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CloudWalk's visual foundation large model demonstrates strong generalization performance, greatly reducing the data dependency and development cost required for downstream tasks. At the same time, zero-shot significantly improves training and development efficiency, making widespread application and rapid deployment possible.

Recently, the ICCV 2023 Open Fine Grained Activity Detection Challenge concluded successfully, and CloudWalk Technology won the championship in the behavior classification track (hereinafter referred to as OpenFAD23-ICCV23).

CloudWalk Technology has made significant progress in visual large models. Its pedestrian foundation model stood out from Alibaba, Hitachi, and many other well-known universities, enterprises, and research institutions on four datasets—PA-100K, RAP V2, PETA, and HICO-DET—setting new world records.

The paper "Diffusion Model-Based Audio-Driven Talking Face Generation" by the joint research team of CloudWalk Technology and Shanghai Jiao Tong University was accepted by the conference and presented on-site, receiving high attention from many parties.

The paper "PointCMP: Contrastive Mask Prediction for Self-supervised Learning on Point Cloud Videos" by CloudWalk Technology and its joint research team has been accepted.

The globally authoritative NIST Face Recognition Vendor Test (NIST-FRVT) released its latest rankings, with CloudWalk achieving three first-place results.