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Binghui Chen

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5 papers

1 author row

IJCAI Conference 2023 Conference Paper

DAMO-StreamNet: Optimizing Streaming Perception in Autonomous Driving

Jun-Yan He
Zhi-Qi Cheng
Chenyang Li
Wangmeng Xiang
Binghui Chen
Bin Luo
Yifeng Geng
Xuansong Xie

In the realm of autonomous driving, real-time perception or streaming perception remains under-explored. This research introduces DAMO-StreamNet, a novel framework that merges the cutting-edge elements of the YOLO series with a detailed examination of spatial and temporal perception techniques. DAMO-StreamNet's main inventions include: (1) a robust neck structure employing deformable convolution, bolstering receptive field and feature alignment capabilities; (2) a dual-branch structure synthesizing short-path semantic features and long-path temporal features, enhancing the accuracy of motion state prediction; (3) logits-level distillation facilitating efficient optimization, which aligns the logits of teacher and student networks in semantic space; and (4) a real-time prediction mechanism that updates the features of support frames with the current frame, providing smooth streaming perception during inference. Our testing shows that DAMO-StreamNet surpasses current state-of-the-art methodologies, achieving 37. 8% (normal size (600, 960)) and 43. 3% (large size (1200, 1920)) sAP without requiring additional data. This study not only establishes a new standard for real-time perception but also offers valuable insights for future research. The source code is at https: //github. com/zhiqic/DAMO-StreamNet.

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AAAI Conference 2023 Conference Paper

Learning Polysemantic Spoof Trace: A Multi-Modal Disentanglement Network for Face Anti-spoofing

Kaicheng Li
Hongyu Yang
Binghui Chen
Pengyu Li
Biao Wang
Di Huang

Along with the widespread use of face recognition systems, their vulnerability has become highlighted. While existing face anti-spoofing methods can be generalized between attack types, generic solutions are still challenging due to the diversity of spoof characteristics. Recently, the spoof trace disentanglement framework has shown great potential for coping with both seen and unseen spoof scenarios, but the performance is largely restricted by the single-modal input. This paper focuses on this issue and presents a multi-modal disentanglement model which targetedly learns polysemantic spoof traces for more accurate and robust generic attack detection. In particular, based on the adversarial learning mechanism, a two-stream disentangling network is designed to estimate spoof patterns from the RGB and depth inputs, respectively. In this case, it captures complementary spoofing clues inhering in different attacks. Furthermore, a fusion module is exploited, which recalibrates both representations at multiple stages to promote the disentanglement in each individual modality. It then performs cross-modality aggregation to deliver a more comprehensive spoof trace representation for prediction. Extensive evaluations are conducted on multiple benchmarks, demonstrating that learning polysemantic spoof traces favorably contributes to anti-spoofing with more perceptible and interpretable results.

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AAAI Conference 2021 Conference Paper

Deep Metric Learning with Graph Consistency

Binghui Chen
Pengyu Li
Zhaoyi Yan
Biao Wang
Lei Zhang

Deep Metric Learning (DML) has been more attractive and widely applied in many computer vision tasks, in which a discriminative embedding is requested such that the image features belonging to the same class are gathered together and the ones belonging to different classes are pushed apart. Most existing works insist to learn this discriminative embedding by either devising powerful pair-based loss functions or hardsample mining strategies. However, in this paper, we start from another perspective and propose Deep Consistent Graph Metric Learning (CGML) framework to enhance the discrimination of the learned embedding. It is mainly achieved by rethinking the conventional distance constraints as a graph regularization and then introducing a Graph Consistency regularization term, which intends to optimize the feature distribution from a global graph perspective. Inspired by the characteristic of our defined ’Discriminative Graph’, which regards DML from another novel perspective, the Graph Consistency regularization term encourages the sub-graphs randomly sampled from the training set to be consistent. We show that our CGML indeed serves as an efficient technique for learning towards discriminative embedding and is applicable to various popular metric objectives, e. g. Triplet, N-Pair and Binomial losses. This paper empirically and experimentally demonstrates the effectiveness of our graph regularization idea, achieving competitive results on the popular CUB, CARS, Stanford Online Products and In-Shop datasets.

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AAAI Conference 2019 Conference Paper

Energy Confused Adversarial Metric Learning for Zero-Shot Image Retrieval and Clustering

Binghui Chen
Weihong Deng

Deep metric learning has been widely applied in many computer vision tasks, and recently, it is more attractive in zeroshot image retrieval and clustering (ZSRC) where a good embedding is requested such that the unseen classes can be distinguished well. Most existing works deem this ’good’ embedding just to be the discriminative one and thus race to devise powerful metric objectives or hard-sample mining strategies for leaning discriminative embedding. However, in this paper, we first emphasize that the generalization ability is a core ingredient of this ’good’ embedding as well and largely affects the metric performance in zero-shot settings as a matter of fact. Then, we propose the Energy Confused Adversarial Metric Learning (ECAML) framework to explicitly optimize a robust metric. It is mainly achieved by introducing an interesting Energy Confusion regularization term, which daringly breaks away from the traditional metric learning idea of discriminative objective devising, and seeks to ’confuse’ the learned model so as to encourage its generalization ability by reducing overfitting on the seen classes. We train this confusion term together with the conventional metric objective in an adversarial manner. Although it seems weird to ’confuse’ the network, we show that our ECAML indeed serves as an efficient regularization technique for metric learning and is applicable to various conventional metric methods. This paper empirically and experimentally demonstrates the importance of learning embedding with good generalization, achieving state-of-theart performances on the popular CUB, CARS, Stanford Online Products and In-Shop datasets for ZSRC tasks. Code available at http://www.bhchen.cn/.

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NeurIPS Conference 2018 Conference Paper

Virtual Class Enhanced Discriminative Embedding Learning

Binghui Chen
Weihong Deng
Haifeng Shen

Recently, learning discriminative features to improve the recognition performances gradually becomes the primary goal of deep learning, and numerous remarkable works have emerged. In this paper, we propose a novel yet extremely simple method Virtual Softmax to enhance the discriminative property of learned features by injecting a dynamic virtual negative class into the original softmax. Injecting virtual class aims to enlarge inter-class margin and compress intra-class distribution by strengthening the decision boundary constraint. Although it seems weird to optimize with this additional virtual class, we show that our method derives from an intuitive and clear motivation, and it indeed encourages the features to be more compact and separable. This paper empirically and experimentally demonstrates the superiority of Virtual Softmax, improving the performances on a variety of object classification and face verification tasks.

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