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Found 4 papers, 2 papers with code
Soft Masked Transformer for Point Cloud Processing with Skip Attention-Based Upsampling
This strategy allows various transformer blocks to share the same position information over the same resolution points, thereby reducing network parameters and training time without compromising accuracy. Experimental comparisons with existing methods on multiple datasets demonstrate the efficacy of SMTransformer and skip-attention-based up-sampling for point cloud processing tasks, including semantic segmentation and classification.
OST: Refining Text Knowledge with Optimal Spatio-Temporal Descriptor for General Video Recognition
Due to the resource-intensive nature of training vision-language models on expansive video data, a majority of studies have centered on adapting pre-trained image-language models to the video domain.
Ranked #2 on
Zero-Shot Action Recognition
on Kinetics
Multi-scale Alternated Attention Transformer for Generalized Stereo Matching
Recent stereo matching networks achieves dramatic performance by introducing epipolar line constraint to limit the matching range of dual-view.
AShapeFormer: Semantics-Guided Object-Level Active Shape Encoding for 3D Object Detection via Transformers
In this work, we propose AShapeFormer, a semantics-guided object-level shape encoding module for 3D object detection.
