The success of existing salient object detection models relies on a large pixel-wise labeled training dataset.
The calibrated person representation is subtly decomposed into the identity-relevant feature, domain feature, and the remaining entangled one.
Despite the fact that deep neural networks (DNNs) have achieved prominent performance in various applications, it is well known that DNNs are vulnerable to adversarial examples/samples (AEs) with imperceptible perturbations in clean/original samples.
Chatbot is increasingly thriving in different domains, however, because of unexpected discourse complexity and training data sparseness, its potential distrust hatches vital apprehension.
When it comes to customising these algorithms for real-world applications, none of the existing libraries can offer both the flexibility of developing custom pose estimation algorithms and the high-performance of executing these algorithms on commodity devices.
The purpose of the task was to extract triples from a paper in the Nature Language Processing field for constructing an Open Research Knowledge Graph.
Occluded person re-identification (ReID) aims to match person images with occlusion.
Then, we concatenate it with the input image and feed it to the confidence estimation network to produce an one channel confidence map. We generate dynamic supervision for the confidence estimation network, representing the agreement of camouflage prediction with the ground truth camouflage map.
It describes unseen target domain as a combination of the known source ones, and explicitly learns domain-specific representation with target distribution to improve the model's generalization by a meta-learning pipeline.
The key factor for video person re-identification is to effectively exploit both spatial and temporal clues from video sequences.
Within this pipeline, the class activation map (CAM) is obtained and further processed to serve as a pseudo label to train the semantic segmentation model in a fully-supervised manner.
Our framework extracts the knowledge of an arbitrary learned GNN model (teacher model), and injects it into a well-designed student model.
Ranked #1 on Node Classification on Cora (0.5%)
This hard selection strategy is able to fuse the strong-relevant multi-modality features for alleviating the problem of matching redundancy.
Ranked #6 on Text based Person Retrieval on CUHK-PEDES
The experiments on the dataset for training show that our automatic label correction algorithm can improve the accuracy of manual labels and further improve the positioning accuracy of overlapping cells with deep learning models.
TALNet simultaneously exploits human attributes and appearance to learn comprehensive and effective pedestrian representations from videos.
We present a spectral co-design of a statistical multiple-input-multiple-output (MIMO) radar and an in-band full-duplex (IBFD) multi-user MIMO (MU-MIMO) communications system both of which concurrently operate within the same frequency band.
The results demonstrate the effectiveness of our method, which outperforms the baseline on 10 kinds of data and achieves nearly 50 percent improvement on average.
Person re-identification aims at identifying a certain pedestrian across non-overlapping camera networks.
In particular, ATNet consists of a transfer network composed by multiple factor-wise CycleGANs and an ensemble CycleGAN as well as a selection network that infers the affects of different factors on transferring each image.
Current state-of-art feature-engineered and end-to-end Automated Essay Score (AES) methods are proven to be unable to detect adversarial samples, e. g. the essays composed of permuted sentences and the prompt-irrelevant essays.
An appearance network is developed to learn appearance features from the full body, horizontal and vertical body parts of pedestrians with spatial dependencies among body parts.
Experiments on word similarity, syntactic analogy and text classification are conducted to validate the feasibility of our models.