Search Results for author:
Found 11 papers, 2 papers with code
PromptTA: Prompt-driven Text Adapter for Source-free Domain Generalization
In this work, we propose Prompt-Driven Text Adapter (PromptTA) method, which is designed to better capture the distribution of style features and employ resampling to ensure thorough coverage of domain knowledge.
Semantic-aware Representation Learning for Homography Estimation
In our work, we seek another way to use the semantic information, that is semantic-aware feature representation learning framework. Based on this, we propose SRMatcher, a new detector-free feature matching method, which encourages the network to learn integrated semantic feature representation. Specifically, to capture precise and rich semantics, we leverage the capabilities of recently popularized vision foundation models (VFMs) trained on extensive datasets.
A General Theory for Compositional Generalization
Compositional Generalization (CG) embodies the ability to comprehend novel combinations of familiar concepts, representing a significant cognitive leap in human intellectual advancement.
Closing the Gap Between the Upper Bound and the Lower Bound of Adam's Iteration Complexity
Recently, Arjevani et al. [1] established a lower bound of iteration complexity for the first-order optimization under an $L$-smooth condition and a bounded noise variance assumption.
Breaking through the learning plateaus of in-context learning in Transformer
To study the mechanism behind the learning plateaus, we conceptually seperate a component within the model's internal representation that is exclusively affected by the model's weights.
Generalization error bounds for iterative learning algorithms with bounded updates
This paper explores the generalization characteristics of iterative learning algorithms with bounded updates for non-convex loss functions, employing information-theoretic techniques.
When and Why Momentum Accelerates SGD:An Empirical Study
In the comparison of SGDM and SGD with the same effective learning rate and the same batch size, we observe a consistent pattern: when $\eta_{ef}$ is small, SGDM and SGD experience almost the same empirical training losses; when $\eta_{ef}$ surpasses a certain threshold, SGDM begins to perform better than SGD.
StructVPR: Distill Structural Knowledge with Weighting Samples for Visual Place Recognition
In this paper, we propose StructVPR, a novel training architecture for VPR, to enhance structural knowledge in RGB global features and thus improve feature stability in a constantly changing environment.
Understanding Mobile GUI: from Pixel-Words to Screen-Sentences
A dataset, RICO-PW, of screenshots with Pixel-Words annotations is built based on the public RICO dataset, which will be released to help to address the lack of high-quality training data in this area.
Model Adaption Object Detection System for Robot
Object detection for robot guidance is a crucial mission for autonomous robots, which has provoked extensive attention for researchers.
Recognition Of Surface Defects On Steel Sheet Using Transfer Learning
Automatic defect recognition is one of the research hotspots in steel production, but most of the current methods mainly extract features manually and use machine learning classifiers to recognize defects, which cannot tackle the situation, where there are few data available to train and confine to a certain scene.
