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Found 7 papers, 3 papers with code
Revisiting Multi-Scale Feature Fusion for Semantic Segmentation
It is commonly believed that high internal resolution combined with expensive operations (e. g. atrous convolutions) are necessary for accurate semantic segmentation, resulting in slow speed and large memory usage.
DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object Detection
In this paper, we propose two novel techniques: InverseAug that inverses geometric-related augmentations, e. g., rotation, to enable accurate geometric alignment between lidar points and image pixels, and LearnableAlign that leverages cross-attention to dynamically capture the correlations between image and lidar features during fusion.
A Design Space Study for LISTA and Beyond
Unrolling is believed to incorporate the model-based prior with the learning capacity of deep learning.
Rethinking Co-design of Neural Architectures and Hardware Accelerators
In our work, we target the optimization of hardware and software configurations on an industry-standard edge accelerator.
Spatiotemporal Contrastive Video Representation Learning
Our representations are learned using a contrastive loss, where two augmented clips from the same short video are pulled together in the embedding space, while clips from different videos are pushed away.
Ranked #1 on
Self-Supervised Action Recognition
on Kinetics-600
Go Wide, Then Narrow: Efficient Training of Deep Thin Networks
This is achieved by layerwise imitation, that is, forcing the thin network to mimic the intermediate outputs of the wide network from layer to layer.
SSN: Learning Sparse Switchable Normalization via SparsestMax
Unlike $\ell_1$ and $\ell_0$ constraints that impose difficulties in optimization, we turn this constrained optimization problem into feed-forward computation by proposing SparsestMax, which is a sparse version of softmax.
