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Found 4 papers, 4 papers with code
BBS-Net: RGB-D Salient Object Detection with a Bifurcated Backbone Strategy Network
In particular, we 1) propose a bifurcated backbone strategy (BBS) to split the multi-level features into teacher and student features, and 2) utilize a depth-enhanced module (DEM) to excavate informative parts of depth cues from the channel and spatial views.
No Time to Waste: Squeeze Time into Channel for Mobile Video Understanding
In this paper, we propose to squeeze the time axis of a video sequence into the channel dimension and present a lightweight video recognition network, term as \textit{SqueezeTime}, for mobile video understanding.
Context-Guided Spatial Feature Reconstruction for Efficient Semantic Segmentation
Specifically, it achieves $43. 6\%$ mIoU on ADE20K with only $4. 0$ GFLOPs, which is $0. 9\%$ and $2. 5\%$ mIoU better than SeaFormer and SegNeXt but with about $38. 0\%$ fewer GFLOPs.
Bifurcated backbone strategy for RGB-D salient object detection
In particular, first, we propose to regroup the multi-level features into teacher and student features using a bifurcated backbone strategy (BBS).
Ranked #2 on
RGB-D Salient Object Detection
on RGBD135
