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Found 9 papers, 2 papers with code
RMT: Retentive Networks Meet Vision Transformers
To alleviate these issues, we draw inspiration from the recent Retentive Network (RetNet) in the field of NLP, and propose RMT, a strong vision backbone with explicit spatial prior for general purposes.
ICDAR 2023 Competition on Structured Text Extraction from Visually-Rich Document Images
It is hoped that this competition will attract many researchers in the field of CV and NLP, and bring some new thoughts to the field of Document AI.
ICDAR 2023 Competition on Reading the Seal Title
To promote research in this area, we organized ICDAR 2023 competition on reading the seal title (ReST), which included two tasks: seal title text detection (Task 1) and end-to-end seal title recognition (Task 2).
OccDepth: A Depth-Aware Method for 3D Semantic Scene Completion
3D Semantic Scene Completion (SSC) can provide dense geometric and semantic scene representations, which can be applied in the field of autonomous driving and robotic systems.
Ranked #13 on
3D Semantic Scene Completion
on SemanticKITTI
Occ^2Net: Robust Image Matching Based on 3D Occupancy Estimation for Occluded Regions
Image matching is a fundamental and critical task in various visual applications, such as Simultaneous Localization and Mapping (SLAM) and image retrieval, which require accurate pose estimation.
Ternary and Binary Quantization for Improved Classification
Furthermore, this quantization property could be maintained in the random projections of sparse features, if both the features and random projection matrices are sufficiently sparse.
Cascaded Compressed Sensing Networks: A Reversible Architecture for Layerwise Learning
In the letter, we show that target propagation could be achieved by modeling the network s each layer with compressed sensing, without the need of auxiliary networks.
Deep Learning to Ternary Hash Codes by Continuation
Recently, it has been observed that {0, 1,-1}-ternary codes which are simply generated from deep features by hard thresholding, tend to outperform {-1, 1}-binary codes in image retrieval.
