63 citations · 449 across the 39 of their papers we have counts for
37 papers · 1 filter
Transferable Attack for Semantic Segmentation
Mengqi He, Jing Zhang, Zhaoyuan Yang +3
We analysis performance of semantic segmentation models wrt. adversarial attacks, and observe that the adversarial examples generated from a source model fail to attack the target…
Model Calibration in Dense Classification with Adaptive Label Perturbation
Jiawei Liu, Changkun Ye, Shan Wang +4
For safety-related applications, it is crucial to produce trustworthy deep neural networks whose prediction is associated with confidence that can represent the likelihood of corre…
P2C: Self-Supervised Point Cloud Completion from Single Partial Clouds
Ruikai Cui, Shi Qiu, Saeed Anwar +4
Point cloud completion aims to recover the complete shape based on a partial observation. Existing methods require either complete point clouds or multiple partial observations of…
Measuring and Modeling Uncertainty Degree for Monocular Depth Estimation
Mochu Xiang, Jing Zhang, Nick Barnes +1
Effectively measuring and modeling the reliability of a trained model is essential to the real-world deployment of monocular depth estimation (MDE) models. However, the intrinsic i…
Joint Salient Object Detection and Camouflaged Object Detection via Uncertainty-aware Learning
Aixuan Li, Jing Zhang, Yunqiu Lv +4
Salient objects attract human attention and usually stand out clearly from their surroundings. In contrast, camouflaged objects share similar colors or textures with the environmen…
Weakly-supervised Contrastive Learning for Unsupervised Object Discovery
Yunqiu Lv, Jing Zhang, Nick Barnes +1
Unsupervised object discovery (UOD) refers to the task of discriminating the whole region of objects from the background within a scene without relying on labeled datasets, which b…
Contrastive Conditional Latent Diffusion for Audio-visual Segmentation
Yuxin Mao, Jing Zhang, Mochu Xiang +4
We propose a contrastive conditional latent diffusion model for audio-visual segmentation (AVS) to thoroughly investigate the impact of audio, where the correlation between audio a…
Mutual Information Regularization for Weakly-supervised RGB-D Salient Object Detection
Aixuan Li, Yuxin Mao, Jing Zhang +1
In this paper, we present a weakly-supervised RGB-D salient object detection model via scribble supervision. Specifically, as a multimodal learning task, we focus on effective mult…
Transmission-Guided Bayesian Generative Model for Smoke Segmentation
Siyuan Yan, Jing Zhang, Nick Barnes
Smoke segmentation is essential to precisely localize wildfire so that it can be extinguished in an early phase. Although deep neural networks have achieved promising results on im…
Energy-Based Residual Latent Transport for Unsupervised Point Cloud Completion
Ruikai Cui, Shi Qiu, Saeed Anwar +2
Unsupervised point cloud completion aims to infer the whole geometry of a partial object observation without requiring partial-complete correspondence. Differing from existing dete…
Generalised Co-Salient Object Detection
Jiawei Liu, Jing Zhang, Ruikai Cui +3
We propose a new setting that relaxes an assumption in the conventional Co-Salient Object Detection (CoSOD) setting by allowing the presence of "noisy images" which do not show the…
Salient Object Detection via Bounding-box Supervision
Mengqi He, Jing Zhang, Wenxin Yu
The success of fully supervised saliency detection models depends on a large number of pixel-wise labeling. In this paper, we work on bounding-box based weakly-supervised saliency…
Towards Deeper Understanding of Camouflaged Object Detection
Yunqiu Lv, Jing Zhang, Yuchao Dai +3
Preys in the wild evolve to be camouflaged to avoid being recognized by predators. In this way, camouflage acts as a key defence mechanism across species that is critical to surviv…
Learning Generative Vision Transformer with Energy-Based Latent Space for Saliency Prediction
Jing Zhang, Jianwen Xie, Nick Barnes +1
Vision transformer networks have shown superiority in many computer vision tasks. In this paper, we take a step further by proposing a novel generative vision transformer with late…
Semi-supervised Salient Object Detection with Effective Confidence Estimation
Jiawei Liu, Jing Zhang, Nick Barnes
The success of existing salient object detection models relies on a large pixel-wise labeled training dataset, which is time-consuming and expensive to obtain. We study semi-superv…
GETAM: Gradient-weighted Element-wise Transformer Attention Map for Weakly-supervised Semantic segmentation
Weixuan Sun, Jing Zhang, Zheyuan Liu +2
Weakly Supervised Semantic Segmentation (WSSS) is challenging, particularly when image-level labels are used to supervise pixel level prediction. To bridge their gap, a Class Activ…
Dense Uncertainty Estimation via an Ensemble-based Conditional Latent Variable Model
Jing Zhang, Yuchao Dai, Mehrtash Harandi +3
Uncertainty estimation has been extensively studied in recent literature, which can usually be classified as aleatoric uncertainty and epistemic uncertainty. In current aleatoric u…
A General Divergence Modeling Strategy for Salient Object Detection
Xinyu Tian, Jing Zhang, Yuchao Dai
Salient object detection is subjective in nature, which implies that multiple estimations should be related to the same input image. Most existing salient object detection models a…
Inferring the Class Conditional Response Map for Weakly Supervised Semantic Segmentation
Weixuan Sun, Jing Zhang, Nick Barnes
Image-level weakly supervised semantic segmentation (WSSS) relies on class activation maps (CAMs) for pseudo labels generation. As CAMs only highlight the most discriminative regio…
RGB-D Saliency Detection via Cascaded Mutual Information Minimization
Jing Zhang, Deng-Ping Fan, Yuchao Dai +4
Existing RGB-D saliency detection models do not explicitly encourage RGB and depth to achieve effective multi-modal learning. In this paper, we introduce a novel multi-stage cascad…
Confidence-Aware Learning for Camouflaged Object Detection
Jiawei Liu, Jing Zhang, Nick Barnes
Confidence-aware learning is proven as an effective solution to prevent networks becoming overconfident. We present a confidence-aware camouflaged object detection framework using…
Energy-Based Generative Cooperative Saliency Prediction
Jing Zhang, Jianwen Xie, Zilong Zheng +1
Conventional saliency prediction models typically learn a deterministic mapping from an image to its saliency map, and thus fail to explain the subjective nature of human attention…
Exploring Depth Contribution for Camouflaged Object Detection
Mochu Xiang, Jing Zhang, Yunqiu Lv +3
Camouflaged object detection (COD) aims to segment camouflaged objects hiding in the environment, which is challenging due to the similar appearance of camouflaged objects and thei…
Learning structure-aware semantic segmentation with image-level supervision
Jiawei Liu, Jing Zhang, Yicong Hong +1
Compared with expensive pixel-wise annotations, image-level labels make it possible to learn semantic segmentation in a weakly-supervised manner. Within this pipeline, the class ac…