Zobrazeno 1 - 10
of 364
pro vyhledávání: '"Lai, Jianhuang"'
Human trajectory prediction is a practical task of predicting the future positions of pedestrians on the road, which typically covers all temporal ranges from short-term to long-term within a trajectory. However, existing works attempt to address the
Externí odkaz:
http://arxiv.org/abs/2407.11588
Existing person re-identification methods have achieved remarkable advances in appearance-based identity association across homogeneous cameras, such as ground-ground matching. However, as a more practical scenario, aerial-ground person re-identifica
Externí odkaz:
http://arxiv.org/abs/2403.14513
Siamese Learning with Joint Alignment and Regression for Weakly-Supervised Video Paragraph Grounding
Video Paragraph Grounding (VPG) is an emerging task in video-language understanding, which aims at localizing multiple sentences with semantic relations and temporal order from an untrimmed video. However, existing VPG approaches are heavily reliant
Externí odkaz:
http://arxiv.org/abs/2403.11463
Sparse-view Computed Tomography (SVCT) reconstruction is an ill-posed inverse problem in imaging that aims to acquire high-quality CT images based on sparsely-sampled measurements. Recent works use Implicit Neural Representations (INRs) to build the
Externí odkaz:
http://arxiv.org/abs/2307.05270
Anomaly detectors are widely used in industrial production to detect and localize unknown defects in query images. These detectors are trained on nominal images and have shown success in distinguishing anomalies from most normal samples. However, har
Externí odkaz:
http://arxiv.org/abs/2303.16191
Deep learning techniques have achieved superior performance in computer-aided medical image analysis, yet they are still vulnerable to imperceptible adversarial attacks, resulting in potential misdiagnosis in clinical practice. Oppositely, recent yea
Externí odkaz:
http://arxiv.org/abs/2303.14133
The Enemy of My Enemy is My Friend: Exploring Inverse Adversaries for Improving Adversarial Training
Although current deep learning techniques have yielded superior performance on various computer vision tasks, yet they are still vulnerable to adversarial examples. Adversarial training and its variants have been shown to be the most effective approa
Externí odkaz:
http://arxiv.org/abs/2211.00525
We present a weight similarity measure method that can quantify the weight similarity of non-convex neural networks. To understand the weight similarity of different trained models, we propose to extract the feature representation from the weights of
Externí odkaz:
http://arxiv.org/abs/2208.04369
Deep Learning-based Unsupervised Salient Object Detection (USOD) mainly relies on the noisy saliency pseudo labels that have been generated from traditional handcraft methods or pre-trained networks. To cope with the noisy labels problem, a class of
Externí odkaz:
http://arxiv.org/abs/2207.05921
Spiking neural networks are efficient computation models for low-power environments. Spike-based BP algorithms and ANN-to-SNN (ANN2SNN) conversions are successful techniques for SNN training. Nevertheless, the spike-base BP training is slow and requi
Externí odkaz:
http://arxiv.org/abs/2206.09449