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Found 18 papers, 7 papers with code
NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video: Dataset, Methods and Results
This challenge includes three tracks.
Sparsity Winning Twice: Better Robust Generalization from More Efficient Training
We introduce two alternatives for sparse adversarial training: (i) static sparsity, by leveraging recent results from the lottery ticket hypothesis to identify critical sparse subnetworks arising from the early training; (ii) dynamic sparsity, by allowing the sparse subnetwork to adaptively adjust its connectivity pattern (while sticking to the same sparsity ratio) throughout training.
VAQF: Fully Automatic Software-Hardware Co-Design Framework for Low-Bit Vision Transformer
To the best of our knowledge, this is the first time quantization has been incorporated into ViT acceleration on FPGAs with the help of a fully automatic framework to guide the quantization strategy on the software side and the accelerator implementations on the hardware side given the target frame rate.
Over-the-Air Aggregation for Federated Learning: Waveform Superposition and Prototype Validation
In this paper, we develop an orthogonal-frequency-division-multiplexing (OFDM)-based over-the-air (OTA) aggregation solution for wireless federated learning (FL).
AFTer-UNet: Axial Fusion Transformer UNet for Medical Image Segmentation
One has to either downsample the image or use cropped local patches to reduce GPU memory usage, which limits its performance.
TransFusion: Cross-view Fusion with Transformer for 3D Human Pose Estimation
The 3D position encoding guided by the epipolar field provides an efficient way of encoding correspondences between pixels of different views.
Stingy Teacher: Sparse Logits Suffice to Fail Knowledge Distillation
However, it is unclear why the distorted distribution of the logits is catastrophic to the student model.
SGE net: Video object detection with squeezed GRU and information entropy map
Compared with object detection of static images, video object detection is more challenging due to the motion of objects, while providing rich temporal information.
Undistillable: Making A Nasty Teacher That CANNOT teach students
Knowledge Distillation (KD) is a widely used technique to transfer knowledge from pre-trained teacher models to (usually more lightweight) student models.
Coarse-to-Fine Domain Adaptive Semantic Segmentation with Photometric Alignment and Category-Center Regularization
Unsupervised domain adaptation (UDA) in semantic segmentation is a fundamental yet promising task relieving the need for laborious annotation works.
Spending Your Winning Lottery Better After Drawing It
In this paper, we demonstrate that it is unnecessary for spare retraining to strictly inherit those properties from the dense network.
SIA-GCN: A Spatial Information Aware Graph Neural Network with 2D Convolutions for Hand Pose Estimation
In this paper, we extend GNNs along two directions: a) allowing features at each node to be represented by 2D spatial confidence maps instead of 1D vectors; and b) proposing an efficient operation to integrate information from neighboring nodes through 2D convolutions with different learnable kernels at each edge.
Real-MFF: A Large Realistic Multi-focus Image Dataset with Ground Truth
In this letter, we introduce a large and realistic multi-focus dataset called Real-MFF, which contains 710 pairs of source images with corresponding ground truth images.
Rotation-invariant Mixed Graphical Model Network for 2D Hand Pose Estimation
In this paper, we propose a new architecture named Rotation-invariant Mixed Graphical Model Network (R-MGMN) to solve the problem of 2D hand pose estimation from a monocular RGB image.
Nonparametric Structure Regularization Machine for 2D Hand Pose Estimation
We propose a novel Nonparametric Structure Regularization Machine (NSRM) for 2D hand pose estimation, adopting a cascade multi-task architecture to learn hand structure and keypoint representations jointly.
An α-Matte Boundary Defocus Model Based Cascaded Network for Multi-focus Image Fusion
Based on this {\alpha}-matte defocus model and the generated data, a cascaded boundary aware convolutional network termed MMF-Net is proposed and trained, aiming to achieve clearer fusion results around the FDB.
Adaptive Graphical Model Network for 2D Handpose Estimation
In this paper, we propose a new architecture called Adaptive Graphical Model Network (AGMN) to tackle the task of 2D hand pose estimation from a monocular RGB image.
Boundary Aware Multi-Focus Image Fusion Using Deep Neural Network
Since it is usually difficult to capture an all-in-focus image of a 3D scene directly, various multi-focus image fusion methods are employed to generate it from several images focusing at different depths.
