Search Results for author:
Found 12 papers, 4 papers with code
Accelerating Material Design with the Generative Toolkit for Scientific Discovery
With the growing availability of data within various scientific domains, generative models hold enormous potential to accelerate scientific discovery.
Covering the News with (AI) Style
We introduce a multi-modal discriminative and generative frame-work capable of assisting humans in producing visual content re-lated to a given theme, starting from a collection of documents(textual, visual, or both).
A Broader Study of Cross-Domain Few-Shot Learning
Extensive experiments on the proposed benchmark are performed to evaluate state-of-art meta-learning approaches, transfer learning approaches, and newer methods for cross-domain few-shot learning.
cross-domain few-shot learning
Few-Shot Image Classification
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Collaborative Human-AI (CHAI): Evidence-Based Interpretable Melanoma Classification in Dermoscopic Images
Quantitative relevance of results, according to non-expert similarity, as well as localized image regions, are also significantly improved.
Automatic Curation of Golf Highlights using Multimodal Excitement Features
The production of sports highlight packages summarizing a game's most exciting moments is an essential task for broadcast media.
Deep Learning Ensembles for Melanoma Recognition in Dermoscopy Images
Compared to the average of 8 expert dermatologists on a subset of 100 test images, the proposed system produces a higher accuracy (76% vs. 70. 5%), and specificity (62% vs. 59%) evaluated at an equivalent sensitivity (82%).
Top Rank Supervised Binary Coding for Visual Search
In this paper, we propose a novel supervised binary coding approach, namely Top Rank Supervised Binary Coding (Top-RSBC), which explicitly focuses on optimizing the precision of top positions in a Hamming-distance ranking list towards preserving the supervision information.
Oracle performance for visual captioning
The task of associating images and videos with a natural language description has attracted a great amount of attention recently.
Unsupervised One-Class Learning for Automatic Outlier Removal
Outliers are pervasive in many computer vision and pattern recognition problems.
Learning Locally-Adaptive Decision Functions for Person Verification
This paper proposes to learn a decision function for verification that can be viewed as a joint model of a distance metric and a locally adaptive thresholding rule.
Designing Category-Level Attributes for Discriminative Visual Recognition
In this paper, we propose a novel formulation to automatically design discriminative "category-level attributes", which can be efficiently encoded by a compact category-attribute matrix.
