Camouflage object detection (COD) poses a significant challenge due to the high resemblance between camouflaged objects and their surroundings.
In the second stage, we tune the imitator network by optimizing the style code, in order to find an optimal fusion result for each input pair.
FRDF utilizes the directional information between object pixels to effectively enhance the intra-class compactness of salient regions.
However, it is challenging to determine the network resources and road sensor placements for multi-stage training with multi-modal datasets in multi-variant scenarios.
FNs-player and FPs-player are designed with different strategies: One is to minimize FNs and the other is to minimize FPs.
Our method can integrate the pedestrian's head and body information to enhance the feature expression ability of the density map.
In this article, we propose a simulated crowd counting dataset CrowdX, which has a large scale, accurate labeling, parameterized realization, and high fidelity.
Knowledge Distillation (KD) is extensively used to compress and deploy large pre-trained language models on edge devices for real-world applications.
To narrow this gap, we propose a network fusion architecture, which consists of a multispectral proposal network to generate pedestrian proposals, and a subsequent multispectral classification network to distinguish pedestrian instances from hard negatives.
Multispectral images of color-thermal pairs have shown more effective than a single color channel for pedestrian detection, especially under challenging illumination conditions.