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Found 24 papers, 8 papers with code
A Medical Data-Effective Learning Benchmark for Highly Efficient Pre-training of Foundation Models
This paper introduces data-effective learning, aiming to use data in the most impactful way to pre-train foundation models.
Low-latency Space-time Supersampling for Real-time Rendering
With the rise of real-time rendering and the evolution of display devices, there is a growing demand for post-processing methods that offer high-resolution content in a high frame rate.
Context-Aware Iteration Policy Network for Efficient Optical Flow Estimation
In this paper, we develop a Context-Aware Iteration Policy Network for efficient optical flow estimation, which determines the optimal number of iterations per sample.
Unsupervised Disentangling of Facial Representations with 3D-aware Latent Diffusion Models
Second, we propose a novel representation diffusion model (RDM) to disentangle 3D latent into facial identity and expression.
MVFlow: Deep Optical Flow Estimation of Compressed Videos with Motion Vector Prior
Motion vectors, one of the compression information, record the motion of the video frames.
SAMFlow: Eliminating Any Fragmentation in Optical Flow with Segment Anything Model
Optical Flow Estimation aims to find the 2D dense motion field between two frames.
Uncertainty-Guided Spatial Pruning Architecture for Efficient Frame Interpolation
We can use dynamic spatial pruning method to skip redundant computation, but this method cannot properly identify easy regions in VFI tasks without supervision.
DifFSS: Diffusion Model for Few-Shot Semantic Segmentation
Although various few-shot semantic segmentation (FSS) models with different network structures have been proposed, performance improvement has reached a bottleneck.
Ranked #5 on
Few-Shot Semantic Segmentation
on PASCAL-5i (1-Shot)
Instruct-NeuralTalker: Editing Audio-Driven Talking Radiance Fields with Instructions
Given a short speech video, we first build an efficient talking radiance field, and then apply the latest conditional diffusion model for image editing based on the given instructions and guiding implicit representation optimization towards the editing target.
Learning Survival Distribution with Implicit Survival Function
Survival analysis aims at modeling the relationship between covariates and event occurrence with some untracked (censored) samples.
Multi-Modality Deep Network for JPEG Artifacts Reduction
In recent years, many convolutional neural network-based models are designed for JPEG artifacts reduction, and have achieved notable progress.
Multi-Modality Deep Network for Extreme Learned Image Compression
Image-based single-modality compression learning approaches have demonstrated exceptionally powerful encoding and decoding capabilities in the past few years , but suffer from blur and severe semantics loss at extremely low bitrates.
Geometry-Aware Reference Synthesis for Multi-View Image Super-Resolution
To address these problems, we propose the MVSRnet, which uses geometry information to extract sharp details from all LR multi-view to support the SR of the LR input view.
Learning Parallax Transformer Network for Stereo Image JPEG Artifacts Removal
Under stereo settings, the performance of image JPEG artifacts removal can be further improved by exploiting the additional information provided by a second view.
Rethinking Super-Resolution as Text-Guided Details Generation
To enhance the semantic accuracy and the visual quality of the reconstructed image, we explore the multi-modal fusion learning in SISR by proposing a Text-Guided Super-Resolution (TGSR) framework, which can effectively utilize the information from the text and image modalities.
Perception-Oriented Stereo Image Super-Resolution
Recent studies of deep learning based stereo image super-resolution (StereoSR) have promoted the development of StereoSR.
Learning Robust Image-Based Rendering on Sparse Scene Geometry via Depth Completion
Specifically, GR stage takes sparse depth map and RGB as input to predict dense depth map by exploiting the correlation between two modals.
Feature Pyramid Network for Multi-task Affective Analysis
Affective Analysis is not a single task, and the valence-arousal value, expression class, and action unit can be predicted at the same time.
Perceptual Variousness Motion Deblurring With Light Global Context Refinement
Deep learning algorithms have made significant progress in dynamic scene deblurring.
Frame and Feature-Context Video Super-Resolution
For video super-resolution, current state-of-the-art approaches either process multiple low-resolution (LR) frames to produce each output high-resolution (HR) frame separately in a sliding window fashion or recurrently exploit the previously estimated HR frames to super-resolve the following frame.
RDGAN : Retinex Decomposition Based Adversarial Learning for Low-Light Enhancement
We also present a new RDGAN (Retinex Decomposition based Generative Adversarial Network) loss, which is computed on the decomposed reflectance components of the enhanced an the reference images.
Cycle-IR: Deep Cyclic Image Retargeting
Our idea is built on the reverse mapping from the retargeted images to the given input images.
Feature Super-Resolution: Make Machine See More Clearly
In this paper, different from image super-resolution (ISR), we propose a novel super-resolution technique called feature super-resolution (FSR), which aims at enhancing the discriminatory power of small size image in order to provide high recognition precision for machine.
Understanding the Feedforward Artificial Neural Network Model From the Perspective of Network Flow
Experiments for two types of ANN model including multi-layer MLP and CNN verify that the network flow based on class-pathway is a reasonable explanation for ANN models.
