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Found 14 papers, 5 papers with code
CLNet: A Compact Latent Network for Fast Adjusting Siamese Trackers
To make full use of these sequence-specific samples, {we propose a compact latent network to quickly adjust the tracking model to adapt to new scenes.}
TextFormer: A Query-based End-to-End Text Spotter with Mixed Supervision
End-to-end text spotting is a vital computer vision task that aims to integrate scene text detection and recognition into a unified framework.
Referring Multi-Object Tracking
In this paper, we propose a new and general referring understanding task, termed referring multi-object tracking (RMOT).
Generalized Few-Shot 3D Object Detection of LiDAR Point Cloud for Autonomous Driving
To achieve simultaneous detection for both common and rare objects, we propose a novel task, called generalized few-shot 3D object detection, where we have a large amount of training data for common (base) objects, but only a few data for rare (novel) classes.
Adaptive Siamese Tracking with a Compact Latent Network
Under this viewing, we perform an in-depth analysis for them through visual simulations and real tracking examples, and find that the failure cases in some challenging situations can be regarded as the issue of missing decisive samples in offline training.
Rethinking Clustering-Based Pseudo-Labeling for Unsupervised Meta-Learning
In this work, we prove that the core reason for this is lack of a clustering-friendly property in the embedding space.
Pseudo-Labeling Based Practical Semi-Supervised Meta-Training for Few-Shot Learning
Most existing few-shot learning (FSL) methods require a large amount of labeled data in meta-training, which is a major limit.
Multi-Level Representation Learning With Semantic Alignment for Referring Video Object Segmentation
To address this, we propose a novel multi-level representation learning approach, which explores the inherent structure of the video content to provide a set of discriminative visual embedding, enabling more effective vision-language semantic alignment.
Learning to Fuse Asymmetric Feature Maps in Siamese Trackers
We propose a learnable module, called the asymmetric convolution (ACM), which learns to better capture the semantic correlation information in offline training on large-scale data.
Ranked #21 on
Visual Object Tracking
on TrackingNet
Distilled Siamese Networks for Visual Tracking
This model is intuitively inspired by the one teacher vs. multiple students learning method typically employed in schools.
Triplet Loss in Siamese Network for Object Tracking
In this paper, a novel triplet loss is proposed to extract expressive deep feature for object tracking by adding it into Siamese network framework instead of pairwise loss for training.
Salient Object Detection Driven by Fixation Prediction
Salient object detection is then viewed as fine-grained object-level saliency segmentation and is progressively optimized with the guidance of the fixation map in a top-down manner.
Hyperparameter Optimization for Tracking With Continuous Deep Q-Learning
Hyperparameters are numerical presets whose values are assigned prior to the commencement of the learning process.
Quadruplet Network with One-Shot Learning for Fast Visual Object Tracking
In this paper, we propose a new quadruplet deep network to examine the potential connections among the training instances, aiming to achieve a more powerful representation.
