Oct 11, 2023 · In this work, we present a new feature fusion module which tackles this problem and enables language-guided paradigm to be applied to lightweight networks.
Dec 25, 2023 · The large-scale pretrained model CLIP, trained on 400 million image-text pairs, offers a promising paradigm for tackling vision tasks, ...
Dec 25, 2023 · The large-scale pretrained model CLIP, trained on 400 million image-text pairs, offers a promising paradigm for tackling vision tasks, ...
The large-scale pretrained model CLIP, trained on 400 million image-text pairs, offers a promising paradigm for tackling vision tasks, albeit at the image ...
In this paper, we propose WeCLIP, a CLIP-based single-stage pipeline, for weakly supervised semantic segmentation. Specifically, the frozen CLIP model is ...
With such information, our method CLIP-DINOiser performs zero-shot open-vocabulary semantic segmentation in a single pass of CLIP model and with two light extra.
Mar 12, 2024 · This paper develops a Boundary-guide dual-resolution lightweight network with multi-scale Semantic Context, called BSCNet, for semantic segmentation.
(arXiv 2023.01) Head-Free Lightweight Semantic Segmentation with Linear Transformer, [Paper], [Code] ... (arXiv 2023.10) CLIP for Lightweight Semantic ...
We introduce segment matching loss and multi-scaled feature distillation loss, which are crucial for enabling open-vocabulary semantic segmentation from CLIP.
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Oct 7, 2024 · In this work, we propose a lightweight approach to enhance realtime semantic segmentation ... (CLIP) to generate rich semantic embeddings ...