Search Results for author:
Found 14 papers, 5 papers with code
AIDE: An Automatic Data Engine for Object Detection in Autonomous Driving
This necessitates an expensive process of continuously curating and annotating data with significant human effort.
Evidential Active Recognition: Intelligent and Prudent Open-World Embodied Perception
Active recognition enables robots to intelligently explore novel observations, thereby acquiring more information while circumventing undesired viewing conditions.
Understanding Self-attention Mechanism via Dynamical System Perspective
The self-attention mechanism (SAM) is widely used in various fields of artificial intelligence and has successfully boosted the performance of different models.
Exploring Compositional Visual Generation with Latent Classifier Guidance
We show that this paradigm based on latent classifier guidance is agnostic to pre-trained generative models, and present competitive results for both image generation and sequential manipulation of real and synthetic images.
On Robust Numerical Solver for ODE via Self-Attention Mechanism
With the development of deep learning techniques, AI-enhanced numerical solvers are expected to become a new paradigm for solving differential equations due to their versatility and effectiveness in alleviating the accuracy-speed trade-off in traditional numerical solvers.
A Generic Shared Attention Mechanism for Various Backbone Neural Networks
The self-attention mechanism has emerged as a critical component for improving the performance of various backbone neural networks.
The Lottery Ticket Hypothesis for Self-attention in Convolutional Neural Network
Recently many plug-and-play self-attention modules (SAMs) are proposed to enhance the model generalization by exploiting the internal information of deep convolutional neural networks (CNNs).
AlterSGD: Finding Flat Minima for Continual Learning by Alternative Training
Deep neural networks suffer from catastrophic forgetting when learning multiple knowledge sequentially, and a growing number of approaches have been proposed to mitigate this problem.
Blending Pruning Criteria for Convolutional Neural Networks
One filter could be important according to a certain criterion, while it is unnecessary according to another one, which indicates that each criterion is only a partial view of the comprehensive "importance".
CAP: Context-Aware Pruning for Semantic-Segmentation
In this paper, we advocate the importance of contextual information during channel pruning for semantic segmentation networks by presenting a novel Context-aware Pruning framework.
CAP-Context-Aware-Pruning-for-Semantic-Segmentation
In this paper, we advocate the importance of contextual information during channel pruning for semantic segmentation networks by presenting a novel Context-aware Pruning framework.
Efficient Attention Network: Accelerate Attention by Searching Where to Plug
Recently, many plug-and-play self-attention modules are proposed to enhance the model generalization by exploiting the internal information of deep convolutional neural networks (CNNs).
Instance Enhancement Batch Normalization: an Adaptive Regulator of Batch Noise
Batch Normalization (BN)(Ioffe and Szegedy 2015) normalizes the features of an input image via statistics of a batch of images and hence BN will bring the noise to the gradient of the training loss.
DIANet: Dense-and-Implicit Attention Network
Attention networks have successfully boosted the performance in various vision problems.
Ranked #139 on
Image Classification
on CIFAR-100
(using extra training data)
