Search Results for author:
Found 24 papers, 8 papers with code
A Flexible Recurrent Residual Pyramid Network for Video Frame Interpolation
Inspired by classical pyramid energy minimization optical flow algorithms, this paper proposes a recurrent residual pyramid network (RRPN) for video frame interpolation.
PKU-DyMVHumans: A Multi-View Video Benchmark for High-Fidelity Dynamic Human Modeling
To facilitate the development of these fields, in this paper, we present PKU-DyMVHumans, a versatile human-centric dataset for high-fidelity reconstruction and rendering of dynamic human scenarios from dense multi-view videos.
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.
Audio-Infused Automatic Image Colorization by Exploiting Audio Scene Semantics
Second, the natural co-occurrence of audio and video is utilized to learn the color semantic correlations between audio and visual scenes.
TLIC: Learned Image Compression with ROI-Weighted Distortion and Bit Allocation
This short paper describes our method for the track of image compression.
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
HFLIC: Human Friendly Perceptual Learned Image Compression with Reinforced Transform
In recent years, there has been rapid development in learned image compression techniques that prioritize ratedistortion-perceptual compression, preserving fine details even at lower bit-rates.
LLIC: Large Receptive Field Transform Coding with Adaptive Weights for Learned Image Compression
To tackle this issue, we propose Large Receptive Field Transform Coding with Adaptive Weights for Learned Image Compression (LLIC).
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.
GeoMVSNet: Learning Multi-View Stereo With Geometry Perception
To intensify the full-scene geometry perception of our model, we present the depth distribution similarity loss based on the Gaussian-Mixture Model assumption.
Ranked #2 on
Point Clouds
on Tanks and Temples
Revisiting the Stack-Based Inverse Tone Mapping
In this paper, we revisit the stack-based ITM approaches and propose a novel method to reconstruct HDR radiance from a single image, which only needs to estimate two exposure images.
CL-MVSNet: Unsupervised Multi-View Stereo with Dual-Level Contrastive Learning
On the one hand, we present an image-level contrastive branch to guide the model to acquire more context awareness, thus leading to more complete depth estimation in indistinguishable regions.
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
Improving Multi-generation Robustness of Learned Image Compression
Benefit from flexible network designs and end-to-end joint optimization approach, learned image compression (LIC) has demonstrated excellent coding performance and practical feasibility in recent years.
Rethinking Depth Estimation for Multi-View Stereo: A Unified Representation
Depth estimation is solved as a regression or classification problem in existing learning-based multi-view stereo methods.
Ranked #9 on
3D Reconstruction
on DTU
DeepFGS: Fine-Grained Scalable Coding for Learned Image Compression
In this paper, we propose the first learned fine-grained scalable image compression model (DeepFGS) to overcome the above two shortcomings.
Multi-frame Joint Enhancement for Early Interlaced Videos
Early interlaced videos usually contain multiple and interlacing and complex compression artifacts, which significantly reduce the visual quality.
Ranked #1 on
Video Deinterlacing
on MSU Deinterlacer Benchmark
Excavating the Potential Capacity of Self-Supervised Monocular Depth Estimation
Self-supervised methods play an increasingly important role in monocular depth estimation due to their great potential and low annotation cost.
Super-Resolving Compressed Video in Coding Chain
Scaling and lossy coding are widely used in video transmission and storage.
COLA-Net: Collaborative Attention Network for Image Restoration
Local and non-local attention-based methods have been well studied in various image restoration tasks while leading to promising performance.
Rethinking deinterlacing for early interlaced videos
Traditional deinterlacing approaches are mainly focused on early interlacing scanning systems and thus cannot handle the complex and complicated artifacts in real-world early interlaced videos.
Beyond Monte Carlo Tree Search: Playing Go with Deep Alternative Neural Network and Long-Term Evaluation
We show such setting can preserve more contexts of local features and its evolutions which are beneficial for move prediction.
Local Patch Encoding-Based Method for Single Image Super-Resolution
The proposed method consists of a learning stage and a reconstructing stage.
Deep Alternative Neural Network: Exploring Contexts as Early as Possible for Action Recognition
This paper instead explores contexts as early as possible and leverages their evolutions for action recognition.
