TabTransformer is a deep tabular data modeling architecture for supervised and semi-supervised learning. The TabTransformer is built upon self-attention based Transformers. The Transformer layers transform the embeddings of categorical features into robust contextual embeddings to achieve higher prediction accuracy. As an overview, the architecture comprises a column embedding layer, a stack of $N$ Transformer layers, and a multi-layer perceptron (MLP). The contextual embeddings (outputted by the Transformer layer) are concatenated along with continuous features which is inputted to an MLP. The loss function is then minimized to learn all the parameters in an end-to-end learning.
Contrastive Video Representation Learning, or CVRL, is a self-supervised contrastive learning framework for learning spatiotemporal visual representations from unlabeled videos. Representations are learned using a contrastive loss, where two clips from the same short video are pulled together in the embedding space, while clips from different videos are pushed away. Data augmentations are designed involving spatial and temporal cues. Concretely, a temporally consistent spatial augmentation method is used to impose strong spatial augmentations on each frame of the video while maintaining the temporal consistency across frames. A sampling-based temporal augmentation method is also used to avoid overly enforcing invariance on clips that are distant in time. End-to-end, from a raw video, we first sample a temporal interval from a monotonically decreasing distribution. The temporal interval represents the number of frames between the start points of two clips, and we sample two clips from a video according to this interval. Afterwards we apply a temporally consistent spatial augmentation to each of the clips and feed them into a 3D backbone with an MLP head. The contrastive loss is used to train the network to attract the clips from the same video and repel the clips from different videos in the embedding space.
Model to adapt: We use Ultra Fast Structure-aware Deep Lane Detection (UFLD) as baseline and strictly adopt its training scheme and hyperparameters. UFLD treats lane detection as a row-based classification problem and utilizes the row anchors defined by TuSimple. Unsupervised Domain Adaptation with SGPCS: SGPCS builds upon PCS and performs in-domain contrastive learning and crossdomain self-supervised learning via cluster prototypes. We reformulate the pseudo label selection mechanism from SGADA. For each lane, we select the highest confidence value from the griding cells of each row anchor. Based on their griding cell position, the confidence values are divided into two cases: absent lane points and present lane points. Thereby, the last griding cell represents absent lane points as in. For each case, we calculate the mean confidence over the corresponding lanes. We then use the thresholds defined by SGADA to decide whether the prediction is treated as a pseudo label. Our overall objective function comprises the in-domain and cross-domain loss from PCS, the losses defined by UFLD, and our adopted pseudo loss from SGADA. We adjust the momentum for memory bank feature updates to 0.5 and use spherical K-means with K = 2,500 to cluster them into prototypes.