Search Results for author:
Found 11 papers, 5 papers with code
m-RevNet: Deep Reversible Neural Networks with Momentum
In recent years, the connections between deep residual networks and first-order Ordinary Differential Equations (ODEs) have been disclosed.
Representative Batch Normalization With Feature Calibration
We propose to add a simple yet effective feature calibration scheme into the centering and scaling operations of BatchNorm, enhancing the instance-specific representations with the negligible computational cost.
Global2Local: Efficient Structure Search for Video Action Segmentation
Our search scheme exploits both global search to find the coarse combinations and local search to get the refined receptive field combination patterns further.
Ranked #20 on
Action Segmentation
on Breakfast
iNAS: Integral NAS for Device-Aware Salient Object Detection
Existing salient object detection (SOD) models usually focus on either backbone feature extractors or saliency heads, ignoring their relations.
Split to Be Slim: An Overlooked Redundancy in Vanilla Convolution
Therefore, instead of directly removing uncertain redundant features, we propose a \textbf{sp}lit based \textbf{conv}olutional operation, namely SPConv, to tolerate features with similar patterns but require less computation.
JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation
The chest CT scan test provides a valuable complementary tool to the RT-PCR test, and it can identify the patients in the early-stage with high sensitivity.
Highly Efficient Salient Object Detection with 100K Parameters
Salient object detection models often demand a considerable amount of computation cost to make precise prediction for each pixel, making them hardly applicable on low-power devices.
Res2Net: A New Multi-scale Backbone Architecture
We evaluate the Res2Net block on all these models and demonstrate consistent performance gains over baseline models on widely-used datasets, e. g., CIFAR-100 and ImageNet.
Ranked #2 on
Image Classification
on GasHisSDB
Optimizing the F-measure for Threshold-free Salient Object Detection
By reformulating the standard F-measure we propose the relaxed F-measure which is differentiable w. r. t the posterior and can be easily appended to the back of CNNs as the loss function.
Hi-Fi: Hierarchical Feature Integration for Skeleton Detection
In natural images, the scales (thickness) of object skeletons may dramatically vary among objects and object parts, making object skeleton detection a challenging problem.
Ranked #2 on
Object Skeleton Detection
on SK-LARGE
