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Found 9 papers, 0 papers with code
From Google Maps to a Fine-Grained Catalog of Street trees
We believe this is the first work to exploit publicly available image data for fine-grained tree mapping at city-scale, respectively over many thousands of trees.
Learned Video Compression
We present a new algorithm for video coding, learned end-to-end for the low-latency mode.
Lean Multiclass Crowdsourcing
We introduce a method for efficiently crowdsourcing multiclass annotations in challenging, real world image datasets.
Lean Crowdsourcing: Combining Humans and Machines in an Online System
We develop specialized models and algorithms for binary annotation, part keypoint annotation, and sets of bounding box annotations.
Building a Bird Recognition App and Large Scale Dataset With Citizen Scientists: The Fine Print in Fine-Grained Dataset Collection
We worked with bird experts to measure the quality of popular datasets like CUB-200-2011 and ImageNet and found class label error rates of at least 4%.
Bird Species Categorization Using Pose Normalized Deep Convolutional Nets
We perform a detailed investigation of state-of-the-art deep convolutional feature implementations and fine-tuning feature learning for fine-grained classification.
Similarity Comparisons for Interactive Fine-Grained Categorization
Current human-in-the-loop fine-grained visual categorization systems depend on a predefined vocabulary of attributes and parts, usually determined by experts.
Active Annotation Translation
Human annotators may be thought of as helping translate the old annotations into the new ones.
Efficient Large-Scale Structured Learning
Our method is shown to be 10-50 times faster than SVMstruct for cost-sensitive multiclass classification while being about as fast as the fastest 1-vs-all methods for multiclass classification.
