314 research outputs found
Shape from Shading through Shape Evolution
In this paper, we address the shape-from-shading problem by training deep
networks with synthetic images. Unlike conventional approaches that combine
deep learning and synthetic imagery, we propose an approach that does not need
any external shape dataset to render synthetic images. Our approach consists of
two synergistic processes: the evolution of complex shapes from simple
primitives, and the training of a deep network for shape-from-shading. The
evolution generates better shapes guided by the network training, while the
training improves by using the evolved shapes. We show that our approach
achieves state-of-the-art performance on a shape-from-shading benchmark
MeshAdv: Adversarial Meshes for Visual Recognition
Highly expressive models such as deep neural networks (DNNs) have been widely
applied to various applications. However, recent studies show that DNNs are
vulnerable to adversarial examples, which are carefully crafted inputs aiming
to mislead the predictions. Currently, the majority of these studies have
focused on perturbation added to image pixels, while such manipulation is not
physically realistic. Some works have tried to overcome this limitation by
attaching printable 2D patches or painting patterns onto surfaces, but can be
potentially defended because 3D shape features are intact. In this paper, we
propose meshAdv to generate "adversarial 3D meshes" from objects that have rich
shape features but minimal textural variation. To manipulate the shape or
texture of the objects, we make use of a differentiable renderer to compute
accurate shading on the shape and propagate the gradient. Extensive experiments
show that the generated 3D meshes are effective in attacking both classifiers
and object detectors. We evaluate the attack under different viewpoints. In
addition, we design a pipeline to perform black-box attack on a photorealistic
renderer with unknown rendering parameters.Comment: Published in IEEE CVPR201
Rel3D: A Minimally Contrastive Benchmark for Grounding Spatial Relations in 3D
Understanding spatial relations (e.g., "laptop on table") in visual input is
important for both humans and robots. Existing datasets are insufficient as
they lack large-scale, high-quality 3D ground truth information, which is
critical for learning spatial relations. In this paper, we fill this gap by
constructing Rel3D: the first large-scale, human-annotated dataset for
grounding spatial relations in 3D. Rel3D enables quantifying the effectiveness
of 3D information in predicting spatial relations on large-scale human data.
Moreover, we propose minimally contrastive data collection -- a novel
crowdsourcing method for reducing dataset bias. The 3D scenes in our dataset
come in minimally contrastive pairs: two scenes in a pair are almost identical,
but a spatial relation holds in one and fails in the other. We empirically
validate that minimally contrastive examples can diagnose issues with current
relation detection models as well as lead to sample-efficient training. Code
and data are available at https://github.com/princeton-vl/Rel3D.Comment: Accepted to NeurIPS 202
Meso-scale Study of Water Transport in Mortar Influenced by Sodium Chloride and Freeze-thaw Cycles
Salt frost damage of concrete is an important durability issue to concern since it can threaten to the structural safety. The mechanical properties of concrete could be degraded while the corrosion of steel bar can be initiated because of the penetration of chloride ion. After freeze-thaw cycles (FTCs), due to the increase of connectivity, the water transport property could be changed which is the main reason of steel corrosion. However, because of the non-uniform salt frost damage of concrete in depth direction, how the water transport in mortar influenced by the combined effects of sodium chloride and FTCs is still not clear. In this study, the water transport behavior of meso-scale salt frost damaged mortar samples was studied. Different water-to-cement ratios (0.3 and 0.7) and salt solution concentrations (DI water, 5% NaCl, 15% NaCl and 20% NaCl) were adopted for comparisons. In total, 30 FTCs were tested. After three-point bending test, the central part was removed and the remaining specimens (30×30×5 mm) were immersed into deionized water for evaluation of the transport property. The results show that the porosity increased clearly with FTCs for pure frost damage case, whereas different tendency was observed in salt frost damage cases. Finally, the relationship between the mechanical degradation and water transport property change is discussed, which can promote the understanding of salt frost damage mechanism
Folate‐conjugated thermo‐responsive micelles for tumor targeting
Folate‐conjugated and thermo‐responsive poly(( N ‐isopropylacrylamide)‐ co ‐ acrylamide‐ co ‐(octadecyl acrylate)‐ co ‐(folate‐(polyethylene glycol)‐(acrylic acid))) (P(NIPA‐ co ‐AAm‐ co ‐ODA‐ co ‐FPA)) micelles with mean diameter of about 60 nm and lower critical solution temperature (LCST) of about 39°C were synthesized by free radical random copolymerization. Single‐factor tests of acrylamide and octadecyl acrylate were carried out to modulate micelles' LCST and diameter, respectively. LCST, diameter, and morphology of micelles were determined by UV–vis spectrophotometer, laser particle size analyzer, and transmittance electron microscope (TEM), respectively. Fluorescein was then used as a model drug to investigate the drug loading content of micelles. Micelles with maximum amount of octadecyl acrylate (180 mg) were found to yield drug loading content of 10.48%. Near infrared dye No.10 was chosen as the tracer to monitor micelles in vivo . The targeting behaviors of micelles in folate receptor positive Bel‐7402 tumor bearing nude mice were assessed by a self‐constructed near infrared imaging system. Results showed satisfactory targeting capability of the thermo‐responsive micelles toward Bel‐7402 tumors, and targeting accumulation could last for more than 96 h, enabling P(NIPA‐ co ‐AAm‐ co ‐ODA‐ co ‐FPA) micelles to function as a diagnostic reagent as well as a targeted tumor therapy. © 2012 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 100A:3134–3142, 2012.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/93660/1/34230_ftp.pd
Inorganic nanozyme with combined self-oxygenation/degradable capabilities for sensitized cancer immunochemotherapy
Recently emerged cancer immunochemotherapy has provided enormous new possibilities to replace traditional chemotherapy in fighting tumor. However, the treatment efficacy is hampered by tumor hypoxia-induced immunosuppression in tumor microenvironment (TME). Herein, we fabricated a self-oxygenation/degradable inorganic nanozyme with a core–shell structure to relieve tumor hypoxia in cancer immunochemotherapy. By integrating the biocompatible CaO2 as the oxygen-storing component, this strategy is more effective than the earlier designed nanocarriers for delivering oxygen or H2O2, and thus provides remarkable oxygenation and long-term capability in relieving hypoxia throughout the tumor tissue. Consequently, in vivo tests validate that the delivery system can successfully relieve hypoxia and reverse the immunosuppressive TME to favor antitumor immune responses, leading to enhanced chemoimmunotherapy with cytotoxic T lymphocyte-associated antigen 4 blockade. Overall, a facile, robust and effective strategy is proposed to improve tumor oxygenation by using self-decomposable and biocompatible inorganic nanozyme reactor, which will not only provide an innovative pathway to relieve intratumoral hypoxia, but also present potential applications in other oxygen-favored cancer therapies or oxygen deficiency-originated diseases
Gold nanoparticle-based beacon to detect STAT5b mRNA expression in living cells: a case optimized by bioinformatics screen
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