Seminars relevant to Informatics

[Steph Smith <Steph.Smith AT ed.ac.uk>]

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ORGANIZER;CN=Steph Smith:mailto:Steph.Smith AT ed.ac.uk
ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN=Bob Fisher
  via ipab-members:mailto:ipab-members AT inf.ed.ac.uk
ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN=inf-semina
 rs AT mlist.is.ed.ac.uk :mailto:inf-seminars AT mlist.is.ed.ac.uk
ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN=Servinar C
 heema:mailto:servinar.cheema AT ed.ac.uk
ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN=Jakub Zadr
 ozny:mailto:Jakub.Zadrozny AT ed.ac.uk
DESCRIPTION;LANGUAGE=en-GB:Speaker: Servinar Cheema\nTitle: Investigating t
 he effectiveness and behavioural affordances in scenario-based black-box t
 esting of automated driving systems.\nAbstract: Simulation-based testing v
 alidates black-box agents by generating scenarios to test for failures und
 er a given specification. However\, current pipelines often decouple gener
 ation from specifications\, flatten metrics\, and rely on lagging indicato
 rs alone. We applied a mutation-based testing approach and found that mode
 rn simulation-based testing toolchains did not fully detect the injected d
 riving faults. We propose a preprocessing step that couples scenario gener
 ation with specifications in an active test loop. By constructing a soluti
 on space of valid behaviours from kinematic and behavioural constraints be
 fore scene creation\, we obtain known and preserved compliance bounds. Thi
 s shifts evaluation from retrospective scoring to leading metrics based on
  proximity to failure boundaries.\n\nSpeaker: Jakub Zadrozny\nTitle: Boots
 trapping Articulated 3D Reconstruction from 2D Image Collections\nAbstract
 : 3D reconstruction of articulated objects from a single image is challeng
 ing because large training datasets with paired image and 3D supervision a
 re difficult to obtain. Recent point map-based methods achieve strong perf
 ormance but rely on synthetic datasets rendered from manually created arti
 culated 3D assets with carefully curated pose distributions. While camera 
 viewpoints can be easily sampled\, generating realistic object articulatio
 ns remains costly and labor-intensive. We propose a training framework tha
 t reduces this requirement by leveraging unannotated 2D images collections
  with only a single rigged canonical mesh per category. Starting from a we
 ak 3D shape predictor trained on canonical-pose renders\, we iteratively e
 stimate object articulation and camera pose by fitting the mesh to predict
 ed point maps. The recovered articulations and viewpoints are then used to
  render updated synthetic training data\, progressively improving the pred
 ictor. Despite using substantially weaker 3D supervision\, our models achi
 eve performance comparable with DualPM\, which requires manually curated a
 rticulated training datasets.\n\n\n_______________________________________
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SUMMARY;LANGUAGE=en-GB:IPAB Workshop - 15th October in G.03\, IF or Teams: 
 Servinar Cheema and Jakub Zadrozny
DTSTART;TZID=GMT Standard Time:20261015T130000
DTEND;TZID=GMT Standard Time:20261015T140000
CLASS:PUBLIC
PRIORITY:5
DTSTAMP:20261009T131054Z
TRANSP:OPAQUE
STATUS:CONFIRMED
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