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Deep High-Resolution Representation Learning for Visual Recognition
TLDR
The superiority of the proposed HRNet in a wide range of applications, including human pose estimation, semantic segmentation, and object detection, is shown, suggesting that the HRNet is a stronger backbone for computer vision problems.
Discrimination-aware Channel Pruning for Deep Neural Networks
TLDR
This work investigates a simple-yet-effective method, called discrimination-aware channel pruning, to choose those channels that really contribute to discriminative power and proposes a greedy algorithm to conduct channel selection and parameter optimization in an iterative way.
Dense Regression Network for Video Grounding
TLDR
A novel dense regression network (DRN) is designed to regress the distances between the frame within the ground truth and the starting (ending) frame of the video segment described by the query to improve the video grounding accuracy.
Graph Convolutional Networks for Temporal Action Localization
TLDR
This paper builds an action proposal graph, where each proposal is represented as a node and their relations between two proposals as an edge and applies the GCNs over the graph to model the relations among different proposals and learn powerful representations for the action classification and localization.
Domain-Symmetric Networks for Adversarial Domain Adaptation
TLDR
This paper proposes a new domain adaptation method called Domain-Symmetric Networks (SymNets), which is based on a symmetric design of source and target task classifiers, based on which an additional classifier is constructed that shares with them its layer neurons.
Learning Sparse SVM for Feature Selection on Very High Dimensional Datasets
TLDR
Comprehensive experimental results show that the proposed method can obtain better or competitive performance compared with existing SVM-based feature selection methods in term of sparsity and generalization performance, and can effectively handle large-scale and extremely high dimensional problems.
Heterogeneous Domain Adaptation for Multiple Classes
TLDR
An efficient multi-class heterogeneous domain adaptation method, where data from source and target domains are represented by heterogeneous features of different dimensions, to reconstruct a sparse feature transformation matrix to map the weight vector of classifiers learned from the source domain to the target domain.
Generative Low-bitwidth Data Free Quantization
TLDR
This paper proposes a knowledge matching generator to produce meaningful fake data by exploiting classification boundary knowledge and distribution information in the pre-trained model with much higher accuracy on 4-bit quantization than the existing data free quantization method.
RSPNet: Relative Speed Perception for Unsupervised Video Representation Learning
TLDR
A new way to perceive the playback speed and exploit the relative speed between two video clips as labels is proposed to provide more effective and stable supervision for representation learning and ensure the learning of appearance features.
Towards Effective Low-Bitwidth Convolutional Neural Networks
TLDR
This paper tackles the problem of training a deep convolutional neural network with both low-precision weights and low-bitwidth activations by proposing a two-stage optimization strategy to progressively find good local minima and adopting a novel learning scheme to jointly train a full- Precision model alongside the low-Precision one.
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