Models optimized by CLIPSelf achieve state-of-the-art performance on open-vocabulary object detection and image segmentation tasks. CLIPSelf provides an effective and general solution for dense prediction tasks based on CLIP vision transformers. 最近,开放词汇的密集预测任务,如目标检测和图像分割,受到广泛...
CLIPSelf: Vision Transformer Distills Itself for Open-Vocabulary Dense Prediction 小记 草木如织 Working on NN ... 目录 收起 Observation Method Experiments (ICLR 2024 Spotlight) CLIP for dense tasks Observation比较CLIP Resnet/ViT,Image Crop/Dense Feature 的classification accuracy,发现: ...
CLIPSelf: Vision Transformer Distills Itself for Open-Vocabulary Dense Prediction, Size Wu, Wenwei Zhang, Lumin Xu, Sheng Jin, Xiangtai Li, Wentao Liu, Chen Change Loy Bibetex TODO Code and models of CLIPSelf Code and models of F-ViT ...
ViT-Adapter是一种简单 yet powerful的dense prediction任务适配器,旨在解决Vision Transformer(ViT)在dense prediction任务中的性能劣势问题。与最近的变体相比,ViT-Adapter不需要在架构中引入视觉特定的归纳偏见,而是通过在预训练阶段引入图像相关的归纳偏见,来提高模型的性能。 ViT-Adapter的架构主要由两个部分组成:一个...
Open-vocabulary object detection via vision and language knowledge distillation (2021) arXiv preprint arXiv:2104.13921 Google Scholar [50] Wu S., Zhang W., Xu L., Jin S., Li X., Liu W., Loy C.C. CLIPSelf: Vision transformer distill itself for open-vocabulary dense prediction (2023) ...
2023arXivvlm.DST-Det: Simple Dynamic Self-Training for Open-Vocabulary Object DetectionCode 2023arXivvlm.Taming Self-Training for Open-Vocabulary Object DetectionCode 2024ICLRunify.,vlm.,pre.CLIPSelf: Vision Transformer Distills Itself for Open-Vocabulary Dense PredictionCode ...
1. Introduction Open-vocabulary semantic segmentation aims to assign each pixel in an image to a class label from an unbounded range, defined by text descriptions. To handle the chal- lenge of associating an image with a wide variety of text descriptions, pre-trained v...
Ghiasi, G., Gu, X., Cui, Y., & Lin, T.-Y. (2022). Scaling open-vocabulary image segmentation with image-level labels. InEuropean conference on computer vision, pp. 540–557. Springer. Girshick, R. (2015) Fast r-cnn. InProceedings of the IEEE international conference on computer visi...
We leverage these dense and rich diffusion features to perform open-vocabulary panoptic segmentation (right figure). Abstract 1. Introduction We present ODISE: Open-vocabulary DIffusion-based panoptic SEgmentation, which unifies pre-trained text- image diffusion and discri...
Fig. 1. Proposed pipeline for open vocabulary land cover mapping. The input remote sensing image is fed to a segmentation network with two heads: a standard classification head trained with a cross-entropy loss and a semantic head that produces a dense semantic output. The land cover labels ar...