Session-based recommendation (SBR), which makes the next-item recommendation based on previous anonymous actions, has drawn increasing attention. The last decade has seen multiple deep learning-based modeling choices applied on SBR successfully, …
Previous multi-task dense prediction studies developed complex pipelines such as multi-modal distillations in multiple stages or searching for task relational contexts for each task. The core insight beyond these methods is to maximize the mutual …
In this work, we focus on open vocabulary instance segmentation to expand a segmentation model to classify and segment instance-level novel categories. Previous approaches have relied on massive caption datasets and complex pipelines to establish …
Panoptic Part Segmentation (PPS) aims to unify panoptic segmentation and part segmentation into one task. Previous work mainly utilizes separated approaches to handle thing, stuff, and part predictions individually without performing any shared …
The Depth-aware Video Panoptic Segmentation (DVPS) is a new challenging vision problem that aims to predict panoptic segmentation and depth in a video simultaneously. The previous work solves this task by extending the existing panoptic segmentation …
Human fashion understanding is one crucial computer vision task since it has comprehensive information for real-world applications. This focus on joint human fashion segmentation and attribute recognition. Contrary to the previous works that …
Starting from DETR, query based detection and segmentation methods achieve comparable results as previous works with a simplified and elegant pipeline. In this work, a novel, simple and unified baseline, named QueryPanSeg, is proposed for panoptic …
Large-scale pre-trained language models have attracted extensive attentions in the research community and shown promising results on various tasks of natural language processing. However, the attention maps, which record the attention scores between …
Time-series anomaly detection plays an important role in various applications. In a commercial system, anomaly detection models are either unsupervised or pre-trained in a self-supervised manner offline; while in the online serving stage, an …
Graph Neural Networks (GNNs) have shown advantages in various graph-based applications. Most existing GNNs assume strong homophily of graph structure and apply permutation-invariant local aggregation of neighbors to learn a representation for each …