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{
"methods": [
{
"label": "Qwen-init",
"checkpoint": "/inspire/hdd/global_user/liuxiaotong-253108540242/yanggang/lihao/lh/or/MLLM-SEG/outputs/fair_7b_onepass6_stamp4_e2_v2/onepass7b/onepass_qwen7b.pt",
"rows_csv": "/inspire/hdd/global_user/liuxiaotong-253108540242/yanggang/lihao/lh/or/MLLM-SEG/outputs/fair_7b_onepass6_stamp4_e2_v2/onepass7b_eval_val/onepass7b_eval_rows.csv",
"initialization": "base_qwen2_vl_without_stamp_weights",
"use_seg_grounding": false,
"seg_fusion_alpha": 0.0,
"selected_inference_seconds": 11.155093079432845
},
{
"label": "STAMP-LoRA-init",
"checkpoint": "/inspire/hdd/global_user/liuxiaotong-253108540242/yanggang/lihao/lh/or/MLLM-SEG/outputs/onepass7b_stamp_lora_warmstart_e2/onepass_qwen7b.pt",
"rows_csv": "/inspire/hdd/global_user/liuxiaotong-253108540242/yanggang/lihao/lh/or/MLLM-SEG/outputs/onepass7b_stamp_lora_warmstart_e2_eval_val/onepass7b_eval_rows.csv",
"initialization": "stamp_7b_lora_warm_start",
"use_seg_grounding": false,
"seg_fusion_alpha": 0.0,
"selected_inference_seconds": 7.2885101940482855
},
{
"label": "SEG-grounding",
"checkpoint": "/inspire/hdd/global_user/liuxiaotong-253108540242/yanggang/lihao/lh/or/MLLM-SEG/outputs/onepass7b_stamp_lora_seg_grounding_e2/onepass_qwen7b.pt",
"rows_csv": "/inspire/hdd/global_user/liuxiaotong-253108540242/yanggang/lihao/lh/or/MLLM-SEG/outputs/onepass7b_seg_grounding_eval_val/onepass7b_eval_rows.csv",
"initialization": "onepass_checkpoint_seg_grounding_finetune",
"use_seg_grounding": true,
"seg_fusion_alpha": -0.001273852656595409,
"selected_inference_seconds": 7.553720632568002
}
],
"distribution": {
"common_samples": 4896,
"runs": [
{
"label": "Qwen-init",
"mean_sample_iou": 0.5861785892137817,
"iou_below_0.5_fraction": 0.27879901960784315,
"iou_above_0.75_fraction": 0.2992238562091503
},
{
"label": "STAMP-LoRA-init",
"mean_sample_iou": 0.5856717786875781,
"iou_below_0.5_fraction": 0.28431372549019607,
"iou_above_0.75_fraction": 0.3059640522875817
},
{
"label": "SEG-grounding",
"mean_sample_iou": 0.5899042908731187,
"iou_below_0.5_fraction": 0.27124183006535946,
"iou_above_0.75_fraction": 0.3165849673202614
}
],
"pairwise": [
{
"comparison": "STAMP-LoRA-init - Qwen-init",
"mean_delta_iou": -0.0005068105262035741,
"improved_by_more_than_0.01_fraction": 0.3547794117647059,
"regressed_by_more_than_0.01_fraction": 0.3396650326797386,
"nearly_unchanged_fraction": 0.3055555555555556
},
{
"comparison": "SEG-grounding - STAMP-LoRA-init",
"mean_delta_iou": 0.0042325121855405715,
"improved_by_more_than_0.01_fraction": 0.29064542483660133,
"regressed_by_more_than_0.01_fraction": 0.19709967320261437,
"nearly_unchanged_fraction": 0.5122549019607843
}
]
},
"selected_cases": [
{
"index": 118,
"category": "all_methods_fail",
"instruction": "Please segment the object this sentence describes: \"a black motor bike with two head lights\".",
"ious": {
"Qwen-init": 0.0,
"STAMP-LoRA-init": 0.0,
"SEG-grounding": 0.0
}
},
{
"index": 121,
"category": "all_methods_fail",
"instruction": "Please segment the object this sentence describes: \"the chair behind the guy wearing the stripes\".",
"ious": {
"Qwen-init": 0.0,
"STAMP-LoRA-init": 0.4044166946027489,
"SEG-grounding": 0.43144398837799197
}
},
{
"index": 3163,
"category": "stamp_lora_gain",
"instruction": "Please segment the object this sentence describes: \"a woman in an apron and uniform holding two pots\".",
"ious": {
"Qwen-init": 0.0004290519667742157,
"STAMP-LoRA-init": 0.6620860738008585,
"SEG-grounding": 0.0
}
},
{
"index": 1045,
"category": "stamp_lora_gain",
"instruction": "Please segment the object this sentence describes: \"red sox baseball player posing for picture with right hand on end of bat that is to his right side\".",
"ious": {
"Qwen-init": 0.8401373877657687,
"STAMP-LoRA-init": 0.837453531598513,
"SEG-grounding": 0.8371484389398798
}
},
{
"index": 808,
"category": "seg_grounding_gain",
"instruction": "Please segment the object this sentence describes: \"a asian woman wholding her hand to her face\".",
"ious": {
"Qwen-init": 0.0,
"STAMP-LoRA-init": 0.0,
"SEG-grounding": 0.0
}
},
{
"index": 3822,
"category": "seg_grounding_gain",
"instruction": "Please segment the object this sentence describes: \"a cow statue with two other cows standing on its back\".",
"ious": {
"Qwen-init": 0.005878735702865576,
"STAMP-LoRA-init": 0.004945503433043646,
"SEG-grounding": 0.018551152600495332
}
},
{
"index": 1641,
"category": "seg_grounding_regression",
"instruction": "Please segment the object this sentence describes: \"monitor closer to the mouse\".",
"ious": {
"Qwen-init": 0.0,
"STAMP-LoRA-init": 0.0,
"SEG-grounding": 0.0
}
},
{
"index": 2162,
"category": "seg_grounding_regression",
"instruction": "Please segment the object this sentence describes: \"there is a grey colored sofa chair in front of three people\".",
"ious": {
"Qwen-init": 0.7193003052359709,
"STAMP-LoRA-init": 0.8150576082638061,
"SEG-grounding": 0.6546229181652188
}
},
{
"index": 105,
"category": "largest_method_disagreement",
"instruction": "Please segment the object this sentence describes: \"back image of a woman in a green shirt\".",
"ious": {
"Qwen-init": 0.0010589775446730094,
"STAMP-LoRA-init": 0.0006794162053346754,
"SEG-grounding": 0.0006869800524450625
}
},
{
"index": 1021,
"category": "largest_method_disagreement",
"instruction": "Please segment the object this sentence describes: \"german shephard dog sleeping\".",
"ious": {
"Qwen-init": 0.8497363796133568,
"STAMP-LoRA-init": 0.8622080375998349,
"SEG-grounding": 0.8624919098959731
}
},
{
"index": 3778,
"category": "all_methods_succeed",
"instruction": "Please segment the object this sentence describes: \"the pizza that is closer to the camera than the other . it is in front of the glasses and the water\".",
"ious": {
"Qwen-init": 0.95012587968157,
"STAMP-LoRA-init": 0.9503128588754253,
"SEG-grounding": 0.935734395750332
}
},
{
"index": 754,
"category": "all_methods_succeed",
"instruction": "Please segment the object this sentence describes: \"chocolate in a white mug\".",
"ious": {
"Qwen-init": 0.8922919149643838,
"STAMP-LoRA-init": 0.8976021463305572,
"SEG-grounding": 0.8803074262121297
}
}
],
"legend": {
"green": "true positive",
"red": "false positive / over-segmentation",
"blue": "false negative / under-segmentation"
}
}