1 code implementation • 7 Mar 2024 • Ali Hassani, Wen-mei Hwu, Humphrey Shi
We observe that our fused kernels successfully circumvent some of the unavoidable inefficiencies in unfused implementations.
no code implementations • 29 Jun 2023 • Feng Liu, Ryan Ashbaugh, Nicholas Chimitt, Najmul Hassan, Ali Hassani, Ajay Jaiswal, Minchul Kim, Zhiyuan Mao, Christopher Perry, Zhiyuan Ren, Yiyang Su, Pegah Varghaei, Kai Wang, Xingguang Zhang, Stanley Chan, Arun Ross, Humphrey Shi, Zhangyang Wang, Anil Jain, Xiaoming Liu
Whole-body biometric recognition is an important area of research due to its vast applications in law enforcement, border security, and surveillance.
2 code implementations • 10 Nov 2022 • Steven Walton, Ali Hassani, Xingqian Xu, Zhangyang Wang, Humphrey Shi
Image generation has been a long sought-after but challenging task, and performing the generation task in an efficient manner is similarly difficult.
Ranked #2 on Image Generation on FFHQ 256 x 256
2 code implementations • CVPR 2023 • Jitesh Jain, Jiachen Li, MangTik Chiu, Ali Hassani, Nikita Orlov, Humphrey Shi
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 Panoptic Segmentation on COCO minival
5 code implementations • 29 Sep 2022 • Ali Hassani, Humphrey Shi
These models typically employ localized attention mechanisms, such as the sliding-window Neighborhood Attention (NA) or Swin Transformer's Shifted Window Self Attention.
Ranked #4 on Panoptic Segmentation on COCO minival
no code implementations • 27 Sep 2022 • Yulin Wang, Yang Yue, Xinhong Xu, Ali Hassani, Victor Kulikov, Nikita Orlov, Shiji Song, Humphrey Shi, Gao Huang
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.
no code implementations • 2 Sep 2022 • Ali Hassani, Zaid El Shair, Rafi Ud Duala Refat, Hafiz Malik
This paper demonstrates a novel approach to improve face-recognition pose-invariance using semantic-segmentation features.
1 code implementation • CVPR 2022 • Xinglong Sun, Ali Hassani, Zhangyang Wang, Gao Huang, Humphrey Shi
We analyzed the pruning masks generated with DiSparse and observed strikingly similar sparse network architecture identified by each task even before the training starts.
5 code implementations • CVPR 2023 • Ali Hassani, Steven Walton, Jiachen Li, Shen Li, Humphrey Shi
We present Neighborhood Attention (NA), the first efficient and scalable sliding-window attention mechanism for vision.
Ranked #119 on Semantic Segmentation on ADE20K
no code implementations • 22 Feb 2022 • Ngoc Dung Huynh, Mohamed Reda Bouadjenek, Imran Razzak, Kevin Lee, Chetan Arora, Ali Hassani, Arkady Zaslavsky
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.
4 code implementations • 9 Sep 2021 • Jiachen Li, Ali Hassani, Steven Walton, Humphrey Shi
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 #8 on Image Classification on Flowers-102 (using extra training data)
8 code implementations • 12 Apr 2021 • Ali Hassani, Steven Walton, Nikhil Shah, Abulikemu Abuduweili, Jiachen Li, Humphrey Shi
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)
Fine-Grained Image Classification Superpixel Image Classification
no code implementations • 12 Nov 2019 • Ali Hassani, Amir Iranmanesh, Najme Mansouri
Together, these feature vectors create a new feature space much more suitable for clustering.
1 code implementation • IJMLC 2020 • Ali Hassani, Amir Iranmanesh, Mahdi Eftekhari, Abbas Salemi
One of the applications of center-based clustering algorithms such as K-Means is partitioning data points into K clusters.