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Found 11 papers, 5 papers with code
BIRNAT: Bidirectional Recurrent Neural Networks with Adversarial Training for Video Snapshot Compressive Imaging
This measurement and the modulation masks are fed into our Recurrent Neural Network (RNN) to reconstruct the desired high-speed frames.
Kernel Semi-Implicit Variational Inference
This way, the upper-level objective becomes the kernel Stein discrepancy (KSD), which is readily computable for stochastic gradient descent due to the hierarchical structure of semi-implicit variational distributions.
Reflected Flow Matching
We propose reflected flow matching (RFM) to train the velocity model in reflected CNFs by matching the conditional velocity fields in a simulation-free manner, similar to the vanilla FM.
The Limits and Potentials of Local SGD for Distributed Heterogeneous Learning with Intermittent Communication
In this paper, we provide new lower bounds for local SGD under existing first-order data heterogeneity assumptions, showing that these assumptions are insufficient to prove the effectiveness of local update steps.
SnapCap: Efficient Snapshot Compressive Video Captioning
To address these problems, in this paper, we propose a novel VC pipeline to generate captions directly from the compressed measurement, which can be captured by a snapshot compressive sensing camera and we dub our model SnapCap.
Momentum Benefits Non-IID Federated Learning Simply and Provably
When all clients participate in the training process, we demonstrate that incorporating momentum allows FedAvg to converge without relying on the assumption of bounded data heterogeneity even using a constant local learning rate.
Joint Graph Learning and Model Fitting in Laplacian Regularized Stratified Models
Laplacian regularized stratified models (LRSM) are models that utilize the explicit or implicit network structure of the sub-problems as defined by the categorical features called strata (e. g., age, region, time, forecast horizon, etc.
Motion-aware Dynamic Graph Neural Network for Video Compressive Sensing
Video snapshot compressive imaging (SCI) utilizes a 2D detector to capture sequential video frames and compresses them into a single measurement.
Dual-view Snapshot Compressive Imaging via Optical Flow Aided Recurrent Neural Network
In this paper, we propose an optical flow-aided recurrent neural network for dual video SCI systems, which provides high-quality decoding in seconds.
Memory-Efficient Network for Large-scale Video Compressive Sensing
With the knowledge of masks, optimization algorithms or deep learning methods are employed to reconstruct the desired high-speed video frames from this snapshot measurement.
MetaSCI: Scalable and Adaptive Reconstruction for Video Compressive Sensing
To capture high-speed videos using a two-dimensional detector, video snapshot compressive imaging (SCI) is a promising system, where the video frames are coded by different masks and then compressed to a snapshot measurement.
