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tarnish    音标拼音: [t'ɑrnɪʃ]
n. 晦暗,玷污,污点
vt. 使失去光泽,玷污
vi. 失去光泽,被玷污

晦暗,玷污,污点使失去光泽,玷污失去光泽,被玷污

tarnish
n 1: discoloration of metal surface caused by oxidation
v 1: make dirty or spotty, as by exposure to air; also used
metaphorically; "The silver was tarnished by the long
exposure to the air"; "Her reputation was sullied after the
affair with a married man" [synonym: {tarnish}, {stain},
{maculate}, {sully}, {defile}]

Tarnish \Tar"nish\, v. i.
To lose luster; to become dull; as, gilding will tarnish in a
foul air.
[1913 Webster]

Till thy fresh glories, which now shine so bright,
Grow stale and tarnish with our daily sight. --Dryden.
[1913 Webster]


Tarnish \Tar"nish\, n.
1. The quality or state of being tarnished; stain; soil;
blemish.
[1913 Webster]

2. (Min.) A thin film on the surface of a metal, usually due
to a slight alteration of the original color; as, the
steel tarnish in columbite.
[1913 Webster]


Tarnish \Tar"nish\, v. t. [imp. & p. p. {Tarnished}; p. pr. &
vb. n. {Tarnishing}.] [F. ternir, fr. OHG. tarnen to darken,
to conceal, hide; akin to OS. dernian to hide, AS. dernan,
dyrnan, OHG. tarni hidden, OS. derni, AS. derne, dyrne. Cf.
{Dern}, a., and see {-ish}.]
To soil, or change the appearance of, especially by an
alternation induced by the air, or by dust, or the like; to
diminish, dull, or destroy the luster of; to sully; as, to
tarnish a metal; to tarnish gilding; to tarnish the purity of
color. "Tarnished lace." --Fuller. Used also figuratively;
as, to tarnish one's honor.
[1913 Webster]

Syn: To sully; stain; dim.
[1913 Webster]

143 Moby Thesaurus words for "tarnish":
achromatize, asperse, aspersion, attaint, badge of infamy,
bar sinister, baton, bedaub, befoul, begrime, benasty,
bend sinister, besmear, besmirch, besmoke, besoil, bespatter,
bestain, black eye, black mark, blacken, blanch, bleach, blemish,
bloodstain, blot, blotch, blow upon, blur, brand, broad arrow,
call names, calumniate, censure, champain, contaminate, corrupt,
dab, damage, darken, daub, debase, decolor, decolorize, defame,
defile, degrade, denigrate, dim, dirty, disapprove, discolor,
disgrace, dishonor, disparage, disparagement, drain,
drain of color, dull, embarrass, engage in personalities, etiolate,
expose, expose to infamy, eyesore, fade, fleck, flick, flyspeck,
foul, fume, gibbet, grime, hang in effigy, harm, heap dirt upon,
hurt, impair, imputation, infect, injure, macula, maculation,
macule, mar, mark, mark of Cain, mess, mess up, muckrake, muddy,
nasty, onus, pale, patch, peroxide, pillory, pillorying,
point champain, pollute, prejudice, reflection, reprimand,
reproach, revile, ruin, scorch, sear, singe, slander, slubber,
slur, smear, smirch, smoke, smooch, smouch, smudge, smut, smutch,
soil, spatter, speck, speckle, splash, splatter, splotch, spoil,
spot, stain, stigma, stigmatism, stigmatization, stigmatize, sully,
taint, tar, throw mud at, tone down, vilify, vitiate, wash out,
whiten


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  • MIT Places Database for Scene Recognition
    By MIT Computer Science and Artificial Intelligence Laboratory Scene recognition is one of the hallmark tasks of computer vision, allowing defining a context for object recognition Here we introduce a new scene-centric database called Places, with 205 scene categories and 2 5 millions of images with a category label Using convolutional neural network (CNN), we learn deep scene features for
  • Places205 dataset - Massachusetts Institute of Technology
    Places205: We release 2 5 million images from 205 scene categories to the public
  • Places: An Image Database for Deep Scene Understanding
    Scene-centric datasets correspond to images labeled with a scene, or place name, as opposed to an object name Fig 6 illustrates the differences among the number of images found in Places, ImageNet and SUN for a set of scene categories common to all three datasets Places Database is the largest scene-centric image dataset so far
  • Places CNN
    CNN trained on Places Database could be directly used for scene recognition, while the deep scene features from the higher level layer of CNN could be used as generic features for visual recognition We share the following pre-trained CNNs using Caffe deep learning toolbox For each CNN, we provide the network deploy file and the trained network model which could be directly loaded using Caffe
  • MIT Places Database for Scene Recognition
    categoryName (ImageNo), more images will be shown when click the category name a_abbey (46368)
  • Learning Deep Features for Scene Recognition using Places Database
    Here we introduce Places, a scene-centric image dataset 60 times larger than the SUN database [24] With this database and a standard CNN architecture, we establish new baselines of accuracies on various scene datasets (Scene15 [17, 13], MIT Indoor67 [19], SUN database [24], and SUN Attribute Database [18]), significantly outperforming the results obtained by the deep features from the same
  • Visualization of Places-CNN and ImageNet CNN
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  • places. csail. mit. edu
    #! bin bash # # This script performs the following operations: # 1 Downloads the Flowers dataset
  • places. csail. mit. edu
    PARSER_EVAL=bazel-bin syntaxnet parser_eval CONTEXT=syntaxnet models parsey_universal context pbtxt INPUT_FORMAT=stdin-untoken MODEL_DIR=$1
  • places. csail. mit. edu
    û÷–Yc @s d„Zd S( c Cs5d d lm } | ƒ} | j ƒGH| j |ƒ | S( Niÿÿÿÿ( t CustomDatasetDataLoader( t data custom_dataset_data_loaderRt namet initialize( t optRt data_loader((s_ data vision torralba deepscene small-projects pytorch-CycleGAN-and-pix2pix data data_loader pyt CreateDataLoader s





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