|TREND||DATASET||BEST METHOD||PAPER TITLE||PAPER||CODE||COMPARE|
Point cloud is an important type of geometric data structure.
Ranked #2 on Scene Segmentation on ScanNet
Compared to YOLOv2 on the MS-COCO object detection, ESPNetv2 delivers 4. 4% higher accuracy with 6x fewer FLOPs.
Ranked #26 on Object Detection on PASCAL VOC 2007
Finally, we gather up the decoder layers with equivalent scales (sizes) to develop a feature pyramid for object detection, in which every feature map consists of the layers (features) from multiple levels.
Ranked #38 on Object Detection on COCO test-dev
Since the output of event cameras is fundamentally different from conventional cameras, it is commonly accepted that they require the development of specialized algorithms to accommodate the particular nature of events.
We analyze key properties of the approach that make it work, finding that the contrastive loss outperforms a popular alternative based on cross-view prediction, and that the more views we learn from, the better the resulting representation captures underlying scene semantics.
Ranked #18 on Self-Supervised Action Recognition on UCF101
This work transfers concepts such as residual/dense connections and dilated convolutions from CNNs to GCNs in order to successfully train very deep GCNs.
Ranked #4 on Node Classification on PPI
In this paper, we address the problem of reducing the memory footprint of convolutional network architectures.
Conventional deep convolutional neural networks (CNNs) apply convolution operators uniformly in space across all feature maps for hundreds of layers - this incurs a high computational cost for real-time applications.
We learn rich natural sound representations by capitalizing on large amounts of unlabeled sound data collected in the wild.