Search Results for author:
Found 17 papers, 6 papers with code
Domain-Rectifying Adapter for Cross-Domain Few-Shot Segmentation
Instead, our key idea is to adapt a small adapter for rectifying diverse target domain styles to the source domain.
OrthCaps: An Orthogonal CapsNet with Sparse Attention Routing and Pruning
Redundancy is a persistent challenge in Capsule Networks (CapsNet), leading to high computational costs and parameter counts.
SA$^2$VP: Spatially Aligned-and-Adapted Visual Prompt
Typical methods for visual prompt tuning follow the sequential modeling paradigm stemming from NLP, which represents an input image as a flattened sequence of token embeddings and then learns a set of unordered parameterized tokens prefixed to the sequence representation as the visual prompts for task adaptation of large vision models.
Deep adaptive hiding network for image hiding using attentive frequency extraction and gradual depth extraction
Image hiding secures information security in multimedia communication.
Universal Object Detection with Large Vision Model
In this study, our focus is on a specific challenge: the large-scale, multi-domain universal object detection problem, which contributes to the broader goal of achieving a universal vision system.
Semantic-Aware Local-Global Vision Transformer
Second, instead of only performing local self-attention within local windows as Swin Transformer does, the proposed SALG performs both 1) local intra-region self-attention for learning fine-grained features within each region and 2) global inter-region feature propagation for modeling global dependencies among all regions.
Ranked #857 on
Image Classification
on ImageNet
Few-Shot Object Detection by Knowledge Distillation Using Bag-of-Visual-Words Representations
In this work we design a novel knowledge distillation framework to guide the learning of the object detector and thereby restrain the overfitting in both the pre-training stage on base classes and fine-tuning stage on novel classes.
Learning Generalizable Latent Representations for Novel Degradations in Super Resolution
The real-world degradations can be beyond the simulation scope by the handcrafted degradations, which are referred to as novel degradations.
Multi-Faceted Distillation of Base-Novel Commonality for Few-shot Object Detection
Most of existing methods for few-shot object detection follow the fine-tuning paradigm, which potentially assumes that the class-agnostic generalizable knowledge can be learned and transferred implicitly from base classes with abundant samples to novel classes with limited samples via such a two-stage training strategy.
Global-Local Stepwise Generative Network for Ultra High-Resolution Image Restoration
While the research on image background restoration from regular size of degraded images has achieved remarkable progress, restoring ultra high-resolution (e. g., 4K) images remains an extremely challenging task due to the explosion of computational complexity and memory usage, as well as the deficiency of annotated data.
BCRLSP: An Offline Reinforcement Learning Framework for Sequential Targeted Promotion
During deployment, we combine the offline RL model with the LP model to generate a robust policy under the budget constraints.
Crystal Transformer: Self-learning neural language model for Generative and Tinkering Design of Materials
Here we propose BLMM Crystal Transformer, a neural network based probabilistic generative model for generative and tinkering design of inorganic materials.
Pedestrian Detection by Exemplar-Guided Contrastive Learning
Instead of learning each of these diverse pedestrian appearance features individually as most existing methods do, we propose to perform contrastive learning to guide the feature learning in such a way that the semantic distance between pedestrians with different appearances in the learned feature space is minimized to eliminate the appearance diversities, whilst the distance between pedestrians and background is maximized.
Ranked #1 on
Pedestrian Detection
on TJU-Ped-campus
Stepwise-Refining Speech Separation Network via Fine-Grained Encoding in High-order Latent Domain
Existing methods for speech separation either transform the speech signals into frequency domain to perform separation or seek to learn a separable embedding space by constructing a latent domain based on convolutional filters.
Ranked #7 on
Speech Separation
on WHAMR!
Generative Memory-Guided Semantic Reasoning Model for Image Inpainting
Most existing methods for image inpainting focus on learning the intra-image priors from the known regions of the current input image to infer the content of the corrupted regions in the same image.
Deep-Masking Generative Network: A Unified Framework for Background Restoration from Superimposed Images
In this work we present the Deep-Masking Generative Network (DMGN), which is a unified framework for background restoration from the superimposed images and is able to cope with different types of noise.
Studying Product Competition Using Representation Learning
We show that, compared with state-of-the-art models, our approach is faster, and can produce more accurate demand forecasts and price elasticities.
