Search Results for author:
Found 16 papers, 8 papers with code
End-To-End Underwater Video Enhancement: Dataset and Model
To fill this gap, we construct the Synthetic Underwater Video Enhancement (SUVE) dataset, comprising 840 diverse underwater-style videos paired with ground-truth reference videos.
Self-Supervised Representation Learning with Meta Comprehensive Regularization
Specifically, we update our proposed model through a bi-level optimization mechanism, enabling it to capture comprehensive features.
FreeStyle: Free Lunch for Text-guided Style Transfer using Diffusion Models
In the decoder, we further modulate features from the dual streams based on a given content image and the corresponding style text prompt for precise style transfer.
Rethinking Causal Relationships Learning in Graph Neural Networks
In order to comprehensively analyze various GNN models from a causal learning perspective, we constructed an artificially synthesized dataset with known and controllable causal relationships between data and labels.
UIEDP:Underwater Image Enhancement with Diffusion Prior
To address this issue, we propose UIE with Diffusion Prior (UIEDP), a novel framework treating UIE as a posterior distribution sampling process of clear images conditioned on degraded underwater inputs.
PrototypeFormer: Learning to Explore Prototype Relationships for Few-shot Image Classification
In particular, our method achieves 97. 07% and 90. 88% on 5-way 5-shot and 5-way 1-shot tasks of miniImageNet, which surpasses the state-of-the-art results with accuracy of 7. 27% and 8. 72%, respectively.
Cartoondiff: Training-free Cartoon Image Generation with Diffusion Transformer Models
Image cartoonization has attracted significant interest in the field of image generation.
Background Debiased SAR Target Recognition via Causal Interventional Regularizer
Based on the constructed SCM, we propose a causal intervention based regularization method to eliminate the negative impact of background on feature semantic learning and achieve background debiased SAR-ATR.
A Dimensional Structure based Knowledge Distillation Method for Cross-Modal Learning
To address this issue, in this paper, we discover the correlation between feature discriminability and dimensional structure (DS) by analyzing and observing features extracted from simple and hard tasks.
Introducing Expertise Logic into Graph Representation Learning from A Causal Perspective
By further observing the ramifications of introducing expertise logic into graph representation learning, we conclude that leading the GNNs to learn human expertise can improve the model performance.
Timestamp-Supervised Action Segmentation from the Perspective of Clustering
Most existing methods generate pseudo-labels for all frames in each video to train the segmentation model.
Disentangle and Remerge: Interventional Knowledge Distillation for Few-Shot Object Detection from A Conditional Causal Perspective
To understand the reasons behind this phenomenon, we revisit the learning paradigm of knowledge distillation on the few-shot object detection task from the causal theoretic standpoint, and accordingly, develop a Structural Causal Model.
Robust Causal Graph Representation Learning against Confounding Effects
This observation reveals that there exist confounders in graphs, which may interfere with the model learning semantic information, and current graph representation learning methods have not eliminated their influence.
Do we really need temporal convolutions in action segmentation?
Most state-of-the-art methods focus on designing temporal convolution-based models, but the inflexibility of temporal convolutions and the difficulties in modeling long-term temporal dependencies restrict the potential of these models.
Ranked #1 on
Action Segmentation
on 50Salads
Bootstrapping Informative Graph Augmentation via A Meta Learning Approach
To this end, we propose a novel approach to learning a graph augmenter that can generate an augmentation with uniformity and informativeness.
SimViT: Exploring a Simple Vision Transformer with sliding windows
Although vision Transformers have achieved excellent performance as backbone models in many vision tasks, most of them intend to capture global relations of all tokens in an image or a window, which disrupts the inherent spatial and local correlations between patches in 2D structure.
