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Found 11 papers, 8 papers with code
Window Attention is Bugged: How not to Interpolate Position Embeddings
To fix it, we introduce a simple absolute window position embedding strategy, which solves the bug outright in Hiera and allows us to increase both speed and performance of the model in ViTDet.
Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles
Modern hierarchical vision transformers have added several vision-specific components in the pursuit of supervised classification performance.
Ranked #1 on
Image Classification
on iNaturalist 2019
(using extra training data)
ZipIt! Merging Models from Different Tasks without Training
While this works for models trained on the same task, we find that this fails to account for the differences in models trained on disjoint tasks.
Token Merging for Fast Stable Diffusion
In the process, we speed up image generation by up to 2x and reduce memory consumption by up to 5. 6x.
Token Merging: Your ViT But Faster
Off-the-shelf, ToMe can 2x the throughput of state-of-the-art ViT-L @ 512 and ViT-H @ 518 models on images and 2. 2x the throughput of ViT-L on video with only a 0. 2-0. 3% accuracy drop in each case.
Ranked #13 on
Efficient ViTs
on ImageNet-1K (with DeiT-S)
Hydra Attention: Efficient Attention with Many Heads
While transformers have begun to dominate many tasks in vision, applying them to large images is still computationally difficult.
Scalable Diverse Model Selection for Accessible Transfer Learning
In this paper, we formalize this setting as "Scalable Diverse Model Selection" and propose several benchmarks for evaluating on this task.
Likelihood Landscapes: A Unifying Principle Behind Many Adversarial Defenses
Convolutional Neural Networks have been shown to be vulnerable to adversarial examples, which are known to locate in subspaces close to where normal data lies but are not naturally occurring and of low probability.
TIDE: A General Toolbox for Identifying Object Detection Errors
We introduce TIDE, a framework and associated toolbox for analyzing the sources of error in object detection and instance segmentation algorithms.
YOLACT++: Better Real-time Instance Segmentation
Then we produce instance masks by linearly combining the prototypes with the mask coefficients.
Ranked #15 on
Real-time Instance Segmentation
on MSCOCO
(using extra training data)
YOLACT: Real-time Instance Segmentation
Then we produce instance masks by linearly combining the prototypes with the mask coefficients.
Ranked #21 on
Real-time Instance Segmentation
on MSCOCO
(using extra training data)
