InstAP introduces instance-aware pre-training with a new dual-granularity dataset InstVL that improves both fine-grained instance retrieval and global video understanding over standard VLP baselines.
Oscar: Object-semantics aligned pre-training for vision-language tasks
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DBAC is a new directional metric for bias amplification in image captions that is less sensitive to sentence encoders and more accurate than LIC, validated on COCO gender and race attributes.
PVCap combines instance-mixing data augmentation with pseudo-labels and a voxel-based captioning network to achieve new state-of-the-art on 3D dense captioning benchmarks ScanRefer and Nr3D.
citing papers explorer
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InstAP: Instance-Aware Vision-Language Pre-Train for Spatial-Temporal Understanding
InstAP introduces instance-aware pre-training with a new dual-granularity dataset InstVL that improves both fine-grained instance retrieval and global video understanding over standard VLP baselines.
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A Woman with a Knife or A Knife with a Woman? Measuring Directional Bias Amplification in Image Captions
DBAC is a new directional metric for bias amplification in image captions that is less sensitive to sentence encoders and more accurate than LIC, validated on COCO gender and race attributes.
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PVCap: Towards Accurate 3D Dense Captioning via PseudoCap and VoxelCapNet
PVCap combines instance-mixing data augmentation with pseudo-labels and a voxel-based captioning network to achieve new state-of-the-art on 3D dense captioning benchmarks ScanRefer and Nr3D.