Search Results for author:
Found 23 papers, 15 papers with code
Autoregressive Queries for Adaptive Tracking with Spatio-TemporalTransformers
Firstly, we introduce a set of learnable and autoregressive queries to capture the instantaneous target appearance changes in a sliding window fashion.
End-to-End Human Instance Matting
Finally, an instance matting network decodes the image features and united semantics guidance to predict all instance-level alpha mattes.
Explicit Visual Prompts for Visual Object Tracking
Specifically, we utilize spatio-temporal tokens to propagate information between consecutive frames without focusing on updating templates.
ODTrack: Online Dense Temporal Token Learning for Visual Tracking
To alleviate the above problem, we propose a simple, flexible and effective video-level tracking pipeline, named \textbf{ODTrack}, which densely associates the contextual relationships of video frames in an online token propagation manner.
Ranked #1 on
Visual Object Tracking
on TrackingNet
Semi-Supervised Video Object Segmentation
Visual Object Tracking
+1
Towards Unified Token Learning for Vision-Language Tracking
In this paper, we present a simple, flexible and effective vision-language (VL) tracking pipeline, termed \textbf{MMTrack}, which casts VL tracking as a token generation task.
Rethinking Context Aggregation in Natural Image Matting
For natural image matting, context information plays a crucial role in estimating alpha mattes especially when it is challenging to distinguish foreground from its background.
Ranked #1 on
Image Matting
on Composition-1K
Visualizing and Understanding Patch Interactions in Vision Transformer
Vision Transformer (ViT) has become a leading tool in various computer vision tasks, owing to its unique self-attention mechanism that learns visual representations explicitly through cross-patch information interactions.
Long-Range Feature Propagating for Natural Image Matting
Finally, we use the matting module which takes the image, trimap and context features to estimate the alpha matte.
Ranked #6 on
Image Matting
on Composition-1K
(using extra training data)
Discover Cross-Modality Nuances for Visible-Infrared Person Re-Identification
In this paper, we propose a joint Modality and Pattern Alignment Network (MPANet) to discover cross-modality nuances in different patterns for visible-infrared person Re-ID, which introduces a modality alleviation module and a pattern alignment module to jointly extract discriminative features.
Distractor-Aware Fast Tracking via Dynamic Convolutions and MOT Philosophy
A practical long-term tracker typically contains three key properties, i. e. an efficient model design, an effective global re-detection strategy and a robust distractor awareness mechanism.
Learning to Filter: Siamese Relation Network for Robust Tracking
RD performs in a meta-learning way to obtain a learning ability to filter the distractors from the background while RM aims to effectively integrate the proposed RD into the Siamese framework to generate accurate tracking result.
EC-DARTS: Inducing Equalized and Consistent Optimization Into DARTS
EC-DARTS decouples different operations based on their categories to optimize the operation weights so that the operation gap between them is shrinked.
Projection & Probability-Driven Black-Box Attack
For reducing the solution space, we first model the adversarial perturbation optimization problem as a process of recovering frequency-sparse perturbations with compressed sensing, under the setting that random noise in the low-frequency space is more likely to be adversarial.
What Can Be Transferred: Unsupervised Domain Adaptation for Endoscopic Lesions Segmentation
Unsupervised domain adaptation has attracted growing research attention on semantic segmentation.
Siamese Box Adaptive Network for Visual Tracking
Most of the existing trackers usually rely on either a multi-scale searching scheme or pre-defined anchor boxes to accurately estimate the scale and aspect ratio of a target.
Residual Non-local Attention Networks for Image Restoration
To address this issue, we design local and non-local attention blocks to extract features that capture the long-range dependencies between pixels and pay more attention to the challenging parts.
Representative Task Self-selection for Flexible Clustered Lifelong Learning
Consider the lifelong machine learning paradigm whose objective is to learn a sequence of tasks depending on previous experiences, e. g., knowledge library or deep network weights.
Residual Dense Network for Image Restoration
We fully exploit the hierarchical features from all the convolutional layers.
Image Super-Resolution Using Very Deep Residual Channel Attention Networks
To solve these problems, we propose the very deep residual channel attention networks (RCAN).
Ranked #15 on
Image Super-Resolution
on BSD100 - 4x upscaling
Predictive Local Smoothness for Stochastic Gradient Methods
To address this problem, we propose a simple yet effective method for improving stochastic gradient methods named predictive local smoothness (PLS).
Residual Dense Network for Image Super-Resolution
In this paper, we propose a novel residual dense network (RDN) to address this problem in image SR. We fully exploit the hierarchical features from all the convolutional layers.
Ranked #3 on
Color Image Denoising
on CBSD68 sigma50
An Effective Unconstrained Correlation Filter and Its Kernelization for Face Recognition
In this paper, an effective unconstrained correlation filter called Uncon- strained Optimal Origin Tradeoff Filter (UOOTF) is presented and applied to robust face recognition.
Understanding Image Structure via Hierarchical Shape Parsing
A hierarchical shape parsing strategy is proposed to partition and organize image components into a hierarchical structure in the scale space.
