150 research outputs found
Effect of Ti content on microstructure and strength of the self-joining of ZrB2–SiC with Pd–Co–Ti filler metals
Micellar emulsions composed of mPEG-PCL/MCT as novel nanocarriers for systemic delivery of genistein: a comparative study with micelles
Balancing Logit Variation for Long-tailed Semantic Segmentation
Semantic segmentation usually suffers from a long-tail data distribution. Due
to the imbalanced number of samples across categories, the features of those
tail classes may get squeezed into a narrow area in the feature space. Towards
a balanced feature distribution, we introduce category-wise variation into the
network predictions in the training phase such that an instance is no longer
projected to a feature point, but a small region instead. Such a perturbation
is highly dependent on the category scale, which appears as assigning smaller
variation to head classes and larger variation to tail classes. In this way, we
manage to close the gap between the feature areas of different categories,
resulting in a more balanced representation. It is noteworthy that the
introduced variation is discarded at the inference stage to facilitate a
confident prediction. Although with an embarrassingly simple implementation,
our method manifests itself in strong generalizability to various datasets and
task settings. Extensive experiments suggest that our plug-in design lends
itself well to a range of state-of-the-art approaches and boosts the
performance on top of them
EA-BEV: Edge-aware Bird' s-Eye-View Projector for 3D Object Detection
In recent years, great progress has been made in the Lift-Splat-Shot-based
(LSS-based) 3D object detection method, which converts features of 2D camera
view and 3D lidar view to Bird's-Eye-View (BEV) for feature fusion. However,
inaccurate depth estimation (e.g. the 'depth jump' problem) is an obstacle to
develop LSS-based methods. To alleviate the 'depth jump' problem, we proposed
Edge-Aware Bird's-Eye-View (EA-BEV) projector. By coupling proposed edge-aware
depth fusion module and depth estimate module, the proposed EA-BEV projector
solves the problem and enforces refined supervision on depth. Besides, we
propose sparse depth supervision and gradient edge depth supervision, for
constraining learning on global depth and local marginal depth information. Our
EA-BEV projector is a plug-and-play module for any LSS-based 3D object
detection models, and effectively improves the baseline performance. We
demonstrate the effectiveness on the nuScenes benchmark. On the nuScenes 3D
object detection validation dataset, our proposed EA-BEV projector can boost
several state-of-the-art LLS-based baselines on nuScenes 3D object detection
benchmark and nuScenes BEV map segmentation benchmark with negligible increment
of inference time
Molecular oxygen-assisted in defect-rich ZnO for catalytic depolymerization of polyethylene terephthalate
Polyethylene terephthalate (PET) is the most produced polyester plastic; its waste has a disruptive impact on the environment and ecosystem. Here, we report a catalytic depolymerization of PET into bis(2-hydroxyethyl) terephthalate (BHET) using molecule oxygen (O2)−assisted in defect-rich ZnO. At air, the PET conversion rate, the BHET yield, and the space-time yield are 3.5, 10.6, and 10.6 times higher than those in nitrogen, respectively. Combining structural characterization with the results of DFT calculations, we conclude that the (100) facet of defect-rich ZnO nanosheets conducive to the formation of reactive oxygen species (∗O2−) and Zn defect, promotes the PET breakage of the ester bond and thus complete the depolymerization processed. This approach demonstrates a sustainable route for PET depolymerization by molecule-assisted defect engineering.publishedVersio
MIAD: A Maintenance Inspection Dataset for Unsupervised Anomaly Detection
Visual anomaly detection plays a crucial role in not only manufacturing
inspection to find defects of products during manufacturing processes, but also
maintenance inspection to keep equipment in optimum working condition
particularly outdoors. Due to the scarcity of the defective samples,
unsupervised anomaly detection has attracted great attention in recent years.
However, existing datasets for unsupervised anomaly detection are biased
towards manufacturing inspection, not considering maintenance inspection which
is usually conducted under outdoor uncontrolled environment such as varying
camera viewpoints, messy background and degradation of object surface after
long-term working. We focus on outdoor maintenance inspection and contribute a
comprehensive Maintenance Inspection Anomaly Detection (MIAD) dataset which
contains more than 100K high-resolution color images in various outdoor
industrial scenarios. This dataset is generated by a 3D graphics software and
covers both surface and logical anomalies with pixel-precise ground truth.
Extensive evaluations of representative algorithms for unsupervised anomaly
detection are conducted, and we expect MIAD and corresponding experimental
results can inspire research community in outdoor unsupervised anomaly
detection tasks. Worthwhile and related future work can be spawned from our new
dataset
Learning Audio-Visual Source Localization via False Negative Aware Contrastive Learning
Self-supervised audio-visual source localization aims to locate sound-source
objects in video frames without extra annotations. Recent methods often
approach this goal with the help of contrastive learning, which assumes only
the audio and visual contents from the same video are positive samples for each
other. However, this assumption would suffer from false negative samples in
real-world training. For example, for an audio sample, treating the frames from
the same audio class as negative samples may mislead the model and therefore
harm the learned representations e.g., the audio of a siren wailing may
reasonably correspond to the ambulances in multiple images). Based on this
observation, we propose a new learning strategy named False Negative Aware
Contrastive (FNAC) to mitigate the problem of misleading the training with such
false negative samples. Specifically, we utilize the intra-modal similarities
to identify potentially similar samples and construct corresponding adjacency
matrices to guide contrastive learning. Further, we propose to strengthen the
role of true negative samples by explicitly leveraging the visual features of
sound sources to facilitate the differentiation of authentic sounding source
regions. FNAC achieves state-of-the-art performances on Flickr-SoundNet,
VGG-Sound, and AVSBench, which demonstrates the effectiveness of our method in
mitigating the false negative issue. The code is available at
\url{https://github.com/OpenNLPLab/FNAC_AVL}.Comment: CVPR202
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