Search Results for author:
Found 10 papers, 3 papers with code
BCTR: Bidirectional Conditioning Transformer for Scene Graph Generation
To address this limitation, we propose a novel bidirectional conditioning factorization in a semantic-aligned space for SGG, enabling efficient and generalizable interaction between entities and predicates.
HyCIR: Boosting Zero-Shot Composed Image Retrieval with Synthetic Labels
Composed Image Retrieval (CIR) aims to retrieve images based on a query image with text.
FF-Former: Swin Fourier Transformer for Nighttime Flare Removal
And the flare can cause the dark regions to become brighter and result in a loss of contrast and alteration of the frequency characteristics of the image.
Ranked #1 on
Flare Removal
on Flare7K
CLIP-Cluster: CLIP-Guided Attribute Hallucination for Face Clustering
Furthermore, we develop a neighbor-aware proxy generator that fuses the features describing various attributes into a proxy feature to build a bridge among different sub-clusters and reduce the intra-class variance.
DMTNet: Dynamic Multi-scale Network for Dual-pixel Images Defocus Deblurring with Transformer
Recent works achieve excellent results in defocus deblurring task based on dual-pixel data using convolutional neural network (CNN), while the scarcity of data limits the exploration and attempt of vision transformer in this task.
Ranked #3 on
Image Defocus Deblurring
on DPD (Dual-view)
SwinFIR: Revisiting the SwinIR with Fast Fourier Convolution and Improved Training for Image Super-Resolution
Transformer-based methods have achieved impressive image restoration performance due to their capacities to model long-range dependency compared to CNN-based methods.
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.
Arbitrary Shape Scene Text Detection with Adaptive Text Region Representation
Here, recurrent neural network based adaptive text region representation is proposed for text region refinement, where a pair of boundary points are predicted each time step until no new points are found.
Deep Residual Text Detection Network for Scene Text
Scene text detection is a challenging problem in computer vision.
R2CNN: Rotational Region CNN for Orientation Robust Scene Text Detection
In this paper, we propose a novel method called Rotational Region CNN (R2CNN) for detecting arbitrary-oriented texts in natural scene images.
