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Found 15 papers, 9 papers with code
UCVC: A Unified Contextual Video Compression Framework with Joint P-frame and B-frame Coding
This paper presents a learned video compression method in response to video compression track of the 6th Challenge on Learned Image Compression (CLIC), at DCC 2024. Specifically, we propose a unified contextual video compression framework (UCVC) for joint P-frame and B-frame coding.
ConsistNet: Enforcing 3D Consistency for Multi-view Images Diffusion
Given a single image of a 3D object, this paper proposes a novel method (named ConsistNet) that is able to generate multiple images of the same object, as if seen they are captured from different viewpoints, while the 3D (multi-view) consistencies among those multiple generated images are effectively exploited.
A Comprehensive Review of Community Detection in Graphs
This review article delves into the topic of community detection in graphs, which serves as a thorough exposition of various community detection methods from perspectives of modularity-based method, spectral clustering, probabilistic modelling, and deep learning.
Stereo Matching in Time: 100+ FPS Video Stereo Matching for Extended Reality
One of the major difficulties is the lack of high-quality indoor video stereo training datasets captured by head-mounted VR/AR glasses.
MLIC++: Linear Complexity Multi-Reference Entropy Modeling for Learned Image Compression
Additionally, to capture global contexts, we propose the linear complexity attention-based global correlations capturing by leveraging the decomposition of the softmax operation.
Ranked #1 on
Image Compression
on kodak
Parametric Depth Based Feature Representation Learning for Object Detection and Segmentation in Bird's Eye View
In contrast, we propose to use parametric depth distribution modeling for feature transformation.
LLIC: Large Receptive Field Transform Coding with Adaptive Weights for Learned Image Compression
To demonstrate the effectiveness of proposed transform coding, we align the entropy model to compare with existing transform methods and obtain models LLIC-STF, LLIC-ELIC, LLIC-TCM.
Butterfly: Multiple Reference Frames Feature Propagation Mechanism for Neural Video Compression
Using more reference frames can significantly improve the compression efficiency in neural video compression.
Parametric Depth Based Feature Representation Learning for Object Detection and Segmentation in Bird's-Eye View
In contrast, we propose to use parametric depth distribution modeling for feature transformation.
MLIC: Multi-Reference Entropy Model for Learned Image Compression
Based on MEM and MEM$^+$, we propose image compression models MLIC and MLIC$^+$.
Ranked #1 on
Image Compression
on kodak
Non-parametric Depth Distribution Modelling based Depth Inference for Multi-view Stereo
Boundary pixels usually follow a multi-modal distribution as they represent different depths; Therefore, the assumption results in an erroneous depth prediction at the coarser level of the cost volume pyramid and can not be corrected in the refinement levels leading to wrong depth predictions.
NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Methods and Results
This paper reviews the first NTIRE challenge on quality enhancement of compressed video, with a focus on the proposed methods and results.
Self-supervised Learning of Depth Inference for Multi-view Stereo
Here, we propose a self-supervised learning framework for multi-view stereo that exploit pseudo labels from the input data.
Super-Resolving Compressed Video in Coding Chain
Scaling and lossy coding are widely used in video transmission and storage.
Cost Volume Pyramid Based Depth Inference for Multi-View Stereo
We propose a cost volume-based neural network for depth inference from multi-view images.
Ranked #14 on
3D Reconstruction
on DTU
