R. Venkatesh Babu

Indian Institute of Science Bangalore

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Jogendra Nath Kundu

META Health

Konda Reddy Mopuri

Indian Institute of Technology Hyderabad

Varun Jampani

Google (United States)

Sravanti Addepalli

Indian Institute of Science Bangalore

Anirban Chakraborty

Indian Institute of Science Bangalore

Ravi Kiran Sarvadevabhatla

Indian Institute of Technology Hyderabad

Suraj Srinivas

Idiap Research Institute

Harsh Rangwani

Indian Institute of Science Bangalore

Tejan Karmali

Google (United States)

Srinivas S S Kruthiventi

Samsung (India)

Cooperative Institutions

Indian Institute of Science Bangalore

143

Carnegie Mellon University

Indian Institute of Technology Bombay

Indian Institute of Technology Kharagpur

Dr. Hari Singh Gour University

Nanyang Technological University

International Institute of Information Technology Bangalore

Indian Institute of Technology Hyderabad

Indian Institute of Technology Guwahati

Indian Institute of Technology Roorkee

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Evaluating Multiexposure Fusion Using Image Information

IEEE Signal Processing Letters (2017)

Hisham Rahman Rajiv Soundararajan R. Venkatesh Babu

Multiexposure fusion (MEF) refers to image fusion methods that capture high dynamic range natural scenes from a set of low dynamic range camera images. In this letter, we study the problem of designing quality assessment (QA) algorithms to estimate the perceptual quality of images generated by different MEF algorithms. We develop our quality index by evaluating individual quality maps between a given fused test image and individual over-/underexposed images at multiple scales and orientations and then combining these maps across the over-/underexposed images. Our approach works on the premise that the true undistorted reference is contained across the over-/underexposed source images. We identify this true reference based on the notion of perceived image information using natural scene statistical models. It is shown that our approach outperforms the state of the art QA algorithms in terms of correlation with human perception of quality on a publicly available MEF database.

Information fusion

10.1109/lsp.2017.2752233

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Citations (12)

Towards Efficient and Effective Self-Supervised Learning of Visual Representations

arXiv (Cornell University) (2022)

Sravanti Addepalli Kaushal Bhogale Priyam Dey R. Venkatesh Babu

Self-supervision has emerged as a propitious method for visual representation learning after the recent paradigm shift from handcrafted pretext tasks to instance-similarity based approaches. Most state-of-the-art methods enforce similarity between various augmentations of a given image, while some methods additionally use contrastive approaches to explicitly ensure diverse representations. While these approaches have indeed shown promising direction, they require a significantly larger number of training iterations when compared to the supervised counterparts. In this work, we explore reasons for the slow convergence of these methods, and further propose to strengthen them using well-posed auxiliary tasks that converge significantly faster, and are also useful for representation learning. The proposed method utilizes the task of rotation prediction to improve the efficiency of existing state-of-the-art methods. We demonstrate significant gains in performance using the proposed method on multiple datasets, specifically for lower training epochs.

Representation

Similarity (geometry)

Pretext

Feature Learning

10.48550/arxiv.2210.09866

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Aligning Non-Causal Factors for Transformer-Based Source-Free Domain Adaptation

arXiv (Cornell University) (2023)

Sunandini Sanyal Ashish Ramayee Asokan Suvaansh Bhambri Pradyumna YM Akshay Kulkarni

Conventional domain adaptation algorithms aim to achieve better generalization by aligning only the task-discriminative causal factors between a source and target domain. However, we find that retaining the spurious correlation between causal and non-causal factors plays a vital role in bridging the domain gap and improving target adaptation. Therefore, we propose to build a framework that disentangles and supports causal factor alignment by aligning the non-causal factors first. We also investigate and find that the strong shape bias of vision transformers, coupled with its multi-head attention, make it a suitable architecture for realizing our proposed disentanglement. Hence, we propose to build a Causality-enforcing Source-Free Transformer framework (C-SFTrans) to achieve disentanglement via a novel two-stage alignment approach: a) non-causal factor alignment: non-causal factors are aligned using a style classification task which leads to an overall global alignment, b) task-discriminative causal factor alignment: causal factors are aligned via target adaptation. We are the first to investigate the role of vision transformers (ViTs) in a privacy-preserving source-free setting. Our approach achieves state-of-the-art results in several DA benchmarks.

Spurious relationship

Discriminative model

Domain Adaptation

Causal structure

10.48550/arxiv.2311.16294

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Recognition of human actions using motion history information extracted from the compressed video

Image and Vision Computing (2004)

R. Venkatesh Babu K.R. Ramakrishnan

Feature (linguistics)

Optical Flow

Quarter-pixel motion

10.1016/j.imavis.2003.11.004

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Citations (84)

Feature Fusion for Robust Object Tracking Using Fragmented Particles

Chhabi Nigam R. Venkatesh Babu S. Kumar Raja K.R. Ramakrishnan

Tracking people or objects across multiple cameras and maintaining a track within a camera is a challenging task in applications such as video surveillance. Some of the major challenges while tracking a target are illumination/scale changes and partial occlusion. In this paper, we propose a novel tracking framework using particle filter to efficiently track an object within a camera and a blob-based target association scheme for tracking across cameras. The proposed particle filter tracking algorithm uses a fragment-based approach to model the target and track it by fusing color and gradient features. Also, the proposed solution incorporates coarser level spatial information by fragmenting each particle and is shown to be beneficial for tracking under partial occlusion. A fast yet robust model update is employed to overcome illumination changes. Experimental results show (i) the robustness of the fragment-based tracking approach with respect to illumination/scale change and partial occlusion and (ii) tracking persons across two cameras.

Robustness

Tracking (education)

Active appearance model

Tracking system

10.1109/icdsc.2007.4357535

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Optic disk localization using L<inf>1</inf> minimization

Neelam Sinha R. Venkatesh Babu

Automatic eye screening for conditions like diabetic retinopathy critically hinges on detection and localization of Optic disk (OD). In this paper, we present a novel scale-embedded dictionary-based method that poses the problem of OD localization as that of classification, carried out in sparse representation framework. A dictionary is created with manually marked fixed-sized sub-images that contain OD at the center, for multiple scales. For a given test image, all subimages are sparsely represented as a linear combination of OD dictionary elements. A confidence measure indicating the likelihood of the presence of OD is obtained from these coefficients. Red channel and gray intensity images are processed independently, and their respective confidence measures are fused to form a confidence map. A blob detector is run on the confidence map, whose peak response is considered to be at the location of the OD. The proposed method is evaluated on publicly available databases such as DIARETDB0, DIARETDB1 and DRIVE. The OD was correctly localized in 253 out of 259 images, with an average computation time of 3.8 seconds/image and accuracy of 97.6%. Comparisons with two existing techniques are also discussed.

Minification

Optic disk

10.1109/icip.2012.6467488

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Citations (37)

Enhancing Salient Object Segmentation Through Attention

arXiv (Cornell University) (2019)

Anuj Pahuja Avishek Majumder Anirban Chakraborty R. Venkatesh Babu

Segmenting salient objects in an image is an important vision task with ubiquitous applications. The problem becomes more challenging in the presence of a cluttered and textured background, low resolution and/or low contrast images. Even though existing algorithms perform well in segmenting most of the object(s) of interest, they often end up segmenting false positives due to resembling salient objects in the background. In this work, we tackle this problem by iteratively attending to image patches in a recurrent fashion and subsequently enhancing the predicted segmentation mask. Saliency features are estimated independently for every image patch, which are further combined using an aggregation strategy based on a Convolutional Gated Recurrent Unit (ConvGRU) network. The proposed approach works in an end-to-end manner, removing background noise and false positives incrementally. Through extensive evaluation on various benchmark datasets, we show superior performance to the existing approaches without any post-processing.

Market Segmentation

Benchmark (surveying)

Source

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Ask, Acquire, and Attack: Data-free UAP Generation using Class Impressions

arXiv (Cornell University) (2018)

Konda Reddy Mopuri Phani Krishna Uppala R. Venkatesh Babu

Deep learning models are susceptible to input specific noise, called adversarial perturbations. Moreover, there exist input-agnostic noise, called Universal Adversarial Perturbations (UAP) that can affect inference of the models over most input samples. Given a model, there exist broadly two approaches to craft UAPs: (i) data-driven: that require data, and (ii) data-free: that do not require data samples. Data-driven approaches require actual samples from the underlying data distribution and craft UAPs with high success (fooling) rate. However, data-free approaches craft UAPs without utilizing any data samples and therefore result in lesser success rates. In this paper, for data-free scenarios, we propose a novel approach that emulates the effect of data samples with class impressions in order to craft UAPs using data-driven objectives. Class impression for a given pair of category and model is a generic representation (in the input space) of the samples belonging to that category. Further, we present a neural network based generative model that utilizes the acquired class impressions to learn crafting UAPs. Experimental evaluation demonstrates that the learned generative model, (i) readily crafts UAPs via simple feed-forwarding through neural network layers, and (ii) achieves state-of-the-art success rates for data-free scenario and closer to that for data-driven setting without actually utilizing any data samples.

Generative model

Ask price

Craft

Representation

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S$^3$VAADA: Submodular Subset Selection for Virtual Adversarial Active Domain Adaptation

arXiv (Cornell University) (2021)

Harsh Rangwani Arihant Jain Sumukh K Aithal R. Venkatesh Babu

Unsupervised domain adaptation (DA) methods have focused on achieving maximal performance through aligning features from source and target domains without using labeled data in the target domain. Whereas, in the real-world scenario's it might be feasible to get labels for a small proportion of target data. In these scenarios, it is important to select maximally-informative samples to label and find an effective way to combine them with the existing knowledge from source data. Towards achieving this, we propose S$^3$VAADA which i) introduces a novel submodular criterion to select a maximally informative subset to label and ii) enhances a cluster-based DA procedure through novel improvements to effectively utilize all the available data for improving generalization on target. Our approach consistently outperforms the competing state-of-the-art approaches on datasets with varying degrees of domain shifts.

Submodular set function

Domain Adaptation

Labeled data

10.48550/arxiv.2109.08901

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Citations (3)

Ensemble classifiers for an objective prediction of severity of uveitis based on measurement of aqueous flare and first-order patient characteristics

Investigative Ophthalmology & Visual Science (2021)

Bhavya Harjai T. Ravi Hadiya Farhath Pattan Prema Padmanabhan Naoki Okumura

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