Search Results for author:
Found 14 papers, 11 papers with code
Asymmetric Two-Stream Architecture for Accurate RGB-D Saliency Detection
In this paper, we propose an asymmetric two-stream architecture taking account of the inherent differences between RGB and depth data for saliency detection.
Ranked #19 on
Thermal Image Segmentation
on RGB-T-Glass-Segmentation
MFNet: Multi-filter Directive Network for Weakly Supervised Salient Object Detection
The multiple accurate cues from multiple DFs are then simultaneously propagated to the saliency network with a multi-guidance loss.
Joint Semantic Mining for Weakly Supervised RGB-D Salient Object Detection
As a by-product, a CapS dataset is constructed by augmenting existing benchmark training set with additional image tags and captions.
To be Critical: Self-Calibrated Weakly Supervised Learning for Salient Object Detection
Despite of the success of previous works, explorations on an effective training strategy for the saliency network and accurate matches between image-level annotations and salient objects are still inadequate.
Calibrated RGB-D Salient Object Detection
Complex backgrounds and similar appearances between objects and their surroundings are generally recognized as challenging scenarios in Salient Object Detection (SOD).
Ranked #13 on
Thermal Image Segmentation
on RGB-T-Glass-Segmentation
Learning Multi-modal Information for Robust Light Field Depth Estimation
We first excavate the internal spatial correlation by designing a context reasoning unit which separately extracts comprehensive contextual information from the focal stack and RGB images.
Dynamic Fusion Network For Light Field Depth Estimation
Focus based methods have shown promising results for the task of depth estimation.
Dynamic Context-Sensitive Filtering Network for Video Salient Object Detection
Our bidirectional dynamic fusion strategy encourages the interaction of spatial and temporal information in a dynamic manner.
Ranked #12 on
Video Polyp Segmentation
on SUN-SEG-Easy (Unseen)
DUT-LFSaliency: Versatile Dataset and Light Field-to-RGB Saliency Detection
The success of learning-based light field saliency detection is heavily dependent on how a comprehensive dataset can be constructed for higher generalizability of models, how high dimensional light field data can be effectively exploited, and how a flexible model can be designed to achieve versatility for desktop computers and mobile devices.
Accurate RGB-D Salient Object Detection via Collaborative Learning
The explicitly extracted edge information goes together with saliency to give more emphasis to the salient regions and object boundaries.
Ranked #19 on
RGB-D Salient Object Detection
on NJU2K
RGB-D Salient Object Detection with Cross-Modality Modulation and Selection
Second, we propose an adaptive feature selection (AFS) module to select saliency-related features and suppress the inferior ones.
Ranked #8 on
RGB-D Salient Object Detection
on NJU2K
A2dele: Adaptive and Attentive Depth Distiller for Efficient RGB-D Salient Object Detection
Existing state-of-the-art RGB-D salient object detection methods explore RGB-D data relying on a two-stream architecture, in which an independent subnetwork is required to process depth data.
Ranked #19 on
RGB-D Salient Object Detection
on NJU2K
(Average MAE metric, using extra
training data)
Memory-oriented Decoder for Light Field Salient Object Detection
In this paper, we present a deep-learning-based method where a novel memory-oriented decoder is tailored for light field saliency detection.
Depth-Induced Multi-Scale Recurrent Attention Network for Saliency Detection
In this work, we propose a novel depth-induced multi-scale recurrent attention network for saliency detection.
Ranked #21 on
RGB-D Salient Object Detection
on NJU2K
(using extra training data)
