Search Results for author:
Found 24 papers, 12 papers with code
Weakly Supervised Segmentation of Vertebral Bodies with Iterative Slice-propagation
In this paper, we propose a Weakly supervised Iterative Spinal Segmentation (WISS) method leveraging only four corner landmark weak labels on a single sagittal slice to achieve automatic volumetric segmentation from CT images for VBs.
Learning-based Bone Quality Classification Method for Spinal Metastasis
In this paper, we explore a learning-based automatic bone quality classification method for spinal metastasis based on CT images.
AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation
AutoGen is an open-source framework that allows developers to build LLM applications via multiple agents that can converse with each other to accomplish tasks.
Boost Video Frame Interpolation via Motion Adaptation
Video frame interpolation (VFI) is a challenging task that aims to generate intermediate frames between two consecutive frames in a video.
Deep Active Learning with Structured Neural Depth Search
Previous work optimizes traditional active learning (AL) processes with incremental neural network architecture search (Active-iNAS) based on data complexity change, which improves the accuracy and learning efficiency.
Intelligent Grimm -- Open-ended Visual Storytelling via Latent Diffusion Models
Generative models have recently exhibited exceptional capabilities in text-to-image generation, but still struggle to generate image sequences coherently.
Boundary-aware Supervoxel-level Iteratively Refined Interactive 3D Image Segmentation with Multi-agent Reinforcement Learning
Interactive segmentation has recently been explored to effectively and efficiently harvest high-quality segmentation masks by iteratively incorporating user hints.
DR2: Diffusion-based Robust Degradation Remover for Blind Face Restoration
However, it is expensive and infeasible to include every type of degradation to cover real-world cases in the training data.
Open-vocabulary Object Segmentation with Diffusion Models
The goal of this paper is to extract the visual-language correspondence from a pre-trained text-to-image diffusion model, in the form of segmentation map, i. e., simultaneously generating images and segmentation masks for the corresponding visual entities described in the text prompt.
A Simple Plugin for Transforming Images to Arbitrary Scales
Existing models on super-resolution often specialized for one scale, fundamentally limiting their use in practical scenarios.
Task Decoupled Framework for Reference-Based Super-Resolution
Those methods conduct the super-resolution task of the input low-resolution(LR) image and the texture transfer task from the reference image together in one module, easily introducing the interference between LR and reference features.
LAR-SR: A Local Autoregressive Model for Image Super-Resolution
Previous super-resolution (SR) approaches often formulate SR as a regression problem and pixel wise restoration, which leads to a blurry and unreal SR output.
Learning to Learn Graph Topologies
The model is trained in an end-to-end fashion with pairs of node data and graph samples.
CaT: Weakly Supervised Object Detection with Category Transfer
To handle overlapping category transfer, we propose a double-supervision mean teacher to gather common category information and bridge the domain gap between two datasets.
Adaptive Mutual Supervision for Weakly-Supervised Temporal Action Localization
To solve this issue, we introduce an adaptive mutual supervision framework (AMS) with two branches, where the base branch adopts CAS to localize the most discriminative action regions, while the supplementary branch localizes the less discriminative action regions through a novel adaptive sampler.
Ranked #7 on
Weakly Supervised Action Localization
on THUMOS14
Weakly Supervised Action Localization
Weakly-supervised Temporal Action Localization
+1
Two-Stream Compare and Contrast Network for Vertebral Compression Fracture Diagnosis
In this paper, we explore to model VCFs diagnosis as a three-class classification problem, i. e. normal vertebrae, benign VCFs, and malignant VCFs.
Content Adaptive and Error Propagation Aware Deep Video Compression
Therefore, the encoder is adaptive to different video contents and achieves better compression performance by reducing the domain gap between the training and testing datasets.
Iteratively-Refined Interactive 3D Medical Image Segmentation with Multi-Agent Reinforcement Learning
We here propose to model the dynamic process of iterative interactive image segmentation as a Markov decision process (MDP) and solve it with reinforcement learning (RL).
Depth-Aware Video Frame Interpolation
The proposed model then warps the input frames, depth maps, and contextual features based on the optical flow and local interpolation kernels for synthesizing the output frame.
Ranked #5 on
Video Frame Interpolation
on Middlebury
DVC: An End-to-end Deep Video Compression Framework
Conventional video compression approaches use the predictive coding architecture and encode the corresponding motion information and residual information.
MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Frame Interpolation and Enhancement
In this work, we propose a motion estimation and motion compensation driven neural network for video frame interpolation.
Ranked #6 on
Video Frame Interpolation
on Middlebury
MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Interpolation and Enhancement
Recently, a number of data-driven frame interpolation methods based on convolutional neural networks have been proposed.
Ranked #22 on
Video Frame Interpolation
on Vimeo90K
Deep Kalman Filtering Network for Video Compression Artifact Reduction
In this paper, we model the video artifact reduction task as a Kalman filtering procedure and restore decoded frames through a deep Kalman filtering network.
Enhancing HEVC Compressed Videos with a Partition-masked Convolutional Neural Network
In this paper, we propose a partition-masked Convolution Neural Network (CNN) to achieve compressed-video enhancement for the state-of-the-art coding standard, High Efficiency Video Coding (HECV).
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