Papers by Shihao Ji
Semantic Structure Based Query Graph Prediction for Question Answering over Knowledge Graph (2022.coling-1)
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| Challenge: | Existing approaches for query graph generation ignore the semantic structure of a question . Existing methods ignore the structure of the question, resulting in noisy query graph candidates. |
| Approach: | They propose to build query graphs from natural language questions to predict semantic structure of a question. |
| Outcome: | The proposed method can predict the semantic structure of a question using six semantic structures from common questions in KGQA. |
SAM3-I: Segment Anything with Instructions (2026.acl-long)
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Jingjing Li, Yue Feng, Yuchen Guo, Jincai Huang, Wei Ji, Qi Bi, Yongri Piao, Miao Zhang, Xiaoqi Zhao, Qiang Chen, Shihao Zou, Huchuan Lu, Li Cheng
| Challenge: | Existing methods for concept-level grounding and instruction-level reasoning use coarse representations and iterative mask filtering. |
| Approach: | They propose an instruction-following extension of the Segment Anything Model 3 family that unifies concept-level grounding and instruction-level reasoning within a single segmentation framework. |
| Outcome: | Experiments show that SAM3-I achieves appealing performance across referring and reasoning-based segmentation while maintaining its strong concept recall ability. |