Search Results for author:
Found 10 papers, 4 papers with code
Localizing Infinity-shaped fishes: Sketch-guided object localization in the wild
This work investigates the problem of sketch-guided object localization (SGOL), where human sketches are used as queries to conduct the object localization in natural images.
One-shot Compositional Data Generation for Low Resource Handwritten Text Recognition
Low resource Handwritten Text Recognition (HTR) is a hard problem due to the scarce annotated data and the very limited linguistic information (dictionaries and language models).
Multi-Modal Reasoning Graph for Scene-Text Based Fine-Grained Image Classification and Retrieval
Scene text instances found in natural images carry explicit semantic information that can provide important cues to solve a wide array of computer vision problems.
Fine-grained Image Classification and Retrieval by Combining Visual and Locally Pooled Textual Features
Text contained in an image carries high-level semantics that can be exploited to achieve richer image understanding.
Ranked #1 on
Fine-Grained Image Classification
on Con-Text
Doodle to Search: Practical Zero-Shot Sketch-based Image Retrieval
Highly abstract amateur human sketches are purposefully sourced to maximize the domain gap, instead of ones included in existing datasets that can often be semi-photorealistic.
Non-deterministic Behavior of Ranking-based Metrics when Evaluating Embeddings
Embedding data into vector spaces is a very popular strategy of pattern recognition methods.
Learning Cross-Modal Deep Embeddings for Multi-Object Image Retrieval using Text and Sketch
In this work we introduce a cross modal image retrieval system that allows both text and sketch as input modalities for the query.
SigNet: Convolutional Siamese Network for Writer Independent Offline Signature Verification
Offline signature verification is one of the most challenging tasks in biometrics and document forensics.
Ranked #1 on
Handwriting Verification
on CEDAR Signature
Evaluation of the Effect of Improper Segmentation on Word Spotting
Word spotting is an important recognition task in historical document analysis.
Local Binary Pattern for Word Spotting in Handwritten Historical Document
Digital libraries store images which can be highly degraded and to index this kind of images we resort to word spot- ting as our information retrieval system.
