VIREO-TNO @ TRECVID 2014 Zero-Shot Event Detection and … · 2014. 11. 25. · VIREO-TNO @ TRECVID...
Transcript of VIREO-TNO @ TRECVID 2014 Zero-Shot Event Detection and … · 2014. 11. 25. · VIREO-TNO @ TRECVID...
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VIREO-TNO @ TRECVID 2014Zero-Shot Event Detection and Recounting
Speaker: Maaike de Boer (TNO)
Yi-Jie Lu1, Hao Zhang1, Chong-Wah Ngo1
Maaike de Boer2, John Schavemaker2, Klamer Schutte2, Wessel Kraaij2
1VIREO Group, City University of Hong Kong, Hong Kong2Netherlands Organization for Applied Scientific Research (TNO), Netherlands
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Outline
0-Shot System
– System Overview
– Findings
MER System
– System Workflow
– Results
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Semantic Query Generation (SQG)
– Given an event query, SQG translates the query description into a representation of semantic concepts
Event Query(Attempting a Bike Trick)
SQG
< Objects >• Bike 0.60• Motorcycle 0.60• Mountain bike 0.60< Actions >• Bike trick 1.00• Ridding bike 0.62• Flipping bike 0.61< Scenes >• Parking lot 0.01
Semantic Query
Relevant ConceptsRelevance ScoreConcept Bank
$
Concept Bank
€TRECVID
SIN
₤Research Collection
ƒ HMDB51
$UCF101
¥ImageNet
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Concept Bank
– Research collection (497 concepts)
– ImageNet ILSVRC’12 (1000 concepts)
– SIN’14 (346 concepts)
$
Concept Bank
€TRECVID
SIN
₤Research Collection
ƒ HMDB51
$UCF101
¥ImageNet
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Event Search
– Ranking according to the SQ and concept responses
< Objects >• Bike 0.60• Motorcycle 0.60• Mountain bike 0.60< Actions >• Bike trick 1.00• Ridding bike 0.62• Flipping bike 0.61< Scenes >• Parking lot 0.01
Semantic Query
Video Ranking
Event Search is iqc
q
Concept Response ic
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Outline
0-Shot System
– System Overview
– Findings
MER System
– System Workflow
– Results
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SQG Experiments
– Exact matching vs. WordNet/ConceptNet matching
– How many concepts are used to represent an event?
– To further improve the weighting:
TF-IDF
Term specificity
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Event ID
WordNet ExactMatching EM-TOP
WordNet
Exact Matching
Exact matching but only retains the top few concepts
7%
Exact matching vs. WordNet matching
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MAP(all)
Hit the best MAP by only retaining the Top 8 concepts
Amount of concepts used to represent event
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Insights
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Event 21: Attempting a bike trick
TrickWheel
Paddle wheel
Car wheelPotter wheel
Person riding
Jumping
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Insights
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Event 31: Beekeeping
Honeycomb (ImageNet)
Bee (ImageNet)
Bee house (ImageNet)Cutting (research collection)
Cutting down tree (research collection)
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Insights
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Event 23: Dog show
Brush dog (research collection)
Dog show (research collection)
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Improvements by TF-IDF and word specificityMethod MAP (on MED14-Test)
Exact Matching Only 0.0306
Exact Matching + TF 0.0420
Exact Matching + TFIDF 0.0495
Exact Matching + TFIDF + Word Specificity 0.0502
0
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EM Only EM + TF EM + TFIDF EM + TFIDF +Spec.
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Findings
1. Exact matching performs better than matching with WordNet and/or ConceptNet
2. Performance is even better by only retaining the top few exactly matched concepts
3. Adding both TF-IDF and Word Specificity increases performance
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Why ontology-based mapping would not work?
A s
amp
le q
uer
y in
TR
ECV
ID 2
00
9
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Why ontology-based mapping would not work?
Dog Show
Concept“dog”
cat
horse
mammal
carnivore
animal
kit fox
red wolf
SIN
ImageNet
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Why ConceptNet mapping would not work?
Tailgating
car
food
helmet
team uniform
portable shelter
parking lot
driver
engine
tailgatingdesires
bus
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Findings
It is difficult to
– harness the ontology-based mapping while constraining the mapping by event context
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In the Ad-Hoc event “Extinguishing a Fire”
– Key concepts are missing:
Fire extinguisher
Firefighter
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Findings
It is reasonable to
– Scale up the number of concepts, thus increasing the chance of exact matching
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MED14-Eval-Full Results
PS 000Ex
– Automatic semantic query generation and search
– Fusion of 0-Shot and OCR system
– Achieves the MAP of 5.2
AH 000Ex
– System is the same as in PS 000Ex
– Achieves the MAP of 2.6
– Performance drops due to the lack of key concepts
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Outline
0-Shot System
– System Overview
– Findings
MER System
– System Workflow
– Results
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MER System
In algorithm design, we aim to optimize– Concept-to-event relevancy
– Evidence diversity
– Viewing time of evidential shots
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MER System
In algorithm design, we aim to optimize– Concept-to-event relevancy
First, we require that candidate shots are relevant to the event;
Second, we do concept-to-shot alignment.
– Evidence diversity
– Viewing time of evidential shots
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MER System
In algorithm design, we aim to optimize– Concept-to-event relevancy
First, we require that candidate shots are relevant to the event;
Second, we do concept-to-shot alignment.
– Evidence diversity In concept-to-shot alignment, we recount each shot with a unique concept
different from other shots.
– Viewing time of evidential shots
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MER System
In algorithm design, we aim to optimize– Concept-to-event relevancy
First, we require that candidate shots are relevant to the event;
Second, we do concept-to-shot alignment.
– Evidence diversity In concept-to-shot alignment, we recount each shot with a unique concept
different from other shots.
– Viewing time of evidential shots Select only the three most confident shots as key evidence
Basically, each shot is in about 5 seconds
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Outline
0-Shot System
– System Overview
– Findings
MER System
– System Workflow
– Results
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Key Evidence Localization
Extract keyframes uniformly
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Key Evidence Localization
Concept Reponses
Apply concept detectors$
Concept Bank
€TRECVID
SIN
₤Research Collection
ƒ HMDB51
$UCF101
¥ImageNet
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Key Evidence Localization
Choose keyframes that are most relevant to this event
• All concepts in semantic query are taken into account by calculating the weighted sum
is iwr
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Key Evidence Localization
Expand keyframes to shots
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Key Evidence Localization
The top 3 shots are selected as key evidences
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Key Evidence Localization
The rests are non-key evidences
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Concept-to-Shot Alignment
The top concept in the key evidence is selected as the representative concept* We choose unique concept for each shot
< Objects >• Bike• Motorcycle• Mountain bike< Actions >• Bike trick• Ridding bike• Flipping bike< Scenes >• Parking lot
Semantic Query
Key
Non-Key
Ridding bikeBike trickBike
Bike trickBikeRidding bike
Key
Key
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MER14 Results
The percentage of strongly agree
(b) Event query quality(a) Evidence quality
0%
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25%
30%
VIREO Team1 Team2 Team3 Team4 Team6 Team5
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MER14 Results
0%
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Team1 Team2 Team3 VIREO Team4 Team5 Team6
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The percentage of both agree and strongly agree
(b) Event query quality(a) Evidence quality
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Summary
0-Shot System
– The simple exact matching performs the best
– The quality of concepts selected to represent an event is more important than quantity
– It’s an open problem of how to harness the ontology-based mapping
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Summary
MER System
– In key evidence localization, we emphasize the event relevancy first, then the hot concepts
– We recommend three shots as key evidences and each in about 5 seconds
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Thanks!