Search Results for author: Aneeshan Sain

Found 17 papers, 6 papers with code

Sketch3T: Test-Time Training for Zero-Shot SBIR

no code implementations28 Mar 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 Sketch-Based Image Retrieval

Partially Does It: Towards Scene-Level FG-SBIR with Partial Input

no code implementations28 Mar 2022 Pinaki Nath Chowdhury, Ayan Kumar Bhunia, Viswanatha Reddy Gajjala, Aneeshan Sain, Tao Xiang, Yi-Zhe Song

We scrutinise an important observation plaguing scene-level sketch research -- that a significant portion of scene sketches are "partial".

Sketch-Based Image Retrieval

Sketching without Worrying: Noise-Tolerant Sketch-Based Image Retrieval

1 code implementation28 Mar 2022 Ayan Kumar Bhunia, Subhadeep Koley, Abdullah Faiz Ur Rahman Khilji, Aneeshan Sain, Pinaki Nath Chowdhury, Tao Xiang, Yi-Zhe Song

We first conducted a pilot study that revealed the secret lies in the existence of noisy strokes, but not so much of the "I can't sketch".

Sketch-Based Image Retrieval

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

no code implementations28 Mar 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).

class-incremental learning Graph Attention +2

FS-COCO: Towards Understanding of Freehand Sketches of Common Objects in Context

no code implementations4 Mar 2022 Pinaki Nath Chowdhury, Aneeshan Sain, Yulia Gryaditskaya, Ayan Kumar Bhunia, Tao Xiang, Yi-Zhe Song

In addition, we propose new solutions enabled by our dataset (i) We adopt meta-learning to show how the retrieval model can be fine-tuned to a new user style given just a small set of sketches, (ii) We extend a popular vector sketch LSTM-based encoder to handle sketches with larger complexity than was supported by previous work.

Image Captioning Image Retrieval +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.

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

PQA: Perceptual Question Answering

1 code implementation CVPR 2021 Yonggang Qi, Kai Zhang, Aneeshan Sain, Yi-Zhe Song

Perceptual organization remains one of the very few established theories on the human visual system.

Question Answering

MetaHTR: Towards Writer-Adaptive Handwritten Text Recognition

no code implementations 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 +1

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 Semi-Supervised Sketch Based Image Retrieval +1

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.

Sketch-Based Image Retrieval

Mind the Gap: Enlarging the Domain Gap in Open Set Domain Adaptation

2 code implementations8 Mar 2020 Dongliang Chang, Aneeshan Sain, Zhanyu Ma, Yi-Zhe Song, Jun Guo

The key insight lies with how we exploit the mutually beneficial information between two networks; (a) to separate samples of known and unknown classes, (b) to maximize the domain confusion between source and target domain without the influence of unknown samples.

Unsupervised Domain Adaptation

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.

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.

Curved Text Detection Text Classification

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