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

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

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