3 citations · 8 across the 4 of their papers we have counts for
6 papers · 1 filter
Instance Retrieval at Fine-grained Level Using Multi-Attribute Recognition
Roshanak Zakizadeh, Yu Qian, Michele Sasdelli +1
In this paper, we present a method for instance ranking and retrieval at fine-grained level based on the global features extracted from a multi-attribute recognition model which is…
Convolutional Recurrent Predictor: Implicit Representation for Multi-target Filtering and Tracking
Mehryar Emambakhsh, Alessandro Bay, Eduard Vazquez
Defining a multi-target motion model, which is an important step of tracking algorithms, can be very challenging. Using fixed models (as in several generative Bayesian algorithms,…
Improving the Annotation of DeepFashion Images for Fine-grained Attribute Recognition
Roshanak Zakizadeh, Michele Sasdelli, Yu Qian +1
DeepFashion is a widely used clothing dataset with 50 categories and more than overall 200k images where each image is annotated with fine-grained attributes. This dataset is often…
Hide and Seek tracker: Real-time recovery from target loss
Alessandro Bay, Panagiotis Sidiropoulos, Eduard Vazquez +1
In this paper, we examine the real-time recovery of a video tracker from a target loss, using information that is already available from the original tracker and without a signific…
FineTag: Multi-attribute Classification at Fine-grained Level in Images
Roshanak Zakizadeh, Michele Sasdelli, Yu Qian +1
In this paper, we address the extraction of the fine-grained attributes of an instance as a `multi-attribute classification' problem. To this end, we propose an end-to-end architec…
Deep Recurrent Neural Network for Multi-target Filtering
Mehryar Emambakhsh, Alessandro Bay, Eduard Vazquez
This paper addresses the problem of fixed motion and measurement models for multi-target filtering using an adaptive learning framework. This is performed by defining target tuples…