677 citations · 696 across the 11 of their papers we have counts for
12 papers
LipSSD: Lipschitz-Constrained Single-Shot Detection for Adversarially Robust Object Detection
Vincent Lébé, Yannick Prudent, Corentin Friedrich +3
Object detectors have many applications in safety-critical systems, but they are known to be sensitive to worst-case perturbations such as adversarial attacks, which limits their a…
Eidos: Efficient, Imperceptible Adversarial 3D Point Clouds
Hanwei Zhang, Luo Cheng, Qisong He +6
Classification of 3D point clouds is a challenging machine learning (ML) task with important real-world applications in a spectrum from autonomous driving and robot-assisted surger…
DP-Net: Learning Discriminative Parts for image recognition
Ronan Sicre, Hanwei Zhang, Julien Dejasmin +3
This paper presents Discriminative Part Network (DP-Net), a deep architecture with strong interpretation capabilities, which exploits a pretrained Convolutional Neural Network (CNN…
A Learning Paradigm for Interpretable Gradients
Felipe Torres Figueroa, Hanwei Zhang, Ronan Sicre +2
This paper studies interpretability of convolutional networks by means of saliency maps. Most approaches based on Class Activation Maps (CAM) combine information from fully connect…
CA-Stream: Attention-based pooling for interpretable image recognition
Felipe Torres, Hanwei Zhang, Ronan Sicre +2
Explanations obtained from transformer-based architectures in the form of raw attention, can be seen as a class-agnostic saliency map. Additionally, attention-based pooling serves…
CAM-Based Methods Can See through Walls
Magamed Taimeskhanov, Ronan Sicre, Damien Garreau
CAM-based methods are widely-used post-hoc interpretability method that produce a saliency map to explain the decision of an image classification model. The saliency map highlights…
Revisiting Transferable Adversarial Images: Systemization, Evaluation, and New Insights
Zhengyu Zhao, Hanwei Zhang, Renjue Li +6
Transferable adversarial images raise critical security concerns for computer vision systems in real-world, black-box attack scenarios. Although many transfer attacks have been pro…
Opti-CAM: Optimizing saliency maps for interpretability
Hanwei Zhang, Felipe Torres, Ronan Sicre +2
Methods based on class activation maps (CAM) provide a simple mechanism to interpret predictions of convolutional neural networks by using linear combinations of feature maps as sa…
Towards Good Practices in Evaluating Transfer Adversarial Attacks
Zhengyu Zhao, Hanwei Zhang, Renjue Li +3
Transfer adversarial attacks raise critical security concerns in real-world, black-box scenarios. However, the actual progress of this field is difficult to assess due to two commo…
Unsupervised part learning for visual recognition
Ronan Sicre, Yannis Avrithis, Ewa Kijak +1
Part-based image classification aims at representing categories by small sets of learned discriminative parts, upon which an image representation is built. Considered as a promisin…
Automatic discovery of discriminative parts as a quadratic assignment problem
Ronan Sicre, Julien Rabin, Yannis Avrithis +2
Part-based image classification consists in representing categories by small sets of discriminative parts upon which a representation of the images is built. This paper addresses t…
Particular object retrieval with integral max-pooling of CNN activations
Giorgos Tolias, Ronan Sicre, Hervé Jégou
Recently, image representation built upon Convolutional Neural Network (CNN) has been shown to provide effective descriptors for image search, outperforming pre-CNN features as sho…