9 research outputs found
Mobile V-MoEs: Scaling Down Vision Transformers via Sparse Mixture-of-Experts
Sparse Mixture-of-Experts models (MoEs) have recently gained popularity due
to their ability to decouple model size from inference efficiency by only
activating a small subset of the model parameters for any given input token. As
such, sparse MoEs have enabled unprecedented scalability, resulting in
tremendous successes across domains such as natural language processing and
computer vision. In this work, we instead explore the use of sparse MoEs to
scale-down Vision Transformers (ViTs) to make them more attractive for
resource-constrained vision applications. To this end, we propose a simplified
and mobile-friendly MoE design where entire images rather than individual
patches are routed to the experts. We also propose a stable MoE training
procedure that uses super-class information to guide the router. We empirically
show that our sparse Mobile Vision MoEs (V-MoEs) can achieve a better trade-off
between performance and efficiency than the corresponding dense ViTs. For
example, for the ViT-Tiny model, our Mobile V-MoE outperforms its dense
counterpart by 3.39% on ImageNet-1k. For an even smaller ViT variant with only
54M FLOPs inference cost, our MoE achieves an improvement of 4.66%
Video Retrieval by Mimicking Poses
We describe a method for real time video retrieval where the task is to match the 2D human pose of a query. A user can form a query by (i) interactively controlling a stickman on a web based GUI, (ii) uploading an image of the desired pose, or (iii) using the Kinect and acting out the query himself. The method is scalable and is applied to a dataset of 18 films totaling more than three million frames. The real time performance is achieved by searching for approximate nearest neighbors to the query using a random forest of K-D trees. Apart from the query modalities, we introduce two other areas of novelty. First, we show that pose retrieval can proceed using a low dimensional representation. Second, we show that the precision of the results can be improved substantially by combining the outputs of independent human pose estimation algorithms. The performance of the system is assessed quantitatively over a range of pose queries