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3404d44 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 | Loading EmbSpatial-Bench dataset... ====================================================================== 2D Heuristic λ΅μ μ νμ§ μμΉ(A/B/C/D) λΆν¬ λΆμ ====================================================================== Heuristic μ μ: FAR: center_yκ° κ°μ₯ μμ 물체 (μ΄λ―Έμ§ μμͺ½ = 'κ°μ₯ λ©λ€') CLOSE: center_yκ° κ°μ₯ ν° λ¬Όμ²΄ (μ΄λ―Έμ§ μλμͺ½ = 'κ°μ₯ κ°κΉλ€') λ§€μΉ μ€ν¨: 0κ° (answer_optionsμ heuristic λ¬Όμ²΄κ° μμ) ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ FAR (n=594) ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ [Heuristic λ΅μ μμΉ λΆν¬] Position Count Ratio A 163 27.4% B 157 26.4% C 143 24.1% D 131 22.1% Std: 2.1%p [GT λ΅μ μμΉ λΆν¬] Position Count Ratio A 159 26.8% B 156 26.3% C 130 21.9% D 149 25.1% Std: 1.9%p Heuristic == GT: 383/594 (64.5%) β μ΄ λΉμ¨μ΄ Consistent μν λΉμ¨κ³Ό μ μ¬ν΄μΌ ν¨ ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ CLOSE (n=612) ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ [Heuristic λ΅μ μμΉ λΆν¬] Position Count Ratio A 155 25.3% B 149 24.3% C 167 27.3% D 141 23.0% Std: 1.6%p [GT λ΅μ μμΉ λΆν¬] Position Count Ratio A 160 26.1% B 134 21.9% C 159 26.0% D 159 26.0% Std: 1.8%p Heuristic == GT: 398/612 (65.0%) β μ΄ λΉμ¨μ΄ Consistent μν λΉμ¨κ³Ό μ μ¬ν΄μΌ ν¨ ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ FAR+CLOSE (n=1206) ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ [Heuristic λ΅μ μμΉ λΆν¬] Position Count Ratio A 318 26.4% B 306 25.4% C 310 25.7% D 272 22.6% Std: 1.5%p [GT λ΅μ μμΉ λΆν¬] Position Count Ratio A 319 26.5% B 290 24.0% C 289 24.0% D 308 25.5% Std: 1.0%p Heuristic == GT: 781/1206 (64.8%) β μ΄ λΉμ¨μ΄ Consistent μν λΉμ¨κ³Ό μ μ¬ν΄μΌ ν¨ ====================================================================== Heuristic λ΅ μμΉ vs GT λ΅ μμΉ κ΅μ°¨ λΆμ ====================================================================== FAR: Heuristic μμΉλ³ GT μμΉ λΆν¬ (ν: Heuristic μμΉ, μ΄: GT μμΉ) Heur\GT A B C D Total ββββββββββββββββββββββββββββββββββββββββββββββββββ A 111(68%) 15(9%) 13(8%) 24(15%) 163 B 12(8%) 106(68%) 18(11%) 21(13%) 157 C 21(15%) 20(14%) 82(57%) 20(14%) 143 D 15(11%) 15(11%) 17(13%) 84(64%) 131 CLOSE: Heuristic μμΉλ³ GT μμΉ λΆν¬ (ν: Heuristic μμΉ, μ΄: GT μμΉ) Heur\GT A B C D Total ββββββββββββββββββββββββββββββββββββββββββββββββββ A 101(65%) 13(8%) 20(13%) 21(14%) 155 B 16(11%) 93(62%) 20(13%) 20(13%) 149 C 27(16%) 16(10%) 105(63%) 19(11%) 167 D 16(11%) 12(9%) 14(10%) 99(70%) 141 ====================================================================== ν΅μ¬ μμ½ ====================================================================== FAR heuristic λ΅ μ΅λ€ μμΉ: A (27.4%) CLOSE heuristic λ΅ μ΅λ€ μμΉ: C (27.3%) FAR heuristic λ΅μ΄ D μμΉ: 131/594 (22.1%) CLOSE heuristic λ΅μ΄ D μμΉ: 141/612 (23.0%) FAR heuristic λ΅μ D μμΉ λΉμ¨μ΄ κ· λ±(22.1%)μ΄λ―λ‘, D biasκ° FARμμ λ μ¬ν κ²μ μ νμ§ λ°°μΉ λλ¬Έμ΄ μλ λ€λ₯Έ μμΈμΌ κ°λ₯μ± |