EditCLEVR: A Paired-Scene Intervention Benchmark for Compositional Faithfulness of Object-Centric Representations
Quick Answer
EditCLEVR introduces a benchmark for evaluating compositional faithfulness in object-centric representations, focusing on controlled semantic edits.
Quick Take
It features a new metric, Scene-Graph Intervention Accuracy (SGIA), which assesses the correctness of predicted scene changes against intended edits. Baseline evaluations reveal that existing models struggle with semantic faithfulness, particularly in out-of-distribution scenarios.
Key Points
- EditCLEVR features before/after pairs for controlled semantic edits in object-centric learning.
- Introduces Scene-Graph Intervention Accuracy (SGIA) for evaluating semantic change correctness.
- Baseline evaluations indicate performance degradation in CoGenT-OOD-core under ground-truth masks.
- Probes reveal that locality or stability alone can misrepresent semantic faithfulness.
- Code for EditCLEVR is publicly available for further research.
Paper Resources
Source Excerpt
Object-centric learning aims to represent scenes as objects whose properties can be reused in new combinations. Existing evaluations usually score segmentation, single-image factor prediction, or downstream accuracy, but these tests do not directly ask whether a per-object representation behaves correctly under a controlled semantic edit. We introduce EditCLEVR, a paired-scene intervention benchmark in which each example contains a before/after pair of CLEVR-style renders with the same object in
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