HMDMV trains a multi-view classifier on all view subsets with uncertainty-weighted fusion and hierarchical mutual distillation, improving accuracy and supporting flexible view counts at inference.
The 2021 Hotel-ID to Combat Human Trafficking Competition Dataset
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
Hotel recognition is an important task for human trafficking investigations since victims are often photographed in hotel rooms. Identifying these hotels is vital to trafficking investigations since they can help track down current and future victims who might be taken to the same places. Hotel recognition is a challenging fine grained visual classification task as there can be little similarity between different rooms within the same hotel, and high similarity between rooms from different hotels (especially if they are from the same chain). Hotel recognition to combat human trafficking poses additional challenges as investigative images are often low quality, contain uncommon camera angles and are highly occluded. Here, we present the 2021 Hotel-ID dataset to help raise awareness for this problem and generate novel approaches. The dataset consists of hotel room images that have been crowd-sourced and uploaded through the TraffickCam mobile application. The quality of these images is similar to investigative images and hence models trained on these images have good chances of accurately narrowing down on the correct hotel.
fields
cs.CV 1years
2024 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Hierarchical mutual distillation for multi-view fusion: Learning from all possible view combinations
HMDMV trains a multi-view classifier on all view subsets with uncertainty-weighted fusion and hierarchical mutual distillation, improving accuracy and supporting flexible view counts at inference.