Graph Convolution for Re-ranking in Person Re-identification

• URL: http://arxiv.org/abs/2107.02220v1
• Date: Mon, 5 Jul 2021 18:40:43 GMT
• Title: Graph Convolution for Re-ranking in Person Re-identification
• Authors: Yuqi Zhang, Qian Qi, Chong Liu, Weihua Chen, Fan Wang, Hao Li, Rong Jin
• Abstract summary: We propose a graph-based re-ranking method to improve learned features while still keeping Euclidean distance as the similarity metric. A simple yet effective method is proposed to generate a profile vector for each tracklet in videos, which helps extend our method to video re-ID.
• Score: 40.9727538382413
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Nowadays, deep learning is widely applied to extract features for similarity computation in person re-identification (re-ID) and have achieved great success. However, due to the non-overlapping between training and testing IDs, the difference between the data used for model training and the testing data makes the performance of learned feature degraded during testing. Hence, re-ranking is proposed to mitigate this issue and various algorithms have been developed. However, most of existing re-ranking methods focus on replacing the Euclidean distance with sophisticated distance metrics, which are not friendly to downstream tasks and hard to be used for fast retrieval of massive data in real applications. In this work, we propose a graph-based re-ranking method to improve learned features while still keeping Euclidean distance as the similarity metric. Inspired by graph convolution networks, we develop an operator to propagate features over an appropriate graph. Since graph is the essential key for the propagation, two important criteria are considered for designing the graph, and three different graphs are explored accordingly. Furthermore, a simple yet effective method is proposed to generate a profile vector for each tracklet in videos, which helps extend our method to video re-ID. Extensive experiments on three benchmark data sets, e.g., Market-1501, Duke, and MARS, demonstrate the effectiveness of our proposed approach.

Related papers

• Deep Homography Estimation for Visual Place Recognition [49.235432979736395]
  We propose a transformer-based deep homography estimation (DHE) network. It takes the dense feature map extracted by a backbone network as input and fits homography for fast and learnable geometric verification. Experiments on benchmark datasets show that our method can outperform several state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-02-25T13:22:17Z)
• Graph Convolution Based Efficient Re-Ranking for Visual Retrieval [29.804582207550478]
  We present an efficient re-ranking method which refines initial retrieval results by updating features. Specifically, we reformulate re-ranking based on Graph Convolution Networks (GCN) and propose a novel Graph Convolution based Re-ranking (GCR) for visual retrieval tasks via feature propagation. In particular, the plain GCR is extended for cross-camera retrieval and an improved feature propagation formulation is presented to leverage affinity relationships across different cameras.
  arXiv  Detail & Related papers  (2023-06-15T00:28:08Z)
• SMARTQUERY: An Active Learning Framework for Graph Neural Networks through Hybrid Uncertainty Reduction [25.77052028238513]
  We propose a framework to learn a graph neural network with very few labeled nodes using a hybrid uncertainty reduction function. We demonstrate the competitive performance of our method against state-of-the-arts on very few labeled data.
  arXiv  Detail & Related papers  (2022-12-02T20:49:38Z)
• A simple way to learn metrics between attributed graphs [11.207372645301094]
  We propose a new Simple Graph Metric Learning - SGML - model with few trainable parameters. This model allows us to build an appropriate distance from a database of labeled (attributed) graphs to improve the performance of simple classification algorithms.
  arXiv  Detail & Related papers  (2022-09-26T14:32:38Z)
• Benchmarking Node Outlier Detection on Graphs [90.29966986023403]
  Graph outlier detection is an emerging but crucial machine learning task with numerous applications. We present the first comprehensive unsupervised node outlier detection benchmark for graphs called UNOD.
  arXiv  Detail & Related papers  (2022-06-21T01:46:38Z)
• Learnable Graph Matching: Incorporating Graph Partitioning with Deep Feature Learning for Multiple Object Tracking [58.30147362745852]
  Data association across frames is at the core of Multiple Object Tracking (MOT) task. Existing methods mostly ignore the context information among tracklets and intra-frame detections. We propose a novel learnable graph matching method to address these issues.
  arXiv  Detail & Related papers  (2021-03-30T08:58:45Z)
• SLADE: A Self-Training Framework For Distance Metric Learning [75.54078592084217]
  We present a self-training framework, SLADE, to improve retrieval performance by leveraging additional unlabeled data. We first train a teacher model on the labeled data and use it to generate pseudo labels for the unlabeled data. We then train a student model on both labels and pseudo labels to generate final feature embeddings.
  arXiv  Detail & Related papers  (2020-11-20T08:26:10Z)
• Knowledge Graph Embedding with Atrous Convolution and Residual Learning [4.582412257655891]
  We propose a simple but effective atrous convolution based knowledge graph embedding method. It effectively increases feature interactions by using atrous convolutions. It addresses the original information forgotten issue and vanishing/exploding gradient issue.
  arXiv  Detail & Related papers  (2020-10-23T00:57:23Z)
• Heuristic Semi-Supervised Learning for Graph Generation Inspired by Electoral College [80.67842220664231]
  We propose a novel pre-processing technique, namely ELectoral COllege (ELCO), which automatically expands new nodes and edges to refine the label similarity within a dense subgraph. In all setups tested, our method boosts the average score of base models by a large margin of 4.7 points, as well as consistently outperforms the state-of-the-art.
  arXiv  Detail & Related papers  (2020-06-10T14:48:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.