Pinaki Nath Chowdhury

Mr Pinaki Nath Chowdhury


My research project


Ayan Kumar Bhunia, PINAKI NATH CHOWDHURY, ANEESHAN SAIN, YONGXIN YANG, TAO XIANG, YI-ZHE SONG (2021)More Photos are All You Need: Semi-Supervised Learning for Fine-Grained Sketch Based Image Retrieval

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. Whilst the number of photos can be easily scaled, each corresponding sketch still needs to be individually produced. In this paper, we aim to mitigate such an upper-bound on sketch data, and study whether unlabelled photos alone (of which they are many) can be cultivated for performance gain. In particular, we introduce a novel semi-supervised framework for cross-modal retrieval that can additionally leverage large-scale unla-belled photos to account for data scarcity. At the center of our semi-supervision design is a sequential photo-to-sketch generation model that aims to generate paired sketches for unlabelled photos. Importantly, we further introduce a discriminator-guided mechanism to guide against unfaithful generation, together with a distillation loss-based regu-larizer to provide tolerance against noisy training samples. Last but not least, we treat generation and retrieval as two conjugate problems, where a joint learning procedure is devised for each module to mutually benefit from each other. Extensive experiments show that our semi-supervised model yields a significant performance boost over the state-of-the-art supervised alternatives, as well as existing methods that can exploit unlabelled photos for FG-SBIR.

AYAN KUMAR BHUNIA, Shuvozit Ghose, Amandeep Kumar, Pinaki Nath Chowdhury, Aneeshan Sain, YI-ZHE SONG (2021)MetaHTR: Towards Writer-Adaptive Handwritten Text Recognition

Handwritten Text Recognition (HTR) remains a challenging problem to date, largely due to the varying writing styles that exist amongst us. Prior works however generally operate with the assumption that there is a limited number of styles, most of which have already been captured by existing datasets. 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. This creates a commercially viable solution – being exposed to the new style, the model has the best shot at adaptation, and the few-sample nature makes it practical to implement. We achieve this via a novel meta-learning framework which exploits additional new-writer data via a support set, and outputs a writer-adapted model via single gradient step update, all during inference (see Figure 1). We discover and leverage on the important insight that there exists few key characters per writer that exhibit relatively larger style discrepancies. For that, we additionally propose to meta-learn instance specific weights for a character-wise cross-entropy loss, which is specifically designed to work with the sequential nature of text data. Our writer-adaptive MetaHTR framework can be easily implemented on the top of most state-of-the-art HTR models. Experiments show an average performance gain of 5-7% can be obtained by observing very few new style data (≤ 16).

AYAN KUMAR BHUNIA, PINAKI NATH CHOWDHURY, YONGXIN YANG, Timothy M. Hospedales, TAO XIANG, YI-ZHE SONG (2021)Vectorization and Rasterization: Self-Supervised Learning for Sketch and Handwriting

Self-supervised learning has gained prominence due to its efficacy at learning powerful representations from un-labelled data that achieve excellent performance on many challenging downstream tasks. However, supervision-free pretext tasks are challenging to design and usually modality specific. Although there is a rich literature of self-supervised methods for either spatial (such as images) or temporal data (sound or text) modalities, a common pretext task that benefits both modalities is largely missing. In this paper, we are interested in defining a self-supervised pretext task for sketches and handwriting data. This data is uniquely characterised by its existence in dual modalities of rasterized images and vector coordinate sequences. We address and exploit this dual representation by proposing two novel cross-modal translation pretext tasks for self-supervised feature learning: Vectorization and Rasteriza-tion. Vectorization learns to map image space to vector coordinates and rasterization maps vector coordinates to image space. We show that our learned encoder modules benefit both raster-based and vector-based downstream approaches to analysing hand-drawn data. Empirical evidence shows that our novel pretext tasks surpass existing single and multi-modal self-supervision methods.