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Found 8 papers, 8 papers with code
CLIP-Guided Source-Free Object Detection in Aerial Images
Domain adaptation is crucial in aerial imagery, as the visual representation of these images can significantly vary based on factors such as geographic location, time, and weather conditions.
Point-Query Quadtree for Crowd Counting, Localization, and More
Such a querying process yields an intuitive, universal modeling of crowd as both the input and output are interpretable and steerable.
Lens-to-lens bokeh effect transformation. NTIRE 2023 challenge report
We present the new Bokeh Effect Transformation Dataset (BETD), and review the proposed solutions for this novel task at the NTIRE 2023 Bokeh Effect Transformation Challenge.
Robust Object Detection With Inaccurate Bounding Boxes
As the crowd-sourcing labeling process and the ambiguities of the objects may raise noisy bounding box annotations, the object detectors will suffer from the degenerated training data.
Interior Attention-Aware Network for Infrared Small Target Detection
Motivated by the fact that pixels from targets or backgrounds are correlated to each other, we propose a coarse-to-fine interior attention-aware network (IAANet) for infrared small target detection.
Represent, Compare, and Learn: A Similarity-Aware Framework for Class-Agnostic Counting
In this work, we propose a similarity-aware CAC framework that jointly learns representation and similarity metric.
Ranked #4 on
Object Counting
on CARPK
From Open Set to Closed Set: Supervised Spatial Divide-and-Conquer for Object Counting
Visual counting, a task that aims to estimate the number of objects from an image/video, is an open-set problem by nature, i. e., the number of population can vary in [0, inf) in theory.
From Open Set to Closed Set: Counting Objects by Spatial Divide-and-Conquer
A dense region can always be divided until sub-region counts are within the previously observed closed set.
Ranked #3 on
Crowd Counting
on TRANCOS
