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Found 11 papers, 2 papers with code
Boosting Pruned Networks with Linear Over-parameterization
To address this challenge, we propose a novel method that first linearly over-parameterizes the compact layers in pruned networks to enlarge the number of fine-tuning parameters and then re-parameterizes them to the original layers after fine-tuning.
COVID-VIT: Classification of COVID-19 from CT chest images based on vision transformer models
The overarching aim is to predict the diagnosis of the COVID-19 virus from chest radiographs, through the development of explainable vision transformer deep learning techniques, leading to population screening in a more rapid, accurate and transparent way.
Can Sophisticated Dispatching Strategy Acquired by Reinforcement Learning? - A Case Study in Dynamic Courier Dispatching System
In this paper, we study a courier dispatching problem (CDP) raised from an online pickup-service platform of Alibaba.
Instance Retrieval at Fine-grained Level Using Multi-Attribute Recognition
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 not dependent on landmarks information or part-based annotations.
Improving the Annotation of DeepFashion Images for Fine-grained Attribute Recognition
After selecting categories with sufficient number of images for training, we remove very scarce attributes and merge the duplicate ones in each category, then we clean the dataset based on the new list of attributes.
FineTag: Multi-attribute Classification at Fine-grained Level in Images
In this paper, we address the extraction of the fine-grained attributes of an instance as a `multi-attribute classification' problem.
A Multi-task Selected Learning Approach for Solving 3D Flexible Bin Packing Problem
A 3D flexible bin packing problem (3D-FBPP) arises from the process of warehouse packing in e-commerce.
Distributed non-parametric deep and wide networks
In recent work, it was shown that combining multi-kernel based support vector machines (SVMs) can lead to near state-of-the-art performance on an action recognition dataset (HMDB-51 dataset).
Pillar Networks++: Distributed non-parametric deep and wide networks
In recent work, it was shown that combining multi-kernel based support vector machines (SVMs) can lead to near state-of-the-art performance on an action recognition dataset (HMDB-51 dataset).
Multi-kernel learning of deep convolutional features for action recognition
Image understanding using deep convolutional network has reached human-level performance, yet a closely related problem of video understanding especially, action recognition has not reached the requisite level of maturity.
Further Theoretical Study of Distribution Separation Method for Information Retrieval
Recently, a Distribution Separation Method (DSM) is proposed for relevant feedback in information retrieval, which aims to approximate the true relevance distribution by separating a seed irrelevance distribution from the mixture one.
