, 6 tweets, 2 min read Read on Twitter
7,500 pictures that image recognition algorithms tend to get wrong. My kind of dataset.
Interesting examples of image recognition algorithms being fooled by color, texture, and background cues.
They also seem to have learned that hummingbird feeder = hummingbird Classifiers may use erroneous background cues for prediction. Pictures of nails tend to<br />
co-occur with a wood background. Likewise, hummingbird feeders tend to appear in images with<br />
hummingbirds but are themselves not hummingbirds.
The image recognition AIs have also learned that bars = rocking chair, shadows+ground = sundial, and thin lines = harvestman spider.
Cruelest of all is the fact that there's no car or van option in ImageNet, but there IS limousine & sports car. Every ride is super fancy.
testing a few examples from the adversarial dataset on visual chatbot. this one worked. Image: a jack o'lantern against the sky<br />
Caption: a bird is flying in the air with a sky background<br />
Me: I don't think it's a bird. I think it's a jack o'lantern.<br />
Bot: no, I don't think so
decided to see which "revolver" images were misclassified. apparently ResNet classified this one as "apron".
visual chatbot is similarly unconcerned.
demo.visualdialog.org image: a young woman in a dress carrying a revolver low to her side.<br />
caption: a woman wearing a hat and a tie<br />
me: is she carrying anything<br />
bot: no<br />
me: nothing to worry about<br />
bot: no
another misclassified "revolver" image <br />
Image: cowboy on horseback shooting a gun. there's a big cloud of smoke from it.<br />
Caption: a man riding a horse in a field<br />
me: is the man holding anything<br />
bot: no<br />
me: is the horse holding anything<br />
bot: no<br />
me: what is making the smoke<br />
bot: nothing<br />
me: just a passing cloud<br />
bot: yes<br />
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