Search Results for author:
Found 19 papers, 13 papers with code
PanGu-Draw: Advancing Resource-Efficient Text-to-Image Synthesis with Time-Decoupled Training and Reusable Coop-Diffusion
Current large-scale diffusion models represent a giant leap forward in conditional image synthesis, capable of interpreting diverse cues like text, human poses, and edges.
A Sentence Speaks a Thousand Images: Domain Generalization through Distilling CLIP with Language Guidance
Domain generalization studies the problem of training a model with samples from several domains (or distributions) and then testing the model with samples from a new, unseen domain.
Ranked #15 on
Domain Generalization
on PACS
Leveraging Large Language Models for Scalable Vector Graphics-Driven Image Understanding
By leveraging the XML-based textual descriptions of SVG representations instead of raster images, we aim to bridge the gap between the visual and textual modalities, allowing LLMs to directly understand and manipulate images without the need for parameterized visual components.
Expeditious Saliency-guided Mix-up through Random Gradient Thresholding
Mix-up training approaches have proven to be effective in improving the generalization ability of Deep Neural Networks.
Ranked #1 on
Classifier calibration
on CIFAR-100
Toward Learning Robust and Invariant Representations with Alignment Regularization and Data Augmentation
Finally, we test this simple technique we identify (worst-case data augmentation with squared l2 norm alignment regularization) and show that the benefits of this method outrun those of the specially designed methods.
The Two Dimensions of Worst-case Training and the Integrated Effect for Out-of-domain Generalization
Training with an emphasis on "hard-to-learn" components of the data has been proven as an effective method to improve the generalization of machine learning models, especially in the settings where robustness (e. g., generalization across distributions) is valued.
The Two Dimensions of Worst-Case Training and Their Integrated Effect for Out-of-Domain Generalization
Training with an emphasis on "hard-to-learn" components of the data has been proven as an effective method to improve the generalization of machine learning models, especially in the settings where robustness (e. g., generalization across distributions) is valued.
On the Integration of Self-Attention and Convolution
In this paper, we show that there exists a strong underlying relation between them, in the sense that the bulk of computations of these two paradigms are in fact done with the same operation.
Toward Learning Human-aligned Cross-domain Robust Models by Countering Misaligned Features
Machine learning has demonstrated remarkable prediction accuracy over i. i. d data, but the accuracy often drops when tested with data from another distribution.
Not All Images are Worth 16x16 Words: Dynamic Transformers for Efficient Image Recognition
Inspired by this phenomenon, we propose a Dynamic Transformer to automatically configure a proper number of tokens for each input image.
Ranked #29 on
Image Classification
on CIFAR-100
(using extra training data)
On the Consistency Loss for Leveraging Augmented Data to Learn Robust and Invariant Representations
Data augmentation is one of the most popular techniques for improving the robustness of neural networks.
Learning Robust Models by Countering Spurious Correlations
In this paper, we formally study the generalization error bound for this setup with the knowledge of how the spurious features are associated with the label.
Squared $\ell_2$ Norm as Consistency Loss for Leveraging Augmented Data to Learn Robust and Invariant Representations
Data augmentation is one of the most popular techniques for improving the robustness of neural networks.
Comprehensive Attention Self-Distillation for Weakly-Supervised Object Detection
Moreover, the image-level category labels do not enforce consistent object detection across different transformations of the same images.
Ranked #1 on
Weakly Supervised Object Detection
on MSCOCO
Self-Challenging Improves Cross-Domain Generalization
We introduce a simple training heuristic, Representation Self-Challenging (RSC), that significantly improves the generalization of CNN to the out-of-domain data.
Ranked #27 on
Domain Generalization
on PACS
Multiple Anchor Learning for Visual Object Detection
In this paper, we propose a Multiple Instance Learning (MIL) approach that selects anchors and jointly optimizes the two modules of a CNN-based object detector.
Ranked #116 on
Object Detection
on COCO test-dev
High Frequency Component Helps Explain the Generalization of Convolutional Neural Networks
We investigate the relationship between the frequency spectrum of image data and the generalization behavior of convolutional neural networks (CNN).
Improving Object Detection with Inverted Attention
Our approach (1) operates along both the spatial and channels dimensions of the feature maps; (2) requires no extra training on hard samples, no extra network parameters for attention estimation, and no testing overheads.
Large Margin Object Tracking with Circulant Feature Maps
Structured output support vector machine (SVM) based tracking algorithms have shown favorable performance recently.
