Weakly supervised object localization and detection: A survey

D Zhang, J Han, G Cheng… - IEEE transactions on …, 2021 - ieeexplore.ieee.org
As an emerging and challenging problem in the computer vision community, weakly
supervised object localization and detection plays an important role for developing new …

GeoAI for large-scale image analysis and machine vision: recent progress of artificial intelligence in geography

W Li, CY Hsu - ISPRS International Journal of Geo-Information, 2022 - mdpi.com
GeoAI, or geospatial artificial intelligence, has become a trending topic and the frontier for
spatial analytics in Geography. Although much progress has been made in exploring the …

Localizing objects with self-supervised transformers and no labels

O Siméoni, G Puy, HV Vo, S Roburin, S Gidaris… - arXiv preprint arXiv …, 2021 - arxiv.org
Localizing objects in image collections without supervision can help to avoid expensive
annotation campaigns. We propose a simple approach to this problem, that leverages the …

Ts-cam: Token semantic coupled attention map for weakly supervised object localization

W Gao, F Wan, X Pan, Z Peng, Q Tian… - Proceedings of the …, 2021 - openaccess.thecvf.com
Weakly supervised object localization (WSOL) is a challenging problem when given image
category labels but requires to learn object localization models. Optimizing a convolutional …

Asm-loc: Action-aware segment modeling for weakly-supervised temporal action localization

B He, X Yang, L Kang, Z Cheng… - Proceedings of the …, 2022 - openaccess.thecvf.com
Weakly-supervised temporal action localization aims to recognize and localize action
segments in untrimmed videos given only video-level action labels for training. Without the …

Refining pseudo labels with clustering consensus over generations for unsupervised object re-identification

X Zhang, Y Ge, Y Qiao, H Li - Proceedings of the IEEE/CVF …, 2021 - openaccess.thecvf.com
Unsupervised object re-identification targets at learning discriminative representations for
object retrieval without any annotations. Clustering-based methods conduct training with the …

What can human sketches do for object detection?

PN Chowdhury, AK Bhunia, A Sain… - Proceedings of the …, 2023 - openaccess.thecvf.com
Sketches are highly expressive, inherently capturing subjective and fine-grained visual
cues. The exploration of such innate properties of human sketches has, however, been …

Comprehensive attention self-distillation for weakly-supervised object detection

Z Huang, Y Zou, BVK Kumar… - Advances in neural …, 2020 - proceedings.neurips.cc
Abstract Weakly Supervised Object Detection (WSOD) has emerged as an effective tool to
train object detectors using only the image-level category labels. However, without object …

Tokencut: Segmenting objects in images and videos with self-supervised transformer and normalized cut

Y Wang, X Shen, Y Yuan, Y Du, M Li… - IEEE transactions on …, 2023 - ieeexplore.ieee.org
In this paper, we describe a graph-based algorithm that uses the features obtained by a self-
supervised transformer to detect and segment salient objects in images and videos. With this …

Exploring simple 3d multi-object tracking for autonomous driving

C Luo, X Yang, A Yuille - Proceedings of the IEEE/CVF …, 2021 - openaccess.thecvf.com
Abstract 3D multi-object tracking in LiDAR point clouds is a key ingredient for self-driving
vehicles. Existing methods are predominantly based on the tracking-by-detection pipeline …