In this paper, we succeed in introducing multi-scale representations into semantic segmentation ViT via window attention mechanism and further improves the performance and efficiency.
Ranked #3 on Semantic Segmentation on Cityscapes test
However, existing methods mostly train the DNNs on uniformly sampled LR-HR patch pairs, which makes them fail to fully exploit informative patches within the image.
Internet video delivery has undergone a tremendous explosion of growth over the past few years.
It is also worth pointing that, given identical strong data augmentations, the performance improvement of ConTNet is more remarkable than that of ResNet.
Graph Reasoning has shown great potential recently in modeling long-range dependencies, which are crucial for various computer vision tasks.
Neural sequence labeling is an important technique employed for many Natural Language Processing (NLP) tasks, such as Named Entity Recognition (NER), slot tagging for dialog systems and semantic parsing.
While self-training serves as an effective mechanism to learn from large amounts of unlabeled data -- meta-learning helps in adaptive sample re-weighting to mitigate error propagation from noisy pseudo-labels.
GI unit is further improved by the SC-loss to enhance the semantic representations over the exemplar-based semantic graph.
To further promote the research of ship detection, we introduced a new fine-grained ship detection datasets, which is named as FGSD.
The proposed loss function, termed as mutual-channel loss (MC-Loss), consists of two channel-specific components: a discriminality component and a diversity component.
Ranked #20 on Fine-Grained Image Classification on FGVC Aircraft
Human parsing is an essential branch of semantic segmentation, which is a fine-grained semantic segmentation task to identify the constituent parts of human.
Learning representations for feature interactions to model user behaviors is critical for recommendation system and click-trough rate (CTR) predictions.
Road extraction is a fundamental task in the field of remote sensing which has been a hot research topic in the past decade.
This evolution has led to large graph-based irregular and sparse models that go beyond what existing deep learning frameworks are designed for.
Its computation is typically characterized by a simple tensor data abstraction to model multi-dimensional matrices, a data-flow graph to model computation, and iterative executions with relatively frequent synchronizations, thereby making it substantially different from Map/Reduce style distributed big data computation.