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Found 6 papers, 4 papers with code
T4P: Test-Time Training of Trajectory Prediction via Masked Autoencoder and Actor-specific Token Memory
Our method surpasses the performance of existing state-of-the-art online learning methods in terms of both prediction accuracy and computational efficiency.
Visual Style Prompting with Swapping Self-Attention
Despite their remarkable capability, existing models still face challenges in achieving controlled generation with a consistent style, requiring costly fine-tuning or often inadequately transferring the visual elements due to content leakage.
Training-free Content Injection using h-space in Diffusion Models
Instead, our method manipulates intermediate features within a feed-forward generative process.
Diffusion Models already have a Semantic Latent Space
To address the problem, we propose asymmetric reverse process (Asyrp) which discovers the semantic latent space in frozen pretrained diffusion models.
Exploring Pixel-level Self-supervision for Weakly Supervised Semantic Segmentation
In our framework, with the help of the proposed Regional Contrastive Module (RCM) and Multi-scale Attentive Module (MAM), MainNet is trained by self-supervision from the SupportNet.
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
SpherePHD: Applying CNNs on a Spherical PolyHeDron Representation of 360 degree Images
This method minimizes thevariance of the spatial resolving power on the sphere sur-face, and includes new convolution and pooling methodsfor the proposed representation.
