204 research outputs found
RCLane: Relay Chain Prediction for Lane Detection
Lane detection is an important component of many real-world autonomous
systems. Despite a wide variety of lane detection approaches have been
proposed, reporting steady benchmark improvements over time, lane detection
remains a largely unsolved problem. This is because most of the existing lane
detection methods either treat the lane detection as a dense prediction or a
detection task, few of them consider the unique topologies (Y-shape,
Fork-shape, nearly horizontal lane) of the lane markers, which leads to
sub-optimal solution. In this paper, we present a new method for lane detection
based on relay chain prediction. Specifically, our model predicts a
segmentation map to classify the foreground and background region. For each
pixel point in the foreground region, we go through the forward branch and
backward branch to recover the whole lane. Each branch decodes a transfer map
and a distance map to produce the direction moving to the next point, and how
many steps to progressively predict a relay station (next point). As such, our
model is able to capture the keypoints along the lanes. Despite its simplicity,
our strategy allows us to establish new state-of-the-art on four major
benchmarks including TuSimple, CULane, CurveLanes and LLAMAS.Comment: ECCV 202
Amodal Segmentation Based on Visible Region Segmentation and Shape Prior
Almost all existing amodal segmentation methods make the inferences of
occluded regions by using features corresponding to the whole image. This is
against the human's amodal perception, where human uses the visible part and
the shape prior knowledge of the target to infer the occluded region. To mimic
the behavior of human and solve the ambiguity in the learning, we propose a
framework, it firstly estimates a coarse visible mask and a coarse amodal mask.
Then based on the coarse prediction, our model infers the amodal mask by
concentrating on the visible region and utilizing the shape prior in the
memory. In this way, features corresponding to background and occlusion can be
suppressed for amodal mask estimation. Consequently, the amodal mask would not
be affected by what the occlusion is given the same visible regions. The
leverage of shape prior makes the amodal mask estimation more robust and
reasonable. Our proposed model is evaluated on three datasets. Experiments show
that our proposed model outperforms existing state-of-the-art methods. The
visualization of shape prior indicates that the category-specific feature in
the codebook has certain interpretability.Comment: Accepted by AAAI 202
Towards Scalable 3D Anomaly Detection and Localization: A Benchmark via 3D Anomaly Synthesis and A Self-Supervised Learning Network
Recently, 3D anomaly detection, a crucial problem involving fine-grained
geometry discrimination, is getting more attention. However, the lack of
abundant real 3D anomaly data limits the scalability of current models. To
enable scalable anomaly data collection, we propose a 3D anomaly synthesis
pipeline to adapt existing large-scale 3Dmodels for 3D anomaly detection.
Specifically, we construct a synthetic dataset, i.e., Anomaly-ShapeNet, basedon
ShapeNet. Anomaly-ShapeNet consists of 1600 point cloud samples under 40
categories, which provides a rich and varied collection of data, enabling
efficient training and enhancing adaptability to industrial scenarios.
Meanwhile,to enable scalable representation learning for 3D anomaly
localization, we propose a self-supervised method, i.e., Iterative Mask
Reconstruction Network (IMRNet). During training, we propose a geometry-aware
sample module to preserve potentially anomalous local regions during point
cloud down-sampling. Then, we randomly mask out point patches and sent the
visible patches to a transformer for reconstruction-based self-supervision.
During testing, the point cloud repeatedly goes through the Mask Reconstruction
Network, with each iteration's output becoming the next input. By merging and
contrasting the final reconstructed point cloud with the initial input, our
method successfully locates anomalies. Experiments show that IMRNet outperforms
previous state-of-the-art methods, achieving 66.1% in I-AUC on Anomaly-ShapeNet
dataset and 72.5% in I-AUC on Real3D-AD dataset. Our dataset will be released
at https://github.com/Chopper-233/Anomaly-ShapeNe
Non-Negative Local Sparse Coding for Subspace Clustering
Subspace sparse coding (SSC) algorithms have proven to be beneficial to
clustering problems. They provide an alternative data representation in which
the underlying structure of the clusters can be better captured. However, most
of the research in this area is mainly focused on enhancing the sparse coding
part of the problem. In contrast, we introduce a novel objective term in our
proposed SSC framework which focuses on the separability of data points in the
coding space. We also provide mathematical insights into how this
local-separability term improves the clustering result of the SSC framework.
Our proposed non-linear local SSC algorithm (NLSSC) also benefits from the
efficient choice of its sparsity terms and constraints. The NLSSC algorithm is
also formulated in the kernel-based framework (NLKSSC) which can represent the
nonlinear structure of data. In addition, we address the possibility of having
redundancies in sparse coding results and its negative effect on graph-based
clustering problems. We introduce the link-restore post-processing step to
improve the representation graph of non-negative SSC algorithms such as ours.
Empirical evaluations on well-known clustering benchmarks show that our
proposed NLSSC framework results in better clusterings compared to the
state-of-the-art baselines and demonstrate the effectiveness of the
link-restore post-processing in improving the clustering accuracy via
correcting the broken links of the representation graph.Comment: 15 pages, IDA 2018 conferenc
Dream3D: Zero-Shot Text-to-3D Synthesis Using 3D Shape Prior and Text-to-Image Diffusion Models
Recent CLIP-guided 3D optimization methods, such as DreamFields and
PureCLIPNeRF, have achieved impressive results in zero-shot text-to-3D
synthesis. However, due to scratch training and random initialization without
prior knowledge, these methods often fail to generate accurate and faithful 3D
structures that conform to the input text. In this paper, we make the first
attempt to introduce explicit 3D shape priors into the CLIP-guided 3D
optimization process. Specifically, we first generate a high-quality 3D shape
from the input text in the text-to-shape stage as a 3D shape prior. We then use
it as the initialization of a neural radiance field and optimize it with the
full prompt. To address the challenging text-to-shape generation task, we
present a simple yet effective approach that directly bridges the text and
image modalities with a powerful text-to-image diffusion model. To narrow the
style domain gap between the images synthesized by the text-to-image diffusion
model and shape renderings used to train the image-to-shape generator, we
further propose to jointly optimize a learnable text prompt and fine-tune the
text-to-image diffusion model for rendering-style image generation. Our method,
Dream3D, is capable of generating imaginative 3D content with superior visual
quality and shape accuracy compared to state-of-the-art methods.Comment: Accepted by CVPR 2023. Project page:
https://bluestyle97.github.io/dream3d
Changes in Life's Essential 8 and risk of cardiovascular disease in Chinese people
Background The American Heart Association recently released an updated algorithm for evaluating cardiovascular health-Life's Essential 8 (LE8). However, the associations between changes in LE8 score over time and risk of cardiovascular disease (CVD) remain unclear. Methods We investigated associations between 6-year changes (2006-12) in LE8 score and risk of subsequent CVD events (2012-20) among 53 363 Chinese men and women from the Kailuan Study, who were free from CVD in 2012. The LE8 score was calculated based on eight components: diet quality, physical activity, smoking status, sleep health, body mass index, blood lipids, blood glucose and blood pressure. Multivariable-adjusted Cox proportional-hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). Results We documented 4281 incident CVD cases during a median of 7.7 years of follow-up. Compared with participants whose LE8 scores remained stable in a 6-year period, those with the large increases of LE8 score over the 6-year period had a lower risk of CVD, heart disease and stroke in the subsequent 8 years [HRs and 95% CIs: 0.67 (0.64, 0.70) for CVD, 0.65 (0.61, 0.69) for heart disease, 0.71 (0.67, 0.76) for stroke, all P-trend < 0.001]. Conversely, those with the large decreases of LE8 score had 47%, 51% and 41% higher risk for CVD, heart disease and stroke, respectively. These associations were consistent across the subgroups stratified by risk factors. Conclusions Improving LE8 score in a short- and moderate-term was associated with a lower CVD risk, whereas decreased LE8 score over time was associated with a higher risk
M3PT: A Multi-Modal Model for POI Tagging
POI tagging aims to annotate a point of interest (POI) with some informative
tags, which facilitates many services related to POIs, including search,
recommendation, and so on. Most of the existing solutions neglect the
significance of POI images and seldom fuse the textual and visual features of
POIs, resulting in suboptimal tagging performance. In this paper, we propose a
novel Multi-Modal Model for POI Tagging, namely M3PT, which achieves enhanced
POI tagging through fusing the target POI's textual and visual features, and
the precise matching between the multi-modal representations. Specifically, we
first devise a domain-adaptive image encoder (DIE) to obtain the image
embeddings aligned to their gold tags' semantics. Then, in M3PT's text-image
fusion module (TIF), the textual and visual representations are fully fused
into the POIs' content embeddings for the subsequent matching. In addition, we
adopt a contrastive learning strategy to further bridge the gap between the
representations of different modalities. To evaluate the tagging models'
performance, we have constructed two high-quality POI tagging datasets from the
real-world business scenario of Ali Fliggy. Upon the datasets, we conducted the
extensive experiments to demonstrate our model's advantage over the baselines
of uni-modality and multi-modality, and verify the effectiveness of important
components in M3PT, including DIE, TIF and the contrastive learning strategy.Comment: Accepted by KDD 202
Studies on the toxicokinetics of intragastricallyadministered paracetamol, aminophenazone, caffeine and chlorphenamine maleate tablets in rats
Purpose: To study the toxicokinetics of paracetamol (PCT), aminophenazone (ACP), caffeine (CFN) and chlorphenamine maleate (CPM) tablets after a single oral gavage, and after oral gavage for 14 consecutive days in rats.
Methods: Eighty Sprague Dawley (SD) rats (half male, half female) were randomly divided into 4 groups with 20 rats in each group. Half of the rats were used for the toxicokinetic test after a single oral gavage of PCT, ACP, CFN and CPM tablets, while rats in the other half were used for the toxicokinetic tests after oral gavage for 14 consecutive days. The doses of the four groups were set as 0, 0.5, 1 and 2 tablets/kg body weight, respectively. Blood was taken from the rats and the plasma concentration of paracetamol was determined.
Results: There was a significant difference in AUC0-∞ between male and female rats at single oral gavage of 2 tablets/kg of each of the drugs. The exposure amount of PCT (AUC0~t, AUC0-∞ and Cmax) increased with increase in dose, and showed a good linear relationship after a single intragastric administration of each drug, and after 14 consecutive days of intragastric administration at low, medium and high doses.
Conclusion: The amount of PCT to which SD rats are exposed after a single intragastric administration of PCT, ACP, CFN and CPM tablets is lower in male than in female rats. However, no significant gender difference in exposure results when these drugs are given intragastrically for 14 consecutive days
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