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QDTrack: Quasi-Dense Similarity Learning for Appearance-Only Multiple Object Tracking., , , , , , and . IEEE Trans. Pattern Anal. Mach. Intell., 45 (12): 15380-15393 (December 2023)Phenomenal Yet Puzzling: Testing Inductive Reasoning Capabilities of Language Models with Hypothesis Refinement., , , , , , , , , and 1 other author(s). CoRR, (2023)Reasoning or Reciting? Exploring the Capabilities and Limitations of Language Models Through Counterfactual Tasks., , , , , , , , and . CoRR, (2023)Evaluating the Impact of Model Scale for Compositional Generalization in Semantic Parsing., , , , , , , and . EMNLP, page 9157-9179. Association for Computational Linguistics, (2022)QDTrack: Quasi-Dense Similarity Learning for Appearance-Only Multiple Object Tracking., , , , , , and . CoRR, (2022)Systematic Generalization on gSCAN: What is Nearly Solved and What is Next?, , , , and . EMNLP (1), page 2180-2188. Association for Computational Linguistics, (2021)Visually Grounded Concept Composition., , , , and . EMNLP (Findings), page 201-215. Association for Computational Linguistics, (2021)Quasi-Dense Instance Similarity Learning., , , , , and . CoRR, (2020)Improving Compositional Generalization with Latent Structure and Data Augmentation., , , , , , and . NAACL-HLT, page 4341-4362. Association for Computational Linguistics, (2022)Generate-and-Retrieve: Use Your Predictions to Improve Retrieval for Semantic Parsing., , , , , , , and . COLING, page 4946-4951. International Committee on Computational Linguistics, (2022)