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Found 10 papers, 5 papers with code
VarSR: Variational Super-Resolution Network for Very Low Resolution Images
As is well known, single image super-resolution (SR) is an ill-posed problem where multiple high resolution (HR) images can be matched to one low resolution (LR) image due to the difference of their representation capabilities.
Adversarial Generation of Hierarchical Gaussians for 3D Generative Model
Specifically, we design a hierarchy of Gaussians where finer-level Gaussians are parameterized by their coarser-level counterparts; the position of finer-level Gaussians would be located near their coarser-level counterparts, and the scale would monotonically decrease as the level becomes finer, modeling both coarse and fine details of the 3D scene.
Diversity-aware Channel Pruning for StyleGAN Compression
Specifically, by assessing channel importance based on their sensitivities to latent vector perturbations, our method enhances the diversity of samples in the compressed model.
Task-Disruptive Background Suppression for Few-Shot Segmentation
Based on these two scores, we define a query background relevant score that captures the similarity between the backgrounds of the query and the support, and utilize it to scale support background features to adaptively restrict the impact of disruptive support backgrounds.
Style Injection in Diffusion: A Training-free Approach for Adapting Large-scale Diffusion Models for Style Transfer
Despite the impressive generative capabilities of diffusion models, existing diffusion model-based style transfer methods require inference-stage optimization (e. g. fine-tuning or textual inversion of style) which is time-consuming, or fails to leverage the generative ability of large-scale diffusion models.
Correlation-guided Query-Dependency Calibration in Video Representation Learning for Temporal Grounding
Dummy tokens conditioned by text query take portions of the attention weights, preventing irrelevant video clips from being represented by the text query.
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Query-Dependent Video Representation for Moment Retrieval and Highlight Detection
As we observe the insignificant role of a given query in transformer architectures, our encoding module starts with cross-attention layers to explicitly inject the context of text query into video representation.
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Disentangled Representation Learning for Unsupervised Neural Quantization
The inverted index is a widely used data structure to avoid the infeasible exhaustive search.
Local Attention Pyramid for Scene Image Generation
To address this, we propose a novel attention module, Local Attention Pyramid (LAP) module tailored for scene image synthesis, that encourages GANs to generate diverse object classes in a high quality by explicit spread of high attention scores to local regions, since objects in scene images are scattered over the entire images.
Self-Supervised Video GANs: Learning for Appearance Consistency and Motion Coherency
The proposed tasks enable the discriminators to learn representations of appearance and temporal context, and force the generator to synthesize videos with consistent appearance and natural flow of motions.
