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Found 7 papers, 0 papers with code
Multi Positive Contrastive Learning with Pose-Consistent Generated Images
Model pre-training has become essential in various recognition tasks.
Mixed-precision Supernet Training from Vision Foundation Models using Low Rank Adapter
To tackle these challenges, first, we study the effective search space design for fine-tuning a VFM by comparing different operators (such as resolution, feature size, width, depth, and bit-widths) in terms of performance and BitOPs reduction.
PQDynamicISP: Dynamically Controlled Image Signal Processor for Any Image Sensors Pursuing Perceptual Quality
Full DNN-based image signal processors (ISPs) have been actively studied and have achieved superior image quality compared to conventional ISPs.
Ranked #2 on
Image Enhancement
on MIT-Adobe 5k
(PSNR on proRGB metric)
Self-Supervised Reversed Image Signal Processing via Reference-Guided Dynamic Parameter Selection
To tackle these limitations, we propose a self-supervised reversed ISP method that does not require metadata and paired images.
Efficient Joint Detection and Multiple Object Tracking with Spatially Aware Transformer
We propose a light-weight and highly efficient Joint Detection and Tracking pipeline for the task of Multi-Object Tracking using a fully-transformer architecture.
DynamicISP: Dynamically Controlled Image Signal Processor for Image Recognition
Image Signal Processors (ISPs) play important roles in image recognition tasks as well as in the perceptual quality of captured images.
Rawgment: Noise-Accounted RAW Augmentation Enables Recognition in a Wide Variety of Environments
We show that our proposed noise-accounted RAW augmentation method doubles the image recognition accuracy in challenging environments only with simple training data.
