19 research outputs found
Unknown Sniffer for Object Detection: Don't Turn a Blind Eye to Unknown Objects
The recently proposed open-world object and open-set detection achieve a
breakthrough in finding never-seen-before objects and distinguishing them from
class-known ones. However, their studies on knowledge transfer from known
classes to unknown ones need to be deeper, leading to the scanty capability for
detecting unknowns hidden in the background. In this paper, we propose the
unknown sniffer (UnSniffer) to find both unknown and known objects. Firstly,
the generalized object confidence (GOC) score is introduced, which only uses
class-known samples for supervision and avoids improper suppression of unknowns
in the background. Significantly, such confidence score learned from
class-known objects can be generalized to unknown ones. Additionally, we
propose a negative energy suppression loss to further limit the non-object
samples in the background. Next, the best box of each unknown is hard to obtain
during inference due to lacking their semantic information in training. To
solve this issue, we introduce a graph-based determination scheme to replace
hand-designed non-maximum suppression (NMS) post-processing. Finally, we
present the Unknown Object Detection Benchmark, the first publicly benchmark
that encompasses precision evaluation for unknown object detection to our
knowledge. Experiments show that our method is far better than the existing
state-of-the-art methods. Code is available at:
https://github.com/Went-Liang/UnSniffer.Comment: CVPR 2023 camera-read
DDNet: Dual-path Decoder Network for Occlusion Relationship Reasoning
Occlusion relationship reasoning based on convolution neural networks
consists of two subtasks: occlusion boundary extraction and occlusion
orientation inference. Due to the essential differences between the two
subtasks in the feature expression at the higher and lower stages, it is
challenging to carry on them simultaneously in one network. To address this
issue, we propose a novel Dual-path Decoder Network, which uniformly extracts
occlusion information at higher stages and separates into two paths to recover
boundary and occlusion orientation respectively in lower stages. Besides,
considering the restriction of occlusion orientation presentation to occlusion
orientation learning, we design a new orthogonal representation for occlusion
orientation and proposed the Orthogonal Orientation Regression loss which can
get rid of the unfitness between occlusion representation and learning and
further prompt the occlusion orientation learning. Finally, we apply a
multi-scale loss together with our proposed orientation regression loss to
guide the boundary and orientation path learning respectively. Experiments
demonstrate that our proposed method achieves state-of-the-art results on PIOD
and BSDS ownership datasets
Reality-Preserving Multiple Parameter Discrete Fractional Angular Transform and Its Application to Color Image Encryption
Object Tracking in Frame-Skipping Video Acquired Using Wireless Consumer Cameras
Object tracking is an important and fundamental task in computer vision and its high-level applications, e.g., intelligent surveillance, motion-based recognition, video indexing, traffic monitoring and vehicle navigation. However, the recent widespread use of wireless consumer cameras often produces low quality videos with frame-skipping and this makes object tracking difficult. Previous tracking methods, for example, generally depend heavily on object appearance or motion continuity and cannot be directly applied to frame-skipping videos. In this paper, we propose an improved particle filter for object tracking to overcome the frame-skipping difficulties. The novelty of our particle filter lies in using the detection result of erratic motion to ameliorate the transition model for a better trial distribution. Experimental results show that the proposed approach improves the tracking accuracy in comparison with the state-of-the-art methods, even when both the object and the consumer are in motion
SWBNet: A Stable White Balance Network for sRGB Images
The white balance methods for sRGB images (sRGB-WB) aim to directly remove their color temperature shifts. Despite achieving promising white balance (WB) performance, the existing methods suffer from WB instability, i.e., their results are inconsistent for images with different color temperatures. We propose a stable white balance network (SWBNet) to alleviate this problem. It learns the color temperature-insensitive features to generate white-balanced images, resulting in consistent WB results. Specifically, the color temperatureinsensitive features are learned by implicitly suppressing lowfrequency information sensitive to color temperatures. Then, a color temperature contrastive loss is introduced to facilitate the most information shared among features of the same scene and different color temperatures. This way, features from the same scene are more insensitive to color temperatures regardless of the inputs. We also present a color temperature sensitivity-oriented transformer that globally perceives multiple color temperature shifts within an image and corrects them by different weights. It helps to improve the accuracy of stabilized SWBNet, especially for multiillumination sRGB images. Experiments indicate that our SWBNet achieves stable and remarkable WB performance