Object Localization
Object Localization is the task of locating an instance of a particular object category in an image, typically by specifying a tightly cropped bounding box centered on the instance. An object proposal specifies a candidate bounding box, and an object proposal is said to be a correct localization if it sufficiently overlaps a human-labeled “ground-truth” bounding box for the given object. In the literature, the “Object Localization” task is to locate one instance of an object category, whereas “object detection” focuses on locating all instances of a category in a given image.
Source: Fast On-Line Kernel Density Estimation for Active Object Localization
Papers
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All datasetsREVERIEKITTI Cars EasyKITTI Pedestrians HardKITTI Pedestrians ModerateKITTI Cars HardKITTI Cyclists EasyKITTI Cyclists ModerateKITTI Pedestrians EasyGRITKITTI Cyclists HardIllusionVQAKITTI Cars Moderate
Benchmark Results
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | OSMaN | RGSPL | 32.99 | — | Unverified |
| 2 | SUSA | RGSPL | 27.31 | — | Unverified |
| 3 | Shanks | RGSPL | 22.85 | — | Unverified |
| 4 | CVPR22 | RGSPL | 22.06 | — | Unverified |
| 5 | damm1 | RGSPL | 15.96 | — | Unverified |
| 6 | 1637 | RGSPL | 14.03 | — | Unverified |
| 7 | init. PREVALENT | RGSPL | 13.51 | — | Unverified |
| 8 | Airbert | RGSPL | 13.28 | — | Unverified |
| 9 | init. OSCAR | RGSPL | 10 | — | Unverified |
| 10 | SIA | RGSPL | 9.2 | — | Unverified |
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | VoxelNet | AP | 89.35 | — | Unverified |
| 2 | VoxelNet | AP | 89.35 | — | Unverified |
| 3 | Frustum PointNets | AP | 88.7 | — | Unverified |
| 4 | Frustum PointNets | AP | 81.2 | — | Unverified |
| 5 | VoxelNet | AP | 77.47 | — | Unverified |
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | Frustrum-PointPillars | AP | 48.3 | — | Unverified |
| 2 | Frustum PointNets | AP | 47.2 | — | Unverified |
| 3 | Frustum PointNets | AP | 40.23 | — | Unverified |
| 4 | VoxelNet | AP | 38.11 | — | Unverified |
| 5 | VoxelNet | AP | 31.51 | — | Unverified |
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | Frustrum-PointPillars | AP | 52.23 | — | Unverified |
| 2 | Frustum PointNets | AP | 50.22 | — | Unverified |
| 3 | Frustum PointNets | AP | 42.15 | — | Unverified |
| 4 | VoxelNet | AP | 40.74 | — | Unverified |
| 5 | VoxelNet | AP | 33.69 | — | Unverified |
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | VoxelNet | AP | 77.39 | — | Unverified |
| 2 | Frustum PointNets | AP | 75.33 | — | Unverified |
| 3 | Frustum PointNets | AP | 62.19 | — | Unverified |
| 4 | VoxelNet | AP | 57.73 | — | Unverified |
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | Frustum PointNets | AP | 75.38 | — | Unverified |
| 2 | Frustum PointNets | AP | 71.96 | — | Unverified |
| 3 | VoxelNet | AP | 66.7 | — | Unverified |
| 4 | VoxelNet | AP | 61.22 | — | Unverified |
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | Frustum PointNets | AP | 61.96 | — | Unverified |
| 2 | Frustum PointNets | AP | 56.77 | — | Unverified |
| 3 | VoxelNet | AP | 54.76 | — | Unverified |
| 4 | VoxelNet | AP | 48.36 | — | Unverified |
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | Frustum PointNets | AP | 58.09 | — | Unverified |
| 2 | Frustum PointNets | AP | 51.21 | — | Unverified |
| 3 | VoxelNet | AP | 46.13 | — | Unverified |
| 4 | VoxelNet | AP | 39.48 | — | Unverified |
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | Unified-IOXL | Localization (ablation) | 67 | — | Unverified |
| 2 | GPV-2 | Localization (ablation) | 53.6 | — | Unverified |
| 3 | Mask R-CNN | Localization (ablation) | 44.7 | — | Unverified |
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | Frustum PointNets | AP | 54.68 | — | Unverified |
| 2 | VoxelNe | AP | 50.55 | — | Unverified |
| 3 | Frustum PointNets | AP | 50.39 | — | Unverified |
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | GPT4-Vision 4-shot+CoT | Accuracy | 49.7 | — | Unverified |
| 2 | Gemini-Pro 4-shot+CoT | Accuracy | 33.9 | — | Unverified |
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | Frustum PointNets | AP | 84 | — | Unverified |
| 2 | VoxelNet | AP | 79.26 | — | Unverified |
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | Frustrum-PointPillars | AP | 60.98 | — | Unverified |
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | Hausdorff Loss | Precision | 88.1 | — | Unverified |
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | ours | CorLoc | 41.2 | — | Unverified |
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | ours | CorLoc | 47.45 | — | Unverified |
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | Hausdorff Loss | F-Score | 88.6 | — | Unverified |
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | Hausdorff Loss | Recall | 89.2 | — | Unverified |