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Found 14 papers, 6 papers with code
Exploring Fine-Grained Audiovisual Categorization with the SSW60 Dataset
We thoroughly benchmark audiovisual classification performance and modality fusion experiments through the use of state-of-the-art transformer methods.
On Label Granularity and Object Localization
Weakly supervised object localization (WSOL) aims to learn representations that encode object location using only image-level category labels.
The Caltech Fish Counting Dataset: A Benchmark for Multiple-Object Tracking and Counting
We present the Caltech Fish Counting Dataset (CFC), a large-scale dataset for detecting, tracking, and counting fish in sonar videos.
Seeing biodiversity: perspectives in machine learning for wildlife conservation
Data acquisition in animal ecology is rapidly accelerating due to inexpensive and accessible sensors such as smartphones, drones, satellites, audio recorders and bio-logging devices.
Benchmarking Representation Learning for Natural World Image Collections
In order to facilitate progress in this area we present two new natural world visual classification datasets, iNat2021 and NeWT.
The iWildCam 2018 Challenge Dataset
Camera traps are a valuable tool for studying biodiversity, but research using this data is limited by the speed of human annotation.
Recognition in Terra Incognita
The challenge is learning recognition in a handful of locations, and generalizing animal detection and classification to new locations where no training data is available.
Lean Multiclass Crowdsourcing
We introduce a method for efficiently crowdsourcing multiclass annotations in challenging, real world image datasets.
The Devil is in the Tails: Fine-grained Classification in the Wild
We find that (a) peak classification performance on well-represented categories is excellent, (b) given enough data, classification performance suffers only minimally from an increase in the number of classes, (c) classification performance decays precipitously as the number of training examples decreases, (d) surprisingly, transfer learning is virtually absent in current methods.
The iNaturalist Species Classification and Detection Dataset
Existing image classification datasets used in computer vision tend to have a uniform distribution of images across object categories.
Ranked #7 on
Image Classification
on iNaturalist
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
