3.5k citations · 9.4k across the 54 of their papers we have counts for
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PASTA: Proportional Amplitude Spectrum Training Augmentation for Syn-to-Real Domain Generalization
Prithvijit Chattopadhyay, Kartik Sarangmath, Vivek Vijaykumar +1
Synthetic data offers the promise of cheap and bountiful training data for settings where labeled real-world data is scarce. However, models trained on synthetic data significantly…
Structure-Encoding Auxiliary Tasks for Improved Visual Representation in Vision-and-Language Navigation
Chia-Wen Kuo, Chih-Yao Ma, Judy Hoffman +1
In Vision-and-Language Navigation (VLN), researchers typically take an image encoder pre-trained on ImageNet without fine-tuning on the environments that the agent will be trained…
Signed Binary Weight Networks
Sachit Kuhar, Alexey Tumanov, Judy Hoffman
Efficient inference of Deep Neural Networks (DNNs) is essential to making AI ubiquitous. Two important algorithmic techniques have shown promise for enabling efficient inference -…
Token Merging: Your ViT But Faster
Daniel Bolya, Cheng-Yang Fu, Xiaoliang Dai +3
We introduce Token Merging (ToMe), a simple method to increase the throughput of existing ViT models without needing to train. ToMe gradually combines similar tokens in a transform…
Hydra Attention: Efficient Attention with Many Heads
Daniel Bolya, Cheng-Yang Fu, Xiaoliang Dai +2
While transformers have begun to dominate many tasks in vision, applying them to large images is still computationally difficult. A large reason for this is that self-attention sca…
Adapting Self-Supervised Vision Transformers by Probing Attention-Conditioned Masking Consistency
Viraj Prabhu, Sriram Yenamandra, Aaditya Singh +1
Visual domain adaptation (DA) seeks to transfer trained models to unseen, unlabeled domains across distribution shift, but approaches typically focus on adapting convolutional neur…