payili Vangmayi, IIT2019118 Shikhar Gupta | Person Synthesis under Different Clothing Style |
| IIITA-VR22-P04 | IIT2019119 Prakash Toppo, IIT2019121 Gurmeet Singh, IIT2019125 Aakash Bishnoi, IIT2019129 Sanyam Agarwal | Person Recognition from Aerial View |
| IIITA-VR22-P05 | IIT2019131 Priyanshu Jain, IIT2019133 Azmeera Mounika, IIT2019137 Harsh Abhijit Thete, IIT2019140 Sagar Barman | Hyperspectral Image Classification |
| IIITA-VR22-P06 | IIT2019141 Khushi Gupta, IIT2019145 Paras Agrawal, IIT2019155 Ritik Parmar, IIT2019158 Aryan Dhakad | Unsupervised Image Rerieval |
| IIITA-VR22-P07 | IIT2019160 Tejas Dutta, IIT2019161 Aadharsh Roshan Nandhakumar, IIT2019162 Vishal Burman, IIT2019164 Saksham Sood | Self-supervised Image Retrieval |
| IIITA-VR22-P08 | IIT2019166 Arun Kumar, IIT2019167 Ansh Verma, IIT2019173 Sankalp Rajendran, IIT2019177 Rohit Kumar Gupta | Network Pruning for Faster Face Recognition |
| IIITA-VR22-P09 | IIT2019179 Sharma Sahil, IIT2019180 Rajveer, IIT2019183 Devender Kumar, IIT2019184 Pratyush Pareek | Person Image Synthesis in Random Poses |
| IIITA-VR22-P10 | IIT2019185 R Shwethaa, IIT2019186 Shah Udgam Birenbhai, IIT2019189 Nidhi Kamewar, IIT2019196 Priyanshu | Face Recognition under Mask |
| IIITA-VR22-P11 | IIT2019202 Jyoti Verma, IIT2019204 Mitta Lekhana Reddy, IIT2019208 Dhanush Vasa, IIT2019219 Gitika Yadav | Facial Micro-expression Recognition |
| IIITA-VR22-P12 | IIT2019221 Divyansh Rai, IIT2019226 Mukul Mohmare, IIT2019229 Navneet Yogesh Bhole, IIT2019230 Eshan Vaid | Histopathological Colon Cancer Recognition |
| IIITA-VR22-P13 | IIT2019236 Noonsavath Sravana Samyukta, IIT2019240 Ayush Khandelwal, IEC2019019 Vishwaas Pratap Singh, IEC2019036 Harsh Ranjan | Identity Recognition using Palmprint |
| IIITA-VR22-P14 | IEC2019053 Chandan Ahire, IEC2019061 Prabhnoor Singh, IEC2019070 Priyansha Gupta, IEC2019071 Anurag Sharma | Identity Recognition using Knuckleprint |
| IIITA-VR22-P15 | IEC2019074 Ravi Agrawal, IEC2019075 Deepak Gupta, IEC2019079 Sachin Kanyal, IEC2019086 Udhav Rana, IIT2017062 Kaustubh Chetan Parmar | Transformer based COVID19 Recognition from X-Ray |
| IIITA-VR22-P16 | MIT2021046 Koppula Krishna Sai, MIT2021059 Anwesh Panda, MIT2021079 Saurav Sagar, MIT2021082 Dhote Anurag Radhesham | Pose Invariant Face Recognition |
| IIITA-VR22-P17 | RSI2022003 Suvramalya Basak | Action Recognition |
| IIITA-VR22-P18 | RSI2021003 Neeraj Baghel | Image Super-resolution |
Grading
- C1 (30%): 10% Written + 20% Practice
- C2 (30%): 10% Written + 20% Practice
- C3 (40%): 20% Written + 20% Practice
Prerequisites
- Computer Programming
- Data Structures and Algorithms
- Machine Learning
- Image and Video Processing
- Ability to deal with abstract mathematical concepts
Books
- Computer Vision: Algorithms and Applications, Richard Szeliski, Springer
- Deep Learning, Ian Goodfellow, Aaron Courville, and Yoshua Bengio, MIT Press
Related Classes / Online Resources
Disclaimer
The content (text, image, and graphics) used in this slide are adopted from many sources for Academic purposes. Broadly, the sources have been given due credit appropriately. However, there is a chance of missing out some original primary sources. The authors of this material do not claim any copyright of such material.