Search Results for author:
Found 15 papers, 9 papers with code
Self-Filtering: A Noise-Aware Sample Selection for Label Noise with Confidence Penalization
Sample selection is an effective strategy to mitigate the effect of label noise in robust learning.
Safe-Student for Safe Deep Semi-Supervised Learning With Unseen-Class Unlabeled Data
To take advantage of these unseen-class data and ensure performance, we propose a safe SSL method called SAFE-STUDENT from the teacher-student view.
A Graph Matching Perspective With Transformers on Video Instance Segmentation
Video Instance Segmentation (VIS) needs to automatically track and segment multiple objects in videos that rely on modeling the spatial-temporal interactions of the instances.
Attentional Prototype Inference for Few-Shot Segmentation
We define a global latent variable to represent the prototype of each object category, which we model as a probabilistic distribution.
Video Object Segmentation with Episodic Graph Memory Networks
How to make a segmentation model efficiently adapt to a specific video and to online target appearance variations are fundamentally crucial issues in the field of video object segmentation.
M2Net: Multi-modal Multi-channel Network for Overall Survival Time Prediction of Brain Tumor Patients
Then, we use a modality-specific network to extract implicit and high-level features from different MR scans.
Learning Video Object Segmentation from Unlabeled Videos
We propose a new method for video object segmentation (VOS) that addresses object pattern learning from unlabeled videos, unlike most existing methods which rely heavily on extensive annotated data.
Human-Aware Motion Deblurring
This paper proposes a human-aware deblurring model that disentangles the motion blur between foreground (FG) humans and background (BG).
Zero-Shot Video Object Segmentation via Attentive Graph Neural Networks
Through parametric message passing, AGNN is able to efficiently capture and mine much richer and higher-order relations between video frames, thus enabling a more complete understanding of video content and more accurate foreground estimation.
See More, Know More: Unsupervised Video Object Segmentation with Co-Attention Siamese Networks
We introduce a novel network, called CO-attention Siamese Network (COSNet), to address the unsupervised video object segmentation task from a holistic view.
Semantic Segmentation
Unsupervised Video Object Segmentation
+2
Distilled Siamese Networks for Visual Tracking
This model is intuitively inspired by the one teacher vs. multiple students learning method typically employed in schools.
Deep Regression Tracking with Shrinkage Loss
Regression trackers directly learn a mapping from regularly dense samples of target objects to soft labels, which are usually generated by a Gaussian function, to estimate target positions.
High Order Structure Descriptors for Scene Images
It is well known that scene images are well characterized by particular arrangements of their local structures, we divide the scene image into the non-overlapping sub-regions and compute the proposed higher order structural features among them.
Efficient Image Categorization with Sparse Fisher Vector
In object recognition, Fisher vector (FV) representation is one of the state-of-art image representations ways at the expense of dense, high dimensional features and increased computation time.
