Papers › Look-into-Object: Self-supervised Structure Modeling for Object Recognition
Look-into-Object: Self-supervised Structure Modeling for Object Recognition
Mohan Zhou, Yalong Bai, Wei zhang, Tiejun Zhao, Tao Mei
Most object recognition approaches predominantly focus on learning discriminative visual patterns while overlooking the holistic object structure. Though important, structure modeling usually requires significant manual annotations and therefore is labor-intensive. In this paper, we propose to "look into object" (explicitly yet intrinsically model the object structure) through incorporating self-supervisions into the traditional framework. We show the recognition backbone can be substantially enhanced for more robust representation learning, without any cost of extra annotation and inference speed. Specifically, we first propose an object-extent learning module for localizing the object according to the visual patterns shared among the instances in the same category. We then design a spatial context learning module for modeling the internal structures of the object, through predicting the relative positions within the extent. These two modules can be easily plugged into any backbone networks during training and detached at inference time. Extensive experiments show that our look-into-object approach (LIO) achieves large performance gain on a number of benchmarks, including generic object recognition (ImageNet) and fine-grained object recognition tasks (CUB, Cars, Aircraft). We also show that this learning paradigm is highly generalizable to other tasks such as object detection and segmentation (MS COCO). Project page: https://github.com/JDAI-CV/LIO.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Fine-Grained Image Classification | CUB-200-2011 | LIO | Accuracy | 88.0 | #22 of 30 | Archive leaderboard | report |
| Fine-Grained Image Classification | FGVC Aircraft | LIO/ResNet-50 (multi-stage) | Accuracy | 92.7% | #38 of 57 | Archive leaderboard | report |
| Fine-Grained Image Classification | Stanford Cars | LIO/ResNet-50 (multi-stage) | Accuracy | 94.5% | #44 of 83 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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