1 citations · 1 across the 4 of their papers we have counts for
5 papers
LVMT: Video Mask Transformer for Long-term Video Segmentation
Narges Norouzi, Niccol`o Cavagnero, Niccolò Cavagnero +4
Existing online video segmentation methods struggle to track objects in long, complex videos with long-term occlusions. We hypothesize that this limitation is caused by (i) the ina…
PMT: Plain Mask Transformer for Image and Video Segmentation with Frozen Vision Encoders
Niccolò Cavagnero, Narges Norouzi, Gijs Dubbelman +1
Vision Foundation Models (VFMs) pre-trained at scale enable a single frozen encoder to serve multiple downstream tasks simultaneously. Recent VFM-based encoder-only models for imag…
VidEoMT: Your ViT is Secretly Also a Video Segmentation Model
Narges Norouzi, Idil Esen Zulfikar, Niccolò Cavagnero +4
Existing online video segmentation models typically combine a per-frame segmenter with complex specialized tracking modules. While effective, these modules introduce significant ar…
Your ViT is Secretly an Image Segmentation Model
Tommie Kerssies, Niccolò Cavagnero, Alexander Hermans +5
Vision Transformers (ViTs) have shown remarkable performance and scalability across various computer vision tasks. To apply single-scale ViTs to image segmentation, existing method…
ALGM: Adaptive Local-then-Global Token Merging for Efficient Semantic Segmentation with Plain Vision Transformers
Narges Norouzi, Svetlana Orlova, Daan de Geus +1
This work presents Adaptive Local-then-Global Merging (ALGM), a token reduction method for semantic segmentation networks that use plain Vision Transformers. ALGM merges tokens in…