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Found 23 papers, 12 papers with code
Hierarchical Spatial-aware Siamese Network for Thermal Infrared Object Tracking
In this paper, we cast the TIR tracking problem as a similarity verification task, which is coupled well to the objective of the tracking task.
Generalized Graph Embedding Models
Many types of relations in physical, biological, social and information systems can be modeled as homogeneous or heterogeneous concept graphs.
PTB-TIR: A Thermal Infrared Pedestrian Tracking Benchmark
The ability to evaluate the TIR pedestrian tracker fairly, on a benchmark dataset, is significant for the development of this field.
Aspect and Sentiment Aware Abstractive Review Summarization
Review text has been widely studied in traditional tasks such as sentiment analysis and aspect extraction.
Learning Deep Multi-Level Similarity for Thermal Infrared Object Tracking
These two similarities complement each other and hence enhance the discriminative capacity of the network for handling distractors.
Multi-Task Driven Feature Models for Thermal Infrared Tracking
These two feature models are learned using a multi-task matching framework and are jointly optimized on the TIR tracking task.
Roundtrip: A Deep Generative Neural Density Estimator
Density estimation is a fundamental problem in both statistics and machine learning.
Accurate Bounding-box Regression with Distance-IoU Loss for Visual Tracking
Moreover, we introduce a classification part that is trained online and optimized with a Conjugate-Gradient-based strategy to guarantee real-time tracking speed.
LSOTB-TIR:A Large-Scale High-Diversity Thermal Infrared Object Tracking Benchmark
We evaluate and analyze more than 30 trackers on LSOTB-TIR to provide a series of baselines, and the results show that deep trackers achieve promising performance.
Thermal Infrared Object Tracking
Vocal Bursts Intensity Prediction
Reinforced Molecular Optimization with Neighborhood-Controlled Grammars
A major challenge in the pharmaceutical industry is to design novel molecules with specific desired properties, especially when the property evaluation is costly.
Ranked #1 on
Molecular Graph Generation
on ZINC
(QED Top-3 metric)
Relational Learning with Gated and Attentive Neighbor Aggregator for Few-Shot Knowledge Graph Completion
Moreover, modeling and inferring complex relations of one-to-many (1-N), many-to-one (N-1), and many-to-many (N-N) by previous knowledge graph completion approaches requires high model complexity and a large amount of training instances.
Boost Neural Networks by Checkpoints
In the experiments, it yields up to 5. 02% higher accuracy over single EfficientNet-B0 on the imbalanced datasets.
Active Learning for Deep Visual Tracking
In this paper, we propose an active learning method for deep visual tracking, which selects and annotates the unlabeled samples to train the deep CNNs model.
An Informative Tracking Benchmark
Along with the rapid progress of visual tracking, existing benchmarks become less informative due to redundancy of samples and weak discrimination between current trackers, making evaluations on all datasets extremely time-consuming.
Graph Convolutional Networks for Multi-modality Medical Imaging: Methods, Architectures, and Clinical Applications
Image-based characterization and disease understanding involve integrative analysis of morphological, spatial, and topological information across biological scales.
Mutual Information Learned Classifiers: an Information-theoretic Viewpoint of Training Deep Learning Classification Systems
Deep learning systems have been reported to achieve state-of-the-art performances in many applications, and a key is the existence of well trained classifiers on benchmark datasets.
Pose-Aided Video-based Person Re-Identification via Recurrent Graph Convolutional Network
Finally, we propose a dual-attention method consisting of node-attention and time-attention to obtain the temporal graph representation from the node embeddings, where the self-attention mechanism is employed to learn the importance of each node and each frame.
Mutual Information Learned Classifiers: an Information-theoretic Viewpoint of Training Deep Learning Classification Systems
Deep learning systems have been reported to acheive state-of-the-art performances in many applications, and one of the keys for achieving this is the existence of well trained classifiers on benchmark datasets which can be used as backbone feature extractors in downstream tasks.
Mutual Information Learned Regressor: an Information-theoretic Viewpoint of Training Regression Systems
We first argue that the MSE minimization approach is equivalent to a conditional entropy learning problem, and then propose a mutual information learning formulation for solving regression problems by using a reparameterization technique.
CausalEGM: a general causal inference framework by encoding generative modeling
In this article, we develop a general framework $\textit{CausalEGM}$ for estimating causal effects by encoding generative modeling, which can be applied in both binary and continuous treatment settings.
Language-Enhanced Session-Based Recommendation with Decoupled Contrastive Learning
Furthermore, we introduce a novel Decoupled Contrastive Learning method to enhance the effectiveness of the language representation.
Aspect-oriented Opinion Alignment Network for Aspect-Based Sentiment Classification
In addition, we design a multi-perspective attention mechanism that align relevant opinion information with respect to the given aspect.
Synergistic Anchored Contrastive Pre-training for Few-Shot Relation Extraction
Specifically, our framework involves a symmetrical contrastive objective that encompasses both sentence-anchored and label-anchored contrastive losses.
