发起 发表于 2025-3-30 12:15:04
Pip Podcasts: When Telling Becomes Listening but they suffer from the ill-posed face pose and depth ambiguity issue. In contrast to previous works that only enforce 2D feature constraints, we propose a self-supervised training architecture by leveraging the multi-view geometry consistency, which provides reliable constraints on face pose andLEERY 发表于 2025-3-30 12:59:58
: A Vision of Community Connectionages different interactions to boost action detection. There are two key designs in it: one is the Interaction Aggregation structure (IA) adopting a uniform paradigm to model and integrate multiple types of interaction; the other is the Asynchronous Memory Update algorithm (AMU) that enables us to ainsipid 发表于 2025-3-30 18:35:52
http://reply.papertrans.cn/24/2343/234211/234211_53.png期满 发表于 2025-3-30 22:08:26
http://reply.papertrans.cn/24/2343/234211/234211_54.png半圆凿 发表于 2025-3-31 03:37:44
Pip Podcasts: When Telling Becomes Listening problem poses many challenges for computers since it requires simultaneously reconstructing objects in the two views while also figuring out their relationship.We propose a new approach that estimates reconstructions, distributions over the camera/object and camera/camera transformations, as well aChivalrous 发表于 2025-3-31 09:01:27
http://reply.papertrans.cn/24/2343/234211/234211_56.pngFLOAT 发表于 2025-3-31 10:02:24
Lake restoration: capabilities and needsdies the non-local block in depth, where we find that its attention computation can be split into two terms, a whitened pairwise term accounting for the relationship between two pixels and a unary term representing the saliency of every pixel. We also observe that the two terms trained alone tend to意外的成功 发表于 2025-3-31 14:43:17
Multiple techniques for lake restorationobject masks referred by the given language expression in the whole video frames. Our algorithm addresses the challenging problem by performing language-based object segmentation and mask propagation jointly using a single deep neural network with a proper combination of two attention models. In add