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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.
Fine-Grained Visual Categorization
General Classification
+2
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.
