Major Events

8th Summer School on Artificial Intelligence With focus on Computer Vision & Machine Learning

IIIT-H is happy to announce the commencement of the 8th episode of its Summer School Series, an exciting opportunity for those interested in diving into the fundamentals of Artificial Intelligence. The 6th summer school will be a research training event with a global scope aiming at equipping participants with the relevant advances in the critical and fast developing area of artificial intelligence.

Artificial Intelligence is poised to become one of the most revolutionary technologies of our time. Every day, we interact with intelligent systems and services in various forms, including apps on our phones, websites, and other devices. Technology never ceases to mimic the human brain and thus AI has gained a lot of interest for decades. AI is the umbrella of several breakthrough fields, machine learning is a subset of AI, wherein computer vision is also the subset of machine learning. The Summer School covers a wide range of topics in Artificial Intelligence from a variety of perspectives. Renowned academics and industry pioneers will lecture and share their views with the audience.

7th Summer School on Artificial Intelligence With focus on Computer Vision & Machine Learning

IIIT-H is happy to announce the commencement of the 7th episode of its Summer School Series, an exciting opportunity for those interested in diving into the fundamentals of Artificial Intelligence. The 6th summer school will be a research training event with a global scope aiming at equipping participants with the relevant advances in the critical and fast developing area of artificial intelligence.

3D Vision Summer School

The domain of 3D Vision deals with acquisition & analysis of 3-dimensional objects/scenes with wider applications in Animation, AR/VR platforms, Autonomous driving, Medical Imaging, etc. The primary representation for 3D objects are Point Cloud, Mesh and Voxels. These 3D objects/scenes are primarily captured either by depth sensors (e.g., RGBD sensors like Kinect and Laser sensors) or by processing images/videos taken from single/multiple RGB cameras. Algorithms for recovering 3D objects from multiple view images involves elaborate theoretical foundation from Multi-View Geometry developed in the last decade. Recently, deep learning methods have also reinvigorated interest in this domain with more efficient/scalable solutions for 3D reconstruction and analysis.

6th Summer School on Artificial Intelligence With focus on Computer Vision & Machine Learning

IIIT-H is happy to announce the commencement of the 6th episode of its Summer School Series, an exciting opportunity for those interested in diving into the fundamentals of Artificial Intelligence. The 6th summer school will be a research training event with a global scope aiming at equipping participants with the relevant advances in the critical and fast developing area of artificial intelligence.

The 18th International Conference on Frontiers of Handwriting Recognition (ICFHR 2022)

The 18 th International Conference on Frontiers of Handwriting Recognition (ICFHR), formerly called International Workshop on Frontiers of Handwriting Recognition (IWFHR), is the most important scientific venue in the field of handwriting recognition. The aim of this conference is to bring together international experts from academia and industry to share their experiences and to promote research and development in all aspects of handwriting recognition and applications.

Scene Understanding Challenge for Autonomous Navigation in Unstructured Environments, at CVPR 2021

As Part of Second International Workshop On Autonomous Navigation in Unconstrained Environments, the workshop adopts a broad view of what is entailed by driving in unconstrained environments. Besides those aspects, this workshop also poses the challenge of autonomous driving in less constrained traffic, along with infrastructure that is not always dependable.

Third International Workshop On Autonomous Navigation in Unconstrained Environments, at CVPR 2021

Autonomous driving has recently emerged as a keystone problem for computer vision and machine learning, with significant interest in both academia and industry. Besides being a rich source of research problems for visual perception, learning, mapping and planning, it is also poised to have an immense societal and economic impact. Several large efforts from the automotive industry have projected the imminent deployment of Level 3 autonomous systems, with a few efforts also geared towards Level 5 autonomy in the near future. But reliable solutions have been trained and validated only in controlled environments, while a vast majority of road conditions deviate from the ideal. This workshop calls for intensive engagement from the research community to address this problem and proposes a benchmark dataset with rich annotations in relatively unconstrained conditions to facilitate the effort. A higher level goal is to percolate autonomous driving to domains where road infrastructure is sub-optimal for computer vision and machine learning but which stand to gain immeasurably from its benefits.

5th Summer School on Artificial Intelligence With focus on Computer Vision & Machine Learning

IIIT-H is happy to announce the commencement of the 5th episode of its Summer School Series, an exciting opportunity for those interested in diving into the fundamentals of Artificial Intelligence. In response to the Covid-19 threat, we had postponed our 5th Summer School scheduled to be held in 2020. However, taking into consideration the continuous requests from previous participants, we have decided to conduct the Summer School Series through an online platform for 2021.
Artificial Intelligence is poised to become one of the most revolutionary technologies of our time. Every day, we interact with intelligent systems and services in various forms, including apps on our phones, websites, and other devices. Technology never ceases to mimic the human brain and thus AI has gained a lot of interest for decades. AI is the umbrella of several breakthrough fields, machine learning is a subset of AI, wherein computer vision is also the subset of machine learning .The 5th Summer School on Artificial Intelligence will be primarily focused on the concepts of Computer Vision and Machine Learning
Computer Vision seeks to automate tasks that human vision can achieve. This involves methods of acquiring, processing, analyzing, and understanding digital images, and extraction of data from the real world to produce information. It also has sub-domains such as object recognition, video tracking, and motion estimation, thus having applications in medicine, navigation, and object modeling.
Machine Learning, a subset of artificial intelligence, is the study of algorithms and statistical models. Systems use it to perform a task without explicit instructions and instead rely on patterns and inference. Thus, it applies to computer vision, software engineering, and pattern recognition.

India Driving Dataset Challenge, at NCVPRIPG 2019

Driving conditions in India are highly unstructured and diverse, with interesting behaviors of traffic participants, compared to the rest of the world. These driving conditions pose unique challenges that are yet unsolved, for research in artificial intelligence (AI) and machine learning (ML) systems, and hence offer immense opportunities for possible technical innovations in AI/ML systems for better road safety.

Scene Understanding Challenge for Autonomous Navigation in Unstructured Environments, at ICCV 2019

Second International Workshop On Autonomous Navigation in Unconstrained Environments, at ICCV 2019

Autonomous driving has recently emerged as a keystone problem for computer vision and machine learning, with significant interest in both academia and industry. Besides being a rich source of research problems for visual perception, learning, mapping and planning, it is also poised to have immense societal and economic impact. Several large efforts from the automotive industry have projected imminent deployment of Level 3 autonomous systems [1], with a few efforts also geared towards Level 5 autonomy in the near future [2]. But reliable solutions have been trained and validated only in controlled environments, while a vast majority of road conditions deviate from the ideal. This workshop calls for intensive engagement from the research community to address this problem and proposes a benchmark dataset with rich annotations in relatively unconstrained conditions to facilitate the effort. A higher level goal is to percolate autonomous driving to domains where road infrastructure is sub-optimal for computer vision and machine learning, but which stand to gain immeasurably from its benefits.

4th Summer School on Machine Learning 2019

Machine Learning finds application in areas as diverse as neuroscience, biomedical informatics, drug discovery, speech recognition, language processing, computer vision, recommender systems, learning theory, robotics and games. In continutian to our previous series of summer schools this year's theme is "Machine Learning".

4th Summer School on Computer Vision 2019

Computer Vision is a rapidly evolving field with its applications being steadily integrated into our day to day lives. The field has received a wide interest from various stakeholders ranging from theoretical researchers, application designers and developers and even business entities. In continutian to our previous summer school this year's theme is "Computer Vision".

Hyderabad Symposium on AI:Computer Vision, Graphics and Image Processing 2019

Hyderabad AI Symposium is a platform for the exchange of idea in the area of Artificial Intelligence.
The event on July 7th, 2019 has a set of eminent speakers worldwide. From the area of Computer Vision Graphic and Image Processing.

Hyderabad Symposium on AI:Computer Vision, Graphics and Image Processing 2018

Hyderabad AI Symposium is a platform for the exchange of idea in the area of Artificial Intelligence.
The event on December 22nd, 2018 has a set of eminent speakers worldwide. From the area of Computer Vision Graphic and Image Processing.

11th Indian Conference on Computer Vision, Graphics and Image Processing 2018

The Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP) is India’s premier conference in Computer Vision, Graphics, Image Processing and related fields. Started in 1998, it is a biennial international conference providing a forum for presentation of technological advances and research findings in these areas. ICVGIP 2018, the 11th conference in this series, is being organized by IIIT Hyderabad in association with the Indian Unit for Pattern Recognition and Artificial Intelligence (IUPRAI), an affiliate of the International Association for Pattern Recognition (IAPR) during December 2018.

Scene Understanding Challenge for Autonomous Navigation in Unstructured Environments, at ECCV 2018

As Part of First International Workshop On Autonomous Navigation in Unconstrained Environments, the workshop adopts a broad view of what is entailed by driving in unconstrained environments. Besides those aspects, this workshop also poses the challenge of autonomous driving in less constrained traffic, along with infrastructure that is not always dependable.

First International Workshop On Autonomous Navigation in Unconstrained Environments, at ECCV 2018

Autonomous driving has recently emerged as a keystone problem for computer vision and machine learning, with significant interest in both academia and industry. Besides being a rich source of research problems for visual perception, learning, mapping and planning, it is also poised to have immense societal and economic impact. Several large efforts from the automotive industry have projected imminent deployment of Level 3 autonomous systems [1], with a few efforts also geared towards Level 5 autonomy in the near future [2]. But reliable solutions have been trained and validated only in controlled environments, while a vast majority of road conditions deviate from the ideal. This workshop calls for intensive engagement from the research community to address this problem and proposes a benchmark dataset with rich annotations in relatively unconstrained conditions to facilitate the effort. A higher level goal is to percolate autonomous driving to domains where road infrastructure is sub-optimal for computer vision and machine learning, but which stand to gain immeasurably from its benefits.

3rd SUMMER SCHOOL ON MACHINE LEARNING: ADVANCES IN MODERN AI 2018

3rd Summer School On COMPUTER VISION: BASICS OF MODERN AI 2018

SUMMER SCHOOL ON MACHINE LEARNING: DEEP LEARNING 2017

The ML summer school is slightly theoretical in nature but sufficient practical exercises would be covered to enable a better understanding of the theoretical track. Primary theme for this year’s summer school is chosen to be Deep Learning, seeing the recent trends of its rise. Experts in the field will deliver talks to share their views and works with the attendees and you are more than welcome to interact and discuss ideas with the speakers. It will also be a good platform to bounce ideas amongst other like-minded enthusiasts. The primary focus of the summer school this time would be on the more recent advancements in the field of Deep Learning.

SUMMER SCHOOL ON COMPUTER VISION: RECENT ADVANCES IN COMPUTER VISION 2017

The summer school curriculum roughly consists of a series of lectures and demo/labs sessions designed to work in tandem to help you make the most out of the program. Experts in the field will deliver talks to share their views and works with the attendees and you are more than welcome to interact and discuss ideas with the speakers. It will also be a good platform to bounce ideas amongst other like-minded enthusiasts. This time we aim to have a special focus on the recent advances in the area.

Short Course on Deep Learning 2016

Deep learning has resulted in the best solutions for many challenging computer vision problems in recent years. The course on Deep Learning at IIIT Hyderabad aims to keep the pace with the rapid growth in this field, and expose the advances to working professionals and researchers. The course will focus on foundations, recent advances with special emphasis to running on limited memory platforms and the practical aspects of using deep learning for a variety of computer vision problems.

Summer School on Deep Learning for Computer Vision 2016

Tutorial on MAP Estimation and Structured Prediction in Document Image Analysis, ICPR 2014

Many problems in document image analysis are being formulated as maximum a posteriori (MAP) estimation in a Markov/conditional random field (MRF/CRF) setting. Examples include restoration, binarization, segmentation,and recognition. This class of formulations has resulted in the development of elegant algorithms for many challenging problems in this area. Often the output of these inferencing algorithms is a structured description (e.g., a string of characters, an array of labelled pixels, a tree/graph description of a document image). In this tutorial, we would like to connect the two well established areas (i.e., document image analysis, structured prediction by MAP estimation), and demonstrate how systematic development of robust algorithms can be enabled.

Tutorial on Building Scalable Solutions for Document Retrieval and Recognition, DAS 2014

Scalability of a given solution is an important consideration towards enabling retrieval and recognition over large collections of document images. However, the definitions of scalability are fast changing with the emergence of huge datasets and digital libraries, as well as the advent of new computing paradigms. In this tutorial, we shall cover three approaches towards building scalable document image retrieval and recognition systems:

Recognition-free retrieval using bag-of-visual-words
Recognition of word-images using indexing schemes
Large-scale testing/deployment using cloud computing

This tutorial shall include a parallel hands-on practical session, where the attendees would have the opportunity to practice the methods described in the tutorial. A dataset, along with the necessary code libraries, will be provided to the audience. Multiple solution stacks shall be deployed and evaluated by the various groups/individuals, with a scalable retrieval system being built by the end of the tutorial.

Workshop on Computer Vision 2008, IIIT Hyderabad

Workshop on Computer Vision 2008, WCV '08 was organized under the Indo-Israeli MoU on research collaboration managed by DST. The workshop was held at IIIT Hyderabad from February 04, 2008 to February 05, 2008. Top researchers from India and Israel participated in the two day event. The respose to the workshop was overwhelming.

Asian Conference on Computer Vision 2006, IIIT Hyderabad

7^th Asian Conference on Computer Vision 2006, ACCV '06 was held from January 13, 2006 to January 16, 2006 at Hyderabad. The conference was organized by IIIT Hyderabad. The past conferences in this biennial series were held in Korea (ACCV'04), Australia (ACCV'02), Taiwan (ACCV'00), Hong Kong (ACCV'98), Singapore (ACCV'95), and Japan (ACCV'93).

Computer Vision on GPUs (CVGPU)

Course at CVPR 2009, The GPUs have emerged as a useful computing co-processor that is readily available and economical. The latest commodity GPUs are rated for a peak performance of around 1 TFLOPs at the cost of $400 or so. The recent demand for high performance techniques has led to the adaptation of GPUs for various computer vision algorithms. Many computer vision algorithms are well-suited for processing on the GPU due to the match of the data-parallel computations to many operations on images. Recent advances such as CUDA and the OpenCL standard have the potential to accelerate the use of GPUs in many areas for more general purpose computing, including Computer vision. This course aims to familiarize computer vision researchers with the emerging and exciting area of fast computer vision algorithms on the GPU. It will give an introduction to the programming of the current state-of-the-art hardware to enable participants to employ the unique capabilities of GPUs.