Search Results for author:
Found 3 papers, 3 papers with code
Compound Text-Guided Prompt Tuning via Image-Adaptive Cues
We introduce text supervision to the optimization of prompts, which enables two benefits: 1) releasing the model reliance on the pre-defined category names during inference, thereby enabling more flexible prompt generation; 2) reducing the number of inputs to the text encoder, which decreases GPU memory consumption significantly.
Reversible Column Networks
Such architectural scheme attributes RevCol very different behavior from conventional networks: during forward propagation, features in RevCol are learned to be gradually disentangled when passing through each column, whose total information is maintained rather than compressed or discarded as other network does.
Ranked #8 on
Semantic Segmentation
on ADE20K
(using extra training data)
Scaling Up Your Kernels to 31x31: Revisiting Large Kernel Design in CNNs
We revisit large kernel design in modern convolutional neural networks (CNNs).
Ranked #75 on
Image Classification
on ImageNet
