Search Results for author:
Found 32 papers, 21 papers with code
Similarity-Guided Layer-Adaptive Vision Transformer for UAV Tracking
Based on this observation, we propose a similarity-guided layer adaptation approach to optimize the structure of ViTs.
Dynamic Updates for Language Adaptation in Visual-Language Tracking
Finally, the dynamic template and language descriptions that record the latest state of the target are used to update the multi-modal references, providing more accurate reference information for subsequent inference and enhancing the robustness of the tracker.
Adaptive Perception for Unified Visual Multi-modal Object Tracking
This strategy allows the model to dynamically adapt to various modalities and tasks without requiring additional fine-tuning between different tasks.
Ranked #6 on
Rgb-T Tracking
on RGBT234
Less is More: Token Context-aware Learning for Object Tracking
Recently, several studies have shown that utilizing contextual information to perceive target states is crucial for object tracking.
Exploiting Multimodal Spatial-temporal Patterns for Video Object Tracking
These temporal information tokens are used to guide the localization of the target in the next time state, establish long-range contextual relationships between video frames, and capture the temporal trajectory of the target.
Ranked #4 on
Rgb-T Tracking
on LasHeR
Robust Tracking via Mamba-based Context-aware Token Learning
To alleviate the above issues, we propose a simple yet robust tracker that separates temporal information learning from appearance modeling and extracts temporal relations from a set of representative tokens rather than several images (or features).
MambaLCT: Boosting Tracking via Long-term Context State Space Model
To address this issue, we propose MambaLCT, which constructs and utilizes target variation cues from the first frame to the current frame for robust tracking.
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.
Ranked #8 on
Visual Object Tracking
on DiDi
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 OTB-2015
Semi-Supervised Video Object Segmentation
Video Object Tracking
+2
Autoregressive Queries for Adaptive Tracking with Spatio-Temporal Transformers
Firstly we introduce a set of learnable and autoregressive queries to capture the instantaneous target appearance changes in a sliding window fashion.
DiffPerformer: Iterative Learning of Consistent Latent Guidance for Diffusion-based Human Video Generation
In this paper we propose a novel framework DiffPerformer to synthesize high-fidelity and temporally consistent human video.
Fourier Priors-Guided Diffusion for Zero-Shot Joint Low-Light Enhancement and Deblurring
Existing joint low-light enhancement and deblurring methods learn pixel-wise mappings from paired synthetic data which results in limited generalization in real-world scenes.
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.
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.
Ranked #1 on
Color Image Denoising
on Kodak24 sigma30
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 #21 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 #5 on
Image Super-Resolution
on IXI
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.
