Search Results for author:
Found 15 papers, 6 papers with code
SceneTrilogy: On Scene Sketches and its Relationship with Text and Photo
We spell out a few insights on the complementarity of each modality for scene understanding, and study for the first time a series of scene-specific applications like joint sketch- and text-based image retrieval, sketch captioning.
Sketch3T: Test-Time Training for Zero-Shot SBIR
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.
Partially Does It: Towards Scene-Level FG-SBIR with Partial Input
We scrutinise an important observation plaguing scene-level sketch research -- that a significant portion of scene sketches are "partial".
Sketching without Worrying: Noise-Tolerant Sketch-Based Image Retrieval
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".
FS-COCO: Towards Understanding of Freehand Sketches of Common Objects in Context
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.
SketchLattice: Latticed Representation for Sketch Manipulation
The key challenge in designing a sketch representation lies with handling the abstract and iconic nature of sketches.
Towards the Unseen: Iterative Text Recognition by Distilling from Errors
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
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.
Joint Visual Semantic Reasoning: Multi-Stage Decoder for Text Recognition
In this paper, we argue that semantic information offers a complementary role in addition to visual only.
MetaHTR: Towards Writer-Adaptive Handwritten Text Recognition
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.
Vectorization and Rasterization: Self-Supervised Learning for Sketch and Handwriting
This data is uniquely characterised by its existence in dual modalities of rasterized images and vector coordinate sequences.
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.
Cross-Modal Retrieval
Semi-Supervised Sketch Based Image Retrieval
+1
UDBNET: Unsupervised Document Binarization Network via Adversarial Game
In this paper, we present a novel approach towards document image binarization by introducing three-player min-max adversarial game.
Modeling Extent-of-Texture Information for Ground Terrain Recognition
Ground Terrain Recognition is a difficult task as the context information varies significantly over the regions of a ground terrain image.
ICDAR2019 Robust Reading Challenge on Multi-lingual Scene Text Detection and Recognition -- RRC-MLT-2019
With the growing cosmopolitan culture of modern cities, the need of robust Multi-Lingual scene Text (MLT) detection and recognition systems has never been more immense.
