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Found 5 papers, 2 papers with code
Align and Distill: Unifying and Improving Domain Adaptive Object Detection
We address these problems by introducing: (1) A unified benchmarking and implementation framework, Align and Distill (ALDI), enabling comparison of DAOD methods and supporting future development, (2) A fair and modern training and evaluation protocol for DAOD that addresses benchmarking pitfalls, (3) A new DAOD benchmark dataset, CFC-DAOD, enabling evaluation on diverse real-world data, and (4) A new method, ALDI++, that achieves state-of-the-art results by a large margin.
Ranked #1 on
Unsupervised Domain Adaptation
on CFC-DAOD
Teaching Computer Vision for Ecology
Computer vision can accelerate ecology research by automating the analysis of raw imagery from sensors like camera traps, drones, and satellites.
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.
Fine-Grained Counting with Crowd-Sourced Supervision
We introduce a new dataset from animal ecology to enable this study that contains 1. 7M crowd-sourced annotations of 8 fine-grained classes.
The Fishnet Open Images Database: A Dataset for Fish Detection and Fine-Grained Categorization in Fisheries
To address this, we present the Fishnet Open Images Database, a large dataset of EM imagery for fish detection and fine-grained categorization onboard commercial fishing vessels.
