A staged knowledge-prompting method (KAAR) improves LLM test accuracy on ARC by about 5 absolute points over repeated-sampling plan-guided code generation, reaching 35% with GPT-o3-mini.
1st Place Solution for PVUW Challenge 2023: Video Panoptic Segmentation
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abstract
Video panoptic segmentation is a challenging task that serves as the cornerstone of numerous downstream applications, including video editing and autonomous driving. We believe that the decoupling strategy proposed by DVIS enables more effective utilization of temporal information for both "thing" and "stuff" objects. In this report, we successfully validated the effectiveness of the decoupling strategy in video panoptic segmentation. Finally, our method achieved a VPQ score of 51.4 and 53.7 in the development and test phases, respectively, and ultimately ranked 1st in the VPS track of the 2nd PVUW Challenge. The code is available at https://github.com/zhang-tao-whu/DVIS
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From Reasoning to Generalization: Knowledge-Augmented LLMs for ARC Benchmark
A staged knowledge-prompting method (KAAR) improves LLM test accuracy on ARC by about 5 absolute points over repeated-sampling plan-guided code generation, reaching 35% with GPT-o3-mini.