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Wisdom of (Binned) Crowds: A Bayesian Stratification Paradigm for Crowd Counting


Abstract

he idea behind our work is to tackle the high variance of error that is ignored when considering de facto statistical performance measures like (MSE,MAE) for performance evaluation in the crowd counting domain. Our recipe involves finding strata that are optimal in a Bayesian sense and later systematically modifying the standard crowd counting pipeline to incorporate decrease of variance at each step.
 
If you want our work to be listed as a network for comparison, please send a pull request to us here. The instructions for pull request is mentioned here.

 

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Please cite our paper if you end up using it for your own research.

Bibtex

 
    @inproceedings{10.1145/3474085.3475522,
        author = {Sravya Vardhani Shivapuja, Mansi Pradeep Khamkar, Divij Bajaj, Ganesh Ramakrishnan, Ravi Kiran Sarvadevabhatla},
        title = {Wisdom of (Binned) Crowds: A Bayesian Stratification Paradigm for Crowd Counting},
        booktitle = {Proceedings of the 2021 ACM Conference on Multimedia},
        year = {2021},
        location = {Virtual Event, China},
        publisher = {ACM},
        address = {China},
        }

Handwritten Text Retrieval from Unseen Collections


Demo Video

The link for video: Demo Video

Efficient and Generic Interactive Segmentation Framework to Correct Mispredictions during Clinical Evaluation of Medical Images


Bhavani Sambaturu*, Ashutosh Gupta   C.V. Jawahar   Chetan Arora

IIIT Hyderabad       IIT Delhi

[Code]   [Paper]   [Supplementary]   [Demo Video]   [Test Sets]

 MICCAI, 2021

first diag

We propose a novel approach to generate annotations for medical images of several modalities in a semi-automated manner. In contrast to the existing methods, our method can be implemented using any semantic segmentation method for medical images, allows correction of multiple labels at the same time and addition of missing labels.

Abstract

Semantic segmentation of medical images is an essential first step in computer-aided diagnosis systems for many applications. However, modern deep neural networks (DNNs) have generally shown inconsistent performance for clinical use. This has led researchers to propose interactive image segmentation techniques where the output of a DNN can be interactively corrected by a medical expert to the desired accuracy. However, these techniques often need separate training data with the associated human interactions, and do not generalize to various diseases, and types of medical images. In this paper, we suggest a novel conditional inference technique for deep neural networks which takes the intervention by a medical expert as test time constraints and performs inference conditioned upon these constraints. Our technique is generic can be used for medical images from any modality. Unlike other methods, our approach can correct multiple structures at the same time and add structures missed at initial segmentation. We report an improvement of 13.3, 12.5, 17.8, 10.2, and 12.4 times in terms of user annotation time compared to full human annotation for the nucleus, multiple cell, liver and tumor, organ, and brain segmentation respectively. In comparison to other interactive segmentation techniques, we report a time saving of 2.8 , 3.0, 1.9, 4.4, and 8.6 fold. Our method can be useful to clinicians for diagnosis and, post-surgical follow-up with minimal intervention from the medical expert.


Paper

  • Paper
    Semi-Automatic Medical Image Annotation

    Bhavani Sambaturu*, Ashutosh Gupta, C.V. Jawahar* and Chetan Arora
    Semi-Automatic Medical Image Annotation, MICCAI, 2021.
    [ PDF ] | [Supplementary] | [BibTeX]

    Updated Soon

Additional Details

Some additional details have been provided which we were unable to put in the paper due to space constraints.

 

Qualitative Results

Multiple Label Segmentation

Our approach has the capability to interactively correct the segmentation of multiple labels at the same time

multiple label2

Missing Label Segmentation

Our method has the capability to add labels missed at the initial segmentation.

missing label

Unseen Organ Segmentation

We can perform interactive segmentation of organs for which the pre-trained model was not trained for.

unseen organs

Network Details

The details of the networks used in our paper has been given here.

Detection-aided liver lesion segmentation using deep learning

The network is based on the DRIU architecture. It is a cascaded architecture where the liver is segmented first followed by the lesion.

Hover-Net

A multiple branch network has been proposed which does nuclear instance segmentation and classification at the same time. The horizontal and vertical distances of nuclear pixels between their centers of masses are leveraged to separate the clustered cells.

Autofocus layer for Semantic Segmentation

An autofocus layer for semantic segmentation has been proposed here. The autofocus layer is used to change the size of the receptive fields which is used to obtain features at various scales. The convolutional layers are paralellized with an attention mechanism.


Contact

  1. Bhavani Sambaturu - This email address is being protected from spambots. You need JavaScript enabled to view it.
  2. Ashutosh Gupta - This email address is being protected from spambots. You need JavaScript enabled to view it.

Towards Speech to Sign Language Generation


Parul Kapoor, Rudrabha Mukhopadhyay Sindhu B Hegde , Vinay Namboodiri and C.V. Jawahar

IIT Kanpur       IIIT Hyderabad       Univ. of Bath

[ Code ]   | [ Demo Video ]   | [ Dataset ]

banner

Previous approaches have only attempted to generate sign-language from the text level, we focus on directly converting speech segments into sign-language. Our work opens up several assistive technology applications and can help effectively communicate with people suffering from hearing loss.

Abstract

We aim to solve the highly challenging task of generating continuous sign language videos solely from speech segments for the first time. Recent efforts in this space have focused on generating such videos from human-annotated text transcripts without considering other modalities. However, replacing speech with sign language proves to be a practical solution while communicating with people suffering from hearing loss. Therefore, we eliminate the need of using text as input and design techniques that work for more natural, continuous, freely uttered speech covering an extensive vocabulary. Since the current datasets are inadequate for generating sign language directly from speech, we collect and release the first Indian sign language dataset comprising speech-level annotations, text transcripts, and the corresponding sign-language videos. Next, we propose a multi-tasking transformer network trained to generate signer's poses from speech segments. With speech-to-text as an auxiliary task and an additional cross-modal discriminator, our model learns to generate continuous sign pose sequences in an end-to-end manner. Extensive experiments and comparisons with other baselines demonstrate the effectiveness of our approach. We also conduct additional ablation studies to analyze the effect of different modules of our network. A demo video containing several results is attached to the supplementary material.


Paper

  • Paper
    Towards Speech to Sign Language Generation

    Parul Kapoor, Rudrabha Mukhopadhyay, Sindhu Hegde Vinay Namboodiri and C.V. Jawahar
    Towards Speech to Sign Language Generation, Interspeech, 2021.
    [PDF ] | [BibTeX]

    Updated Soon

Demo

--- COMING SOON ---


Dataset

--- COMING SOON ---


Contact

  1. Parul Kapoor - This email address is being protected from spambots. You need JavaScript enabled to view it.
  2. Rudrabha Mukhopadhyay - This email address is being protected from spambots. You need JavaScript enabled to view it.
  3. Sindhu Hegde - This email address is being protected from spambots. You need JavaScript enabled to view it.

IIIT-INDIC-HW-WORDS: A Dataset for Indic Handwritten Text Recognition

Santhoshini Gongidi and C. V. Jawahar

[ Teaser Video ] [Paper] [Code]


Overview:

Overview Handwritten text recognition for Indian languages is not yet a well-studied problem. This is primarily due to the unavailability of large annotated datasets in the associated scripts. We introduce a large-scale handwritten dataset for Indic scripts, referred to as the IIIT-INDIC-HW-WORDS dataset. The dataset consists of 872K handwritten instances written by 135 writers in 8 Indic scripts. With the newly introduced dataset and our earlier datasets IIIT-HW-DEV and IIIT-HW-TELUGU in Devanagari and Telugu respectively, the IIIT-INDIC-HW-WORDS dataset contains annotated hand-written word instances in all 10 prominent Indic scripts.

 

We further establish a high baseline for text recognition in eight Indic scripts. Our recognition scheme follows the contemporary design principles from other recognition literature, and yields competitive results on English. We further (i) study the reasons for changes in HTR performance across scripts (ii) explore the utility of pre-training for Indic HTRs. We hope our efforts will catalyze research and fuel applications related to handwritten document understanding in Indic scripts.

A glimpse into the dataset

 IIIT INDIC HW WORDS

Dataset

The zip file for each language contains image folders, label file and a vocabulary file. Please follow the instructions in the README file for more instructions.

 Language  Download link 
Bengali Link
Gujarati Link
Gurumukhi Link
Kannada Link
Odiya Link
Malayalam Link
Tamil Link
Urdu Link

 

For Devanagari and Telugu datasets, please follow the Link


Related Publications

Santhoshini Gongidi, C V Jawahar, INDIC-HW-WORDS: A Dataset for IndicHandwritten Text Recognition International Conference on Document Analysis and Recognition (ICDAR) 2021, [ PDF ]

Contact

For any queries about the dataset, please contact the authors below:

Santhoshini Gongidi: This email address is being protected from spambots. You need JavaScript enabled to view it.

 

 

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