Weakly supervised object localization and detection: A survey
As an emerging and challenging problem in the computer vision community, weakly
supervised object localization and detection plays an important role for developing new …
supervised object localization and detection plays an important role for developing new …
Detecting twenty-thousand classes using image-level supervision
Current object detectors are limited in vocabulary size due to the small scale of detection
datasets. Image classifiers, on the other hand, reason about much larger vocabularies, as …
datasets. Image classifiers, on the other hand, reason about much larger vocabularies, as …
Bridging the gap between object and image-level representations for open-vocabulary detection
H Bangalath, M Maaz, MU Khattak… - Advances in …, 2022 - proceedings.neurips.cc
Existing open-vocabulary object detectors typically enlarge their vocabulary sizes by
leveraging different forms of weak supervision. This helps generalize to novel objects at …
leveraging different forms of weak supervision. This helps generalize to novel objects at …
Exploiting unlabeled data with vision and language models for object detection
Building robust and generic object detection frameworks requires scaling to larger label
spaces and bigger training datasets. However, it is prohibitively costly to acquire annotations …
spaces and bigger training datasets. However, it is prohibitively costly to acquire annotations …
Comprehensive attention self-distillation for weakly-supervised object detection
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 …
train object detectors using only the image-level category labels. However, without object …
Pimnet: a parallel, iterative and mimicking network for scene text recognition
Nowadays, scene text recognition has attracted more and more attention due to its various
applications. Most state-of-the-art methods adopt an encoder-decoder framework with …
applications. Most state-of-the-art methods adopt an encoder-decoder framework with …
Deep learning for weakly-supervised object detection and localization: A survey
Abstract Weakly-Supervised Object Detection (WSOD) and Localization (WSOL), ie.,
detecting multiple and single instances with bounding boxes in an image using image-level …
detecting multiple and single instances with bounding boxes in an image using image-level …
Boosting weakly supervised object detection via learning bounding box adjusters
Weakly-supervised object detection (WSOD) has emerged as an inspiring recent topic to
avoid expensive instance-level object annotations. However, the bounding boxes of most …
avoid expensive instance-level object annotations. However, the bounding boxes of most …
H2fa r-cnn: Holistic and hierarchical feature alignment for cross-domain weakly supervised object detection
Cross-domain weakly supervised object detection (CDWSOD) aims to adapt the detection
model to a novel target domain with easily acquired image-level annotations. How to align …
model to a novel target domain with easily acquired image-level annotations. How to align …
Weakly supervised rotation-invariant aerial object detection network
Object rotation is among long-standing, yet still unexplored, hard issues encountered in the
task of weakly supervised object detection (WSOD) from aerial images. Existing …
task of weakly supervised object detection (WSOD) from aerial images. Existing …