Domain Adaptation for Fair and Robust Computer Vision
Date : 09/01/2024
While recent progress significantly advances the state of the art in computer vision across several tasks, the poor ability of these models to generalize to domains and categories under-represented in the training set remains a problem, posing a direct challenge to fair and inclusive computer vision. In my talk, I will talk about my recent efforts towards improving generalizability and robustness in computer vision using domain adaptation. First, I will talk about our work on scaling domain adaptation to large scale datasets using metric learning. Next, I will introduce our new dataset effort called GeoNet aimed at benchmarking and developing novel algorithms towards geographical robustness in various vision tasks. Finally, I will talk about some research directions for the future in terms of leveraging rich multimodal (vision, language) data to improve adaptation of visual models to new domains.
Tarun Kalluri is a fourth year PhD student at UC San Diego in the Visual Computing Group. Prior to that, he graduated with a bachelors from Indian Institute of Technology, Guwahati and worked as a data scientist in Oracle. His research interests lie in label and data efficient learning from images and videos, domain adaptation and improving fairness in AI. He is a recipient of IPE PhD fellowship for 2020-21.
What Do Generative Image Models Know? Understanding their Latent Grasp of Reality and Limitations
Date : 06/01/2024
Recent generative image models like StyleGAN, Stable Diffusion, and VQGANs have achieved remarkable results, producing images that closely mimic real-world scenes. However, their true understanding of visual information remains unclear. My research delves into this question, exploring whether these models operate on purely abstract representations or possess an understanding akin to traditional rendering principles. My findings reveal that these models:
Encode a wide range of scene attributes like lighting, albedo, depth, and normals, suggesting a nuanced understanding of image composition and structure. Despite this, they exhibit significant shortcomings in depicting projective geometry and shadow consistency, often misrepresenting relationships and light interactions, leading to subtle visual anomalies.
This talk aims to illuminate the complex narrative of what generative image models truly understand about the visual world, highlighting their capabilities and limitations. I’ll conclude with insights into the broader implications of these findings and discuss potential directions for enhancing the realism and utility of generative imagery in applications demanding high fidelity and physical plausibility.
Anand Bhattad is a Research Assistant Professor at the Toyota Technological Institute in Chicago (TTIC). Before this role, he earned his PhD from the University of Illinois Urbana-Champaign (UIUC), working with his advisor David Forsyth. His primary research interests lie in computer vision, with a specific focus on knowledge in generative models, and their applications to computer vision, computer graphics, and computational photography. His recent work, DIVeR, received a best paper nomination at CVPR 2022. He was listed as an Outstanding Reviewer at ICCV 2023 and an Outstanding Emergency Reviewer at CVPR 2021.
Object-centric 3D Scene Understanding from Videos
Date : 06/01/2024
The growing demand for immersive, interactive experiences has underscored the importance of 3D data in understanding our surroundings. Traditional methods for capturing 3D data are often complex and equipment-intensive. In contrast, Yash Bhalgat's research aims to utilize unconstrained videos, such as those from augmented reality glasses, to effortlessly capture scenes and objects in their full 3D complexity.
Yash Bhalgat's talk at IIIT- H on January 6, 2024, consisted of describing a method to incorporate Epipolar Geometry priors in multi-view Transformer models to enable identifying objects across extreme pose variations. Next, he talked about his recent work on 3D object segmentation using 2D pre-trained foundation models. Finally, he touched upon his ongoing work on object-centric dynamic scene representations.
Yash Bhalgat is a 3rd year PhD student at the University of Oxford's Visual Geometry Group supervised by Andrew Zisserman, Andrea Vedaldi, Joao Henriques and Iro Laina. His research is broadly in 3D computer vision and machine learning, with a specific focus on geometry-aware deep networks (transformers), 3D reconstruction, and neural rendering. Yash also works on the intersection of 3D and LLMs. He was a Senior Researcher at Qualcomm AI Research working on efficient deep learning previously. Yash received Masters in Computer Science from the University of Michigan - Ann Arbor, and Bachelors in Electrical Engineering (with CS minor) from IIT Bombay.
3D Representation and Analytics for Computer Vision
Prof. Chandra Kambhamettu
Date : 09/01/2024
Data in computer vision is commonly either in the homogeneous format in 2D projective space (e.g., images and videos) or a heterogeneous format in 3D Euclidean space (e.g., point clouds). One advantage of 3D data is its invariancy towards illumination-based appearance. 3D point clouds are now a vital data source for vision tasks such as autonomous driving, robotic perception, and scene understanding. Thus, deep learning for 3D points has become an essential branch of geometry-based research. However, deep learning over unstructured point clouds is quite challenging. Moreover, due to the 3D data explosion, new representations are necessary for compression and encryption. Therefore, we introduce a new sphere-based representation (3DSaint) to model a 3D scene and further utilize it in deep networks. Our representation produces state-of-the-art results on several 3D understanding tasks, including 3D shape classification. We also present differential geometry-based networks for the 3D analysis tasks.
Dr. Chandra Kambhamettu is a Full Professor of the Computer Science department at the University of Delaware, where he directs the Video/Image Modelling and Synthesis (VIMS) group. His research interests span Artificial Intelligence and Data Science, including computer vision, machine learning, and big data visual analytics. His Lab focuses on novel schemes for multimodal image analysis methodologies. Some of his recent research includes image analysis for the visually impaired, 3D point cloud analysis, drone and vehicular-based camera imagery acquisition, analysis and reconstruction, and plant science image analysis. His work on nonrigid structure from motion was published in CVPR in 2000 and cited as one of the first two papers in the field of Nonrigid Structure from Motion. Several of Dr. Kambhamettu’s works also focus on problems that highly impact earth life, such as arctic sea ice observations with application towards mammal habitat quantification and climate change and hurricane image studies, among several others. Before joining UD, he was a research scientist at NASA-Goddard, where he received the “1995 Excellence in Research Award.” In addition, he received the NSF CAREER award in 2000 and NASA’s “Group Achievement Award” in 2013 for his work in the deployment of the Arctic Collaborative Environment.
Becoming friends with pixels through intermediate representations
Dr. Kuldeep Kulkarni
Date : 30/08/2023
Manipulation of natural images for tasks like object insertion, out-painting or creating animations is extremely difficult if we operate purely in the pixel domain. Dr Kuldeep Kulkarni talked about the advantages of manipulating visual data by directly expressing them in intermediate representations and manipulating them instead of the pixels. Specifically, the focus was on his recent works with image out-painting and animating still images as target applications. He first talked about a semantically-aware novel paradigm to perform image extrapolation that enables the addition of new object instances. Expressing the images in semantic label space allows us to complete the existing objects more effectively and add completely new objects that otherwise are very difficult when working in the pixel domain. Dr. Kulkarni also discussed methods he developed to exploit intermediate representations like optical flow and surface normal maps to generate cinema graphs depicting the animation of fluid elements and human clothing.
Dr Kuldeep Kulkarni is a research scientist at Adobe Research, Bengaluru, working in the BigData Experience Labs. His current research interests broadly span computer vision, with a bent toward synthesizing beautiful and creative images and clips. Before this, Kuldeep did a post-doc stint at Carnegie Mellon University with Prof. Aswin Sankaranarayanan. Kuldeep received his Ph.D. in Electrical Engineering from Arizona State University under the supervision of Prof. Pavan Turaga. His PhD thesis focussed on tackling computer vision problems from compressive cameras at extremely low measurement rates, combining computer vision and compressed sensing.
Automatically Generating Audio Descriptions for Movies
Professor Andrew Zisserman
Date : 21/08/2023
Audio Description is the task of generating descriptions of visual content, at suitable time intervals, for the benefit of visually impaired audiences. For movies, this presents notable challenges - the Audio Description must occur only during existing pauses in dialogue, should refer to characters by name, and ought to aid understanding of the storyline as a whole. This requires a visual-language model that can address all three of the `what', `who', and `when' questions: What is happening in the scene? Who are the characters in the scene? And when should a description be given?
Professor Andrew Zisserman visited IIIT-H and gave a talk on the 21st of August, 2023. He discussed how to build on large pre-trained models to construct a visual-language model that can generate Audio Descriptions addressing the following questions: (i) how to incorporate visual information into a pre-trained language model; (ii) how to train the model using only partial information; (iii) how to use a `character bank' to provide information on who is in a scene; and (iv) how to improve the temporal alignment of an ASR model to obtain clean data for training.
Andrew Zisserman is a Professor at the University of Oxford and is one of the principal architects of modern computer vision. He is best known for his leading role in establishing the computational theory of multiple-view reconstruction and the development of practical algorithms that are widely in use today. This culminated in the publication of his book with Richard Hartley, already regarded as a standard text. He is a fellow of the Royal Society and has won the prestigious Marr Prize three times.
Towards Trustworthy and Fair Medical Image Analysis Models
Date : 15/05/2023
Although Deep Learning (DL) models have been shown to perform very well on various medical imaging tasks, inference in the presence of pathology presents several challenges to common models. These challenges impede the integration of DL models into real clinical workflows. Deployment of these models into real clinical contexts requires: (1) that the confidence in DL model predictions be accurately expressed in the form of uncertainties and (2) that they exhibit robustness and fairness across different sub-populations. In this talk, we will look at our recent work, where we developed an uncertainty quantification score for the task of Brain Tumour Segmentation. We evaluated the score's usefulness during the two consecutive Brain Tumour Segmentation (BraTS) challenges, BraTS 2019 and BraTS 2020. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, highlighting the need for uncertainty quantification in medical image analyses. Additionally, we combine the aspect of uncertainty estimates with fairness across demographic subgroups into the picture. By performing extensive experiments on multiple tasks, we show that popular ML methods for achieving fairness across different subgroups, such as data-balancing and distributionally robust optimization, succeed in terms of the model performances for some of the tasks. However, this can come at the cost of poor uncertainty estimates associated with the model predictions. At last, we talk about our ongoing work on fairness mitigation framework in terms of calibration. Although several methods have been shown to successfully mitigate biases across subgroups in terms of accuracy, they do not consider calibration across different subgroups of these models. To this end, we propose a novel two-stage method Cluster-Focal. Extensive experiments on two different medical image classification datasets show that our method effectively controls calibration error in the worst-performing subgroups while preserving prediction performance, outperforming recent baselines.
Raghav Mehta is a Ph.D. candidate in the Department of Electrical and Computer Engineering at McGill University. He works with Prof. Tal Arbel in the Probabilistic Vision Group, Centre for Intelligent Machines. His primary research is in the field of neuroimage analysis and machine learning. Specifically, he works on quantifying and leveraging uncertainty in deep neural networks for the medical image analysis pipeline. Previously, Raghav completed his master's from IIIT Hyderabad. He was one of the main students in the project, The Construction of Brain Atlas for Young
Robustness and Safety. Raghav has received several awards, including the MEITA scholarship, reviewer award winner for MIDL, and best paper awards at DART and UNSURE workshops in MICCAI.
Computer vision - Self-Supervised Representation Learning
Date : 16/03/2023
Yash Patel during his presentation, discussed a range of topics related to his research interests, which primarily include Self-Supervised Representation Learning, Image Compression, Scene Text Detection and Recognition, Tracking and Segmentation in Videos, and 3D Reconstruction.
Specifically, he discussed the challenges associated with Training Neural Networks on Non-Differentiable Losses and presented various approaches for overcoming these challenges. In particular, he presented his proposed technique for training a neural network by minimizing a surrogate loss that approximates a target evaluation metric that may be non-differentiable. To achieve this, the surrogate is learned via a deep embedding method where the Euclidean distance between the prediction and the ground truth corresponds to the value of the evaluation metric. Additionally, he described his work on proposing a differentiable surrogate loss for the recall metric. To enable training with a very large batch size, which is crucial for metrics computed on the entire retrieval database, the speaker utilized an implementation that sidesteps the hardware constraints of the GPU memory. Furthermore, an efficient mixup approach that operates on pairwise scalar similarities was employed to virtually increase the batch size further.
Yash Patel, a Ph.D. candidate at the Center for Machine Perception, Czech Technical University, advised by Prof. Jiri Matas and an esteemed alumnus of IIIT Hyderabad, visited our institute on 16th March 2023.
He holds a Bachelor in Technology with Honors by Research in Computer Science and Engineering from International Institute of Information Technology, Hyderabad (IIIT-H). During my undergrad, I was working with Prof. C.V. Jawahar at the Center for Visual Information Technology (CVIT). He also holds a Master's degree in Computer Vision from the Robotics Institute of Carnegie Mellon University, where he worked with Prof. Abhinav Gupta.
Yash's research interests are primarily focused on computer vision with expertise in areas such as self-supervised representation learning, image compression, scene text detection and recognition, tracking and segmentation in videos, and 3D reconstruction.
Building Maximal Vision Systems with Minimal Resources
Dr. Ayush Bansal
Date : 19/12/2022
Current vision and robotic systems are like mainframe machines of the 60s -- they require extensive resources: (1) dense data capture and massive human annotations, (2) large parametric models, and (3) intensive computational infrastructure. I build systems that can learn directly from sparse and unconstrained real-world samples with minimal resources, i.e., limited or no supervision, use simple and efficient models, and operate on every day computational devices. Building systems with minimal resources allows us to democratize them for non-experts. My work has impacted important areas such as virtual reality, content creation and audio-visual editing, and providing a natural voice to speech-impaired individuals.
In my talk, I will present my efforts to build vision systems for novel view synthesis. I will discuss Neural Pixel Composition, a novel approach for continuous 3D-4D view synthesis that reliably operates on sparse and wide-baseline multi-view images/videos and can be trained efficiently within a few minutes for high-resolution (12MP) content using 1 GB GPU memory. I will present my efforts to build vision systems for unsupervised audio-visual synthesis. I will primarily discuss Exemplar Autoencoders that enable zero-shot audio-visual retargeting. Exemplar Autoencoders are built on remarkably simple insights: (1) autoencoders project out-of-sample data onto the distribution of the training set; and (2) exemplar learning enables us to capture the voice, stylistic prosody (emotions and ambiance), and visual appearance of the target. These properties enable an autoencoder trained on an individual's voice to generalize for unknown voices in different languages. Exemplar Autoencoders can synthesize natural voices for speech-impaired individuals and do a zero-shot multilingual translation.
Aayush Bansal is currently a short-term research scientist at the Reality Labs Research of Meta Platforms, Inc. He received his Ph.D. in Robotics from Carnegie Mellon University under the supervision of Prof. Deva Ramanan and Prof. Yaser Sheikh. He was a Presidential Fellow at CMU, and a recipient of the Uber Presidential Fellowship (2016-17), Qualcomm Fellowship (2017-18), and Snap Fellowship (2019-20). His research has been covered by various national and international media such as NBC, CBS, WQED, 90.5 WESA FM, France TV, and Journalist. He has also worked with production houses such as BBC Studios, Full Frontal with Samantha Bee (TBS), etc. More details are available on his webpage: https://www.aayushbansal.xyz/
Towards Autonomous Driving in Dense, Heterogeneous, and Unstructured Environments
Dr. Rohan Chandra
Date : 07/11/2022
In this talk, I discuss many key problems in autonomous driving towards handling dense, heterogeneous, and unstructured traffic environments. Autonomous vehicles (AV) at present are restricted to operating on smooth and well-marked roads, in sparse traffic, and among well-behaved drivers. I present new techniques to perceive, predict, and navigate among human drivers in traffic that is significantly denser in terms of a number of traffic-agents, more heterogeneous in terms of size and dynamic constraints of traffic agents, and where many drivers may not follow the traffic rules and have varying behaviors. My talk is structured along three themes—perception, driver behavior modeling, and planning. More specifically, I will talk about Improved tracking and trajectory prediction algorithms for dense and heterogeneous traffic using a combination of computer vision and deep learning techniques. A novel behavior modeling approach using graph theory for characterizing human drivers as aggressive or conservative from their trajectories. Behavior-driven planning and navigation algorithms in mixed and unstructured traffic environments using game theory and risk-aware planning. Finally, I will conclude by discussing the future implications and broader applications of these ideas in the context of social robotics where robots are deployed in warehouses, restaurants, hospitals, and inside homes to assist human beings.
Rohan Chandra is currently a postdoctoral researcher at the University of Texas, Austin, hosted by Dr. Joydeep Biswas. Rohan obtained his B.Tech from the Delhi Technological University, New Delhi in 2016 and completed his MS and PhD in 2018 and 2022 from the University of Maryland advised by Dr. Dinesh Manocha. His doctoral thesis focused on autonomous driving in dense, heterogeneous, and unstructured traffic environments. He is a UMD’20 Future Faculty Fellow, RSS’22 Pioneer, and a recipient of a UMD’20 summer research fellowship. He has published his work in top computer vision and robotics conferences (CVPR, ICRA, IROS) and has interned at NVIDIA in the autonomous driving team. He has served on the program committee of leading conferences in robotics, computer vision, artificial intelligence, and machine learning.