🔥 3.93% improved zero-shot ImageNet classification accuracy when providing foreground alpha-map. 🔥 Plug-in and play with region focus in any work that use CLIP vision encoder. 🔥 A strong visual encoder as versatile tool when foreground mask is available. Training code for Alpha-CLIP and MaskImageNet data.

Alpha-CLIP is a new model that improves on an existing system called CLIP, which helps computers understand images and text. Unlike the original CLIP, Alpha-CLIP can focus on specific parts of images (like a person’s face or an object) without losing important details from the rest of the image.

2023年12月6日 · To fulfill the requirements, we introduce Alpha-CLIP, an enhanced version of CLIP with an auxiliary alpha channel to suggest attentive regions and fine-tuned with constructed millions of RGBA region-text pairs. Alpha-CLIP not only preserves the visual recognition ability of CLIP but also enables precise control over the emphasis of image contents.

Alpha-CLIP not only preserves the visual recognition ability of CLIP but also enables precise control over the emphasis of image contents. It demonstrates effectiveness in various tasks including but not limited to open-world recognition multimodal large language models and conditional 2D / 3D generation.

2019年12月29日 · 腐国官方要求 cvf 可搭载多至 48 架舰载机，每日出动 150 个固定翼架次。 至 2002 年，出动率指标被调低至更加切合实际的 110 架次。 BAE 的分析显示 CATOBAR 构型的排水量将比 STOVL 构型高出 1 万吨。

Papers are assigned to poster sessions such that topics are maximally spread over sessions (attendees will find interesting papers at each session) while grouping similar posters within each poster session to minimize walking distances. We used a 1D t-SNE projection of the SPECTER paper embeddings to realize this assignment.

PyTorch implementation of ALFA-Mix. For details, read the paper Active Learning by Feature Mixing, which is accepted in CVPR 2022. The code includes the implementations of all the baselines presented in the paper. Parts of the code are borrowed from https://github.com/JordanAsh/badge. The dependencies are in requirements.txt.

These research papers are the Open Access versions, provided by the Computer Vision Foundation. Except for the watermark, they are identical to the accepted versions; the final published version of the proceedings is available on IEEE Xplore. This material is presented to ensure timely dissemination of scholarly and technical work.

The proposed Alpha-Refine method leads to a se-ries of strengthened trackers, among which the AR-SiamRPN (Alpha-Refine strengthened SiamRPNpp) and ARDiMP50 (Alpha-Refine strengthened DiMP50) achieve good efficiency-precision trade-off, while ARDiMPsuper (Alpha-Refine strengthened DiMPsuper) achieves state-of-

The continuing COVID-19 pandemic brought us another virtual edition of the conference in 2021. The IEEE Computer Society and Computer Vision Foundation were able to jointly organize an event that attendees still found to be engaging and enjoyable.