SpaMEM is a diagnostic benchmark showing that current vision-language models exhibit a sharp collapse in spatial reasoning when transitioning from text-aided state tracking to purely visual memory in dynamic environments.
arXiv preprint arXiv:2304.03696 (2023),https://arxiv.org/abs/2304.036964
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Motion-aware contrastive learning on mask tubes improves temporal panoptic scene graph generation over pooling-based methods on video and 4D datasets.
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SpaMEM: Benchmarking Dynamic Spatial Reasoning via Perception-Memory Integration in Embodied Environments
SpaMEM is a diagnostic benchmark showing that current vision-language models exhibit a sharp collapse in spatial reasoning when transitioning from text-aided state tracking to purely visual memory in dynamic environments.
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Motion-aware Contrastive Learning for Temporal Panoptic Scene Graph Generation
Motion-aware contrastive learning on mask tubes improves temporal panoptic scene graph generation over pooling-based methods on video and 4D datasets.