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Found 27 papers, 10 papers with code
Informativity in Image Captions vs. Referring Expressions
We hope that an understanding of the systematic differences among these human datasets will ultimately allow them to be leveraged more effectively in the associated engineering tasks.
OmniDrag: Enabling Motion Control for Omnidirectional Image-to-Video Generation
As virtual reality gains popularity, the demand for controllable creation of immersive and dynamic omnidirectional videos (ODVs) is increasing.
MambaCSR: Dual-Interleaved Scanning for Compressed Image Super-Resolution With SSMs
We present MambaCSR, a simple but effective framework based on Mamba for the challenging compressed image super-resolution (CSR) task.
MoE-DiffIR: Task-customized Diffusion Priors for Universal Compressed Image Restoration
We present MoE-DiffIR, an innovative universal compressed image restoration (CIR) method with task-customized diffusion priors.
Potential of Multimodal Large Language Models for Data Mining of Medical Images and Free-text Reports
Our experimental results demonstrated that Gemini-series models excelled in report generation and lesion detection but faces challenges in disease classification and anatomical localization.
PromptCIR: Blind Compressed Image Restoration with Prompt Learning
Existing works on blind CIR often seek assistance from a quality factor prediction network to facilitate their network to restore compressed images.
ResVR: Joint Rescaling and Viewport Rendering of Omnidirectional Images
With the advent of virtual reality technology, omnidirectional image (ODI) rescaling techniques are increasingly embraced for reducing transmitted and stored file sizes while preserving high image quality.
The Ninth NTIRE 2024 Efficient Super-Resolution Challenge Report
In sub-track 1, the practical runtime performance of the submissions was evaluated, and the corresponding score was used to determine the ranking.
Unified Predefined-time Stability Conditions of Nonlinear Systems with Lyapunov Analysis
This brief gives a set of unified Lyapunov stability conditions to guarantee the predefined-time/finite-time stability of a dynamical systems.
Invertible Diffusion Models for Compressed Sensing
To enable such memory-intensive end-to-end finetuning, we propose a novel two-level invertible design to transform both (1) the multi-step sampling process and (2) the noise estimation U-Net in each step into invertible networks.
CAMixerSR: Only Details Need More "Attention"
To satisfy the rapidly increasing demands on the large image (2K-8K) super-resolution (SR), prevailing methods follow two independent tracks: 1) accelerate existing networks by content-aware routing, and 2) design better super-resolution networks via token mixer refining.
Ranked #42 on
Image Super-Resolution
on Set14 - 4x upscaling
Optimal Transcoding Resolution Prediction for Efficient Per-Title Bitrate Ladder Estimation
In this paper, we propose to directly predict the optimal transcoding resolution at each preset bitrate for efficient bitrate ladder construction.
Joint State Estimation and Noise Identification Based on Variational Optimization
In this article, the state estimation problems with unknown process noise and measurement noise covariances for both linear and nonlinear systems are considered.
Holistic Evaluation of GPT-4V for Biomedical Imaging
GPT-4V represents a breakthrough in artificial general intelligence (AGI) for computer vision, with applications in the biomedical domain.
ChatRadio-Valuer: A Chat Large Language Model for Generalizable Radiology Report Generation Based on Multi-institution and Multi-system Data
Radiology report generation, as a key step in medical image analysis, is critical to the quantitative analysis of clinically informed decision-making levels.
Evaluating Large Language Models for Radiology Natural Language Processing
The rise of large language models (LLMs) has marked a pivotal shift in the field of natural language processing (NLP).
Video Compression with Arbitrary Rescaling Network
Most video platforms provide video streaming services with different qualities, and the quality of the services is usually adjusted by the resolution of the videos.
Hybrid Transformer and CNN Attention Network for Stereo Image Super-resolution
Multi-stage strategies are frequently employed in image restoration tasks.
OPDN: Omnidirectional Position-aware Deformable Network for Omnidirectional Image Super-Resolution
Model A aims to enhance the feature extraction ability of 360{\deg} image positional information, while Model B further focuses on the high-frequency information of 360{\deg} images.
Self-Asymmetric Invertible Network for Compression-Aware Image Rescaling
In this paper, we propose the Self-Asymmetric Invertible Network (SAIN) for compression-aware image rescaling.
Ranked #2 on
Image Rescaling
on DIV2K val-q90-4x
Panoramic Video Salient Object Detection with Ambisonic Audio Guidance
In this paper, we aim to tackle the video salient object detection problem for panoramic videos, with their corresponding ambisonic audios.
AIM 2022 Challenge on Super-Resolution of Compressed Image and Video: Dataset, Methods and Results
The homepage of this challenge is at https://github. com/RenYang-home/AIM22_CompressSR.
Discovering Dynamic Functional Brain Networks via Spatial and Channel-wise Attention
More importantly, our proposed hybrid attention modules (SA and CA) do not enforce assumptions of linearity and independence as previous methods, and thus provide a novel approach to better understanding dynamic functional brain networks.
NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video: Dataset, Methods and Results
This challenge includes three tracks.
3D Hierarchical Refinement and Augmentation for Unsupervised Learning of Depth and Pose from Monocular Video
In this framework, the depth and pose estimations are hierarchically and mutually coupled to refine the estimated pose layer by layer.
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.
Modeling 4D fMRI Data via Spatio-Temporal Convolutional Neural Networks (ST-CNN)
Simultaneous modeling of the spatio-temporal variation patterns of brain functional network from 4D fMRI data has been an important yet challenging problem for the field of cognitive neuroscience and medical image analysis.
