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Found 7 papers, 5 papers with code
CFDNet: A Generalizable Foggy Stereo Matching Network with Contrastive Feature Distillation
Stereo matching under foggy scenes remains a challenging task since the scattering effect degrades the visibility and results in less distinctive features for dense correspondence matching.
Global Occlusion-Aware Transformer for Robust Stereo Matching
Despite the remarkable progress facilitated by learning-based stereo-matching algorithms, the performance in the ill-conditioned regions, such as the occluded regions, remains a bottleneck.
Fascinating Supervisory Signals and Where to Find Them: Deep Anomaly Detection with Scale Learning
Due to the unsupervised nature of anomaly detection, the key to fueling deep models is finding supervisory signals.
Dual-Pixel Raindrop Removal
In this paper, we propose the first method using a Dual-Pixel (DP) sensor to better address the raindrop removal.
Single Image Deraining Network with Rain Embedding Consistency and Layered LSTM
For this purpose, an encoder-decoder network draws wide attention, where the encoder is required to encode a high-quality rain embedding which determines the performance of the subsequent decoding stage to reconstruct the rain layer.
Exploit the potential of Multi-column architecture for Crowd Counting
To the best of our knowledge, PSNet is the first work to explicitly address scale limitation and feature similarity in multi-column design.
Deep Density-aware Count Regressor
We seek to improve crowd counting as we perceive limits of currently prevalent density map estimation approach on both prediction accuracy and time efficiency.
