Experiment presentation by Nayan Singhal Motivation Human - PowerPoint PPT Presentation

Jul 09, 2023 •377 likes •875 views

Situation Recognition: Visual Semantic Role Labelling for Image Understanding Mark Yatskar, Luke Zettlemoyer, Ali Farhadi Experiment presentation by Nayan Singhal Motivation Human understanding of image Verbs in English language.

Situation Recognition: Visual Semantic Role Labelling for Image Understanding Mark Yatskar, Luke Zettlemoyer, Ali Farhadi Experiment presentation by Nayan Singhal
Motivation ● Human understanding of image ● Verbs in English language.
Approach ● CRF with CNN CARRYING AGENT WOMAN 1024 CNN CRF ITEM JAR AGENTPART HEAD PLACE OUTDOOR ● Log linear Loss
How object plays role in image understanding?
neighboring images
Remove Cliff Removing Cliff neighboring images
Remove person neighboring images Removing Man
Remove Sky neighboring images Removing Sky
Image (2) neighboring images
Remove Person neighboring images Removing man
Remove Background neighboring images Removing Sky and Man
Conclusion Each object plays a significant role in image understanding.
Experiment 1) Analyzing Failure Cases 2) Different moods of faces
Expt 1: Analyzing Failure Cases
Object Recognition (1) Imsitu Result
Object Recognition (2) Imsitu Result
Object Recognition (3) Imsitu Result
Object Recognition (4) Imsitu Result
Object Recognition (5) Imsitu Result
Object Recognition (6) Imsitu Result
Why is it happening? Are these images difficult to categorize?
Let’s analyze these with ImageNet
Object Recognition (1) Imsitu Result
ImageNet classification
Object Recognition (2) Imsitu Result
Object Recognition (2) ImageNet classification
Object Recognition (3) Imsitu Result
Object Recognition (3) ImageNet classification
Object Recognition (4) Imsitu Result
Object Recognition (4) ImageNet classification
Object Recognition (5) Imsitu Result
Object Recognition (5) ImageNet classification
Object Recognition (6) Imsitu Result
Object Recognition (6) ImageNet classification
Object Recognition (1) Noun Slot Verb Role Noun Potential Labels A (Verb Role Noun Potential) + B (Labels) Post Processing:
Object Recognition (1) Imsitu Result
Object Recognition (1) VGG Imagenet Verb Potential Verb Role Noun Potential Labels Preprocessing
Future Work ● Add labels in preprocessing.
Exp 2: Different moods ● Laughing ● Smiling ● Frowning ● Grimacing ● Winking ● Squinting ● Shouting ● Puckering Winking Squinting Puckering Smiling Laughing Frowning
Success Case Smiling Laughing Shouting Agent place Agent place Agent place man - man - man - 0.35967 0.35777 0.37531
Success Case Frowning Grimacing Agent place Agent place man - man - 0.24378 0.21052
Failure Case Winking Puckering Agent place Agent place man - woman - 0.20954 0.21052
Test Images (25) ● Conclusion: Detect different moods of faces with slight variation.
Some Interest Categorization
Some Interest Categorization Camouflaging Camouflaging Agent owl Agent frog Hiding Item tree Hiding Item pebble Place outdoors Place -
neighboring images
neighboring images
Thank You
No Agent(2) Watering Shredding Agent Person Agent Person Tool Bucket Tool Shreder Place garden Item paper Place -

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