53 citations · 312 across the 79 of their papers we have counts for
18 papers · 2 filters
Hybrid Graph Neural Networks for Few-Shot Learning
Tianyuan Yu, Sen He, Yi-Zhe Song +1
Graph neural networks (GNNs) have been used to tackle the few-shot learning (FSL) problem and shown great potentials under the transductive setting. However under the inductive set…
Clue Me In: Semi-Supervised FGVC with Out-of-Distribution Data
Ruoyi Du, Dongliang Chang, Zhanyu Ma +2
Despite great strides made on fine-grained visual classification (FGVC), current methods are still heavily reliant on fully-supervised paradigms where ample expert labels are calle…
One Sketch for All: One-Shot Personalized Sketch Segmentation
Anran Qi, Yulia Gryaditskaya, Tao Xiang +1
We present the first one-shot personalized sketch segmentation method. We aim to segment all sketches belonging to the same category provisioned with a single sketch with a given p…
One Loss for All: Deep Hashing with a Single Cosine Similarity based Learning Objective
Jiun Tian Hoe, Kam Woh Ng, Tianyu Zhang +3
A deep hashing model typically has two main learning objectives: to make the learned binary hash codes discriminative and to minimize a quantization error. With further constraints…
SketchLattice: Latticed Representation for Sketch Manipulation
Yonggang Qi, Guoyao Su, Pinaki Nath Chowdhury +2
The key challenge in designing a sketch representation lies with handling the abstract and iconic nature of sketches. Existing work predominantly utilizes either, (i) a pixelative…
Disentangled Lifespan Face Synthesis
Sen He, Wentong Liao, Michael Ying Yang +3
A lifespan face synthesis (LFS) model aims to generate a set of photo-realistic face images of a person's whole life, given only one snapshot as reference. The generated face image…
Simpler is Better: Few-shot Semantic Segmentation with Classifier Weight Transformer
Zhihe Lu, Sen He, Xiatian Zhu +3
A few-shot semantic segmentation model is typically composed of a CNN encoder, a CNN decoder and a simple classifier (separating foreground and background pixels). Most existing me…
Joint Visual Semantic Reasoning: Multi-Stage Decoder for Text Recognition
Ayan Kumar Bhunia, Aneeshan Sain, Amandeep Kumar +3
Although text recognition has significantly evolved over the years, state-of-the-art (SOTA) models still struggle in the wild scenarios due to complex backgrounds, varying fonts, u…
Text is Text, No Matter What: Unifying Text Recognition using Knowledge Distillation
Ayan Kumar Bhunia, Aneeshan Sain, Pinaki Nath Chowdhury +1
Text recognition remains a fundamental and extensively researched topic in computer vision, largely owing to its wide array of commercial applications. The challenging nature of th…
Towards the Unseen: Iterative Text Recognition by Distilling from Errors
Ayan Kumar Bhunia, Pinaki Nath Chowdhury, Aneeshan Sain +1
Visual text recognition is undoubtedly one of the most extensively researched topics in computer vision. Great progress have been made to date, with the latest models starting to f…
Towards Unsupervised Sketch-based Image Retrieval
Conghui Hu, Yongxin Yang, Yunpeng Li +2
The practical value of existing supervised sketch-based image retrieval (SBIR) algorithms is largely limited by the requirement for intensive data collection and labeling. In this…
PQA: Perceptual Question Answering
Yonggang Qi, Kai Zhang, Aneeshan Sain +1
Perceptual organization remains one of the very few established theories on the human visual system. It underpinned many pre-deep seminal works on segmentation and detection, yet r…
MetaHTR: Towards Writer-Adaptive Handwritten Text Recognition
Ayan Kumar Bhunia, Shuvozit Ghose, Amandeep Kumar +3
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 wit…
StyleMeUp: Towards Style-Agnostic Sketch-Based Image Retrieval
Aneeshan Sain, Ayan Kumar Bhunia, Yongxin Yang +2
Sketch-based image retrieval (SBIR) is a cross-modal matching problem which is typically solved by learning a joint embedding space where the semantic content shared between photo…
Cloud2Curve: Generation and Vectorization of Parametric Sketches
Ayan Das, Yongxin Yang, Timothy Hospedales +2
Analysis of human sketches in deep learning has advanced immensely through the use of waypoint-sequences rather than raster-graphic representations. We further aim to model sketche…
More Photos are All You Need: Semi-Supervised Learning for Fine-Grained Sketch Based Image Retrieval
Ayan Kumar Bhunia, Pinaki Nath Chowdhury, Aneeshan Sain +3
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…
Vectorization and Rasterization: Self-Supervised Learning for Sketch and Handwriting
Ayan Kumar Bhunia, Pinaki Nath Chowdhury, Yongxin Yang +3
Self-supervised learning has gained prominence due to its efficacy at learning powerful representations from unlabelled data that achieve excellent performance on many challenging…
Context-Aware Layout to Image Generation with Enhanced Object Appearance
Sen He, Wentong Liao, Michael Ying Yang +4
A layout to image (L2I) generation model aims to generate a complicated image containing multiple objects (things) against natural background (stuff), conditioned on a given layout…