The problem of multimodal intent and trajectory prediction for human-driven vehicles in parking lots is addressed in this paper.
Unsupervised Person Re-identification (U-ReID) with pseudo labeling recently reaches a competitive performance compared to fully-supervised ReID methods based on modern clustering algorithms.
In this paper, we propose an embarrassing simple yet highly effective adversarial domain adaptation (ADA) method for effectively training models for alignment.
The inheritance part is learned with a similarity loss to transfer the existing learned knowledge from the teacher model to the student model, while the exploration part is encouraged to learn representations different from the inherited ones with a dis-similarity loss.
Driven by the success of deep learning, the last decade has seen rapid advances in person re-identification (re-ID).
Specifically, we introduce Gait recognition as an auxiliary task to drive the Image ReID model to learn cloth-agnostic representations by leveraging personal unique and cloth-independent gait information, we name this framework as GI-ReID.
The CNN encoder is responsible for efficiently extracting discriminative spatial features while the DI decoder is designed to densely model spatial-temporal inherent interaction across frames.
Ranked #1 on Person Re-Identification on DukeMTMC-reID
In this paper, we propose a novel solution for object-matching based semi-supervised video object segmentation, where the target object masks in the first frame are provided.
Second, different body parts possess different scales, and even the same part in different frames can appear at different locations and scales.
The topology of the adjacency graph is a key factor for modeling the correlations of the input skeletons.
We investigate the problem of predicting driver behavior in parking lots, an environment which is less structured than typical road networks and features complex, interactive maneuvers in a compact space.
In this paper, we propose randomly transforming (rotation, scale, and translation) feature maps of CNNs during the training stage.
Convolutional Neural Networks (CNNs) have demonstrated state-of-the-art performance on many visual recognition tasks.
The proposed quantization function can be learned in a lossless and end-to-end manner and works for any weights and activations of neural networks in a simple and uniform way.