Search Results for author:
Found 6 papers, 3 papers with code
Facing the Elephant in the Room: Visual Prompt Tuning or Full Finetuning?
As the scale of vision models continues to grow, the emergence of Visual Prompt Tuning (VPT) as a parameter-efficient transfer learning technique has gained attention due to its superior performance compared to traditional full-finetuning.
Image Translation as Diffusion Visual Programmers
We introduce the novel Diffusion Visual Programmer (DVP), a neuro-symbolic image translation framework.
CML-MOTS: Collaborative Multi-task Learning for Multi-Object Tracking and Segmentation
The advancement of computer vision has pushed visual analysis tasks from still images to the video domain.
E^2VPT: An Effective and Efficient Approach for Visual Prompt Tuning
Specifically, we introduce a set of learnable key-value prompts and visual prompts into self-attention and input layers, respectively, to improve the effectiveness of model fine-tuning.
Visual Recognition with Deep Nearest Centroids
We devise deep nearest centroids (DNC), a conceptually elegant yet surprisingly effective network for large-scale visual recognition, by revisiting Nearest Centroids, one of the most classic and simple classifiers.
YOLOPv2: Better, Faster, Stronger for Panoptic Driving Perception
Over the last decade, multi-tasking learning approaches have achieved promising results in solving panoptic driving perception problems, providing both high-precision and high-efficiency performance.
Ranked #1 on
Lane Detection
on BDD100K val
