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Found 20 papers, 9 papers with code
DestripeCycleGAN: Stripe Simulation CycleGAN for Unsupervised Infrared Image Destriping
However, when applied to the infrared destriping task, it becomes challenging for the vanilla auxiliary generator to consistently produce vertical noise under unsupervised constraints.
ARCNet: An Asymmetric Residual Wavelet Column Correction Network for Infrared Image Destriping
Our neural model leverages a novel downsampler, residual haar discrete wavelet transform (RHDWT), stripe directional prior knowledge and data-driven learning to induce a model with enriched feature representation of stripe noise and background.
Faster Diffusion: Rethinking the Role of UNet Encoder in Diffusion Models
This finding inspired us to omit the encoder at certain adjacent time-steps and reuse cyclically the encoder features in the previous time-steps for the decoder.
MaTe3D: Mask-guided Text-based 3D-aware Portrait Editing
To address this limitation, we propose \textbf{MaTe3D}: mask-guided text-based 3D-aware portrait editing.
Alleviating Behavior Data Imbalance for Multi-Behavior Graph Collaborative Filtering
To this end, we propose IMGCF, a simple but effective model to alleviate behavior data imbalance for multi-behavior graph collaborative filtering.
Dynamic Prompt Learning: Addressing Cross-Attention Leakage for Text-Based Image Editing
Large-scale text-to-image generative models have been a ground-breaking development in generative AI, with diffusion models showing their astounding ability to synthesize convincing images following an input text prompt.
Trust your Good Friends: Source-free Domain Adaptation by Reciprocal Neighborhood Clustering
We capture this intrinsic structure by defining local affinity of the target data, and encourage label consistency among data with high local affinity.
A Critical Look at the Current Usage of Foundation Model for Dense Recognition Task
In recent years large model trained on huge amount of cross-modality data, which is usually be termed as foundation model, achieves conspicuous accomplishment in many fields, such as image recognition and generation.
Positive Pair Distillation Considered Harmful: Continual Meta Metric Learning for Lifelong Object Re-Identification
Lifelong object re-identification incrementally learns from a stream of re-identification tasks.
OneRing: A Simple Method for Source-free Open-partial Domain Adaptation
In this paper, we investigate Source-free Open-partial Domain Adaptation (SF-OPDA), which addresses the situation where there exist both domain and category shifts between source and target domains.
Attracting and Dispersing: A Simple Approach for Source-free Domain Adaptation
Treating SFDA as an unsupervised clustering problem and following the intuition that local neighbors in feature space should have more similar predictions than other features, we propose to optimize an objective of prediction consistency.
Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptation
In this paper, we address the challenging source-free domain adaptation (SFDA) problem, where the source pretrained model is adapted to the target domain in the absence of source data.
Ranked #3 on
Source-Free Domain Adaptation
on VisDA-2017
Generalized Source-free Domain Adaptation
In this paper, we propose a new domain adaptation paradigm called Generalized Source-free Domain Adaptation (G-SFDA), where the learned model needs to perform well on both the target and source domains, with only access to current unlabeled target data during adaptation.
Ranked #2 on
Source-Free Domain Adaptation
on VisDA-2017
On Implicit Attribute Localization for Generalized Zero-Shot Learning
Zero-shot learning (ZSL) aims to discriminate images from unseen classes by exploiting relations to seen classes via their attribute-based descriptions.
Casting a BAIT for Offline and Online Source-free Domain Adaptation
When adapting to the target domain, the additional classifier initialized from source classifier is expected to find misclassified features.
Source-Free Domain Adaptation
Unsupervised Domain Adaptation
Odd-even layer-number effect and layer-dependent magnetic phase diagrams in MnBi2Te4
However, the layer-dependent magnetism of MnBi2Te4, which is fundamental and crucial for further exploration of quantum phenomena in this system, remains elusive.
Materials Science
Simple and effective localized attribute representations for zero-shot learning
Zero-shot learning (ZSL) aims to discriminate images from unseen classes by exploiting relations to seen classes via their semantic descriptions.
Attention to Refine through Multi-Scales for Semantic Segmentation
This paper proposes a novel attention model for semantic segmentation, which aggregates multi-scale and context features to refine prediction.
Parallel Convolutional Networks for Image Recognition via a Discriminator
The discriminator is core which drives parallel networks to focus on different regions and learn different representations.
D-PCN: Parallel Convolutional Networks for Image Recognition via a Discriminator
The discriminator is core which drives parallel networks to focus on different regions and learn complementary representations.
