Selecting high-quality proposals for weakly supervised object detection with bottom-up aggregated attention and phase-aware loss
Weakly supervised object detection (WSOD) has received widespread attention since it
requires only image-category annotations for detector training. Many advanced approaches …
requires only image-category annotations for detector training. Many advanced approaches …
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 …
Multiple instance graph learning for weakly supervised remote sensing object detection
Weakly supervised object detection (WSOD) has recently attracted much attention in the
field of remote sensing, where only image-level labels that distinguish the existence of an …
field of remote sensing, where only image-level labels that distinguish the existence of an …
Cyclic-bootstrap labeling for weakly supervised object detection
Recent progress in weakly supervised object detection is featured by a combination of
multiple instance detection networks (MIDN) and ordinal online refinement. However, with …
multiple instance detection networks (MIDN) and ordinal online refinement. However, with …
Detecting prohibited objects with physical size constraint from cluttered X-ray baggage images
X-ray baggage image inspection aims to detect prohibited objects. Existing inspection
systems often rely on humans to scrutinize X-ray images. Although several deep-learning …
systems often rely on humans to scrutinize X-ray images. Although several deep-learning …
Learning an invariant and equivariant network for weakly supervised object detection
Weakly Supervised Object Detection (WSOD) is of increasing importance in the community
of computer vision as its extensive applications and low manual cost. Most of the advanced …
of computer vision as its extensive applications and low manual cost. Most of the advanced …
Weakly supervised RGB-D salient object detection with prediction consistency training and active scribble boosting
RGB-D salient object detection (SOD) has attracted increasingly more attention as it shows
more robust results in complex scenes compared with RGB SOD. However, state-of-the-art …
more robust results in complex scenes compared with RGB SOD. However, state-of-the-art …
Parameter-efficient person re-identification in the 3d space
People live in a 3D world. However, existing works on person re-identification (re-id) mostly
consider the semantic representation learning in a 2D space, intrinsically limiting the …
consider the semantic representation learning in a 2D space, intrinsically limiting the …
Cyclic self-training with proposal weight modulation for cross-supervised object detection
Weakly-supervised object detection (WSOD), which requires only image-level annotations
for training detectors, has gained enormous attention. Despite recent rapid advance in …
for training detectors, has gained enormous attention. Despite recent rapid advance in …
Fi-wsod: Foreground information guided weakly supervised object detection
Existing solutions for weakly supervised object detection (WSOD) generally follow the
multiple instance learning (MIL) paradigm to formulate WSOD as a multi-class classification …
multiple instance learning (MIL) paradigm to formulate WSOD as a multi-class classification …