44 research outputs found
Research on the Algorithm of Extracting Weak Signals from Radar Images under the Double-layer Rebars Shielding
AbstractBasement floor is a component of building structure, which is mainly made of reinforced concrete. The defects threaten the safety of building structures and the use of underground buildings all the time. The main defects in the process of construction are that the pouring thickness of floor can’t reach the design demand or the loose of the base. The thickness of the basement floor, which is made of a double-layer rebars structure and often detected by high frequency radar antennae, is usually 0.4 meters. Steel mesh is highly reflective to radar waves, and is shielded from reflection information of the bottom interface of floor, so that it is too hard to reach the goal of interpreting anomalies of images using conventional approaches. Studies have shown that the modern spectrum has a higher resolution than classical spectrum, so that the algorithm of the expectation value of the modern spectrum. Which constructs profile images of radar modern spectrum by short time-window, has been presented. The spectrum profiles made characters of weak shielded signals prominent, proved the algorithm of modern spectrum profiles, by means of procession, interpretation and application of the double-layer rebar reinforced concrete structure and defects, and also demonstrated the effectiveness of the technology of modern spectrum profiles
DreamArtist: Towards Controllable One-Shot Text-to-Image Generation via Contrastive Prompt-Tuning
Large-scale text-to-image generation models with an exponential evolution can
currently synthesize high-resolution, feature-rich, high-quality images based
on text guidance. However, they are often overwhelmed by words of new concepts,
styles, or object entities that always emerge. Although there are some recent
attempts to use fine-tuning or prompt-tuning methods to teach the model a new
concept as a new pseudo-word from a given reference image set, these methods
are not only still difficult to synthesize diverse and high-quality images
without distortion and artifacts, but also suffer from low controllability.
To address these problems, we propose a DreamArtist method that employs a
learning strategy of contrastive prompt-tuning, which introduces both positive
and negative embeddings as pseudo-words and trains them jointly. The positive
embedding aggressively learns characteristics in the reference image to drive
the model diversified generation, while the negative embedding introspects in a
self-supervised manner to rectify the mistakes and inadequacies from positive
embedding in reverse. It learns not only what is correct but also what should
be avoided. Extensive experiments on image quality and diversity analysis,
controllability analysis, model learning analysis and task expansion have
demonstrated that our model learns not only concept but also form, content and
context. Pseudo-words of DreamArtist have similar properties as true words to
generate high-quality images
Adversarially-Aware Robust Object Detector
Object detection, as a fundamental computer vision task, has achieved a
remarkable progress with the emergence of deep neural networks. Nevertheless,
few works explore the adversarial robustness of object detectors to resist
adversarial attacks for practical applications in various real-world scenarios.
Detectors have been greatly challenged by unnoticeable perturbation, with sharp
performance drop on clean images and extremely poor performance on adversarial
images. In this work, we empirically explore the model training for adversarial
robustness in object detection, which greatly attributes to the conflict
between learning clean images and adversarial images. To mitigate this issue,
we propose a Robust Detector (RobustDet) based on adversarially-aware
convolution to disentangle gradients for model learning on clean and
adversarial images. RobustDet also employs the Adversarial Image Discriminator
(AID) and Consistent Features with Reconstruction (CFR) to ensure a reliable
robustness. Extensive experiments on PASCAL VOC and MS-COCO demonstrate that
our model effectively disentangles gradients and significantly enhances the
detection robustness with maintaining the detection ability on clean images.Comment: ECCV2022 oral pape
Masked Images Are Counterfactual Samples for Robust Fine-tuning
Deep learning models are challenged by the distribution shift between the
training data and test data. Recently, the large models pre-trained on diverse
data demonstrate unprecedented robustness to various distribution shifts.
However, fine-tuning on these models can lead to a trade-off between
in-distribution (ID) performance and out-of-distribution (OOD) robustness.
Existing methods for tackling this trade-off do not explicitly address the OOD
robustness problem. In this paper, based on causal analysis on the
aforementioned problems, we propose a novel fine-tuning method, which use
masked images as counterfactual samples that help improving the robustness of
the fine-tuning model. Specifically, we mask either the semantics-related or
semantics-unrelated patches of the images based on class activation map to
break the spurious correlation, and refill the masked patches with patches from
other images. The resulting counterfactual samples are used in feature-based
distillation with the pre-trained model. Extensive experiments verify that
regularizing the fine-tuning with the proposed masked images can achieve a
better trade-off between ID and OOD performance, surpassing previous methods on
the OOD performance. Our code will be publicly available.Comment: Accepted by CVPR 2023 (v2: improve the clarity
Towards Real-World Burst Image Super-Resolution: Benchmark and Method
Despite substantial advances, single-image super-resolution (SISR) is always
in a dilemma to reconstruct high-quality images with limited information from
one input image, especially in realistic scenarios. In this paper, we establish
a large-scale real-world burst super-resolution dataset, i.e., RealBSR, to
explore the faithful reconstruction of image details from multiple frames.
Furthermore, we introduce a Federated Burst Affinity network (FBAnet) to
investigate non-trivial pixel-wise displacements among images under real-world
image degradation. Specifically, rather than using pixel-wise alignment, our
FBAnet employs a simple homography alignment from a structural geometry aspect
and a Federated Affinity Fusion (FAF) strategy to aggregate the complementary
information among frames. Those fused informative representations are fed to a
Transformer-based module of burst representation decoding. Besides, we have
conducted extensive experiments on two versions of our datasets, i.e.,
RealBSR-RAW and RealBSR-RGB. Experimental results demonstrate that our FBAnet
outperforms existing state-of-the-art burst SR methods and also achieves
visually-pleasant SR image predictions with model details. Our dataset, codes,
and models are publicly available at https://github.com/yjsunnn/FBANet.Comment: Accepted by ICCV202
Out-of-Candidate Rectification for Weakly Supervised Semantic Segmentation
Weakly supervised semantic segmentation is typically inspired by class
activation maps, which serve as pseudo masks with class-discriminative regions
highlighted. Although tremendous efforts have been made to recall precise and
complete locations for each class, existing methods still commonly suffer from
the unsolicited Out-of-Candidate (OC) error predictions that not belongs to the
label candidates, which could be avoidable since the contradiction with
image-level class tags is easy to be detected. In this paper, we develop a
group ranking-based Out-of-Candidate Rectification (OCR) mechanism in a
plug-and-play fashion. Firstly, we adaptively split the semantic categories
into In-Candidate (IC) and OC groups for each OC pixel according to their prior
annotation correlation and posterior prediction correlation. Then, we derive a
differentiable rectification loss to force OC pixels to shift to the IC group.
Incorporating our OCR with seminal baselines (e.g., AffinityNet, SEAM,
MCTformer), we can achieve remarkable performance gains on both Pascal VOC
(+3.2%, +3.3%, +0.8% mIoU) and MS COCO (+1.0%, +1.3%, +0.5% mIoU) datasets with
negligible extra training overhead, which justifies the effectiveness and
generality of our OCR.Comment: Accepted to CVPR202
Botulinum Toxin Injection for the Treatment of Upper Esophageal Sphincter Dysfunction
Dysphagia associated with upper esophageal sphincter (UES) dysfunction remarkably affects the quality of life of patients. UES injection of botulinum toxin is an effective treatment for dysphagia. In comparison with skeletal muscles of the limb and trunk, the UES is a special therapeutic target of botulinum toxin injection, owing to its several anatomical, physiological, and pathophysiological features. This review focuses on (1) the anatomy and function of the UES and the pathophysiology of UES dysfunction in dysphagia and why the entire UES rather than the cricopharyngeal muscle before/during botulinum toxin injection should be examined and targeted; (2) the therapeutic mechanisms of botulinum toxin for UES dysfunction, including the choice of injection sites, dose, and volume; (3) the strengths and weaknesses of guiding techniques, including electromyography, ultrasound, computed tomography, and balloon catheter dilation for botulinum toxin injection of the UES