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Found 8 papers, 0 papers with code
Unveiling Backbone Effects in CLIP: Exploring Representational Synergies and Variances
Contrastive Language-Image Pretraining (CLIP) stands out as a prominent method for image representation learning.
Zero-shot Retrieval: Augmenting Pre-trained Models with Search Engines
Large pre-trained models can dramatically reduce the amount of task-specific data required to solve a problem, but they often fail to capture domain-specific nuances out of the box.
Independent Modular Networks
However, a lack of structure in each module's role, and modular network-specific issues such as module collapse have restricted their usability.
Action Capsules: Human Skeleton Action Recognition
Due to the compact and rich high-level representations offered, skeleton-based human action recognition has recently become a highly active research topic.
Ranked #4 on
Skeleton Based Action Recognition
on N-UCLA
A Consistency-Based Loss for Deep Odometry Through Uncertainty Propagation
In such cases where multiple losses are imposed on a network, the uncertainty over each output can be derived to weigh the different loss terms in a maximum likelihood setting.
Deep Inertial Odometry with Accurate IMU Preintegration
Inertial Measurement Units (IMUs) are interceptive modalities that provide ego-motion measurements independent of the environmental factors.
Exploring Self-Attention for Visual Odometry
Visual odometry networks commonly use pretrained optical flow networks in order to derive the ego-motion between consecutive frames.
ARC-Net: Activity Recognition Through Capsules
We provide heatmaps of the priors, learned by the network, to visualize the utilization of each of the data sources by the trained network.
