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Found 7 papers, 5 papers with code
CKGConv: General Graph Convolution with Continuous Kernels
The existing definitions of graph convolution, either from spatial or spectral perspectives, are inflexible and not unified.
Don't Judge by the Look: Towards Motion Coherent Video Representation
Current training pipelines in object recognition neglect Hue Jittering when doing data augmentation as it not only brings appearance changes that are detrimental to classification, but also the implementation is inefficient in practice.
Multi-resolution Time-Series Transformer for Long-term Forecasting
Recent architectures learn complex temporal patterns by segmenting a time-series into patches and using the patches as tokens.
Frame Flexible Network
To fix this issue, we propose a general framework, named Frame Flexible Network (FFN), which not only enables the model to be evaluated at different frames to adjust its computation, but also reduces the memory costs of storing multiple models significantly.
Look More but Care Less in Video Recognition
To tackle this problem, we propose Ample and Focal Network (AFNet), which is composed of two branches to utilize more frames but with less computation.
Parameter-Efficient Masking Networks
We validate the potential of PEMN learning masks on random weights with limited unique values and test its effectiveness for a new compression paradigm based on different network architectures.
Contrastive Learning for Time Series on Dynamic Graphs
We propose a framework called GraphTNC for unsupervised learning of joint representations of the graph and the time-series.
