入伍仪式
发表于 2025-3-30 10:12:12
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debase
发表于 2025-3-30 12:45:33
Massimo G. Colombo,Marco Delmastroose an efficient Attention Guided Adversarial Training mechanism. Specifically, relying on the specialty of self-attention, we actively remove certain patch embeddings of each layer with an attention-guided dropping strategy during adversarial training. The slimmed self-attention modules accelerate
instulate
发表于 2025-3-30 18:02:19
AU-Aware 3D Face Reconstruction through Personalized AU-Specific Blendshape Learning,basis coefficients such that they are semantically mapped to each AU. Our AU-aware 3D reconstruction model generates accurate 3D expressions composed by semantically meaningful AU motion components. Furthermore, the output of the model can be directly applied to generate 3D AU occurrence predictions
octogenarian
发表于 2025-3-30 21:55:44
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咯咯笑
发表于 2025-3-31 04:17:20
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整体
发表于 2025-3-31 08:58:54
,Pre-training Strategies and Datasets for Facial Representation Learning,ncluding their size and quality (labelled, unlabelled or even uncurated). (d) To draw our conclusions, we conducted a very large number of experiments. Our main two findings are: (1) Unsupervised pre-training on completely in-the-wild, uncurated data provides consistent and, in some cases, significa
wall-stress
发表于 2025-3-31 09:14:05
,Look Both Ways: Self-supervising Driver Gaze Estimation and Road Scene Saliency,framework to enforce this consistency, allowing the gaze model to supervise the scene saliency model, and vice versa. We implement a prototype of our method and test it with our dataset, to show that compared to a supervised approach it can yield better gaze estimation and scene saliency estimation
HARP
发表于 2025-3-31 17:25:14
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buoyant
发表于 2025-3-31 18:12:49
,3D Face Reconstruction with Dense Landmarks, facial performance capture in both monocular and multi-view scenarios. Finally, our method is highly efficient: we can predict dense landmarks and fit our 3D face model at over 150FPS on a single CPU thread. Please see our website: ..
FLAGR
发表于 2025-4-1 00:12:42
,Emotion-aware Multi-view Contrastive Learning for Facial Emotion Recognition,entation in the polar coordinate, i.e., the Arousal-Valence space. Experimental results show that the proposed method improves the PCC/CCC performance by more than 10% compared to the runner-up method in the wild datasets and is also qualitatively better in terms of neural activation map. Code is av