However, such panoptic architectures do not truly unify image segmentation because they need to be trained individually on the semantic, instance, or panoptic segmentation to achieve the best performance.
Ranked #1 on Instance Segmentation on ADE20K val
Image generation has been a long sought-after but challenging task, and performing the generation task in an efficient manner is similarly difficult.
Ranked #1 on Image Generation on FFHQ 256 x 256
These models typically employ localized attention mechanisms, such as the sliding-window Neighborhood Attention (NA) or Swin Transformer's Shifted Window Self Attention.
Ranked #2 on Panoptic Segmentation on COCO minival
Recent research has revealed that reducing the temporal and spatial redundancy are both effective approaches towards efficient video recognition, e. g., allocating the majority of computation to a task-relevant subset of frames or the most valuable image regions of each frame.
This paper demonstrates a novel approach to improve face-recognition pose-invariance using semantic-segmentation features.
We analyzed the pruning masks generated with DiSparse and observed strikingly similar sparse network architecture identified by each task even before the training starts.
We present Neighborhood Attention (NA), the first efficient and scalable sliding-window attention mechanism for vision.
Ranked #71 on Semantic Segmentation on ADE20K
Indeed, Adversarial Artificial Intelligence (AI) which refers to a set of techniques that attempt to fool machine learning models with deceptive data, is a growing threat in the AI and machine learning research community, in particular for machine-critical applications.
MLP-based architectures, which consist of a sequence of consecutive multi-layer perceptron blocks, have recently been found to reach comparable results to convolutional and transformer-based methods.
Ranked #7 on Image Classification on Flowers-102 (using extra training data)
Our models are flexible in terms of model size, and can have as little as 0. 28M parameters while achieving competitive results.
Ranked #1 on Image Classification on Flowers-102 (using extra training data)
Together, these feature vectors create a new feature space much more suitable for clustering.
One of the applications of center-based clustering algorithms such as K-Means is partitioning data points into K clusters.