10 accepted papers
Text-to-point-cloud (T2P) localization aims to infer precise spatial positions within 3D point cloud maps from natural language descriptions, reflecting how humans perceive and communicate spatial layouts through language. However, existing methods largely rely on shallow text-point cloud correspond
Robotic tactile sensors based on Electrical Impedance Tomography (EIT) have gained great attention in robotic sensing applications due to their features such as no internal wiring, "all-in-one" structure, and continuous sensing capabilities. However, the effectiveness of EIT-based tactile sensors is
"Point-supervised temporal action localization pursues high-accuracy action detection under low-cost data annotation. Despite recent advances, a significant challenge remains: sparse labeling of individual frames leads to semantic ambiguity in determining action boundaries due to the lack of continu…
Recent advances in 3D point cloud completion adopt unsupervised deep learning-based methods, which does not rely on labeled data and improves generalization ability. However, existing methods tend to focus more on the generation overall shape rather than detailed structure. To explore unsupervised 3…
The object of Weakly-supervised Temporal Action Localization (WS-TAL) is to localize all action instances in an untrimmed video with only video-level supervision. Due to the lack of frame-level annotations during training, current WS-TAL methods rely on attention mechanisms to localize the foregroun…
Recent studies unveil the vulnerabilities of deep ranking models, where an imperceptible perturbation can trigger dramatic changes in the ranking result. While previous attempts focus on manipulating absolute ranks of certain candidates, the possibility of adjusting their relative order remains unde…
Deep Neural Network (DNN) classifiers are vulnerable to adversarial attack, where an imperceptible perturbation could result in misclassification. However, the vulnerability of DNN-based image ranking systems remains under-explored. In this paper, we propose two attacks against deep ranking systems,…
The image-based fine-grained identification of individual giant pandas (Ailuropoda melanoleuca) is an emerging technology, and it is extraordinarily challenging due to the extremely subtle visual differences between individual giant pandas and limited annotated training data. To address these challe…
Weakly-supervised Temporal Action Localization (W-TAL) aims to classify and localize all action instances in an untrimmed video under only video-level supervision. However, without frame-level annotations, it is challenging for W-TAL methods to identify false positive action proposals and generate a…
Weakly-supervised temporal action localization (WS-TAL) is a promising but challenging task with only video-level action categorical labels available during training. Without requiring temporal action boundary annotations in training data, WS-TAL could possibly exploit automatically retrieved video…