Search Results for author:
Found 18 papers, 13 papers with code
Regression of Instance Boundary by Aggregated CNN and GCN
In particular, thanks to the proposed aggregation GCN, our network benefits from direct feature learning of the instances’ boundary locations and the spatial information propagation across the image.
DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classification
Multiple instance learning (MIL) has been increasingly used in the classification of histopathology whole slide images (WSIs).
3D Dense Face Alignment with Fused Features by Aggregating CNNs and GCNs
In this paper, we propose a novel multi-level aggregation network to regress the coordinates of the vertices of a 3D face from a single 2D image in an end-to-end manner.
Counting with Adaptive Auxiliary Learning
This paper proposes an adaptive auxiliary task learning based approach for object counting problems.
BI-GCN: Boundary-Aware Input-Dependent Graph Convolution Network for Biomedical Image Segmentation
Our model is well-suited to obtain global semantic region information while also accommodates local spatial boundary characteristics simultaneously.
Video Annotation for Visual Tracking via Selection and Refinement
Deep learning based visual trackers entail offline pre-training on large volumes of video datasets with accurate bounding box annotations that are labor-expensive to achieve.
Spatial Uncertainty-Aware Semi-Supervised Crowd Counting
Semi-supervised approaches for crowd counting attract attention, as the fully supervised paradigm is expensive and laborious due to its request for a large number of images of dense crowd scenarios and their annotations.
A Video Is Worth Three Views: Trigeminal Transformers for Video-based Person Re-identification
To capture richer perceptions and extract more comprehensive video representations, in this paper we propose a novel framework named Trigeminal Transformers (TMT) for video-based person Re-ID.
Transformer Tracking
The correlation operation is a simple fusion manner to consider the similarity between the template and the search region.
Ranked #5 on
Visual Tracking
on TNL2K
Watching You: Global-guided Reciprocal Learning for Video-based Person Re-identification
Specifically, we first propose a Global-guided Correlation Estimation (GCE) to generate feature correlation maps of local features and global features, which help to localize the high- and low-correlation regions for identifying the same person.
Alpha-Refine: Boosting Tracking Performance by Precise Bounding Box Estimation
Many recent trackers adopt the multiple-stage tracking strategy to improve the quality of bounding box estimation.
Ranked #15 on
Semi-Supervised Video Object Segmentation
on VOT2020
Semi-Supervised Video Object Segmentation
Visual Object Tracking
Alpha-Refine: Boosting Tracking Performance by Precise Bounding Box Estimation
In recent years, the multiple-stage strategy has become a popular trend for visual tracking.
Jointly Modeling Motion and Appearance Cues for Robust RGB-T Tracking
In this study, we propose a novel RGB-T tracking framework by jointly modeling both appearance and motion cues.
Ranked #4 on
Rgb-T Tracking
on GTOT
High-Performance Long-Term Tracking with Meta-Updater
Most top-ranked long-term trackers adopt the offline-trained Siamese architectures, thus, they cannot benefit from great progress of short-term trackers with online update.
Ranked #10 on
Visual Object Tracking
on LaSOT-ext
Cooling-Shrinking Attack: Blinding the Tracker with Imperceptible Noises
An effective and efficient perturbation generator is trained with a carefully designed adversarial loss, which can simultaneously cool hot regions where the target exists on the heatmaps and force the predicted bounding box to shrink, making the tracked target invisible to trackers.
GradNet: Gradient-Guided Network for Visual Object Tracking
In this work, we propose a novel gradient-guided network to exploit the discriminative information in gradients and update the template in the siamese network through feed-forward and backward operations.
Ranked #3 on
Visual Object Tracking
on OTB-2015
(Precision metric)
'Skimming-Perusal' Tracking: A Framework for Real-Time and Robust Long-term Tracking
In this work, we present a novel robust and real-time long-term tracking framework based on the proposed skimming and perusal modules.
Cascaded Context Pyramid for Full-Resolution 3D Semantic Scene Completion
To address these issues, in this work we propose a novel deep learning framework, named Cascaded Context Pyramid Network (CCPNet), to jointly infer the occupancy and semantic labels of a volumetric 3D scene from a single depth image.
Ranked #5 on
3D Semantic Scene Completion
on NYUv2
(using extra training data)
