Search Results for author:
Found 10 papers, 4 papers with code
Multi-view Distillation based on Multi-modal Fusion for Few-shot Action Recognition(CLIP-$\mathrm{M^2}$DF)
In each view, we fuse the prompt embedding as consistent information with visual and the global or local temporal context to overcome the overlapping distribution of classes and outliers.
Forced Exploration in Bandit Problems
This paper aims to design a multi-armed bandit algorithm that can be implemented without using information about the reward distribution while still achieving substantial regret upper bounds.
Consistency Prototype Module and Motion Compensation for Few-Shot Action Recognition (CLIP-CP$\mathbf{M^2}$C)
Although some multi-modal works use labels as supplementary to construct prototypes of support videos, they can not use this information for query videos.
Task-Specific Alignment and Multiple Level Transformer for Few-Shot Action Recognition
The second module (MLT) focuses on the Multiple-level feature of the support prototype and query sample to mine more information for the alignment, which operates on different level features.
ISSTAD: Incremental Self-Supervised Learning Based on Transformer for Anomaly Detection and Localization
In the subsequent stage, we apply pixel-level data augmentation techniques to generate corrupted normal images and their corresponding pixel labels.
Non-aligned supervision for Real Image Dehazing
In particular, we explore a non-alignment scenario that a clear reference image, unaligned with the input hazy image, is utilized to supervise the dehazing network.
EAA-Net: Rethinking the Autoencoder Architecture with Intra-class Features for Medical Image Segmentation
However, segmentation networks pay too much attention to the main visual difference between foreground and background, and ignores the detailed edge information, which leads to a reduction in the accuracy of edge segmentation.
TranSiam: Fusing Multimodal Visual Features Using Transformer for Medical Image Segmentation
These differences will affect the results of the automatic segmentation methods.
Industrial Style Transfer with Large-scale Geometric Warping and Content Preservation
Given a source product, a target product, and an art style image, our method produces a neural warping field that warps the source shape to imitate the geometric style of the target and a neural texture transformation network that transfers the artistic style to the warped source product.
ROSE: Real One-Stage Effort to Detect the Fingerprint Singular Point Based on Multi-scale Spatial Attention
Detecting the singular point accurately and efficiently is one of the most important tasks for fingerprint recognition.
