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Found 13 papers, 2 papers with code
AbsGS: Recovering Fine Details for 3D Gaussian Splatting
In this work, we present a comprehensive analysis of the cause of aforementioned artifacts, namely gradient collision, which prevents large Gaussians in over-reconstructed regions from splitting.
TFDMNet: A Novel Network Structure Combines the Time Domain and Frequency Domain Features
To get the balance between the computation complexity and memory usage, we propose a new network structure, namely Time-Frequency Domain Mixture Network (TFDMNet), which combines the advantages of both convolution layers and EMLs.
Rethinking SIGN Training: Provable Nonconvex Acceleration without First- and Second-Order Gradient Lipschitz
Sign-based stochastic methods have gained attention due to their ability to achieve robust performance despite using only the sign information for parameter updates.
PVP: Pre-trained Visual Parameter-Efficient Tuning
Large-scale pre-trained transformers have demonstrated remarkable success in various computer vision tasks.
Ranked #4 on
Image Classification
on VTAB-1k
(using extra training data)
Towards Vision Transformer Unrolling Fixed-Point Algorithm: a Case Study on Image Restoration
It provides a promising way to introduce the Transformer in low-level vision tasks.
Multi-Outputs Is All You Need For Deblur
Therefore, we propose to make the network learn the distribution of feasible solutions, and design based on this consideration a novel multi-head output architecture and corresponding loss function for distribution learning.
Ranked #5 on
Image Deblurring
on GoPro
Visual Tree Convolutional Neural Network in Image Classification
In image classification, Convolutional Neural Network(CNN) models have achieved high performance with the rapid development in deep learning.
IF-TTN: Information Fused Temporal Transformation Network for Video Action Recognition
Focusing on discriminate spatiotemporal feature learning, we propose Information Fused Temporal Transformation Network (IF-TTN) for action recognition on top of popular Temporal Segment Network (TSN) framework.
Exploring Frame Segmentation Networks for Temporal Action Localization
The proposed FSN can make dense predictions at frame-level for a video clip using both spatial and temporal context information.
Learning Generic Diffusion Processes for Image Restoration
On the contrary, the regularization term learned via discriminative approaches are usually trained for a specific image restoration problem, and fail in the problem for which it is not trained.
Exploring Temporal Preservation Networks for Precise Temporal Action Localization
A newly proposed work exploits Convolutional-Deconvolutional-Convolutional (CDC) filters to upsample the predictions of 3D ConvNets, making it possible to perform per-frame action predictions and achieving promising performance in terms of temporal action localization.
Open-Ended Question Answering
Temporal Action Localization
+1
Learning Non-local Image Diffusion for Image Denoising
In order to preserve the expected property that end-to-end training is available, we exploit the NSS prior by a set of non-local filters, and derive our proposed trainable non-local reaction diffusion (TNLRD) model for image denoising.
Image Denoising via Multi-scale Nonlinear Diffusion Models
However, in recent two years, discriminatively trained local approaches have started to outperform previous non-local models and have been attracting increasing attentions due to the additional advantage of computational efficiency.
