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Found 12 papers, 4 papers with code
Generalization Gap in Data Augmentation: Insights from Illumination
In the field of computer vision, data augmentation is widely used to enrich the feature complexity of training datasets with deep learning techniques.
Enabling Collaborative Clinical Diagnosis of Infectious Keratitis by Integrating Expert Knowledge and Interpretable Data-driven Intelligence
This study investigates the performance, interpretability, and clinical utility of KGDM in the diagnosis of infectious keratitis (IK), which is the leading cause of corneal blindness.
Revisiting the Domain Shift and Sample Uncertainty in Multi-source Active Domain Transfer
Active Domain Adaptation (ADA) aims to maximally boost model adaptation in a new target domain by actively selecting a limited number of target data to annotate. This setting neglects the more practical scenario where training data are collected from multiple sources.
Panoptic Scene Graph Generation with Semantics-Prototype Learning
To promise consistency and accuracy during the transfer process, we propose to measure the invariance of representations in each predicate class, and learn unbiased prototypes of predicates with different intensities.
Ranked #3 on
Panoptic Scene Graph Generation
on PSG Dataset
Gradient-Regulated Meta-Prompt Learning for Generalizable Vision-Language Models
Prompt tuning, a recently emerging paradigm, enables the powerful vision-language pre-training models to adapt to downstream tasks in a parameter -- and data -- efficient way, by learning the ``soft prompts'' to condition frozen pre-training models.
IDEAL: Toward High-efficiency Device-Cloud Collaborative and Dynamic Recommendation System
The aforementioned two trends enable the device-cloud collaborative and dynamic recommendation, which deeply exploits the recommendation pattern among cloud-device data and efficiently characterizes different instances with different underlying distributions based on the cost of frequent device-cloud communication.
Are Binary Annotations Sufficient? Video Moment Retrieval via Hierarchical Uncertainty-Based Active Learning
Moreover, we treat the uncertainty score of frames in a video as a whole, and estimate the difficulty of each video, which can further relieve the burden of video selection.
WINNER: Weakly-Supervised hIerarchical decompositioN and aligNment for Spatio-tEmporal Video gRounding
WINNER first builds the language decomposition tree in a bottom-up manner, upon which the structural attention mechanism and top-down feature backtracking jointly build a multi-modal decomposition tree, permitting a hierarchical understanding of unstructured videos.
BOSS: Bottom-up Cross-modal Semantic Composition with Hybrid Counterfactual Training for Robust Content-based Image Retrieval
In this scenario, the input image serves as an intuitive context and background for the search, while the corresponding language expressly requests new traits on how specific characteristics of the query image should be modified in order to get the intended target image.
Enhancing Fairness of Visual Attribute Predictors
The performance of deep neural networks for image recognition tasks such as predicting a smiling face is known to degrade with under-represented classes of sensitive attributes.
End-to-End Modeling via Information Tree for One-Shot Natural Language Spatial Video Grounding
To achieve effective grounding under a limited annotation budget, we investigate one-shot video grounding, and learn to ground natural language in all video frames with solely one frame labeled, in an end-to-end manner.
Geometry of the Minimum Volume Confidence Sets
Prior work showed the minimum-volume confidence sets are the level-sets of a discontinuous function defined by an exact p-value.
