Increase the threshold corresponding to the feature-frame; add the corresponding original image of smkd

SignX/detailed_prediction_20251225_192957/sample_000/frame_alignment.json

@@ -1,86 +0,0 @@
-{
-  "translation": "<unk> NOW-WEEK STUDENT IX HAVE NONE/NOTHING GO NONE/NOTHING",
-  "words": [
-    "<unk>",
-    "NOW-WEEK",
-    "STUDENT",
-    "IX",
-    "HAVE",
-    "NONE/NOTHING",
-    "GO",
-    "NONE/NOTHING"
-  ],
-  "total_video_frames": 24,
-  "frame_ranges": [
-    {
-      "word": "<unk>",
-      "start_frame": 0,
-      "end_frame": 23,
-      "peak_frame": 0,
-      "avg_attention": 0.06790952384471893,
-      "confidence": "low"
-    },
-    {
-      "word": "NOW-WEEK",
-      "start_frame": 2,
-      "end_frame": 3,
-      "peak_frame": 2,
-      "avg_attention": 0.4792596399784088,
-      "confidence": "medium"
-    },
-    {
-      "word": "STUDENT",
-      "start_frame": 1,
-      "end_frame": 23,
-      "peak_frame": 21,
-      "avg_attention": 0.13404551148414612,
-      "confidence": "low"
-    },
-    {
-      "word": "IX",
-      "start_frame": 1,
-      "end_frame": 23,
-      "peak_frame": 3,
-      "avg_attention": 0.09226731956005096,
-      "confidence": "low"
-    },
-    {
-      "word": "HAVE",
-      "start_frame": 4,
-      "end_frame": 6,
-      "peak_frame": 5,
-      "avg_attention": 0.27426692843437195,
-      "confidence": "medium"
-    },
-    {
-      "word": "NONE/NOTHING",
-      "start_frame": 7,
-      "end_frame": 8,
-      "peak_frame": 7,
-      "avg_attention": 0.3239603638648987,
-      "confidence": "medium"
-    },
-    {
-      "word": "GO",
-      "start_frame": 7,
-      "end_frame": 23,
-      "peak_frame": 7,
-      "avg_attention": 0.1878073364496231,
-      "confidence": "low"
-    },
-    {
-      "word": "NONE/NOTHING",
-      "start_frame": 8,
-      "end_frame": 8,
-      "peak_frame": 8,
-      "avg_attention": 0.7333312630653381,
-      "confidence": "high"
-    }
-  ],
-  "statistics": {
-    "avg_confidence": 0.2866059858351946,
-    "high_confidence_words": 1,
-    "medium_confidence_words": 3,
-    "low_confidence_words": 4
-  }
-}

SignX/detailed_prediction_20251225_192957/sample_000/translation.txt

@@ -1,2 +0,0 @@
-With BPE: <unk> NOW@@ -@@ WEEK STUDENT I@@ X HAVE NONE/NOTHING GO NONE/NOTHING
-Clean: <unk> NOW-WEEK STUDENT IX HAVE NONE/NOTHING GO NONE/NOTHING

SignX/detailed_prediction_20251225_193758/sample_000/frame_alignment.json

@@ -1,86 +0,0 @@
-{
-  "translation": "<unk> NOW-WEEK STUDENT IX HAVE NONE/NOTHING GO NONE/NOTHING",
-  "words": [
-    "<unk>",
-    "NOW-WEEK",
-    "STUDENT",
-    "IX",
-    "HAVE",
-    "NONE/NOTHING",
-    "GO",
-    "NONE/NOTHING"
-  ],
-  "total_video_frames": 24,
-  "frame_ranges": [
-    {
-      "word": "<unk>",
-      "start_frame": 0,
-      "end_frame": 23,
-      "peak_frame": 0,
-      "avg_attention": 0.06790952384471893,
-      "confidence": "low"
-    },
-    {
-      "word": "NOW-WEEK",
-      "start_frame": 2,
-      "end_frame": 3,
-      "peak_frame": 2,
-      "avg_attention": 0.4792596399784088,
-      "confidence": "medium"
-    },
-    {
-      "word": "STUDENT",
-      "start_frame": 1,
-      "end_frame": 23,
-      "peak_frame": 21,
-      "avg_attention": 0.13404551148414612,
-      "confidence": "low"
-    },
-    {
-      "word": "IX",
-      "start_frame": 1,
-      "end_frame": 23,
-      "peak_frame": 3,
-      "avg_attention": 0.09226731956005096,
-      "confidence": "low"
-    },
-    {
-      "word": "HAVE",
-      "start_frame": 4,
-      "end_frame": 6,
-      "peak_frame": 5,
-      "avg_attention": 0.27426692843437195,
-      "confidence": "medium"
-    },
-    {
-      "word": "NONE/NOTHING",
-      "start_frame": 7,
-      "end_frame": 8,
-      "peak_frame": 7,
-      "avg_attention": 0.3239603638648987,
-      "confidence": "medium"
-    },
-    {
-      "word": "GO",
-      "start_frame": 7,
-      "end_frame": 23,
-      "peak_frame": 7,
-      "avg_attention": 0.1878073364496231,
-      "confidence": "low"
-    },
-    {
-      "word": "NONE/NOTHING",
-      "start_frame": 8,
-      "end_frame": 8,
-      "peak_frame": 8,
-      "avg_attention": 0.7333312630653381,
-      "confidence": "high"
-    }
-  ],
-  "statistics": {
-    "avg_confidence": 0.2866059858351946,
-    "high_confidence_words": 1,
-    "medium_confidence_words": 3,
-    "low_confidence_words": 4
-  }
-}

SignX/detailed_prediction_20251225_193758/sample_000/translation.txt

@@ -1,2 +0,0 @@
-With BPE: <unk> NOW@@ -@@ WEEK STUDENT I@@ X HAVE NONE/NOTHING GO NONE/NOTHING
-Clean: <unk> NOW-WEEK STUDENT IX HAVE NONE/NOTHING GO NONE/NOTHING

SignX/{detailed_prediction_20251225_192957 → detailed_prediction_20251226_155113}/sample_000/analysis_report.txt

@@ -2,43 +2,45 @@
   Sign Language Recognition - Attention分析报告
 ================================================================================
 
-生成时间: 2025-12-25 19:30:01
+生成时间: 2025-12-26 15:51:17
 
 翻译结果:
 --------------------------------------------------------------------------------
-<unk> NOW-WEEK STUDENT IX HAVE NONE/NOTHING GO NONE/NOTHING
+<unk> #IF FRIEND GROUP/TOGETHER DEPART PARTY IX-1p FINISH JOIN IX-1p
 
 视频信息:
 --------------------------------------------------------------------------------
-总帧数: 24
-词数量: 8
+总帧数: 28
+词数量: 10
 
 Attention权重信息:
 --------------------------------------------------------------------------------
-形状: (29, 8, 24)
-  - 解码步数: 29
+形状: (28, 8, 28)
+  - 解码步数: 28
   - Batch大小: 8
 
 词-帧对应详情:
 ================================================================================
 No.   Word                 Frames          Peak     Attn     Conf      
 --------------------------------------------------------------------------------
-1     <unk>                0-23            0        0.068    low       
-2     NOW-WEEK             2-3             2        0.479    medium    
-3     STUDENT              1-23            21       0.134    low       
-4     IX                   1-23            3        0.092    low       
-5     HAVE                 4-6             5        0.274    medium    
-6     NONE/NOTHING         7-8             7        0.324    medium    
-7     GO                   7-23            7        0.188    low       
-8     NONE/NOTHING         8-8             8        0.733    high      
+1     <unk>                0-0             0        0.133    low       
+2     #IF                  2-3             2        0.359    medium    
+3     FRIEND               5-5             5        0.449    medium    
+4     GROUP/TOGETHER       8-8             8        0.371    medium    
+5     DEPART               27-27           27       0.305    medium    
+6     PARTY                27-27           27       0.296    medium    
+7     IX-1p                27-27           27       0.326    medium    
+8     FINISH               11-12           12       0.467    medium    
+9     JOIN                 13-14           14       0.317    medium    
+10    IX-1p                17-17           17       0.358    medium    
 
 ================================================================================
 
 统计摘要:
 --------------------------------------------------------------------------------
-平均attention权重: 0.287
-高置信度词: 1 (12.5%)
-中置信度词: 3 (37.5%)
-低置信度词: 4 (50.0%)
+平均attention权重: 0.338
+高置信度词: 0 (0.0%)
+中置信度词: 9 (90.0%)
+低置信度词: 1 (10.0%)
 
 ================================================================================

SignX/{detailed_prediction_20251225_193758 → detailed_prediction_20251226_155113}/sample_000/attention_weights.npy

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:25434051e14c2b1741bf1376aaae36ca9a9fc276b01859a40b74bab3b603bcf8
-size 22400
+oid sha256:7414e5ab870540255a4bc963aa612d837eca27b95da7b4603c4c8e39f82b8c01
+size 25216

SignX/{detailed_prediction_20251225_193758 → detailed_prediction_20251226_155113}/sample_000/debug_video_path.txt

@@ -1,4 +1,4 @@
-video_path = '/common/users/sf895/output/huggingface_asllrp_repo/SignX/eval/tiny_test_data/videos/666.mp4'
+video_path = '/common/users/sf895/output/huggingface_asllrp_repo/SignX/eval/tiny_test_data/videos/632051.mp4'
 video_path type = <class 'str'>
 video_path is None: False
 bool(video_path): True

SignX/detailed_prediction_20251226_155113/sample_000/feature_frame_mapping.json

@@ -0,0 +1,176 @@
+{
+  "original_frame_count": 106,
+  "feature_count": 28,
+  "downsampling_ratio": 3.7857142857142856,
+  "fps": 24.0,
+  "mapping": [
+    {
+      "feature_index": 0,
+      "frame_start": 0,
+      "frame_end": 3,
+      "frame_count": 3
+    },
+    {
+      "feature_index": 1,
+      "frame_start": 3,
+      "frame_end": 7,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 2,
+      "frame_start": 7,
+      "frame_end": 11,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 3,
+      "frame_start": 11,
+      "frame_end": 15,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 4,
+      "frame_start": 15,
+      "frame_end": 18,
+      "frame_count": 3
+    },
+    {
+      "feature_index": 5,
+      "frame_start": 18,
+      "frame_end": 22,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 6,
+      "frame_start": 22,
+      "frame_end": 26,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 7,
+      "frame_start": 26,
+      "frame_end": 30,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 8,
+      "frame_start": 30,
+      "frame_end": 34,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 9,
+      "frame_start": 34,
+      "frame_end": 37,
+      "frame_count": 3
+    },
+    {
+      "feature_index": 10,
+      "frame_start": 37,
+      "frame_end": 41,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 11,
+      "frame_start": 41,
+      "frame_end": 45,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 12,
+      "frame_start": 45,
+      "frame_end": 49,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 13,
+      "frame_start": 49,
+      "frame_end": 53,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 14,
+      "frame_start": 53,
+      "frame_end": 56,
+      "frame_count": 3
+    },
+    {
+      "feature_index": 15,
+      "frame_start": 56,
+      "frame_end": 60,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 16,
+      "frame_start": 60,
+      "frame_end": 64,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 17,
+      "frame_start": 64,
+      "frame_end": 68,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 18,
+      "frame_start": 68,
+      "frame_end": 71,
+      "frame_count": 3
+    },
+    {
+      "feature_index": 19,
+      "frame_start": 71,
+      "frame_end": 75,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 20,
+      "frame_start": 75,
+      "frame_end": 79,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 21,
+      "frame_start": 79,
+      "frame_end": 83,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 22,
+      "frame_start": 83,
+      "frame_end": 87,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 23,
+      "frame_start": 87,
+      "frame_end": 90,
+      "frame_count": 3
+    },
+    {
+      "feature_index": 24,
+      "frame_start": 90,
+      "frame_end": 94,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 25,
+      "frame_start": 94,
+      "frame_end": 98,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 26,
+      "frame_start": 98,
+      "frame_end": 102,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 27,
+      "frame_start": 102,
+      "frame_end": 106,
+      "frame_count": 4
+    }
+  ]
+}

SignX/detailed_prediction_20251226_155113/sample_000/frame_alignment.json

@@ -0,0 +1,104 @@
+{
+  "translation": "<unk> #IF FRIEND GROUP/TOGETHER DEPART PARTY IX-1p FINISH JOIN IX-1p",
+  "words": [
+    "<unk>",
+    "#IF",
+    "FRIEND",
+    "GROUP/TOGETHER",
+    "DEPART",
+    "PARTY",
+    "IX-1p",
+    "FINISH",
+    "JOIN",
+    "IX-1p"
+  ],
+  "total_video_frames": 28,
+  "frame_ranges": [
+    {
+      "word": "<unk>",
+      "start_frame": 0,
+      "end_frame": 0,
+      "peak_frame": 0,
+      "avg_attention": 0.13272422552108765,
+      "confidence": "low"
+    },
+    {
+      "word": "#IF",
+      "start_frame": 2,
+      "end_frame": 3,
+      "peak_frame": 2,
+      "avg_attention": 0.35901427268981934,
+      "confidence": "medium"
+    },
+    {
+      "word": "FRIEND",
+      "start_frame": 5,
+      "end_frame": 5,
+      "peak_frame": 5,
+      "avg_attention": 0.4494199752807617,
+      "confidence": "medium"
+    },
+    {
+      "word": "GROUP/TOGETHER",
+      "start_frame": 8,
+      "end_frame": 8,
+      "peak_frame": 8,
+      "avg_attention": 0.3710141181945801,
+      "confidence": "medium"
+    },
+    {
+      "word": "DEPART",
+      "start_frame": 27,
+      "end_frame": 27,
+      "peak_frame": 27,
+      "avg_attention": 0.30533191561698914,
+      "confidence": "medium"
+    },
+    {
+      "word": "PARTY",
+      "start_frame": 27,
+      "end_frame": 27,
+      "peak_frame": 27,
+      "avg_attention": 0.2963099479675293,
+      "confidence": "medium"
+    },
+    {
+      "word": "IX-1p",
+      "start_frame": 27,
+      "end_frame": 27,
+      "peak_frame": 27,
+      "avg_attention": 0.3264133930206299,
+      "confidence": "medium"
+    },
+    {
+      "word": "FINISH",
+      "start_frame": 11,
+      "end_frame": 12,
+      "peak_frame": 12,
+      "avg_attention": 0.46679070591926575,
+      "confidence": "medium"
+    },
+    {
+      "word": "JOIN",
+      "start_frame": 13,
+      "end_frame": 14,
+      "peak_frame": 14,
+      "avg_attention": 0.3172740340232849,
+      "confidence": "medium"
+    },
+    {
+      "word": "IX-1p",
+      "start_frame": 17,
+      "end_frame": 17,
+      "peak_frame": 17,
+      "avg_attention": 0.3579559326171875,
+      "confidence": "medium"
+    }
+  ],
+  "statistics": {
+    "avg_confidence": 0.33822485208511355,
+    "high_confidence_words": 0,
+    "medium_confidence_words": 9,
+    "low_confidence_words": 1
+  }
+}

SignX/detailed_prediction_20251226_155113/sample_000/interactive_alignment.html

@@ -0,0 +1,579 @@
+<!DOCTYPE html>
+<html lang="zh-CN">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Interactive Word-Frame Alignment</title>
+    <style>
+        body {
+            font-family: 'Arial', sans-serif;
+            margin: 20px;
+            background-color: #f5f5f5;
+        }
+        .container {
+            max-width: 1800px;
+            margin: 0 auto;
+            background-color: white;
+            padding: 30px;
+            border-radius: 8px;
+            box-shadow: 0 2px 10px rgba(0,0,0,0.1);
+        }
+        h1 {
+            color: #333;
+            border-bottom: 3px solid #4CAF50;
+            padding-bottom: 10px;
+            margin-bottom: 20px;
+        }
+        .stats {
+            background-color: #E3F2FD;
+            padding: 15px;
+            border-radius: 5px;
+            margin-bottom: 20px;
+            border-left: 4px solid #2196F3;
+            font-size: 14px;
+        }
+        .controls {
+            background-color: #f9f9f9;
+            padding: 20px;
+            border-radius: 5px;
+            margin-bottom: 30px;
+            border: 1px solid #ddd;
+        }
+        .control-group {
+            margin-bottom: 15px;
+        }
+        label {
+            font-weight: bold;
+            display: inline-block;
+            width: 250px;
+            color: #555;
+        }
+        input[type="range"] {
+            width: 400px;
+            vertical-align: middle;
+        }
+        .value-display {
+            display: inline-block;
+            width: 80px;
+            font-family: monospace;
+            font-size: 14px;
+            color: #2196F3;
+            font-weight: bold;
+        }
+        .reset-btn {
+            margin-top: 15px;
+            padding: 10px 25px;
+            background-color: #2196F3;
+            color: white;
+            border: none;
+            border-radius: 5px;
+            cursor: pointer;
+            font-size: 14px;
+            font-weight: bold;
+        }
+        .reset-btn:hover {
+            background-color: #1976D2;
+        }
+        canvas {
+            border: 1px solid #999;
+            display: block;
+            margin: 20px auto;
+            background: white;
+        }
+        .legend {
+            margin-top: 20px;
+            padding: 15px;
+            background-color: #fff;
+            border: 1px solid #ddd;
+            border-radius: 5px;
+        }
+        .legend-item {
+            display: inline-block;
+            margin-right: 25px;
+            font-size: 13px;
+            margin-bottom: 10px;
+        }
+        .color-box {
+            display: inline-block;
+            width: 30px;
+            height: 15px;
+            margin-right: 8px;
+            vertical-align: middle;
+            border: 1px solid #666;
+        }
+        .info-panel {
+            margin-top: 20px;
+            padding: 15px;
+            background-color: #f9f9f9;
+            border-radius: 5px;
+            border: 1px solid #ddd;
+        }
+        .confidence {
+            display: inline-block;
+            padding: 3px 10px;
+            border-radius: 10px;
+            font-weight: bold;
+            font-size: 11px;
+            text-transform: uppercase;
+        }
+        .confidence.high {
+            background-color: #4CAF50;
+            color: white;
+        }
+        .confidence.medium {
+            background-color: #FF9800;
+            color: white;
+        }
+        .confidence.low {
+            background-color: #f44336;
+            color: white;
+        }
+    </style>
+</head>
+<body>
+    <div class="container">
+        <h1>🎯 Interactive Word-to-Frame Alignment Visualizer</h1>
+
+        <div class="stats">
+            <strong>Translation:</strong> <unk> #IF FRIEND GROUP/TOGETHER DEPART PARTY IX-1p FINISH JOIN IX-1p<br>
+            <strong>Total Words:</strong> 10 |
+            <strong>Total Features:</strong> 8
+        </div>
+
+        <div class="controls">
+            <h3>⚙️ Threshold Controls</h3>
+
+            <div class="control-group">
+                <label for="peak-threshold">Peak Threshold (% of max):</label>
+                <input type="range" id="peak-threshold" min="1" max="100" value="90" step="1">
+                <span class="value-display" id="peak-threshold-value">90%</span>
+                <br>
+                <small style="margin-left: 255px; color: #666;">
+                    帧的注意力权重 ≥ (峰值权重 × 阈值%) 时被认为是"显著帧"
+                </small>
+            </div>
+
+            <div class="control-group">
+                <label for="confidence-high">High Confidence (avg attn >):</label>
+                <input type="range" id="confidence-high" min="0" max="100" value="50" step="1">
+                <span class="value-display" id="confidence-high-value">0.50</span>
+            </div>
+
+            <div class="control-group">
+                <label for="confidence-medium">Medium Confidence (avg attn >):</label>
+                <input type="range" id="confidence-medium" min="0" max="100" value="20" step="1">
+                <span class="value-display" id="confidence-medium-value">0.20</span>
+            </div>
+
+            <button class="reset-btn" onclick="resetDefaults()">
+                Reset to Defaults
+            </button>
+        </div>
+
+        <div>
+            <h3>Word-to-Frame Alignment</h3>
+            <p style="color: #666; font-size: 13px;">
+                每个词显示为彩色矩形，宽度表示该词对应的特征帧范围。★ = 峰值帧。矩形内部显示注意力权重波形。
+            </p>
+            <canvas id="alignment-canvas" width="1600" height="600"></canvas>
+
+            <h3 style="margin-top: 30px;">Timeline Progress Bar</h3>
+            <canvas id="timeline-canvas" width="1600" height="100"></canvas>
+
+            <div class="legend">
+                <strong>Legend:</strong><br><br>
+                <div class="legend-item">
+                    <span class="confidence high">High</span>
+                    <span class="confidence medium">Medium</span>
+                    <span class="confidence low">Low</span>
+                    Confidence Levels (opacity reflects confidence)
+                </div>
+                <div class="legend-item">
+                    <span style="color: red; font-size: 20px;">★</span>
+                    Peak Frame (highest attention)
+                </div>
+                <div class="legend-item">
+                    <span style="color: blue;">━</span>
+                    Attention Waveform (within word region)
+                </div>
+            </div>
+        </div>
+
+        <div class="info-panel">
+            <h3>Alignment Details</h3>
+            <div id="alignment-details"></div>
+        </div>
+    </div>
+
+    <script>
+        // Attention data from Python
+        const attentionData = [{"word": "<unk>", "word_idx": 0, "weights": [0.13272422552108765, 0.13272422552108765, 0.13272422552108765, 0.13272422552108765, 0.13272422552108765, 0.13272422552108765, 0.13272422552108765, 0.13272422552108765]}, {"word": "#IF", "word_idx": 1, "weights": [0.035573869943618774, 0.03952416777610779, 0.03952416777610779, 0.03952416777610779, 0.03952416777610779, 0.03952416777610779, 0.03952416777610779, 0.04215671867132187]}, {"word": "FRIEND", "word_idx": 2, "weights": [0.0035305300261825323, 0.06121520698070526, 0.038338951766490936, 0.03721731901168823, 0.0396437793970108, 0.06121520698070526, 0.03798501193523407, 0.06121520698070526]}, {"word": "GROUP/TOGETHER", "word_idx": 3, "weights": [0.0032519344240427017, 0.06749927252531052, 0.06769093871116638, 0.06797394156455994, 0.05660902336239815, 0.0032519344240427017, 0.06791087239980698, 0.06749927252531052]}, {"word": "DEPART", "word_idx": 4, "weights": [0.1269645392894745, 0.011337662115693092, 0.011169591918587685, 0.011129447259008884, 0.011337662115693092, 0.14123021066188812, 0.007393963634967804, 0.011116056703031063]}, {"word": "PARTY", "word_idx": 5, "weights": [0.08145920932292938, 0.003315121866762638, 0.24030542373657227, 0.0034980459604412317, 0.0034834302496165037, 0.16299134492874146, 0.003404060145840049, 0.0034867869690060616]}, {"word": "IX-1p", "word_idx": 6, "weights": [0.08820953965187073, 0.14399001002311707, 0.00422912510111928, 0.09592650085687637, 0.004478602670133114, 0.004475735127925873, 0.00422912510111928, 0.00439990172162652]}, {"word": "FINISH", "word_idx": 7, "weights": [0.0013184626586735249, 0.0013184626586735249, 0.0013184626586735249, 0.1313352882862091, 0.0013184626586735249, 0.14886727929115295, 0.14913317561149597, 0.004780622664839029]}, {"word": "JOIN", "word_idx": 8, "weights": [0.00555413169786334, 0.006608393043279648, 0.00555413169786334, 0.1470479518175125, 0.13233929872512817, 0.09560006111860275, 0.00555413169786334, 0.08190114796161652]}, {"word": "IX-1p", "word_idx": 9, "weights": [0.03655996546149254, 0.03403225541114807, 0.27225977182388306, 0.35054582357406616, 0.03655996546149254, 0.27422454953193665, 0.03655996546149254, 0.09239426255226135]}];
+        const numGlosses = 10;
+        const numFeatures = 8;
+
+        // Colors for different words (matching matplotlib tab20)
+        const colors = [
+            '#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd',
+            '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf',
+            '#aec7e8', '#ffbb78', '#98df8a', '#ff9896', '#c5b0d5',
+            '#c49c94', '#f7b6d2', '#c7c7c7', '#dbdb8d', '#9edae5'
+        ];
+
+        // Get controls
+        const peakThresholdSlider = document.getElementById('peak-threshold');
+        const peakThresholdValue = document.getElementById('peak-threshold-value');
+        const confidenceHighSlider = document.getElementById('confidence-high');
+        const confidenceHighValue = document.getElementById('confidence-high-value');
+        const confidenceMediumSlider = document.getElementById('confidence-medium');
+        const confidenceMediumValue = document.getElementById('confidence-medium-value');
+        const alignmentCanvas = document.getElementById('alignment-canvas');
+        const timelineCanvas = document.getElementById('timeline-canvas');
+        const alignmentCtx = alignmentCanvas.getContext('2d');
+        const timelineCtx = timelineCanvas.getContext('2d');
+
+        // Update displays when sliders change
+        peakThresholdSlider.oninput = function() {
+            peakThresholdValue.textContent = this.value + '%';
+            updateVisualization();
+        };
+
+        confidenceHighSlider.oninput = function() {
+            confidenceHighValue.textContent = (this.value / 100).toFixed(2);
+            updateVisualization();
+        };
+
+        confidenceMediumSlider.oninput = function() {
+            confidenceMediumValue.textContent = (this.value / 100).toFixed(2);
+            updateVisualization();
+        };
+
+        function resetDefaults() {
+            peakThresholdSlider.value = 90;
+            confidenceHighSlider.value = 50;
+            confidenceMediumSlider.value = 20;
+            peakThresholdValue.textContent = '90%';
+            confidenceHighValue.textContent = '0.50';
+            confidenceMediumValue.textContent = '0.20';
+            updateVisualization();
+        }
+
+        function calculateAlignment(weights, peakThreshold) {
+            // Find peak
+            let peakIdx = 0;
+            let peakWeight = weights[0];
+            for (let i = 1; i < weights.length; i++) {
+                if (weights[i] > peakWeight) {
+                    peakWeight = weights[i];
+                    peakIdx = i;
+                }
+            }
+
+            // Find significant frames
+            const threshold = peakWeight * (peakThreshold / 100);
+            let startIdx = peakIdx;
+            let endIdx = peakIdx;
+            let sumWeight = 0;
+            let count = 0;
+
+            for (let i = 0; i < weights.length; i++) {
+                if (weights[i] >= threshold) {
+                    if (i < startIdx) startIdx = i;
+                    if (i > endIdx) endIdx = i;
+                    sumWeight += weights[i];
+                    count++;
+                }
+            }
+
+            const avgWeight = count > 0 ? sumWeight / count : peakWeight;
+
+            return {
+                startIdx: startIdx,
+                endIdx: endIdx,
+                peakIdx: peakIdx,
+                peakWeight: peakWeight,
+                avgWeight: avgWeight,
+                threshold: threshold
+            };
+        }
+
+        function getConfidenceLevel(avgWeight, highThreshold, mediumThreshold) {
+            if (avgWeight > highThreshold) return 'high';
+            if (avgWeight > mediumThreshold) return 'medium';
+            return 'low';
+        }
+
+        function drawAlignmentChart() {
+            const peakThreshold = parseInt(peakThresholdSlider.value);
+            const highThreshold = parseInt(confidenceHighSlider.value) / 100;
+            const mediumThreshold = parseInt(confidenceMediumSlider.value) / 100;
+
+            // Canvas dimensions
+            const width = alignmentCanvas.width;
+            const height = alignmentCanvas.height;
+            const leftMargin = 180;
+            const rightMargin = 50;
+            const topMargin = 60;
+            const bottomMargin = 80;
+
+            const plotWidth = width - leftMargin - rightMargin;
+            const plotHeight = height - topMargin - bottomMargin;
+
+            const rowHeight = plotHeight / numGlosses;
+            const featureWidth = plotWidth / numFeatures;
+
+            // Clear canvas
+            alignmentCtx.clearRect(0, 0, width, height);
+
+            // Draw title
+            alignmentCtx.fillStyle = '#333';
+            alignmentCtx.font = 'bold 18px Arial';
+            alignmentCtx.textAlign = 'center';
+            alignmentCtx.fillText('Word-to-Frame Alignment', width / 2, 30);
+            alignmentCtx.font = '13px Arial';
+            alignmentCtx.fillText('(based on attention peaks, ★ = peak frame)', width / 2, 48);
+
+            // Calculate alignments
+            const alignments = [];
+            for (let wordIdx = 0; wordIdx < numGlosses; wordIdx++) {
+                const data = attentionData[wordIdx];
+                const alignment = calculateAlignment(data.weights, peakThreshold);
+                alignment.word = data.word;
+                alignment.wordIdx = wordIdx;
+                alignment.weights = data.weights;
+                alignments.push(alignment);
+            }
+
+            // Draw grid
+            alignmentCtx.strokeStyle = '#e0e0e0';
+            alignmentCtx.lineWidth = 0.5;
+            for (let i = 0; i <= numFeatures; i++) {
+                const x = leftMargin + i * featureWidth;
+                alignmentCtx.beginPath();
+                alignmentCtx.moveTo(x, topMargin);
+                alignmentCtx.lineTo(x, topMargin + plotHeight);
+                alignmentCtx.stroke();
+            }
+
+            // Draw word regions
+            for (let wordIdx = 0; wordIdx < numGlosses; wordIdx++) {
+                const alignment = alignments[wordIdx];
+                const confidence = getConfidenceLevel(alignment.avgWeight, highThreshold, mediumThreshold);
+                const y = topMargin + wordIdx * rowHeight;
+
+                // Alpha based on confidence
+                const alpha = confidence === 'high' ? 0.9 : confidence === 'medium' ? 0.7 : 0.5;
+
+                // Draw rectangle for word region
+                const startX = leftMargin + alignment.startIdx * featureWidth;
+                const rectWidth = (alignment.endIdx - alignment.startIdx + 1) * featureWidth;
+
+                alignmentCtx.fillStyle = colors[wordIdx % 20];
+                alignmentCtx.globalAlpha = alpha;
+                alignmentCtx.fillRect(startX, y, rectWidth, rowHeight * 0.8);
+                alignmentCtx.globalAlpha = 1.0;
+
+                // Draw border
+                alignmentCtx.strokeStyle = '#000';
+                alignmentCtx.lineWidth = 2;
+                alignmentCtx.strokeRect(startX, y, rectWidth, rowHeight * 0.8);
+
+                // Draw attention waveform inside rectangle
+                alignmentCtx.strokeStyle = 'rgba(0, 0, 255, 0.8)';
+                alignmentCtx.lineWidth = 1.5;
+                alignmentCtx.beginPath();
+                for (let i = alignment.startIdx; i <= alignment.endIdx; i++) {
+                    const x = leftMargin + i * featureWidth + featureWidth / 2;
+                    const weight = alignment.weights[i];
+                    const maxWeight = alignment.peakWeight;
+                    const normalizedWeight = weight / (maxWeight * 1.2); // Scale for visibility
+                    const waveY = y + rowHeight * 0.8 - (normalizedWeight * rowHeight * 0.6);
+
+                    if (i === alignment.startIdx) {
+                        alignmentCtx.moveTo(x, waveY);
+                    } else {
+                        alignmentCtx.lineTo(x, waveY);
+                    }
+                }
+                alignmentCtx.stroke();
+
+                // Draw word label
+                const labelX = startX + rectWidth / 2;
+                const labelY = y + rowHeight * 0.4;
+
+                alignmentCtx.fillStyle = 'rgba(0, 0, 0, 0.7)';
+                alignmentCtx.fillRect(labelX - 60, labelY - 12, 120, 24);
+                alignmentCtx.fillStyle = '#fff';
+                alignmentCtx.font = 'bold 13px Arial';
+                alignmentCtx.textAlign = 'center';
+                alignmentCtx.textBaseline = 'middle';
+                alignmentCtx.fillText(alignment.word, labelX, labelY);
+
+                // Mark peak frame with star
+                const peakX = leftMargin + alignment.peakIdx * featureWidth + featureWidth / 2;
+                const peakY = y + rowHeight * 0.4;
+
+                // Draw star
+                alignmentCtx.fillStyle = '#ff0000';
+                alignmentCtx.strokeStyle = '#ffff00';
+                alignmentCtx.lineWidth = 1.5;
+                alignmentCtx.font = '20px Arial';
+                alignmentCtx.textAlign = 'center';
+                alignmentCtx.strokeText('★', peakX, peakY);
+                alignmentCtx.fillText('★', peakX, peakY);
+
+                // Y-axis label (word names)
+                alignmentCtx.fillStyle = '#333';
+                alignmentCtx.font = '12px Arial';
+                alignmentCtx.textAlign = 'right';
+                alignmentCtx.textBaseline = 'middle';
+                alignmentCtx.fillText(alignment.word, leftMargin - 10, y + rowHeight * 0.4);
+            }
+
+            // Draw horizontal grid lines
+            alignmentCtx.strokeStyle = '#ccc';
+            alignmentCtx.lineWidth = 0.5;
+            for (let i = 0; i <= numGlosses; i++) {
+                const y = topMargin + i * rowHeight;
+                alignmentCtx.beginPath();
+                alignmentCtx.moveTo(leftMargin, y);
+                alignmentCtx.lineTo(leftMargin + plotWidth, y);
+                alignmentCtx.stroke();
+            }
+
+            // Draw axes
+            alignmentCtx.strokeStyle = '#000';
+            alignmentCtx.lineWidth = 2;
+            alignmentCtx.strokeRect(leftMargin, topMargin, plotWidth, plotHeight);
+
+            // X-axis labels (frame indices)
+            alignmentCtx.fillStyle = '#000';
+            alignmentCtx.font = '11px Arial';
+            alignmentCtx.textAlign = 'center';
+            alignmentCtx.textBaseline = 'top';
+            for (let i = 0; i < numFeatures; i++) {
+                const x = leftMargin + i * featureWidth + featureWidth / 2;
+                alignmentCtx.fillText(i.toString(), x, topMargin + plotHeight + 10);
+            }
+
+            // Axis titles
+            alignmentCtx.fillStyle = '#333';
+            alignmentCtx.font = 'bold 14px Arial';
+            alignmentCtx.textAlign = 'center';
+            alignmentCtx.fillText('Feature Frame Index', leftMargin + plotWidth / 2, height - 20);
+
+            alignmentCtx.save();
+            alignmentCtx.translate(30, topMargin + plotHeight / 2);
+            alignmentCtx.rotate(-Math.PI / 2);
+            alignmentCtx.fillText('Generated Word', 0, 0);
+            alignmentCtx.restore();
+
+            return alignments;
+        }
+
+        function drawTimeline(alignments) {
+            const highThreshold = parseInt(confidenceHighSlider.value) / 100;
+            const mediumThreshold = parseInt(confidenceMediumSlider.value) / 100;
+
+            const width = timelineCanvas.width;
+            const height = timelineCanvas.height;
+            const leftMargin = 180;
+            const rightMargin = 50;
+            const plotWidth = width - leftMargin - rightMargin;
+            const featureWidth = plotWidth / numFeatures;
+
+            // Clear canvas
+            timelineCtx.clearRect(0, 0, width, height);
+
+            // Background bar
+            timelineCtx.fillStyle = '#ddd';
+            timelineCtx.fillRect(leftMargin, 30, plotWidth, 40);
+            timelineCtx.strokeStyle = '#000';
+            timelineCtx.lineWidth = 2;
+            timelineCtx.strokeRect(leftMargin, 30, plotWidth, 40);
+
+            // Draw word regions on timeline
+            for (let wordIdx = 0; wordIdx < alignments.length; wordIdx++) {
+                const alignment = alignments[wordIdx];
+                const confidence = getConfidenceLevel(alignment.avgWeight, highThreshold, mediumThreshold);
+                const alpha = confidence === 'high' ? 0.9 : confidence === 'medium' ? 0.7 : 0.5;
+
+                const startX = leftMargin + alignment.startIdx * featureWidth;
+                const rectWidth = (alignment.endIdx - alignment.startIdx + 1) * featureWidth;
+
+                timelineCtx.fillStyle = colors[wordIdx % 20];
+                timelineCtx.globalAlpha = alpha;
+                timelineCtx.fillRect(startX, 30, rectWidth, 40);
+                timelineCtx.globalAlpha = 1.0;
+                timelineCtx.strokeStyle = '#000';
+                timelineCtx.lineWidth = 0.5;
+                timelineCtx.strokeRect(startX, 30, rectWidth, 40);
+            }
+
+            // Title
+            timelineCtx.fillStyle = '#333';
+            timelineCtx.font = 'bold 13px Arial';
+            timelineCtx.textAlign = 'left';
+            timelineCtx.fillText('Timeline Progress Bar', leftMargin, 20);
+        }
+
+        function updateDetailsPanel(alignments, highThreshold, mediumThreshold) {
+            const panel = document.getElementById('alignment-details');
+            let html = '<table style="width: 100%; border-collapse: collapse;">';
+            html += '<tr style="background: #f0f0f0; font-weight: bold;">';
+            html += '<th style="padding: 8px; border: 1px solid #ddd;">Word</th>';
+            html += '<th style="padding: 8px; border: 1px solid #ddd;">Feature Range</th>';
+            html += '<th style="padding: 8px; border: 1px solid #ddd;">Peak</th>';
+            html += '<th style="padding: 8px; border: 1px solid #ddd;">Span</th>';
+            html += '<th style="padding: 8px; border: 1px solid #ddd;">Avg Attention</th>';
+            html += '<th style="padding: 8px; border: 1px solid #ddd;">Confidence</th>';
+            html += '</tr>';
+
+            for (const align of alignments) {
+                const confidence = getConfidenceLevel(align.avgWeight, highThreshold, mediumThreshold);
+                const span = align.endIdx - align.startIdx + 1;
+
+                html += '<tr>';
+                html += `<td style="padding: 8px; border: 1px solid #ddd;"><strong>${align.word}</strong></td>`;
+                html += `<td style="padding: 8px; border: 1px solid #ddd;">${align.startIdx} → ${align.endIdx}</td>`;
+                html += `<td style="padding: 8px; border: 1px solid #ddd;">${align.peakIdx}</td>`;
+                html += `<td style="padding: 8px; border: 1px solid #ddd;">${span}</td>`;
+                html += `<td style="padding: 8px; border: 1px solid #ddd;">${align.avgWeight.toFixed(4)}</td>`;
+                html += `<td style="padding: 8px; border: 1px solid #ddd;"><span class="confidence ${confidence}">${confidence}</span></td>`;
+                html += '</tr>';
+            }
+
+            html += '</table>';
+            panel.innerHTML = html;
+        }
+
+        function updateVisualization() {
+            const alignments = drawAlignmentChart();
+            drawTimeline(alignments);
+            const highThreshold = parseInt(confidenceHighSlider.value) / 100;
+            const mediumThreshold = parseInt(confidenceMediumSlider.value) / 100;
+            updateDetailsPanel(alignments, highThreshold, mediumThreshold);
+        }
+
+        // Event listeners for sliders
+        peakSlider.addEventListener('input', function() {
+            peakValue.textContent = peakSlider.value + '%';
+            updateVisualization();
+        });
+
+        confidenceHighSlider.addEventListener('input', function() {
+            const val = parseInt(confidenceHighSlider.value) / 100;
+            confidenceHighValue.textContent = val.toFixed(2);
+            updateVisualization();
+        });
+
+        confidenceMediumSlider.addEventListener('input', function() {
+            const val = parseInt(confidenceMediumSlider.value) / 100;
+            confidenceMediumValue.textContent = val.toFixed(2);
+            updateVisualization();
+        });
+
+        // Initial visualization
+        updateVisualization();
+    </script>
+</body>
+</html>

SignX/detailed_prediction_20251226_155113/sample_000/translation.txt

@@ -0,0 +1,2 @@
+With BPE: <unk> #IF FRIEND GROUP/TOGE@@ TH@@ E@@ R DEPART PARTY IX-1p FINISH JO@@ I@@ N IX-1p
+Clean: <unk> #IF FRIEND GROUP/TOGETHER DEPART PARTY IX-1p FINISH JOIN IX-1p

SignX/{detailed_prediction_20251225_193758 → detailed_prediction_20251226_161814}/sample_000/analysis_report.txt

@@ -2,43 +2,45 @@
   Sign Language Recognition - Attention分析报告
 ================================================================================
 
-生成时间: 2025-12-25 19:38:00
+生成时间: 2025-12-26 16:18:17
 
 翻译结果:
 --------------------------------------------------------------------------------
-<unk> NOW-WEEK STUDENT IX HAVE NONE/NOTHING GO NONE/NOTHING
+<unk> #IF FRIEND GROUP/TOGETHER DEPART PARTY IX-1p FINISH JOIN IX-1p
 
 视频信息:
 --------------------------------------------------------------------------------
-总帧数: 24
-词数量: 8
+总帧数: 28
+词数量: 10
 
 Attention权重信息:
 --------------------------------------------------------------------------------
-形状: (29, 8, 24)
-  - 解码步数: 29
+形状: (28, 8, 28)
+  - 解码步数: 28
   - Batch大小: 8
 
 词-帧对应详情:
 ================================================================================
 No.   Word                 Frames          Peak     Attn     Conf      
 --------------------------------------------------------------------------------
-1     <unk>                0-23            0        0.068    low       
-2     NOW-WEEK             2-3             2        0.479    medium    
-3     STUDENT              1-23            21       0.134    low       
-4     IX                   1-23            3        0.092    low       
-5     HAVE                 4-6             5        0.274    medium    
-6     NONE/NOTHING         7-8             7        0.324    medium    
-7     GO                   7-23            7        0.188    low       
-8     NONE/NOTHING         8-8             8        0.733    high      
+1     <unk>                0-0             0        0.133    low       
+2     #IF                  2-3             2        0.359    medium    
+3     FRIEND               5-5             5        0.449    medium    
+4     GROUP/TOGETHER       8-8             8        0.371    medium    
+5     DEPART               27-27           27       0.305    medium    
+6     PARTY                27-27           27       0.296    medium    
+7     IX-1p                27-27           27       0.326    medium    
+8     FINISH               11-12           12       0.467    medium    
+9     JOIN                 13-14           14       0.317    medium    
+10    IX-1p                17-17           17       0.358    medium    
 
 ================================================================================
 
 统计摘要:
 --------------------------------------------------------------------------------
-平均attention权重: 0.287
-高置信度词: 1 (12.5%)
-中置信度词: 3 (37.5%)
-低置信度词: 4 (50.0%)
+平均attention权重: 0.338
+高置信度词: 0 (0.0%)
+中置信度词: 9 (90.0%)
+低置信度词: 1 (10.0%)
 
 ================================================================================

SignX/{detailed_prediction_20251225_192957 → detailed_prediction_20251226_161814}/sample_000/attention_weights.npy

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:25434051e14c2b1741bf1376aaae36ca9a9fc276b01859a40b74bab3b603bcf8
-size 22400
+oid sha256:7414e5ab870540255a4bc963aa612d837eca27b95da7b4603c4c8e39f82b8c01
+size 25216

SignX/{detailed_prediction_20251225_192957 → detailed_prediction_20251226_161814}/sample_000/debug_video_path.txt

@@ -1,4 +1,4 @@
-video_path = '/common/users/sf895/output/huggingface_asllrp_repo/SignX/eval/tiny_test_data/videos/666.mp4'
+video_path = '/common/users/sf895/output/huggingface_asllrp_repo/SignX/eval/tiny_test_data/videos/632051.mp4'
 video_path type = <class 'str'>
 video_path is None: False
 bool(video_path): True

SignX/detailed_prediction_20251226_161814/sample_000/feature_frame_mapping.json

@@ -0,0 +1,176 @@
+{
+  "original_frame_count": 106,
+  "feature_count": 28,
+  "downsampling_ratio": 3.7857142857142856,
+  "fps": 24.0,
+  "mapping": [
+    {
+      "feature_index": 0,
+      "frame_start": 0,
+      "frame_end": 3,
+      "frame_count": 3
+    },
+    {
+      "feature_index": 1,
+      "frame_start": 3,
+      "frame_end": 7,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 2,
+      "frame_start": 7,
+      "frame_end": 11,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 3,
+      "frame_start": 11,
+      "frame_end": 15,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 4,
+      "frame_start": 15,
+      "frame_end": 18,
+      "frame_count": 3
+    },
+    {
+      "feature_index": 5,
+      "frame_start": 18,
+      "frame_end": 22,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 6,
+      "frame_start": 22,
+      "frame_end": 26,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 7,
+      "frame_start": 26,
+      "frame_end": 30,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 8,
+      "frame_start": 30,
+      "frame_end": 34,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 9,
+      "frame_start": 34,
+      "frame_end": 37,
+      "frame_count": 3
+    },
+    {
+      "feature_index": 10,
+      "frame_start": 37,
+      "frame_end": 41,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 11,
+      "frame_start": 41,
+      "frame_end": 45,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 12,
+      "frame_start": 45,
+      "frame_end": 49,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 13,
+      "frame_start": 49,
+      "frame_end": 53,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 14,
+      "frame_start": 53,
+      "frame_end": 56,
+      "frame_count": 3
+    },
+    {
+      "feature_index": 15,
+      "frame_start": 56,
+      "frame_end": 60,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 16,
+      "frame_start": 60,
+      "frame_end": 64,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 17,
+      "frame_start": 64,
+      "frame_end": 68,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 18,
+      "frame_start": 68,
+      "frame_end": 71,
+      "frame_count": 3
+    },
+    {
+      "feature_index": 19,
+      "frame_start": 71,
+      "frame_end": 75,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 20,
+      "frame_start": 75,
+      "frame_end": 79,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 21,
+      "frame_start": 79,
+      "frame_end": 83,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 22,
+      "frame_start": 83,
+      "frame_end": 87,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 23,
+      "frame_start": 87,
+      "frame_end": 90,
+      "frame_count": 3
+    },
+    {
+      "feature_index": 24,
+      "frame_start": 90,
+      "frame_end": 94,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 25,
+      "frame_start": 94,
+      "frame_end": 98,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 26,
+      "frame_start": 98,
+      "frame_end": 102,
+      "frame_count": 4
+    },
+    {
+      "feature_index": 27,
+      "frame_start": 102,
+      "frame_end": 106,
+      "frame_count": 4
+    }
+  ]
+}

SignX/detailed_prediction_20251226_161814/sample_000/frame_alignment.json

@@ -0,0 +1,104 @@
+{
+  "translation": "<unk> #IF FRIEND GROUP/TOGETHER DEPART PARTY IX-1p FINISH JOIN IX-1p",
+  "words": [
+    "<unk>",
+    "#IF",
+    "FRIEND",
+    "GROUP/TOGETHER",
+    "DEPART",
+    "PARTY",
+    "IX-1p",
+    "FINISH",
+    "JOIN",
+    "IX-1p"
+  ],
+  "total_video_frames": 28,
+  "frame_ranges": [
+    {
+      "word": "<unk>",
+      "start_frame": 0,
+      "end_frame": 0,
+      "peak_frame": 0,
+      "avg_attention": 0.13272422552108765,
+      "confidence": "low"
+    },
+    {
+      "word": "#IF",
+      "start_frame": 2,
+      "end_frame": 3,
+      "peak_frame": 2,
+      "avg_attention": 0.35901427268981934,
+      "confidence": "medium"
+    },
+    {
+      "word": "FRIEND",
+      "start_frame": 5,
+      "end_frame": 5,
+      "peak_frame": 5,
+      "avg_attention": 0.4494199752807617,
+      "confidence": "medium"
+    },
+    {
+      "word": "GROUP/TOGETHER",
+      "start_frame": 8,
+      "end_frame": 8,
+      "peak_frame": 8,
+      "avg_attention": 0.3710141181945801,
+      "confidence": "medium"
+    },
+    {
+      "word": "DEPART",
+      "start_frame": 27,
+      "end_frame": 27,
+      "peak_frame": 27,
+      "avg_attention": 0.30533191561698914,
+      "confidence": "medium"
+    },
+    {
+      "word": "PARTY",
+      "start_frame": 27,
+      "end_frame": 27,
+      "peak_frame": 27,
+      "avg_attention": 0.2963099479675293,
+      "confidence": "medium"
+    },
+    {
+      "word": "IX-1p",
+      "start_frame": 27,
+      "end_frame": 27,
+      "peak_frame": 27,
+      "avg_attention": 0.3264133930206299,
+      "confidence": "medium"
+    },
+    {
+      "word": "FINISH",
+      "start_frame": 11,
+      "end_frame": 12,
+      "peak_frame": 12,
+      "avg_attention": 0.46679070591926575,
+      "confidence": "medium"
+    },
+    {
+      "word": "JOIN",
+      "start_frame": 13,
+      "end_frame": 14,
+      "peak_frame": 14,
+      "avg_attention": 0.3172740340232849,
+      "confidence": "medium"
+    },
+    {
+      "word": "IX-1p",
+      "start_frame": 17,
+      "end_frame": 17,
+      "peak_frame": 17,
+      "avg_attention": 0.3579559326171875,
+      "confidence": "medium"
+    }
+  ],
+  "statistics": {
+    "avg_confidence": 0.33822485208511355,
+    "high_confidence_words": 0,
+    "medium_confidence_words": 9,
+    "low_confidence_words": 1
+  }
+}

SignX/detailed_prediction_20251226_161814/sample_000/interactive_alignment.html

@@ -0,0 +1,579 @@
+<!DOCTYPE html>
+<html lang="zh-CN">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Interactive Word-Frame Alignment</title>
+    <style>
+        body {
+            font-family: 'Arial', sans-serif;
+            margin: 20px;
+            background-color: #f5f5f5;
+        }
+        .container {
+            max-width: 1800px;
+            margin: 0 auto;
+            background-color: white;
+            padding: 30px;
+            border-radius: 8px;
+            box-shadow: 0 2px 10px rgba(0,0,0,0.1);
+        }
+        h1 {
+            color: #333;
+            border-bottom: 3px solid #4CAF50;
+            padding-bottom: 10px;
+            margin-bottom: 20px;
+        }
+        .stats {
+            background-color: #E3F2FD;
+            padding: 15px;
+            border-radius: 5px;
+            margin-bottom: 20px;
+            border-left: 4px solid #2196F3;
+            font-size: 14px;
+        }
+        .controls {
+            background-color: #f9f9f9;
+            padding: 20px;
+            border-radius: 5px;
+            margin-bottom: 30px;
+            border: 1px solid #ddd;
+        }
+        .control-group {
+            margin-bottom: 15px;
+        }
+        label {
+            font-weight: bold;
+            display: inline-block;
+            width: 250px;
+            color: #555;
+        }
+        input[type="range"] {
+            width: 400px;
+            vertical-align: middle;
+        }
+        .value-display {
+            display: inline-block;
+            width: 80px;
+            font-family: monospace;
+            font-size: 14px;
+            color: #2196F3;
+            font-weight: bold;
+        }
+        .reset-btn {
+            margin-top: 15px;
+            padding: 10px 25px;
+            background-color: #2196F3;
+            color: white;
+            border: none;
+            border-radius: 5px;
+            cursor: pointer;
+            font-size: 14px;
+            font-weight: bold;
+        }
+        .reset-btn:hover {
+            background-color: #1976D2;
+        }
+        canvas {
+            border: 1px solid #999;
+            display: block;
+            margin: 20px auto;
+            background: white;
+        }
+        .legend {
+            margin-top: 20px;
+            padding: 15px;
+            background-color: #fff;
+            border: 1px solid #ddd;
+            border-radius: 5px;
+        }
+        .legend-item {
+            display: inline-block;
+            margin-right: 25px;
+            font-size: 13px;
+            margin-bottom: 10px;
+        }
+        .color-box {
+            display: inline-block;
+            width: 30px;
+            height: 15px;
+            margin-right: 8px;
+            vertical-align: middle;
+            border: 1px solid #666;
+        }
+        .info-panel {
+            margin-top: 20px;
+            padding: 15px;
+            background-color: #f9f9f9;
+            border-radius: 5px;
+            border: 1px solid #ddd;
+        }
+        .confidence {
+            display: inline-block;
+            padding: 3px 10px;
+            border-radius: 10px;
+            font-weight: bold;
+            font-size: 11px;
+            text-transform: uppercase;
+        }
+        .confidence.high {
+            background-color: #4CAF50;
+            color: white;
+        }
+        .confidence.medium {
+            background-color: #FF9800;
+            color: white;
+        }
+        .confidence.low {
+            background-color: #f44336;
+            color: white;
+        }
+    </style>
+</head>
+<body>
+    <div class="container">
+        <h1>🎯 Interactive Word-to-Frame Alignment Visualizer</h1>
+
+        <div class="stats">
+            <strong>Translation:</strong> <unk> #IF FRIEND GROUP/TOGETHER DEPART PARTY IX-1p FINISH JOIN IX-1p<br>
+            <strong>Total Words:</strong> 10 |
+            <strong>Total Features:</strong> 8
+        </div>
+
+        <div class="controls">
+            <h3>⚙️ Threshold Controls</h3>
+
+            <div class="control-group">
+                <label for="peak-threshold">Peak Threshold (% of max):</label>
+                <input type="range" id="peak-threshold" min="1" max="100" value="90" step="1">
+                <span class="value-display" id="peak-threshold-value">90%</span>
+                <br>
+                <small style="margin-left: 255px; color: #666;">
+                    帧的注意力权重 ≥ (峰值权重 × 阈值%) 时被认为是"显著帧"
+                </small>
+            </div>
+
+            <div class="control-group">
+                <label for="confidence-high">High Confidence (avg attn >):</label>
+                <input type="range" id="confidence-high" min="0" max="100" value="50" step="1">
+                <span class="value-display" id="confidence-high-value">0.50</span>
+            </div>
+
+            <div class="control-group">
+                <label for="confidence-medium">Medium Confidence (avg attn >):</label>
+                <input type="range" id="confidence-medium" min="0" max="100" value="20" step="1">
+                <span class="value-display" id="confidence-medium-value">0.20</span>
+            </div>
+
+            <button class="reset-btn" onclick="resetDefaults()">
+                Reset to Defaults
+            </button>
+        </div>
+
+        <div>
+            <h3>Word-to-Frame Alignment</h3>
+            <p style="color: #666; font-size: 13px;">
+                每个词显示为彩色矩形，宽度表示该词对应的特征帧范围。★ = 峰值帧。矩形内部显示注意力权重波形。
+            </p>
+            <canvas id="alignment-canvas" width="1600" height="600"></canvas>
+
+            <h3 style="margin-top: 30px;">Timeline Progress Bar</h3>
+            <canvas id="timeline-canvas" width="1600" height="100"></canvas>
+
+            <div class="legend">
+                <strong>Legend:</strong><br><br>
+                <div class="legend-item">
+                    <span class="confidence high">High</span>
+                    <span class="confidence medium">Medium</span>
+                    <span class="confidence low">Low</span>
+                    Confidence Levels (opacity reflects confidence)
+                </div>
+                <div class="legend-item">
+                    <span style="color: red; font-size: 20px;">★</span>
+                    Peak Frame (highest attention)
+                </div>
+                <div class="legend-item">
+                    <span style="color: blue;">━</span>
+                    Attention Waveform (within word region)
+                </div>
+            </div>
+        </div>
+
+        <div class="info-panel">
+            <h3>Alignment Details</h3>
+            <div id="alignment-details"></div>
+        </div>
+    </div>
+
+    <script>
+        // Attention data from Python
+        const attentionData = [{"word": "<unk>", "word_idx": 0, "weights": [0.13272422552108765, 0.13272422552108765, 0.13272422552108765, 0.13272422552108765, 0.13272422552108765, 0.13272422552108765, 0.13272422552108765, 0.13272422552108765]}, {"word": "#IF", "word_idx": 1, "weights": [0.035573869943618774, 0.03952416777610779, 0.03952416777610779, 0.03952416777610779, 0.03952416777610779, 0.03952416777610779, 0.03952416777610779, 0.04215671867132187]}, {"word": "FRIEND", "word_idx": 2, "weights": [0.0035305300261825323, 0.06121520698070526, 0.038338951766490936, 0.03721731901168823, 0.0396437793970108, 0.06121520698070526, 0.03798501193523407, 0.06121520698070526]}, {"word": "GROUP/TOGETHER", "word_idx": 3, "weights": [0.0032519344240427017, 0.06749927252531052, 0.06769093871116638, 0.06797394156455994, 0.05660902336239815, 0.0032519344240427017, 0.06791087239980698, 0.06749927252531052]}, {"word": "DEPART", "word_idx": 4, "weights": [0.1269645392894745, 0.011337662115693092, 0.011169591918587685, 0.011129447259008884, 0.011337662115693092, 0.14123021066188812, 0.007393963634967804, 0.011116056703031063]}, {"word": "PARTY", "word_idx": 5, "weights": [0.08145920932292938, 0.003315121866762638, 0.24030542373657227, 0.0034980459604412317, 0.0034834302496165037, 0.16299134492874146, 0.003404060145840049, 0.0034867869690060616]}, {"word": "IX-1p", "word_idx": 6, "weights": [0.08820953965187073, 0.14399001002311707, 0.00422912510111928, 0.09592650085687637, 0.004478602670133114, 0.004475735127925873, 0.00422912510111928, 0.00439990172162652]}, {"word": "FINISH", "word_idx": 7, "weights": [0.0013184626586735249, 0.0013184626586735249, 0.0013184626586735249, 0.1313352882862091, 0.0013184626586735249, 0.14886727929115295, 0.14913317561149597, 0.004780622664839029]}, {"word": "JOIN", "word_idx": 8, "weights": [0.00555413169786334, 0.006608393043279648, 0.00555413169786334, 0.1470479518175125, 0.13233929872512817, 0.09560006111860275, 0.00555413169786334, 0.08190114796161652]}, {"word": "IX-1p", "word_idx": 9, "weights": [0.03655996546149254, 0.03403225541114807, 0.27225977182388306, 0.35054582357406616, 0.03655996546149254, 0.27422454953193665, 0.03655996546149254, 0.09239426255226135]}];
+        const numGlosses = 10;
+        const numFeatures = 8;
+
+        // Colors for different words (matching matplotlib tab20)
+        const colors = [
+            '#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd',
+            '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf',
+            '#aec7e8', '#ffbb78', '#98df8a', '#ff9896', '#c5b0d5',
+            '#c49c94', '#f7b6d2', '#c7c7c7', '#dbdb8d', '#9edae5'
+        ];
+
+        // Get controls
+        const peakThresholdSlider = document.getElementById('peak-threshold');
+        const peakThresholdValue = document.getElementById('peak-threshold-value');
+        const confidenceHighSlider = document.getElementById('confidence-high');
+        const confidenceHighValue = document.getElementById('confidence-high-value');
+        const confidenceMediumSlider = document.getElementById('confidence-medium');
+        const confidenceMediumValue = document.getElementById('confidence-medium-value');
+        const alignmentCanvas = document.getElementById('alignment-canvas');
+        const timelineCanvas = document.getElementById('timeline-canvas');
+        const alignmentCtx = alignmentCanvas.getContext('2d');
+        const timelineCtx = timelineCanvas.getContext('2d');
+
+        // Update displays when sliders change
+        peakThresholdSlider.oninput = function() {
+            peakThresholdValue.textContent = this.value + '%';
+            updateVisualization();
+        };
+
+        confidenceHighSlider.oninput = function() {
+            confidenceHighValue.textContent = (this.value / 100).toFixed(2);
+            updateVisualization();
+        };
+
+        confidenceMediumSlider.oninput = function() {
+            confidenceMediumValue.textContent = (this.value / 100).toFixed(2);
+            updateVisualization();
+        };
+
+        function resetDefaults() {
+            peakThresholdSlider.value = 90;
+            confidenceHighSlider.value = 50;
+            confidenceMediumSlider.value = 20;
+            peakThresholdValue.textContent = '90%';
+            confidenceHighValue.textContent = '0.50';
+            confidenceMediumValue.textContent = '0.20';
+            updateVisualization();
+        }
+
+        function calculateAlignment(weights, peakThreshold) {
+            // Find peak
+            let peakIdx = 0;
+            let peakWeight = weights[0];
+            for (let i = 1; i < weights.length; i++) {
+                if (weights[i] > peakWeight) {
+                    peakWeight = weights[i];
+                    peakIdx = i;
+                }
+            }
+
+            // Find significant frames
+            const threshold = peakWeight * (peakThreshold / 100);
+            let startIdx = peakIdx;
+            let endIdx = peakIdx;
+            let sumWeight = 0;
+            let count = 0;
+
+            for (let i = 0; i < weights.length; i++) {
+                if (weights[i] >= threshold) {
+                    if (i < startIdx) startIdx = i;
+                    if (i > endIdx) endIdx = i;
+                    sumWeight += weights[i];
+                    count++;
+                }
+            }
+
+            const avgWeight = count > 0 ? sumWeight / count : peakWeight;
+
+            return {
+                startIdx: startIdx,
+                endIdx: endIdx,
+                peakIdx: peakIdx,
+                peakWeight: peakWeight,
+                avgWeight: avgWeight,
+                threshold: threshold
+            };
+        }
+
+        function getConfidenceLevel(avgWeight, highThreshold, mediumThreshold) {
+            if (avgWeight > highThreshold) return 'high';
+            if (avgWeight > mediumThreshold) return 'medium';
+            return 'low';
+        }
+
+        function drawAlignmentChart() {
+            const peakThreshold = parseInt(peakThresholdSlider.value);
+            const highThreshold = parseInt(confidenceHighSlider.value) / 100;
+            const mediumThreshold = parseInt(confidenceMediumSlider.value) / 100;
+
+            // Canvas dimensions
+            const width = alignmentCanvas.width;
+            const height = alignmentCanvas.height;
+            const leftMargin = 180;
+            const rightMargin = 50;
+            const topMargin = 60;
+            const bottomMargin = 80;
+
+            const plotWidth = width - leftMargin - rightMargin;
+            const plotHeight = height - topMargin - bottomMargin;
+
+            const rowHeight = plotHeight / numGlosses;
+            const featureWidth = plotWidth / numFeatures;
+
+            // Clear canvas
+            alignmentCtx.clearRect(0, 0, width, height);
+
+            // Draw title
+            alignmentCtx.fillStyle = '#333';
+            alignmentCtx.font = 'bold 18px Arial';
+            alignmentCtx.textAlign = 'center';
+            alignmentCtx.fillText('Word-to-Frame Alignment', width / 2, 30);
+            alignmentCtx.font = '13px Arial';
+            alignmentCtx.fillText('(based on attention peaks, ★ = peak frame)', width / 2, 48);
+
+            // Calculate alignments
+            const alignments = [];
+            for (let wordIdx = 0; wordIdx < numGlosses; wordIdx++) {
+                const data = attentionData[wordIdx];
+                const alignment = calculateAlignment(data.weights, peakThreshold);
+                alignment.word = data.word;
+                alignment.wordIdx = wordIdx;
+                alignment.weights = data.weights;
+                alignments.push(alignment);
+            }
+
+            // Draw grid
+            alignmentCtx.strokeStyle = '#e0e0e0';
+            alignmentCtx.lineWidth = 0.5;
+            for (let i = 0; i <= numFeatures; i++) {
+                const x = leftMargin + i * featureWidth;
+                alignmentCtx.beginPath();
+                alignmentCtx.moveTo(x, topMargin);
+                alignmentCtx.lineTo(x, topMargin + plotHeight);
+                alignmentCtx.stroke();
+            }
+
+            // Draw word regions
+            for (let wordIdx = 0; wordIdx < numGlosses; wordIdx++) {
+                const alignment = alignments[wordIdx];
+                const confidence = getConfidenceLevel(alignment.avgWeight, highThreshold, mediumThreshold);
+                const y = topMargin + wordIdx * rowHeight;
+
+                // Alpha based on confidence
+                const alpha = confidence === 'high' ? 0.9 : confidence === 'medium' ? 0.7 : 0.5;
+
+                // Draw rectangle for word region
+                const startX = leftMargin + alignment.startIdx * featureWidth;
+                const rectWidth = (alignment.endIdx - alignment.startIdx + 1) * featureWidth;
+
+                alignmentCtx.fillStyle = colors[wordIdx % 20];
+                alignmentCtx.globalAlpha = alpha;
+                alignmentCtx.fillRect(startX, y, rectWidth, rowHeight * 0.8);
+                alignmentCtx.globalAlpha = 1.0;
+
+                // Draw border
+                alignmentCtx.strokeStyle = '#000';
+                alignmentCtx.lineWidth = 2;
+                alignmentCtx.strokeRect(startX, y, rectWidth, rowHeight * 0.8);
+
+                // Draw attention waveform inside rectangle
+                alignmentCtx.strokeStyle = 'rgba(0, 0, 255, 0.8)';
+                alignmentCtx.lineWidth = 1.5;
+                alignmentCtx.beginPath();
+                for (let i = alignment.startIdx; i <= alignment.endIdx; i++) {
+                    const x = leftMargin + i * featureWidth + featureWidth / 2;
+                    const weight = alignment.weights[i];
+                    const maxWeight = alignment.peakWeight;
+                    const normalizedWeight = weight / (maxWeight * 1.2); // Scale for visibility
+                    const waveY = y + rowHeight * 0.8 - (normalizedWeight * rowHeight * 0.6);
+
+                    if (i === alignment.startIdx) {
+                        alignmentCtx.moveTo(x, waveY);
+                    } else {
+                        alignmentCtx.lineTo(x, waveY);
+                    }
+                }
+                alignmentCtx.stroke();
+
+                // Draw word label
+                const labelX = startX + rectWidth / 2;
+                const labelY = y + rowHeight * 0.4;
+
+                alignmentCtx.fillStyle = 'rgba(0, 0, 0, 0.7)';
+                alignmentCtx.fillRect(labelX - 60, labelY - 12, 120, 24);
+                alignmentCtx.fillStyle = '#fff';
+                alignmentCtx.font = 'bold 13px Arial';
+                alignmentCtx.textAlign = 'center';
+                alignmentCtx.textBaseline = 'middle';
+                alignmentCtx.fillText(alignment.word, labelX, labelY);
+
+                // Mark peak frame with star
+                const peakX = leftMargin + alignment.peakIdx * featureWidth + featureWidth / 2;
+                const peakY = y + rowHeight * 0.4;
+
+                // Draw star
+                alignmentCtx.fillStyle = '#ff0000';
+                alignmentCtx.strokeStyle = '#ffff00';
+                alignmentCtx.lineWidth = 1.5;
+                alignmentCtx.font = '20px Arial';
+                alignmentCtx.textAlign = 'center';
+                alignmentCtx.strokeText('★', peakX, peakY);
+                alignmentCtx.fillText('★', peakX, peakY);
+
+                // Y-axis label (word names)
+                alignmentCtx.fillStyle = '#333';
+                alignmentCtx.font = '12px Arial';
+                alignmentCtx.textAlign = 'right';
+                alignmentCtx.textBaseline = 'middle';
+                alignmentCtx.fillText(alignment.word, leftMargin - 10, y + rowHeight * 0.4);
+            }
+
+            // Draw horizontal grid lines
+            alignmentCtx.strokeStyle = '#ccc';
+            alignmentCtx.lineWidth = 0.5;
+            for (let i = 0; i <= numGlosses; i++) {
+                const y = topMargin + i * rowHeight;
+                alignmentCtx.beginPath();
+                alignmentCtx.moveTo(leftMargin, y);
+                alignmentCtx.lineTo(leftMargin + plotWidth, y);
+                alignmentCtx.stroke();
+            }
+
+            // Draw axes
+            alignmentCtx.strokeStyle = '#000';
+            alignmentCtx.lineWidth = 2;
+            alignmentCtx.strokeRect(leftMargin, topMargin, plotWidth, plotHeight);
+
+            // X-axis labels (frame indices)
+            alignmentCtx.fillStyle = '#000';
+            alignmentCtx.font = '11px Arial';
+            alignmentCtx.textAlign = 'center';
+            alignmentCtx.textBaseline = 'top';
+            for (let i = 0; i < numFeatures; i++) {
+                const x = leftMargin + i * featureWidth + featureWidth / 2;
+                alignmentCtx.fillText(i.toString(), x, topMargin + plotHeight + 10);
+            }
+
+            // Axis titles
+            alignmentCtx.fillStyle = '#333';
+            alignmentCtx.font = 'bold 14px Arial';
+            alignmentCtx.textAlign = 'center';
+            alignmentCtx.fillText('Feature Frame Index', leftMargin + plotWidth / 2, height - 20);
+
+            alignmentCtx.save();
+            alignmentCtx.translate(30, topMargin + plotHeight / 2);
+            alignmentCtx.rotate(-Math.PI / 2);
+            alignmentCtx.fillText('Generated Word', 0, 0);
+            alignmentCtx.restore();
+
+            return alignments;
+        }
+
+        function drawTimeline(alignments) {
+            const highThreshold = parseInt(confidenceHighSlider.value) / 100;
+            const mediumThreshold = parseInt(confidenceMediumSlider.value) / 100;
+
+            const width = timelineCanvas.width;
+            const height = timelineCanvas.height;
+            const leftMargin = 180;
+            const rightMargin = 50;
+            const plotWidth = width - leftMargin - rightMargin;
+            const featureWidth = plotWidth / numFeatures;
+
+            // Clear canvas
+            timelineCtx.clearRect(0, 0, width, height);
+
+            // Background bar
+            timelineCtx.fillStyle = '#ddd';
+            timelineCtx.fillRect(leftMargin, 30, plotWidth, 40);
+            timelineCtx.strokeStyle = '#000';
+            timelineCtx.lineWidth = 2;
+            timelineCtx.strokeRect(leftMargin, 30, plotWidth, 40);
+
+            // Draw word regions on timeline
+            for (let wordIdx = 0; wordIdx < alignments.length; wordIdx++) {
+                const alignment = alignments[wordIdx];
+                const confidence = getConfidenceLevel(alignment.avgWeight, highThreshold, mediumThreshold);
+                const alpha = confidence === 'high' ? 0.9 : confidence === 'medium' ? 0.7 : 0.5;
+
+                const startX = leftMargin + alignment.startIdx * featureWidth;
+                const rectWidth = (alignment.endIdx - alignment.startIdx + 1) * featureWidth;
+
+                timelineCtx.fillStyle = colors[wordIdx % 20];
+                timelineCtx.globalAlpha = alpha;
+                timelineCtx.fillRect(startX, 30, rectWidth, 40);
+                timelineCtx.globalAlpha = 1.0;
+                timelineCtx.strokeStyle = '#000';
+                timelineCtx.lineWidth = 0.5;
+                timelineCtx.strokeRect(startX, 30, rectWidth, 40);
+            }
+
+            // Title
+            timelineCtx.fillStyle = '#333';
+            timelineCtx.font = 'bold 13px Arial';
+            timelineCtx.textAlign = 'left';
+            timelineCtx.fillText('Timeline Progress Bar', leftMargin, 20);
+        }
+
+        function updateDetailsPanel(alignments, highThreshold, mediumThreshold) {
+            const panel = document.getElementById('alignment-details');
+            let html = '<table style="width: 100%; border-collapse: collapse;">';
+            html += '<tr style="background: #f0f0f0; font-weight: bold;">';
+            html += '<th style="padding: 8px; border: 1px solid #ddd;">Word</th>';
+            html += '<th style="padding: 8px; border: 1px solid #ddd;">Feature Range</th>';
+            html += '<th style="padding: 8px; border: 1px solid #ddd;">Peak</th>';
+            html += '<th style="padding: 8px; border: 1px solid #ddd;">Span</th>';
+            html += '<th style="padding: 8px; border: 1px solid #ddd;">Avg Attention</th>';
+            html += '<th style="padding: 8px; border: 1px solid #ddd;">Confidence</th>';
+            html += '</tr>';
+
+            for (const align of alignments) {
+                const confidence = getConfidenceLevel(align.avgWeight, highThreshold, mediumThreshold);
+                const span = align.endIdx - align.startIdx + 1;
+
+                html += '<tr>';
+                html += `<td style="padding: 8px; border: 1px solid #ddd;"><strong>${align.word}</strong></td>`;
+                html += `<td style="padding: 8px; border: 1px solid #ddd;">${align.startIdx} → ${align.endIdx}</td>`;
+                html += `<td style="padding: 8px; border: 1px solid #ddd;">${align.peakIdx}</td>`;
+                html += `<td style="padding: 8px; border: 1px solid #ddd;">${span}</td>`;
+                html += `<td style="padding: 8px; border: 1px solid #ddd;">${align.avgWeight.toFixed(4)}</td>`;
+                html += `<td style="padding: 8px; border: 1px solid #ddd;"><span class="confidence ${confidence}">${confidence}</span></td>`;
+                html += '</tr>';
+            }
+
+            html += '</table>';
+            panel.innerHTML = html;
+        }
+
+        function updateVisualization() {
+            const alignments = drawAlignmentChart();
+            drawTimeline(alignments);
+            const highThreshold = parseInt(confidenceHighSlider.value) / 100;
+            const mediumThreshold = parseInt(confidenceMediumSlider.value) / 100;
+            updateDetailsPanel(alignments, highThreshold, mediumThreshold);
+        }
+
+        // Event listeners for sliders
+        peakSlider.addEventListener('input', function() {
+            peakValue.textContent = peakSlider.value + '%';
+            updateVisualization();
+        });
+
+        confidenceHighSlider.addEventListener('input', function() {
+            const val = parseInt(confidenceHighSlider.value) / 100;
+            confidenceHighValue.textContent = val.toFixed(2);
+            updateVisualization();
+        });
+
+        confidenceMediumSlider.addEventListener('input', function() {
+            const val = parseInt(confidenceMediumSlider.value) / 100;
+            confidenceMediumValue.textContent = val.toFixed(2);
+            updateVisualization();
+        });
+
+        // Initial visualization
+        updateVisualization();
+    </script>
+</body>
+</html>

SignX/detailed_prediction_20251226_161814/sample_000/translation.txt

@@ -0,0 +1,2 @@
+With BPE: <unk> #IF FRIEND GROUP/TOGE@@ TH@@ E@@ R DEPART PARTY IX-1p FINISH JO@@ I@@ N IX-1p
+Clean: <unk> #IF FRIEND GROUP/TOGETHER DEPART PARTY IX-1p FINISH JOIN IX-1p

SignX/eval/attention_analysis.py

@@ -121,8 +121,8 @@ class AttentionAnalyzer:
             peak_frame = int(np.argmax(attn_weights))
             peak_weight = attn_weights[peak_frame]
 
-            # 计算显著帧范围（权重 >= 最大值的30%）
-            threshold = peak_weight * 0.3
+            # 计算显著帧范围（权重 >= 最大值的90%）
+            threshold = peak_weight * 0.9
             significant_frames = np.where(attn_weights >= threshold)[0]
 
             if len(significant_frames) > 0:
@@ -253,8 +253,24 @@ class AttentionAnalyzer:
             print("  跳过对齐图: matplotlib未安装")
             return
 
-        fig = plt.figure(figsize=(18, 8))
-        gs = GridSpec(3, 1, height_ratios=[4, 1, 0.5], hspace=0.4)
+        # Try to load feature-to-frame mapping
+        feature_mapping = None
+        output_dir = Path(output_path).parent
+        mapping_file = output_dir / "feature_frame_mapping.json"
+        if mapping_file.exists():
+            try:
+                with open(mapping_file, 'r') as f:
+                    feature_mapping = json.load(f)
+            except Exception as e:
+                print(f"  Warning: Failed to load feature mapping: {e}")
+
+        # Adjust layout based on whether we have feature mapping
+        if feature_mapping:
+            fig = plt.figure(figsize=(18, 10))
+            gs = GridSpec(4, 1, height_ratios=[4, 1, 1, 0.5], hspace=0.4)
+        else:
+            fig = plt.figure(figsize=(18, 8))
+            gs = GridSpec(3, 1, height_ratios=[4, 1, 0.5], hspace=0.4)
 
         # === 上图: 词-帧对齐 ===
         ax1 = fig.add_subplot(gs[0])
@@ -299,7 +315,7 @@ class AttentionAnalyzer:
         ax1.set_yticks(range(len(self.words)))
         ax1.set_yticklabels([w['word'] for w in self.word_frame_ranges], fontsize=10)
 
-        # === 中图: 时间线进度条 ===
+        # === 中图1: SMKD特征帧时间线进度条 ===
         ax2 = fig.add_subplot(gs[1])
 
         # 背景
@@ -318,18 +334,58 @@ class AttentionAnalyzer:
 
         ax2.set_xlim(-2, self.video_frames + 2)
         ax2.set_ylim(-0.4, 0.4)
-        ax2.set_xlabel('Frame Index', fontsize=12, fontweight='bold')
+        ax2.set_xlabel('SMKD Feature Frame Index', fontsize=12, fontweight='bold')
         ax2.set_yticks([])
-        ax2.set_title('Timeline Progress Bar', fontsize=13, fontweight='bold')
+        ax2.set_title('SMKD Feature Timeline', fontsize=13, fontweight='bold')
         ax2.grid(True, alpha=0.3, axis='x', linestyle='--')
 
+        # === 中图2: 原始视频帧时间线进度条 (如果有feature mapping) ===
+        if feature_mapping:
+            ax3 = fig.add_subplot(gs[2])
+
+            original_frame_count = feature_mapping['original_frame_count']
+
+            # 背景
+            ax3.barh(0, original_frame_count, height=0.6, color='lightgray',
+                    edgecolor='black', linewidth=2)
+
+            # 每个词对应的原始帧区域
+            for i, word_info in enumerate(self.word_frame_ranges):
+                feat_start = word_info['start_frame']
+                feat_end = word_info['end_frame']
+                confidence = word_info['confidence']
+                alpha = 0.9 if confidence == 'high' else 0.7 if confidence == 'medium' else 0.5
+
+                # 从feature mapping中找到对应的原始帧范围
+                # 使用特征帧的起始和结束索引来查找原始帧范围
+                mapping_list = feature_mapping['mapping']
+                if feat_start < len(mapping_list) and feat_end < len(mapping_list):
+                    orig_start = mapping_list[feat_start]['frame_start']
+                    orig_end = mapping_list[feat_end]['frame_end']
+
+                    ax3.barh(0, orig_end - orig_start, left=orig_start, height=0.6,
+                            color=colors[i % 20], alpha=alpha, edgecolor='black', linewidth=0.5)
+
+            ax3.set_xlim(-2, original_frame_count + 2)
+            ax3.set_ylim(-0.4, 0.4)
+            ax3.set_xlabel('Original Video Frame Index', fontsize=12, fontweight='bold')
+            ax3.set_yticks([])
+            ax3.set_title(f'Original Video Timeline ({original_frame_count} frames, '
+                         f'{feature_mapping["downsampling_ratio"]:.2f}x downsampling)',
+                         fontsize=13, fontweight='bold')
+            ax3.grid(True, alpha=0.3, axis='x', linestyle='--')
+
+            legend_row = 3
+        else:
+            legend_row = 2
+
         # === 下图: 置信度图例 ===
-        ax3 = fig.add_subplot(gs[2])
-        ax3.axis('off')
+        ax_legend = fig.add_subplot(gs[legend_row])
+        ax_legend.axis('off')
 
         legend_text = "Confidence:  ■ High (avg attn > 0.5)  ■ Medium (0.2-0.5)  ■ Low (< 0.2)"
-        ax3.text(0.5, 0.5, legend_text, ha='center', va='center',
-                fontsize=11, transform=ax3.transAxes)
+        ax_legend.text(0.5, 0.5, legend_text, ha='center', va='center',
+                fontsize=11, transform=ax_legend.transAxes)
 
         plt.tight_layout()
         plt.savefig(output_path, dpi=150, bbox_inches='tight')
@@ -580,10 +636,10 @@ class AttentionAnalyzer:
             print("  ⓘ No video frames extracted, skipping visualization")
             return
 
-        # 创建figure
+        # 创建figure (4列布局: Gloss | Feature Index | Frame Info | Video Frames)
         n_words = len(self.words)
-        fig = plt.figure(figsize=(20, 3 * n_words))
-        gs = gridspec.GridSpec(n_words, 3, width_ratios=[1.5, 2, 6], hspace=0.3, wspace=0.2)
+        fig = plt.figure(figsize=(24, 3 * n_words))
+        gs = gridspec.GridSpec(n_words, 4, width_ratios=[1.5, 1.5, 2, 6], hspace=0.3, wspace=0.2)
 
         for row_idx, (word, word_info) in enumerate(zip(self.words, self.word_frame_ranges)):
             # 列1: Gloss文本
@@ -592,23 +648,36 @@ class AttentionAnalyzer:
                          ha='center', va='center', wrap=True)
             ax_gloss.axis('off')
 
-            # 列2: 时间和帧信息
-            ax_info = fig.add_subplot(gs[row_idx, 1])
+            # 列2: 特征索引信息 (Feature Index Layer)
+            ax_feature = fig.add_subplot(gs[row_idx, 1])
 
             # 特征帧信息
             feat_start = word_info['start_frame']
             feat_end = word_info['end_frame']
             feat_peak = word_info['peak_frame']
 
+            feature_text = f"SMKD Feature Index\n"
+            feature_text += f"{'='*20}\n\n"
+            feature_text += f"Range:\n  {feat_start} → {feat_end}\n\n"
+            feature_text += f"Peak:\n  {feat_peak}\n\n"
+            feature_text += f"Count:\n  {feat_end - feat_start + 1} features\n\n"
+            feature_text += f"Position:\n  {(feat_start/self.video_frames)*100:.1f}% - {(feat_end/self.video_frames)*100:.1f}%"
+
+            ax_feature.text(0.5, 0.5, feature_text, fontsize=11, family='monospace',
+                           va='center', ha='center',
+                           bbox=dict(boxstyle='round,pad=0.8', facecolor='lightblue',
+                                   edgecolor='darkblue', linewidth=2, alpha=0.7))
+            ax_feature.axis('off')
+
+            # 列3: 原始视频帧信息
+            ax_info = fig.add_subplot(gs[row_idx, 2])
+
             # 相对时间 (0-100%)
             rel_start = (feat_start / self.video_frames) * 100
             rel_end = (feat_end / self.video_frames) * 100
             rel_peak = (feat_peak / self.video_frames) * 100
 
-            info_text = f"Feature Frames:\n"
-            info_text += f"  Range: {feat_start}-{feat_end}\n"
-            info_text += f"  Peak: {feat_peak}\n\n"
-            info_text += f"Relative Time:\n"
+            info_text = f"Relative Time:\n"
             info_text += f"  Range: {rel_start:.1f}%-{rel_end:.1f}%\n"
             info_text += f"  Peak: {rel_peak:.1f}%\n"
 
@@ -617,17 +686,18 @@ class AttentionAnalyzer:
                 orig_start = self._map_feature_frame_to_original(feat_start)
                 orig_end = self._map_feature_frame_to_original(feat_end)
                 orig_peak = self._map_feature_frame_to_original(feat_peak)
-                info_text += f"\nOriginal Video:\n"
+                info_text += f"\nOriginal Video Frames:\n"
                 info_text += f"  Total: {self.original_video_total_frames} frames\n"
                 info_text += f"  Range: {orig_start}-{orig_end}\n"
                 info_text += f"  Peak: {orig_peak}\n"
+                info_text += f"  Count: {orig_end - orig_start} frames\n"
 
             ax_info.text(0.05, 0.5, info_text, fontsize=10, family='monospace',
                         va='center', ha='left')
             ax_info.axis('off')
 
-            # 列3: 视频帧
-            ax_frames = fig.add_subplot(gs[row_idx, 2])
+            # 列4: 视频帧缩略图
+            ax_frames = fig.add_subplot(gs[row_idx, 3])
 
             # 选择要显示的帧: start, peak, end
             frames_to_show = []
@@ -656,7 +726,7 @@ class AttentionAnalyzer:
 
             ax_frames.axis('off')
 
-        plt.suptitle(f"Gloss-to-Frames Alignment\nTranslation: {self.translation}",
+        plt.suptitle(f"Three-Layer Alignment: Gloss ↔ Feature Index ↔ Original Frames\nTranslation: {self.translation}",
                      fontsize=16, weight='bold', y=0.995)
 
         plt.savefig(output_path, dpi=150, bbox_inches='tight', facecolor='white')

SignX/eval/generate_feature_mapping.py

@@ -0,0 +1,119 @@
+#!/usr/bin/env python
+"""
+生成特征-帧映射文件
+
+Usage:
+    python generate_feature_mapping.py <sample_dir> <video_path>
+
+Example:
+    python generate_feature_mapping.py detailed_prediction_20251226_155113/sample_000 \\
+        eval/tiny_test_data/videos/632051.mp4
+"""
+
+import sys
+import os
+import json
+import numpy as np
+from pathlib import Path
+
+def generate_feature_mapping(sample_dir, video_path):
+    """为指定样本生成特征-帧映射文件"""
+    sample_dir = Path(sample_dir)
+
+    # Check if attention_weights.npy exists
+    attn_file = sample_dir / "attention_weights.npy"
+    if not attn_file.exists():
+        print(f"错误: 找不到attention_weights.npy: {attn_file}")
+        return False
+
+    # Load attention weights to get feature count
+    attn_weights = np.load(attn_file)
+    feature_count = attn_weights.shape[2]  # Shape: (time, beam, features)
+
+    print(f"特征数量: {feature_count}")
+
+    # Get original frame count from video
+    try:
+        import cv2
+        cap = cv2.VideoCapture(str(video_path))
+        if not cap.isOpened():
+            print(f"错误: 无法打开视频文件: {video_path}")
+            return False
+
+        original_frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+        fps = cap.get(cv2.CAP_PROP_FPS)
+        cap.release()
+
+        print(f"原始帧数: {original_frame_count}, FPS: {fps}")
+
+    except ImportError:
+        print("警告: OpenCV不可用，使用估算值")
+        # 假设30fps，根据特征数估算
+        original_frame_count = feature_count * 3  # 默认3倍下采样
+        fps = 30.0
+
+    # Calculate uniform mapping: feature i -> frames [start, end]
+    frame_mapping = []
+    for feat_idx in range(feature_count):
+        start_frame = int(feat_idx * original_frame_count / feature_count)
+        end_frame = int((feat_idx + 1) * original_frame_count / feature_count)
+        frame_mapping.append({
+            "feature_index": feat_idx,
+            "frame_start": start_frame,
+            "frame_end": end_frame,
+            "frame_count": end_frame - start_frame
+        })
+
+    # Save mapping
+    mapping_data = {
+        "original_frame_count": original_frame_count,
+        "feature_count": feature_count,
+        "downsampling_ratio": original_frame_count / feature_count,
+        "fps": fps,
+        "mapping": frame_mapping
+    }
+
+    output_file = sample_dir / "feature_frame_mapping.json"
+    with open(output_file, 'w') as f:
+        json.dump(mapping_data, f, indent=2)
+
+    print(f"\n✓ 已生成映射文件: {output_file}")
+    print(f"  原始帧数: {original_frame_count}")
+    print(f"  特征数量: {feature_count}")
+    print(f"  下采样比例: {mapping_data['downsampling_ratio']:.2f}x")
+
+    # Print sample mappings
+    print("\n映射示例:")
+    for i in range(min(3, len(frame_mapping))):
+        mapping = frame_mapping[i]
+        print(f"  特征 {mapping['feature_index']}: 帧 {mapping['frame_start']}-{mapping['frame_end']} "
+              f"({mapping['frame_count']} 帧)")
+    if len(frame_mapping) > 3:
+        print("  ...")
+        mapping = frame_mapping[-1]
+        print(f"  特征 {mapping['feature_index']}: 帧 {mapping['frame_start']}-{mapping['frame_end']} "
+              f"({mapping['frame_count']} 帧)")
+
+    return True
+
+if __name__ == "__main__":
+    if len(sys.argv) != 3:
+        print("用法: python generate_feature_mapping.py <sample_dir> <video_path>")
+        print("\n示例:")
+        print("  python generate_feature_mapping.py detailed_prediction_20251226_155113/sample_000 \\")
+        print("      eval/tiny_test_data/videos/632051.mp4")
+        sys.exit(1)
+
+    sample_dir = sys.argv[1]
+    video_path = sys.argv[2]
+
+    if not os.path.exists(sample_dir):
+        print(f"错误: 样本目录不存在: {sample_dir}")
+        sys.exit(1)
+
+    if not os.path.exists(video_path):
+        print(f"错误: 视频文件不存在: {video_path}")
+        sys.exit(1)
+
+    success = generate_feature_mapping(sample_dir, video_path)
+    sys.exit(0 if success else 1)

SignX/eval/generate_interactive_alignment.py

@@ -205,8 +205,8 @@ def generate_interactive_html(sample_dir, output_path):
 
             <div class="control-group">
                 <label for="peak-threshold">Peak Threshold (% of max):</label>
-                <input type="range" id="peak-threshold" min="1" max="100" value="30" step="1">
-                <span class="value-display" id="peak-threshold-value">30%</span>
+                <input type="range" id="peak-threshold" min="1" max="100" value="90" step="1">
+                <span class="value-display" id="peak-threshold-value">90%</span>
                 <br>
                 <small style="margin-left: 255px; color: #666;">
                     帧的注意力权重 ≥ (峰值权重 × 阈值%) 时被认为是"显著帧"
@@ -308,10 +308,10 @@ def generate_interactive_html(sample_dir, output_path):
         }};
 
         function resetDefaults() {{
-            peakThresholdSlider.value = 30;
+            peakThresholdSlider.value = 90;
             confidenceHighSlider.value = 50;
             confidenceMediumSlider.value = 20;
-            peakThresholdValue.textContent = '30%';
+            peakThresholdValue.textContent = '90%';
             confidenceHighValue.textContent = '0.50';
             confidenceMediumValue.textContent = '0.20';
             updateVisualization();

SignX/eval/regenerate_visualizations.py

@@ -0,0 +1,123 @@
+#!/usr/bin/env python3
+"""
+重新生成所有可视化（使用最新的attention_analysis.py代码）
+
+使用方法:
+    python regenerate_visualizations.py <detailed_prediction_dir> <video_path>
+
+例如:
+    python regenerate_visualizations.py detailed_prediction_20251226_161117 ./eval/tiny_test_data/videos/632051.mp4
+"""
+
+import sys
+import os
+from pathlib import Path
+
+# 添加项目根目录到path
+SCRIPT_DIR = Path(__file__).parent.parent
+sys.path.insert(0, str(SCRIPT_DIR))
+
+from eval.attention_analysis import AttentionAnalyzer
+import numpy as np
+
+
+def regenerate_sample_visualizations(sample_dir, video_path):
+    """为单个样本重新生成所有可视化"""
+    sample_dir = Path(sample_dir)
+
+    if not sample_dir.exists():
+        print(f"错误: 样本目录不存在: {sample_dir}")
+        return False
+
+    # 加载数据
+    attn_file = sample_dir / "attention_weights.npy"
+    trans_file = sample_dir / "translation.txt"
+
+    if not attn_file.exists() or not trans_file.exists():
+        print(f"  跳过 {sample_dir.name}: 缺少必要文件")
+        return False
+
+    # 读取数据
+    attention_weights = np.load(attn_file)
+    with open(trans_file, 'r') as f:
+        lines = f.readlines()
+        # 找到 "Clean:" 后的翻译
+        translation = None
+        for line in lines:
+            if line.startswith('Clean:'):
+                translation = line.replace('Clean:', '').strip()
+                break
+        if translation is None:
+            translation = lines[0].strip()  # 后备方案
+
+    # 获取特征数量（video_frames）
+    if len(attention_weights.shape) == 4:
+        video_frames = attention_weights.shape[3]
+    elif len(attention_weights.shape) == 3:
+        video_frames = attention_weights.shape[2]
+    else:
+        video_frames = attention_weights.shape[1]
+
+    print(f"  样本: {sample_dir.name}")
+    print(f"    Attention shape: {attention_weights.shape}")
+    print(f"    Translation: {translation}")
+    print(f"    Features: {video_frames}")
+
+    # 创建分析器
+    analyzer = AttentionAnalyzer(
+        attentions=attention_weights,
+        translation=translation,
+        video_frames=video_frames,
+        video_path=str(video_path) if video_path else None
+    )
+
+    # 重新生成frame_alignment.png (带原始帧层)
+    print(f"    重新生成 frame_alignment.png...")
+    analyzer.plot_frame_alignment(sample_dir / "frame_alignment.png")
+
+    # 重新生成gloss_to_frames.png (带特征索引层)
+    if video_path and Path(video_path).exists():
+        print(f"    重新生成 gloss_to_frames.png...")
+        try:
+            analyzer.generate_gloss_to_frames_visualization(sample_dir / "gloss_to_frames.png")
+        except Exception as e:
+            print(f"      警告: gloss_to_frames生成失败: {e}")
+
+    return True
+
+
+def main():
+    if len(sys.argv) < 2:
+        print("用法: python regenerate_visualizations.py <detailed_prediction_dir> [<video_path>]")
+        print("\n示例:")
+        print("  python regenerate_visualizations.py detailed_prediction_20251226_161117 ./eval/tiny_test_data/videos/632051.mp4")
+        sys.exit(1)
+
+    pred_dir = Path(sys.argv[1])
+    video_path = Path(sys.argv[2]) if len(sys.argv) > 2 else None
+
+    if not pred_dir.exists():
+        print(f"错误: 预测目录不存在: {pred_dir}")
+        sys.exit(1)
+
+    if video_path and not video_path.exists():
+        print(f"警告: 视频文件不存在: {video_path}")
+        video_path = None
+
+    print(f"重新生成可视化:")
+    print(f"  预测目录: {pred_dir}")
+    print(f"  视频路径: {video_path if video_path else 'N/A'}")
+    print()
+
+    # 处理所有样本
+    success_count = 0
+    for sample_dir in sorted(pred_dir.glob("sample_*")):
+        if sample_dir.is_dir():
+            if regenerate_sample_visualizations(sample_dir, video_path):
+                success_count += 1
+
+    print(f"\n✓ 完成！成功处理 {success_count} 个样本")
+
+
+if __name__ == "__main__":
+    main()

SignX/inference.sh

@@ -247,33 +247,50 @@ if [ -f "$TEMP_DIR/prediction.txt" ]; then
             sample_count=$(find "$dest_path" -maxdepth 1 -type d -name "sample_*" | wc -l)
             echo "  ✓ 已保存 $sample_count 个样本的详细分析到: $dest_path"
 
-            # 后处理：生成 gloss-to-frames 可视化
+            # 步骤1：生成特征-帧映射 (Feature-to-Frame Mapping)
             echo ""
-            echo -e "${BLUE}生成 Gloss-to-Frames 可视化...${NC}"
-            if [ -f "$SCRIPT_DIR/eval/generate_gloss_frames.py" ]; then
-                # 切换回 signx-slt 环境 (有 matplotlib 和 cv2)
+            echo -e "${BLUE}生成特征-帧映射...${NC}"
+            if [ -f "$SCRIPT_DIR/eval/generate_feature_mapping.py" ]; then
+                # 切换到 signx-slt 环境 (有 cv2)
                 conda activate signx-slt
-                python "$SCRIPT_DIR/eval/generate_gloss_frames.py" "$dest_path" "$VIDEO_PATH"
-
-                # 生成交互式HTML可视化
-                echo ""
-                echo -e "${BLUE}生成交互式HTML可视化...${NC}"
-                if [ -f "$SCRIPT_DIR/eval/generate_interactive_alignment.py" ]; then
-                    # 处理所有样本
-                    for sample_dir in "$dest_path"/sample_*; do
-                        if [ -d "$sample_dir" ]; then
-                            python "$SCRIPT_DIR/eval/generate_interactive_alignment.py" "$sample_dir"
-                        fi
-                    done
-                else
-                    echo "  ⓘ generate_interactive_alignment.py 未找到，跳过交互式HTML生成"
+                for sample_dir in "$dest_path"/sample_*; do
+                    if [ -d "$sample_dir" ]; then
+                        python "$SCRIPT_DIR/eval/generate_feature_mapping.py" "$sample_dir" "$VIDEO_PATH" 2>&1 | grep -E "(特征数量|原始帧数|已生成映射|错误)"
+                    fi
+                done
+            else
+                echo "  ⓘ generate_feature_mapping.py 未找到，跳过特征映射生成"
+            fi
+
+            # 步骤2：重新生成所有可视化（使用最新代码）
+            echo ""
+            echo -e "${BLUE}重新生成可视化（使用最新代码）...${NC}"
+            if [ -f "$SCRIPT_DIR/eval/regenerate_visualizations.py" ]; then
+                # 已在 signx-slt 环境
+                python "$SCRIPT_DIR/eval/regenerate_visualizations.py" "$dest_path" "$VIDEO_PATH"
+            else
+                echo "  ⓘ regenerate_visualizations.py 未找到，使用旧版本"
+                if [ -f "$SCRIPT_DIR/eval/generate_gloss_frames.py" ]; then
+                    python "$SCRIPT_DIR/eval/generate_gloss_frames.py" "$dest_path" "$VIDEO_PATH"
                 fi
+            fi
 
-                # 切换回 slt_tf1 环境
-                conda activate slt_tf1
+            # 步骤3：生成交互式HTML可视化
+            echo ""
+            echo -e "${BLUE}生成交互式HTML可视化...${NC}"
+            if [ -f "$SCRIPT_DIR/eval/generate_interactive_alignment.py" ]; then
+                # 处理所有样本
+                for sample_dir in "$dest_path"/sample_*; do
+                    if [ -d "$sample_dir" ]; then
+                        python "$SCRIPT_DIR/eval/generate_interactive_alignment.py" "$sample_dir"
+                    fi
+                done
             else
-                echo "  ⓘ generate_gloss_frames.py 未找到，跳过后处理"
+                echo "  ⓘ generate_interactive_alignment.py 未找到，跳过交互式HTML生成"
             fi
+
+            # 切换回 slt_tf1 环境
+            conda activate slt_tf1
         done
     fi
 

SignX/inference_output.txt

@@ -1 +1 @@
-<unk> NOW@@ -@@ WEEK STUDENT I@@ X HAVE NONE/NOTHING GO NONE/NOTHING
+<unk> #IF FRIEND GROUP/TOGE@@ TH@@ E@@ R DEPART PARTY IX-1p FINISH JO@@ I@@ N IX-1p

SignX/inference_output.txt.clean

@@ -1 +1 @@
-<unk> NOW-WEEK STUDENT IX HAVE NONE/NOTHING GO NONE/NOTHING
+<unk> #IF FRIEND GROUP/TOGETHER DEPART PARTY IX-1p FINISH JOIN IX-1p

SignX/models/evalu.py

@@ -386,6 +386,48 @@ def dump_detailed_attention_output(tranes, output, indices, attentions, video_pa
                         f.write(f"With BPE: {trans}\n")
                         f.write(f"Clean: {trans_clean}\n")
 
+                    # Calculate and save feature-to-frame mapping
+                    if video_path and os.path.exists(video_path):
+                        try:
+                            import cv2
+                            import json
+
+                            # Get original frame count from video
+                            cap = cv2.VideoCapture(video_path)
+                            original_frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+                            cap.release()
+
+                            # Feature count from attention matrix
+                            feature_count = sample_attn.shape[2]
+
+                            # Calculate uniform mapping: feature i -> frames [start, end]
+                            frame_mapping = []
+                            for feat_idx in range(feature_count):
+                                start_frame = int(feat_idx * original_frame_count / feature_count)
+                                end_frame = int((feat_idx + 1) * original_frame_count / feature_count)
+                                frame_mapping.append({
+                                    "feature_index": feat_idx,
+                                    "frame_start": start_frame,
+                                    "frame_end": end_frame,
+                                    "frame_count": end_frame - start_frame
+                                })
+
+                            # Save mapping
+                            mapping_data = {
+                                "original_frame_count": original_frame_count,
+                                "feature_count": feature_count,
+                                "downsampling_ratio": original_frame_count / feature_count,
+                                "mapping": frame_mapping
+                            }
+
+                            with open(sample_dir / "feature_frame_mapping.json", 'w') as f:
+                                json.dump(mapping_data, f, indent=2)
+
+                            tf.logging.info(f"  ✓ Feature-to-frame mapping saved ({original_frame_count} frames → {feature_count} features)")
+
+                        except Exception as e:
+                            tf.logging.warning(f"Failed to generate feature-to-frame mapping: {e}")
+
                     # 使用attention_analysis模块生成可视化
                     try:
                         # 添加eval目录到路径