Search Results for author: Ayan Kumar Bhunia

Found 58 papers, 18 papers with code

SketchINR: A First Look into Sketches as Implicit Neural Representations

no code implementations14 Mar 2024 Hmrishav Bandyopadhyay, Ayan Kumar Bhunia, Pinaki Nath Chowdhury, Aneeshan Sain, Tao Xiang, Timothy Hospedales, Yi-Zhe Song

(ii) SketchINR's auto-decoder provides a much higher-fidelity representation than other learned vector sketch representations, and is uniquely able to scale to complex vector sketches such as FS-COCO.

Data Compression

What Sketch Explainability Really Means for Downstream Tasks

no code implementations14 Mar 2024 Hmrishav Bandyopadhyay, Pinaki Nath Chowdhury, Ayan Kumar Bhunia, Aneeshan Sain, Tao Xiang, Yi-Zhe Song

In this paper, we explore the unique modality of sketch for explainability, emphasising the profound impact of human strokes compared to conventional pixel-oriented studies.

Retrieval

It's All About Your Sketch: Democratising Sketch Control in Diffusion Models

1 code implementation12 Mar 2024 Subhadeep Koley, Ayan Kumar Bhunia, Deeptanshu Sekhri, Aneeshan Sain, Pinaki Nath Chowdhury, Tao Xiang, Yi-Zhe Song

This paper unravels the potential of sketches for diffusion models, addressing the deceptive promise of direct sketch control in generative AI.

Retrieval Sketch-Based Image Retrieval

Text-to-Image Diffusion Models are Great Sketch-Photo Matchmakers

no code implementations12 Mar 2024 Subhadeep Koley, Ayan Kumar Bhunia, Aneeshan Sain, Pinaki Nath Chowdhury, Tao Xiang, Yi-Zhe Song

This paper, for the first time, explores text-to-image diffusion models for Zero-Shot Sketch-based Image Retrieval (ZS-SBIR).

Retrieval Sketch-Based Image Retrieval

DemoCaricature: Democratising Caricature Generation with a Rough Sketch

no code implementations7 Dec 2023 Dar-Yen Chen, Ayan Kumar Bhunia, Subhadeep Koley, Aneeshan Sain, Pinaki Nath Chowdhury, Yi-Zhe Song

In this paper, we democratise caricature generation, empowering individuals to effortlessly craft personalised caricatures with just a photo and a conceptual sketch.

Caricature Model Editing

Doodle Your 3D: From Abstract Freehand Sketches to Precise 3D Shapes

no code implementations7 Dec 2023 Hmrishav Bandyopadhyay, Subhadeep Koley, Ayan Das, Aneeshan Sain, Pinaki Nath Chowdhury, Tao Xiang, Ayan Kumar Bhunia, Yi-Zhe Song

In this paper, we democratise 3D content creation, enabling precise generation of 3D shapes from abstract sketches while overcoming limitations tied to drawing skills.

Position

What Can Human Sketches Do for Object Detection?

no code implementations CVPR 2023 Pinaki Nath Chowdhury, Ayan Kumar Bhunia, Aneeshan Sain, Subhadeep Koley, Tao Xiang, Yi-Zhe Song

In particular, we first perform independent prompting on both sketch and photo branches of an SBIR model to build highly generalisable sketch and photo encoders on the back of the generalisation ability of CLIP.

Object object-detection +3

Exploiting Unlabelled Photos for Stronger Fine-Grained SBIR

no code implementations CVPR 2023 Aneeshan Sain, Ayan Kumar Bhunia, Subhadeep Koley, Pinaki Nath Chowdhury, Soumitri Chattopadhyay, Tao Xiang, Yi-Zhe Song

This paper advances the fine-grained sketch-based image retrieval (FG-SBIR) literature by putting forward a strong baseline that overshoots prior state-of-the-arts by ~11%.

Knowledge Distillation Sketch-Based Image Retrieval

Picture that Sketch: Photorealistic Image Generation from Abstract Sketches

no code implementations CVPR 2023 Subhadeep Koley, Ayan Kumar Bhunia, Aneeshan Sain, Pinaki Nath Chowdhury, Tao Xiang, Yi-Zhe Song

We further introduce specific designs to tackle the abstract nature of human sketches, including a fine-grained discriminative loss on the back of a trained sketch-photo retrieval model, and a partial-aware sketch augmentation strategy.

Image Generation Retrieval +1

Data-Free Sketch-Based Image Retrieval

1 code implementation CVPR 2023 Abhra Chaudhuri, Ayan Kumar Bhunia, Yi-Zhe Song, Anjan Dutta

For the first time, we identify that for data-scarce tasks like Sketch-Based Image Retrieval (SBIR), where the difficulty in acquiring paired photos and hand-drawn sketches limits data-dependent cross-modal learning algorithms, DFL can prove to be a much more practical paradigm.

Retrieval Sketch-Based Image Retrieval

Democratising 2D Sketch to 3D Shape Retrieval Through Pivoting

no code implementations ICCV 2023 Pinaki Nath Chowdhury, Ayan Kumar Bhunia, Aneeshan Sain, Subhadeep Koley, Tao Xiang, Yi-Zhe Song

We perform pivoting on two existing datasets, each from a distant research domain to the other: 2D sketch and photo pairs from the sketch-based image retrieval field (SBIR), and 3D shapes from ShapeNet.

3D Shape Retrieval Retrieval +1

Towards Practicality of Sketch-Based Visual Understanding

no code implementations27 Oct 2022 Ayan Kumar Bhunia

Sketches have been used to conceptualise and depict visual objects from pre-historic times.

Image Generation Image Retrieval +1

Adaptive Fine-Grained Sketch-Based Image Retrieval

1 code implementation4 Jul 2022 Ayan Kumar Bhunia, Aneeshan Sain, Parth Shah, Animesh Gupta, Pinaki Nath Chowdhury, Tao Xiang, Yi-Zhe Song

To solve this new problem, we introduce a novel model-agnostic meta-learning (MAML) based framework with several key modifications: (1) As a retrieval task with a margin-based contrastive loss, we simplify the MAML training in the inner loop to make it more stable and tractable.

Meta-Learning Retrieval +1

Doodle It Yourself: Class Incremental Learning by Drawing a Few Sketches

no code implementations CVPR 2022 Ayan Kumar Bhunia, Viswanatha Reddy Gajjala, Subhadeep Koley, Rohit Kundu, Aneeshan Sain, Tao Xiang, Yi-Zhe Song

In this paper, we push the boundary further for FSCIL by addressing two key questions that bottleneck its ubiquitous application (i) can the model learn from diverse modalities other than just photo (as humans do), and (ii) what if photos are not readily accessible (due to ethical and privacy constraints).

Few-Shot Class-Incremental Learning Graph Attention +2

Sketch3T: Test-Time Training for Zero-Shot SBIR

no code implementations CVPR 2022 Aneeshan Sain, Ayan Kumar Bhunia, Vaishnav Potlapalli, Pinaki Nath Chowdhury, Tao Xiang, Yi-Zhe Song

In this paper, we question to argue that this setup by definition is not compatible with the inherent abstract and subjective nature of sketches, i. e., the model might transfer well to new categories, but will not understand sketches existing in different test-time distribution as a result.

Meta-Learning Retrieval +1

Text is Text, No Matter What: Unifying Text Recognition using Knowledge Distillation

no code implementations ICCV 2021 Ayan Kumar Bhunia, Aneeshan Sain, Pinaki Nath Chowdhury, Yi-Zhe Song

In this paper, for the first time, we argue for their unification -- we aim for a single model that can compete favourably with two separate state-of-the-art STR and HTR models.

Handwriting Recognition Knowledge Distillation +1

Towards the Unseen: Iterative Text Recognition by Distilling from Errors

no code implementations ICCV 2021 Ayan Kumar Bhunia, Pinaki Nath Chowdhury, Aneeshan Sain, Yi-Zhe Song

Our framework is iterative in nature, in that it utilises predicted knowledge of character sequences from a previous iteration, to augment the main network in improving the next prediction.

MetaHTR: Towards Writer-Adaptive Handwritten Text Recognition

1 code implementation CVPR 2021 Ayan Kumar Bhunia, Shuvozit Ghose, Amandeep Kumar, Pinaki Nath Chowdhury, Aneeshan Sain, Yi-Zhe Song

In this paper, we take a completely different perspective -- we work on the assumption that there is always a new style that is drastically different, and that we will only have very limited data during testing to perform adaptation.

Handwritten Text Recognition Meta-Learning

StyleMeUp: Towards Style-Agnostic Sketch-Based Image Retrieval

no code implementations CVPR 2021 Aneeshan Sain, Ayan Kumar Bhunia, Yongxin Yang, Tao Xiang, Yi-Zhe Song

With this meta-learning framework, our model can not only disentangle the cross-modal shared semantic content for SBIR, but can adapt the disentanglement to any unseen user style as well, making the SBIR model truly style-agnostic.

Disentanglement Meta-Learning +2

More Photos are All You Need: Semi-Supervised Learning for Fine-Grained Sketch Based Image Retrieval

1 code implementation CVPR 2021 Ayan Kumar Bhunia, Pinaki Nath Chowdhury, Aneeshan Sain, Yongxin Yang, Tao Xiang, Yi-Zhe Song

A fundamental challenge faced by existing Fine-Grained Sketch-Based Image Retrieval (FG-SBIR) models is the data scarcity -- model performances are largely bottlenecked by the lack of sketch-photo pairs.

Cross-Modal Retrieval Retrieval +2

Cross-Modal Hierarchical Modelling for Fine-Grained Sketch Based Image Retrieval

1 code implementation29 Jul 2020 Aneeshan Sain, Ayan Kumar Bhunia, Yongxin Yang, Tao Xiang, Yi-Zhe Song

In this paper, we study a further trait of sketches that has been overlooked to date, that is, they are hierarchical in terms of the levels of detail -- a person typically sketches up to various extents of detail to depict an object.

Retrieval Sketch-Based Image Retrieval

Sketch Less for More: On-the-Fly Fine-Grained Sketch Based Image Retrieval

1 code implementation24 Feb 2020 Ayan Kumar Bhunia, Yongxin Yang, Timothy M. Hospedales, Tao Xiang, Yi-Zhe Song

Fine-grained sketch-based image retrieval (FG-SBIR) addresses the problem of retrieving a particular photo instance given a user's query sketch.

Cross-Modal Retrieval On-the-Fly Sketch Based Image Retrieval +1

Facial Micro-Expression Spotting and Recognition using Time Contrasted Feature with Visual Memory

no code implementations9 Feb 2019 Sauradip Nag, Ayan Kumar Bhunia, Aishik Konwer, Partha Pratim Roy

Facial micro-expressions are sudden involuntary minute muscle movements which reveal true emotions that people try to conceal.

Micro-Expression Spotting

Texture Synthesis Guided Deep Hashing for Texture Image Retrieval

no code implementations4 Nov 2018 Ayan Kumar Bhunia, Perla Sai Raj Kishore, Pranay Mukherjee, Abhirup Das, Partha Pratim Roy

In the next stage, a second network gathers the multi-scale feature representations from the TSN's intermediate layers using channel-wise attention, combines them in a progressive manner to a dense continuous representation which is finally converted into a binary hash code with the help of individual and pairwise label information.

Data Augmentation Deep Hashing +3

A Deep One-Shot Network for Query-based Logo Retrieval

2 code implementations4 Nov 2018 Ayan Kumar Bhunia, Ankan Kumar Bhunia, Shuvozit Ghose, Abhirup Das, Partha Pratim Roy, Umapada Pal

Logo detection in real-world scene images is an important problem with applications in advertisement and marketing.

Marketing object-detection +4

User Constrained Thumbnail Generation using Adaptive Convolutions

2 code implementations31 Oct 2018 Perla Sai Raj Kishore, Ayan Kumar Bhunia, Shuvozit Ghose, Partha Pratim Roy

We use Global Context Aggregation (GCA) and a modified Region Proposal Network (RPN) with adaptive convolutions to generate thumbnails in real time.

Region Proposal User Constrained Thumbnail Generation

Indic Handwritten Script Identification using Offline-Online Multimodal Deep Network

no code implementations23 Feb 2018 Ayan Kumar Bhunia, Subham Mukherjee, Aneeshan Sain, Ankan Kumar Bhunia, Partha Pratim Roy, Umapada Pal

In this paper, we propose a novel approach of word-level Indic script identification using only character-level data in training stage.

Word Level Font-to-Font Image Translation using Convolutional Recurrent Generative Adversarial Networks

no code implementations22 Jan 2018 Ankan Kumar Bhunia, Ayan Kumar Bhunia, Prithaj Banerjee, Aishik Konwer, Abir Bhowmick, Partha Pratim Roy, Umapada Pal

We employ a novel convolutional recurrent model architecture in the Generator that efficiently deals with the word images of arbitrary width.

Translation

Handwriting Trajectory Recovery using End-to-End Deep Encoder-Decoder Network

no code implementations22 Jan 2018 Ayan Kumar Bhunia, Abir Bhowmick, Ankan Kumar Bhunia, Aishik Konwer, Prithaj Banerjee, Partha Pratim Roy, Umapada Pal

Our encoder module consists of Convolutional LSTM network, which takes an offline character image as the input and encodes the feature sequence to a hidden representation.

Retrieval

Script Identification in Natural Scene Image and Video Frame using Attention based Convolutional-LSTM Network

1 code implementation1 Jan 2018 Ankan Kumar Bhunia, Aishik Konwer, Ayan Kumar Bhunia, Abir Bhowmick, Partha P. Roy, Umapada Pal

In this paper, we propose a novel method that involves extraction of local and global features using CNN-LSTM framework and weighting them dynamically for script identification.

Cross-language Framework for Word Recognition and Spotting of Indic Scripts

no code implementations19 Dec 2017 Ayan Kumar Bhunia, Partha Pratim Roy, Akash Mohta, Umapada Pal

This paper presents a novel cross language platform for handwritten word recognition and spotting for such low-resource scripts where training is performed with a sufficiently large dataset of an available script (considered as source script) and testing is done on other scripts (considered as target script).

Zone-based Keyword Spotting in Bangla and Devanagari Documents

no code implementations5 Dec 2017 Ayan Kumar Bhunia, Partha Pratim Roy, Umapada Pal

Also, we propose a novel feature combining foreground and background information of text line images for keyword-spotting by character filler models.

Keyword Spotting Segmentation

Local Neighborhood Intensity Pattern: A new texture feature descriptor for image retrieval

no code implementations7 Sep 2017 Prithaj Banerjee, Ayan Kumar Bhunia, Avirup Bhattacharyya, Partha Pratim Roy, Subrahmanyam Murala

The proposed method is based on the concept that neighbors of a particular pixel hold a significant amount of texture information that can be considered for efficient texture representation for CBIR.

Content-Based Image Retrieval Retrieval

Word Searching in Scene Image and Video Frame in Multi-Script Scenario using Dynamic Shape Coding

no code implementations18 Aug 2017 Partha Pratim Roy, Ayan Kumar Bhunia, Avirup Bhattacharyya, Umapada Pal

To evaluate the proposed system for searching keyword from natural scene image and video frames, we have considered two popular Indic scripts such as Bangla (Bengali) and Devanagari along with English.

Keyword Spotting Optical Character Recognition (OCR) +2

HMM-based Indic Handwritten Word Recognition using Zone Segmentation

no code implementations1 Aug 2017 Partha Pratim Roy, Ayan Kumar Bhunia, Ayan Das, Prasenjit Dey, Umapada Pal

To avoid character segmentation in such scripts, HMM-based sequence modeling has been used earlier in holistic way.

Segmentation

Multi-Oriented Text Detection and Verification in Video Frames and Scene Images

no code implementations22 Jul 2017 Aneeshan Sain, Ayan Kumar Bhunia, Partha Pratim Roy, Umapada Pal

Until now only a few methods have been proposed that look into curved text detection in video frames, wherein lies our novelty.

Clustering Curved Text Detection +3

Date-Field Retrieval in Scene Image and Video Frames using Text Enhancement and Shape Coding

no code implementations21 Jul 2017 Partha Pratim Roy, Ayan Kumar Bhunia, Umapada Pal

We propose a line based date spotting approach using Hidden Markov Model (HMM) which is used to detect the date information in a given text.

Information Retrieval Retrieval

HMM-based Writer Identification in Music Score Documents without Staff-Line Removal

no code implementations21 Jul 2017 Partha Pratim Roy, Ayan Kumar Bhunia, Umapada Pal

A novel Factor Analysis based feature selection technique is applied in sliding window features to reduce the noise appearing from staff lines which proves efficiency in writer identification performance. In our framework we have also proposed a novel score line detection approach in musical sheet using HMM.

feature selection Line Detection

Text Recognition in Scene Image and Video Frame using Color Channel Selection

no code implementations21 Jul 2017 Ayan Kumar Bhunia, Gautam Kumar, Partha Pratim Roy, R. Balasubramanian, Umapada Pal

In this paper, we present a novel approach based on color channel selection for text recognition from scene images and video frames.

Binarization Optical Character Recognition (OCR)

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