Search Results for author:
Found 9 papers, 4 papers with code
Unsupervised Deep Asymmetric Stereo Matching With Spatially-Adaptive Self-Similarity
In this paper, we present a novel spatially-adaptive self-similarity (SASS) for unsupervised asymmetric stereo matching.
Context-Preserving Instance-Level Augmentation and Deformable Convolution Networks for SAR Ship Detection
By learning sampling offset to the grid of standard convolution, the network can robustly extract the features from targets with shape variations for SAR ship detection.
Dual Prototypical Contrastive Learning for Few-shot Semantic Segmentation
We address the problem of few-shot semantic segmentation (FSS), which aims to segment novel class objects in a target image with a few annotated samples.
Looking into Your Speech: Learning Cross-modal Affinity for Audio-visual Speech Separation
In this paper, we address the problem of separating individual speech signals from videos using audio-visual neural processing.
On the confidence of stereo matching in a deep-learning era: a quantitative evaluation
Stereo matching is one of the most popular techniques to estimate dense depth maps by finding the disparity between matching pixels on two, synchronized and rectified images.
Adaptive confidence thresholding for monocular depth estimation
To cope with the prediction error of the confidence map itself, we also leverage the threshold network that learns the threshold dynamically conditioned on the pseudo depth maps.
Context-Aware Emotion Recognition Networks
We present deep networks for context-aware emotion recognition, called CAER-Net, that exploit not only human facial expression but also context information in a joint and boosting manner.
Ranked #1 on
Emotion Recognition in Context
on CAER-Dynamic
LAF-Net: Locally Adaptive Fusion Networks for Stereo Confidence Estimation
The proposed network, termed as Locally Adaptive Fusion Networks (LAF-Net), learns locally-varying attention and scale maps to fuse the tri-modal confidence features.
Semantic Attribute Matching Networks
SAM-Net accomplishes this through an iterative process of establishing reliable correspondences by reducing the attribute discrepancy between the images and synthesizing attribute transferred images using the learned correspondences.
