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Found 3 papers, 1 papers with code
HEAM : Hashed Embedding Acceleration using Processing-In-Memory
This paper introduces HEAM, a heterogeneous memory architecture that integrates 3D-stacked DRAM with DIMM to accelerate recommendation systems in which compositional embedding is utilized-a technique aimed at reducing the size of embedding tables.
EGformer: Equirectangular Geometry-biased Transformer for 360 Depth Estimation
However, to apply local attention successfully for EIs, a specific strategy, which addresses distorted equirectangular geometry and limited receptive field simultaneously, is required.
Improving 360 Monocular Depth Estimation via Non-local Dense Prediction Transformer and Joint Supervised and Self-supervised Learning
Due to difficulties in acquiring ground truth depth of equirectangular (360) images, the quality and quantity of equirectangular depth data today is insufficient to represent the various scenes in the world.
Ranked #3 on
Depth Estimation
on Stanford2D3D Panoramic
