Hugging Face's logo

Datasets:
xiaomoguhzz
/
DeCLIP-TPAMI

Tags:
open-vocabulary
dense-prediction
clip
cvpr-2025
tpami
Dataset card Files
xet
 Community
DeCLIP-TPAMI
File size: 5,084 Bytes
c50dde6
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Patch

# =========================
# 数据与预处理
# =========================
labels = [
    'MATH-500', 'AIME24', 'AIME25', 'GSM8K', 'AMC23', 'Olympiad',
    'MATH-500', 'AIME24', 'AIME25', 'GSM8K', 'AMC23', 'Olympiad',
    'MATH-500', 'AIME24', 'AIME25', 'GSM8K', 'AMC23', 'Olympiad',
    'MATH-500', 'AIME24', 'AIME25', 'GSM8K', 'AMC23', 'Olympiad',
]

ReBalance_value = [83.0, 36.7, 30.0, 78.3, 80.0, 43.9,
                   92.6, 56.7, 40.0, 91.6, 95.0, 57.0,
                   94.0, 73.3, 56.7, 96.3, 100.0, 66.3,
                   95.2, 70.0, 63.3, 96.8, 95.0, 68.6]

CoD_value = [80.2, 33.3, 13.3, 69.5, 62.5, 35.2,
             90.0, 46.7, 36.7, 84.5, 85.0, 47.5,
             93.8, 66.7, 53.3, 95.6, 95.0, 62.2,
             93.8, 63.3, 46.7, 96.2, 92.5, 67.7]

SEAL_value = [78.6, 23.3, 26.7, 76.4, 53.8, 32.7,
              90.6, 43.3, 26.7, 88.4, 77.5, 53.9,
              93.4, 63.3, 50.0, 95.7, 90.0, 62.3,
              92.6, 63.3, 56.7, 96.2, 95.0, 67.5]

def normalize_values(target, base_values, method_values):
    normalized = []
    for base, value in zip(base_values, method_values):
        if base == 0 or value == 0:
            normalized.append(0)
        else:
            normalized.append((value / base) * target)
    return normalized

def enhance_contrast(values, power=2.0, target_max=40):
    arr = np.array(values, dtype=float)
    arr_scaled = arr ** power
    return (arr_scaled / arr_scaled.max()) * target_max

# 数据准备
ReBalance_target = 40
ReBalance = normalize_values(ReBalance_target, ReBalance_value, ReBalance_value)
CoD = normalize_values(ReBalance_target, ReBalance_value, CoD_value)
SEAL = normalize_values(ReBalance_target, ReBalance_value, SEAL_value)

ReBalance = enhance_contrast(ReBalance, power=2.0, target_max=ReBalance_target)
CoD = enhance_contrast(CoD, power=2.0, target_max=ReBalance_target)
SEAL = enhance_contrast(SEAL, power=2.0, target_max=ReBalance_target)

# 设置角度
num_groups = 4
tasks_per_group = 6
gap_deg = 18
group_arc = (360 - num_groups * gap_deg) / num_groups
point_arc = group_arc / tasks_per_group

angles = []
current_angle = 0
for g in range(num_groups):
    for t in range(tasks_per_group):
        angles.append(np.deg2rad(current_angle))
        current_angle += point_arc
    current_angle += gap_deg

# 首尾相连
angles += [angles[0]]
ReBalance = np.concatenate((ReBalance, [ReBalance[0]]))
CoD = np.concatenate((CoD, [CoD[0]]))
SEAL = np.concatenate((SEAL, [SEAL[0]]))

# =========================
# 绘图 (CVPR/NIPS 风格)
# =========================
colors = {
    "ReBalance": "#00E676",   # 荧光绿
    "CoD": "#FFB300",         # 亮橙黄
    "SEAL": "#00B0FF",        # 亮青蓝
}

markers = {"ReBalance": "o", "CoD": "s", "SEAL": "D"}
linestyles = {"ReBalance": "-", "CoD": "--", "SEAL": "-."}

fig, ax = plt.subplots(figsize=(8, 8), subplot_kw=dict(polar=True))

# 额外绘制半径为40的圆圈
theta = np.linspace(0, 2*np.pi, 500)
ax.plot(theta, np.full_like(theta, 40),
        color="black", linewidth=1.2, linestyle="-", zorder=0.5)

ax.set_ylim(0, 44)

# 背景与网格
ax.set_facecolor("white")
ax.grid(True, color="gray", linestyle="--", linewidth=0.6, alpha=0.6)
ax.spines["polar"].set_visible(False)
ax.set_yticks([])           # 移除半径方向的刻度
ax.set_yticklabels([])      # 移除半径方向的刻度标签

# 绘制每个方法
def plot_method(values, angles, label):
    # 淡淡背景填充
    if label=="ReBalance":
        ax.fill(angles, values, color=colors[label], alpha=0.05, zorder=1)
    else:
        ax.fill(angles, values, color=colors[label], alpha=0.1, zorder=1)
    # 折线
    ax.plot(angles, values, color=colors[label],
            linewidth=2.2, linestyle=linestyles[label],
            label=label, zorder=2)
    # 数据点
    ax.scatter(angles, values,
               facecolors="white", edgecolors=colors[label],
               linewidths=1.5, marker=markers[label],
               s=55, zorder=3)

plot_method(ReBalance, angles, "ReBalance")
plot_method(CoD, angles, "CoD")
plot_method(SEAL, angles, "SEAL")

# =========================
# 在中心画一个灰色实心圆点（带黑色边框）
# =========================
ax.scatter(0, 0,
           c="gray", edgecolors="black",
           s=120, zorder=5, linewidths=1.0)

# 标签
ax.set_xticks(angles[:-1])
ax.set_xticklabels(labels, fontsize=12, color="black")
for label in ax.get_xticklabels():
    label.set_horizontalalignment("center")

# 图例
legend_elements = [
    Patch(facecolor=colors["ReBalance"], edgecolor=colors["ReBalance"], label="ReBalance (Ours)"),
    Patch(facecolor=colors["CoD"], edgecolor=colors["CoD"], label="CoD"),
    Patch(facecolor=colors["SEAL"], edgecolor=colors["SEAL"], label="SEAL"),
]
ax.legend(
    handles=legend_elements,
    frameon=False,
    fontsize=14,  
    loc="upper center",
    bbox_to_anchor=(0.78, 1.12),
)

plt.tight_layout()
plt.savefig("radar_chart_cvpr_with_center.png", dpi=300, transparent=True)
plt.close()