8 accepted papers
With the rapid development of intelligent transportation systems and the popularity of smart city infrastructure, Vehicle Re-ID technology has become an important research field. The vehicle Re-ID task faces an important challenge, which is the high similarity between different vehicles. Existing me…
Recurrently refining the optical flow based on a single high-resolution feature demonstrates high performance. We exploit the strength of this strategy to build a novel architecture for the joint learning of optical flow and depth. Our pro-posed architecture is improved to work in the case of traini…
Optical flow is the process of estimating motion in scenes. Each object in the scene has a homogeneous motion, i.e., moves in the same direction with the same velocity. Therefore, connecting the parts of an image globally provides an essential cue for learning accurate motion. Convolution-based meth…
Person search is a challenging retrieval problem which aims at matching pedestrians with the same identity over non-overlapping camera views. In this paper, we adopt Swin Transformer as the backbone network to extract discriminative features. We propose a symmetric online instance matching loss whic…
Person search, which requires both pedestrian detection and person re-identification, is a challenging computer vision task applied to real-world scenarios. The challenges faced by detection and re-identification, such as occlusion, poor illumination, confusing background, are still urgent for perso…
Many deep learning applications benefit from multi-task learning with several related objectives. In autonomous driving scenarios, being able to accurately infer motion and spatial information is essential for scene understanding. In this paper, we combine an adaptive feature refinement module and a…
Variational models for optical flow estimation usually define an energy function that contains prior assumptions to explore rudimentary statistics of images. However, such methods cannot learn motion knowledge from the pre-prepared data and have many parameters that need to be set manually. Nowadays…
In this paper, we proposed new framework for human action representation, which leverages the strengths of convolutional neural networks (CNNs) and the linear dynamical system (LDS) to represent both spatial and temporal structures of actions in videos. We make two principal contributions: first, we…