Search Results for author:
Found 21 papers, 6 papers with code
Shifting Spotlight for Co-supervision: A Simple yet Efficient Single-branch Network to See Through Camouflage
Efficient and accurate camouflaged object detection (COD) poses a challenge in the field of computer vision.
Towards Open-World Co-Salient Object Detection with Generative Uncertainty-aware Group Selective Exchange-Masking
To simultaneously consider the uncertainty introduced by irrelevant images and the consensus features of the remaining relevant images in the group, we designed a latent variable generator branch and CoSOD transformer branch.
Distilling Cross-Temporal Contexts for Continuous Sign Language Recognition
Existing results in [9, 20, 25, 36] have indicated that, as the frontal component of the overall model, the spatial perception module used for spatial feature extraction tends to be insufficiently trained.
Ranked #5 on
Sign Language Recognition
on CSL-Daily
Co-Salient Object Detection With Uncertainty-Aware Group Exchange-Masking
To address this issue, this paper presents a group exchange-masking (GEM) strategy for robust CoSOD model learning.
Learning Self-Supervised Low-Rank Network for Single-Stage Weakly and Semi-Supervised Semantic Segmentation
Semantic segmentation with limited annotations, such as weakly supervised semantic segmentation (WSSS) and semi-supervised semantic segmentation (SSSS), is a challenging task that has attracted much attention recently.
Ranked #34 on
Weakly-Supervised Semantic Segmentation
on COCO 2014 val
Learning Dynamic Compact Memory Embedding for Deformable Visual Object Tracking
To address this issue, segmentation-based trackers have been proposed that employ per-pixel matching to improve the tracking performance of deformable objects effectively.
DeepACG: Co-Saliency Detection via Semantic-Aware Contrast Gromov-Wasserstein Distance
This dense correlation volumes enables the network to accurately discover the structured pair-wise pixel similarities among the common salient objects.
Deep Transport Network for Unsupervised Video Object Segmentation
The popular unsupervised video object segmentation methods fuse the RGB frame and optical flow via a two-stream network.
Ranked #4 on
Unsupervised Video Object Segmentation
on FBMS test
Learning Spatio-Appearance Memory Network for High-Performance Visual Tracking
Existing visual object tracking usually learns a bounding-box based template to match the targets across frames, which cannot accurately learn a pixel-wise representation, thereby being limited in handling severe appearance variations.
Dual Temporal Memory Network for Efficient Video Object Segmentation
We present an end-to-end network which stores short- and long-term video sequence information preceding the current frame as the temporal memories to address the temporal modeling in VOS.
Adaptive Graph Convolutional Network with Attention Graph Clustering for Co-saliency Detection
Second, we develop an attention graph clustering algorithm to discriminate the common objects from all the salient foreground objects in an unsupervised fashion.
Video Saliency Prediction Using Enhanced Spatiotemporal Alignment Network
Due to a variety of motions across different frames, it is highly challenging to learn an effective spatiotemporal representation for accurate video saliency prediction (VSP).
Deep Object Co-segmentation via Spatial-Semantic Network Modulation
With the multi-resolution features of the relevant images as input, we design a spatial modulator to learn a mask for each image.
Co-Saliency Detection via Mask-Guided Fully Convolutional Networks With Multi-Scale Label Smoothing
We next propose a multi-scale label smoothing model to further refine the detection result.
Ranked #8 on
Co-Salient Object Detection
on CoCA
(S-measure metric)
Unsupervised Video Segmentation via Spatio-Temporally Nonlocal Appearance Learning
Then, with the updated appearances, we formulate a spatio-temporal graphical model comprised of the superpixel label consistency potentials.
An Efficient Algorithm for the Piecewise-Smooth Model with Approximately Explicit Solutions
This paper presents an efficient approach to image segmentation that approximates the piecewise-smooth (PS) functional in [12] with explicit solutions.
Visual Tracking via Boolean Map Representations
In this paper, we present a simple yet effective Boolean map based representation that exploits connectivity cues for visual tracking.
Adaptive Compressive Tracking via Online Vector Boosting Feature Selection
Recently, the compressive tracking (CT) method has attracted much attention due to its high efficiency, but it cannot well deal with the large scale target appearance variations due to its data-independent random projection matrix that results in less discriminative features.
Robust Visual Tracking via Convolutional Networks
In this paper we present that, even without offline training with a large amount of auxiliary data, simple two-layer convolutional networks can be powerful enough to develop a robust representation for visual tracking.
Fast Tracking via Spatio-Temporal Context Learning
In this paper, we present a simple yet fast and robust algorithm which exploits the spatio-temporal context for visual tracking.
A Local Active Contour Model for Image Segmentation with Intensity Inhomogeneity
The means of the Gaussian distributions in the transformed domain can be adaptively estimated by multiplying a bias field with the original signal within the window.
