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Found 9 papers, 4 papers with code
Hierarchical Dense Correlation Distillation for Few-Shot Segmentation-Extended Abstract
We hope our work can benefit broader industrial applications where novel classes with limited annotations are required to be decently identified.
Temporal Enhanced Training of Multi-view 3D Object Detector via Historical Object Prediction
The HoP approach is straightforward: given the current timestamp t, we generate a pseudo Bird's-Eye View (BEV) feature of timestamp t-k from its adjacent frames and utilize this feature to predict the object set at timestamp t-k. Our approach is motivated by the observation that enforcing the detector to capture both the spatial location and temporal motion of objects occurring at historical timestamps can lead to more accurate BEV feature learning.
Ranked #3 on
3D Object Detection
on nuScenes Camera Only
Hierarchical Dense Correlation Distillation for Few-Shot Segmentation
Few-shot semantic segmentation (FSS) aims to form class-agnostic models segmenting unseen classes with only a handful of annotations.
Ranked #6 on
Few-Shot Semantic Segmentation
on COCO-20i (1-shot)
Two-person Graph Convolutional Network for Skeleton-based Human Interaction Recognition
To overcome the above shortcoming, we introduce a novel unified two-person graph to represent inter-body and intra-body correlations between joints.
Elastic Functional Coding of Riemannian Trajectories
We propose to learn an embedding such that each action trajectory is mapped to a single point in a low-dimensional Euclidean space, and the trajectories that differ only in temporal rates map to the same point.
Temporally Coherent Interpretations for Long Videos Using Pattern Theory
Graph-theoretical methods have successfully provided semantic and structural interpretations of images and videos.
Elastic Functional Coding of Human Actions: From Vector-Fields to Latent Variables
Learning an accurate low dimensional embedding for actions could have a huge impact in the areas of efficient search and retrieval, visualization, learning, and recognition.
Video-Based Action Recognition Using Rate-Invariant Analysis of Covariance Trajectories
Using a natural Riemannain metric on vector bundles of SPDMs, we compute geodesic paths and geodesic distances between trajectories in the quotient space of this vector bundle, with respect to the re-parameterization group.
Rate-Invariant Analysis of Trajectories on Riemannian Manifolds with Application in Visual Speech Recognition
We apply this framework to the problem of speech recognition using both audio and visual components.
