Research map / Vision-language and multimodal / Human and embodied modeling
Unsupervised Vision-Language Parsing: Seamlessly Bridging Visual Scene Graphs With Language Structures via Dependency Relationships
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add visual cues and joint VL structure construction relative to the language-only D-NDMV baseline | VLGAE · VLParse test | UDA: 70.77 -> 71.43 | Helped |
| Add visual cues and joint VL structure construction relative to the language-only D-NDMV baseline | VLGAE · VLParse test | DDA: 65.88 -> 67.57 | Helped |
| Use VLGAE rather than MAF* | Visual phrase grounding · VLParse test | Zero-AA (All): 27.7 -> 28.7 | Helped |
| Use VLGAE rather than MAF* | Visual phrase grounding · VLParse test | Zero-AA (Obj.): 38.5 -> 36.1 | Hurt |
| Use VLGAE rather than MAF* | Visual phrase grounding · VLParse test | Zero-AA (Rel.): 0.1 -> 10.2 | Helped |
| Use VLGAE rather than MAF* | Visual phrase grounding · VLParse test | First-AA: 0.0 -> 3.4 | Helped |
| Use VLGAE rather than MAF* | Visual phrase grounding · VLParse test | Second-AA: 0.0 -> 0.2 | Helped |
| Use gold scene graphs instead of object proposals and dense connections (ablation) | VLGAE · VLParse test | Zero-AA (All): 28.7 -> 42.3 | Helped |
| Use gold scene graphs instead of object proposals and dense connections (ablation) | VLGAE · VLParse test | Zero-AA (Obj.): 36.1 -> 67.2 | Helped |
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