Search Results for author:
Found 10 papers, 1 papers with code
Unsupervised and semi-supervised co-salient object detection via segmentation frequency statistics
Our unsupervised model is a great pre-training initialization for our semi-supervised model SS-CoSOD, especially when very limited labeled data is available for training.
ProcSim: Proxy-based Confidence for Robust Similarity Learning
Deep Metric Learning (DML) methods aim at learning an embedding space in which distances are closely related to the inherent semantic similarity of the inputs.
Hierarchical Proxy-based Loss for Deep Metric Learning
In this paper, we present a framework that leverages this implicit hierarchy by imposing a hierarchical structure on the proxies and can be used with any existing proxy-based loss.
T-VSE: Transformer-Based Visual Semantic Embedding
Transformer models have recently achieved impressive performance on NLP tasks, owing to new algorithms for self-supervised pre-training on very large text corpora.
Large Scale Open-Set Deep Logo Detection
We present an open-set logo detection (OSLD) system, which can detect (localize and recognize) any number of unseen logo classes without re-training; it only requires a small set of canonical logo images for each logo class.
Semantic Granularity Metric Learning for Visual Search
In view of this, we propose a new deep semantic granularity metric learning (SGML) that develops a novel idea of leveraging attribute semantic space to capture different granularity of similarity, and then integrate this information into deep metric learning.
Remote Detection of Idling Cars Using Infrared Imaging and Deep Networks
First, we detect the cars in each IR image using a convolutional neural network, which is pre-trained on regular RGB images and fine-tuned on IR images for higher accuracy.
Active Canny: Edge Detection and Recovery with Open Active Contour Models
This way, the strong edges are used to recover weak or missing edges by considering the local edge structures, instead of blindly linking them if gradient magnitudes are above some threshold.
Multi-View Product Image Search Using Deep ConvNets Representations
We concluded that (1) multi-view queries with deep ConvNets representations perform significantly better than single view queries, (2) ConvNets perform much better than BoWs and have room for further improvement, (3) pre-training of ConvNets on a different image dataset with background clutter is needed to obtain good performance on cluttered product image queries obtained with a mobile phone.
Mobile Multi-View Object Image Search
High user interaction capability of mobile devices can help improve the accuracy of mobile visual search systems.
