# Hard-Aware Deeply Cascaded Embedding

arXiv: https://arxiv.org/abs/1611.05720 · HTML: https://mlsanity.com/paper/1611.05720/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2016.

## Built on by (most cited first, 30 of 44)

- [GiT: Graph Interactive Transformer for Vehicle Re-identification](https://mlsanity.com/paper/2107.05475/) (2021)
- [Learning Intra-Batch Connections for Deep Metric Learning](https://mlsanity.com/paper/2102.07753/) (2021)
- [Instance-specific and Model-adaptive Supervision for Semi-supervised Semantic Segmentation](https://mlsanity.com/paper/2211.11335/) (2022)
- [Close, But Not There: Boosting Geographic Distance Sensitivity in Visual Place Recognition](https://mlsanity.com/paper/2407.02422/) (2024)
- [Robust Multi-Task Learning with Excess Risks](https://mlsanity.com/paper/2402.02009/) (2024)
- [Deep Relational Metric Learning](https://mlsanity.com/paper/2108.10026/) (2021)
- [Learning with Memory-based Virtual Classes for Deep Metric Learning](https://mlsanity.com/paper/2103.16940/) (2021)
- [Proxy Synthesis: Learning with Synthetic Classes for Deep Metric Learning](https://mlsanity.com/paper/2103.15454/) (2021)
- [Does Negative Sampling Matter? A Review with Insights into its Theory and Applications](https://mlsanity.com/paper/2402.17238/) (2024)
- [Supervised Contrastive Learning with Hard Negative Samples](https://mlsanity.com/paper/2209.00078/) (2022)
- [Attributable Visual Similarity Learning](https://mlsanity.com/paper/2203.14932/) (2022)
- [Informative and Representative Triplet Selection for Multilabel Remote Sensing Image Retrieval](https://mlsanity.com/paper/2105.03647/) (2021)
- [Adaptive Hierarchical Similarity Metric Learning with Noisy Labels](https://mlsanity.com/paper/2111.00006/) (2021)
- [Contrastive Bayesian Analysis for Deep Metric Learning](https://mlsanity.com/paper/2210.04402/) (2022)
- [LFME: A Simple Framework for Learning from Multiple Experts in Domain Generalization](https://mlsanity.com/paper/2410.17020/) (2024)
- [LoOp: Looking for Optimal Hard Negative Embeddings for Deep Metric Learning](https://mlsanity.com/paper/2108.09335/) (2021)
- [Introspective Deep Metric Learning](https://mlsanity.com/paper/2309.09982/) (2023)
- [Multi-level Distance Regularization for Deep Metric Learning](https://mlsanity.com/paper/2102.04223/) (2021)
- [The Group Loss++: A deeper look into group loss for deep metric learning](https://mlsanity.com/paper/2204.01509/) (2022)
- [Improving Deep Metric Learning by Divide and Conquer](https://mlsanity.com/paper/2109.04003/) (2021)
- [DAS: Densely-Anchored Sampling for Deep Metric Learning](https://mlsanity.com/paper/2208.00119/) (2022)
- [Exploring Adversarial Robustness of Deep Metric Learning](https://mlsanity.com/paper/2102.07265/) (2021)
- [SwAMP: Swapped Assignment of Multi-Modal Pairs for Cross-Modal Retrieval](https://mlsanity.com/paper/2111.05814/) (2021)
- [Multi-Head Deep Metric Learning Using Global and Local Representations](https://mlsanity.com/paper/2112.14327/) (2021)
- [Center Contrastive Loss for Metric Learning](https://mlsanity.com/paper/2308.00458/) (2023)
- [Potential Field Based Deep Metric Learning](https://mlsanity.com/paper/2405.18560/) (2024)
- [Deep Metric Learning with Chance Constraints](https://mlsanity.com/paper/2209.09060/) (2022)
- [Mean Field Theory in Deep Metric Learning](https://mlsanity.com/paper/2306.15368/) (2023)
- [Construct Informative Triplet with Two-stage Hard-sample Generation](https://mlsanity.com/paper/2112.02259/) (2021)
- [UAV (Unmanned Aerial Vehicles): Diverse Applications of UAV Datasets in Segmentation, Classification, Detection, and Tracking](https://mlsanity.com/paper/2409.03245/) (2024)

## Abstract (arXiv)

Riding on the waves of deep neural networks, deep metric learning has also achieved promising results in various tasks using triplet network or Siamese network. Though the basic goal of making images from the same category closer than the ones from different categories is intuitive, it is hard to directly optimize due to the quadratic or cubic sample size. To solve the problem, hard example mining which only focuses on a subset of samples that are considered hard is widely used. However, hard is defined relative to a model, where complex models treat most samples as easy ones and vice versa for simple models, and both are not good for training. Samples are also with different hard levels, it is hard to define a model with the just right complexity and choose hard examples adequately. This motivates us to ensemble a set of models with different complexities in cascaded manner and mine hard examples adaptively, a sample is judged by a series of models with increasing complexities and only updates models that consider the sample as a hard case. We evaluate our method on CARS196, CUB-200-2011, Stanford Online Products, VehicleID and DeepFashion datasets. Our method outperforms state-of-the-art methods by a large margin.