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Found 6 papers, 3 papers with code
MOVES: Movable and Moving LiDAR Scene Segmentation in Label-Free settings using Static Reconstruction
In the real world however, LiDAR scans consist of non-stationary dynamic structures - moving and movable objects.
GAFNet: A Global Fourier Self Attention Based Novel Network for multi-modal downstream tasks
We introduce a novel network, GAFNet (Global Attention Fourier Net), which learns through large-scale pre-training over three image-text datasets (COCO, SBU, and CC-3M), for achieving high performance on downstream vision and language tasks.
TPFNet: A Novel Text In-painting Transformer for Text Removal
Further, we use a novel multi-headed decoder that generates a high-pass filtered image and a segmentation map, in addition to a text-free image.
ACLNet: An Attention and Clustering-based Cloud Segmentation Network
We propose a novel deep learning model named ACLNet, for cloud segmentation from ground images.
Ranked #1 on
Semantic Segmentation
on SWINySEG
WaferSegClassNet -- A Light-weight Network for Classification and Segmentation of Semiconductor Wafer Defects
WSCN has a model size of only 0. 51MB and performs only 0. 2M FLOPS.
Ranked #1 on
Semantic Segmentation
on MixedWM38
RRLFSOR: An Efficient Self-Supervised Learning Strategy of Graph Convolutional Networks
Graph Convolutional Networks (GCNs) are widely used in many applications yet still need large amounts of labelled data for training.
