Hugging Face Trending Papers
OFBD: Object-Focused Background Debiasing for Long-Tailed Learning
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arXiv Computer Vision
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OFBD: Object-Focused Background Debiasing for Long-Tailed Learning
arXiv:2609.37331v1 Announce Type: new Abstract: Balancing performance trade-offs on long-tailed data distributions remains a long-standing challenge in visual recognition. Existing methods mainly imp...
arXiv AI
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Learning Dynamics of Logits Debiasing for Long-Tailed Semi-Supervised Learning
arXiv:2608.30699v1 Announce Type: cross Abstract: Long-tailed distributions are prevalent in real-world semi-supervised learning (SSL), where pseudo-labels tend to favor majority classes, leading to...
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ForAug: Mitigating Biases in Image Classification via Controlled Image Compositions
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Hugging Face Trending Papers
Sep 2
Information Density Imbalance in Visual Object Detection
The paper introduces the concept of information density to explain category bias in visual object detection. It finds a strong negative correlation between a category’s information density and its detection accuracy, showing that instance count alone does not account for bias. By incorporating information density into three advanced loss functions, the authors demonstrate significant bias reduction and overall performance gains on Pascal VOC, COCO‑LT, and LVIS datasets.
arXiv Computer Vision
Sep 3
Information Density Imbalance in Visual Object Detection
The paper introduces the concept of information density to explain category bias in visual object detection. It finds a strong negative correlation between a category’s information density and its detection accuracy, showing that instance count alone does not account for bias. By incorporating information density into three advanced loss functions, the authors demonstrate reduced model bias and improved overall performance on Pascal VOC, COCO‑LT, and LVIS datasets.
arXiv AI
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Steerable Visual Representations
arXiv:2604. 02327v2 Announce Type: replace-cross Abstract: Pretrained Vision Transformers (ViTs) such as DINOv2 and MAE provide generic image features that can be applied to a variety of downstream tasks such as retrieval, classification, and segmentation.