Search Results for author:
Found 19 papers, 3 papers with code
FreeFlow: A Comprehensive Understanding on Diffusion Probabilistic Models via Optimal Transport
The blooming diffusion probabilistic models (DPMs) have garnered significant interest due to their impressive performance and the elegant inspiration they draw from physics.
Machine Vision Therapy: Multimodal Large Language Models Can Enhance Visual Robustness via Denoising In-Context Learning
Concretely, by estimating a transition matrix that captures the probability of one class being confused with another, an instruction containing a correct exemplar and an erroneous one from the most probable noisy class can be constructed.
DiFace: Cross-Modal Face Recognition through Controlled Diffusion
In this context, face recognition through textual description presents a unique and promising solution that not only transcends the limitations from application scenarios but also expands the potential for research in the field of cross-modal face recognition.
Hierarchical Semantic Perceptual Listener Head Video Generation: A High-performance Pipeline
In dyadic speaker-listener interactions, the listener's head reactions along with the speaker's head movements, constitute an important non-verbal semantic expression together.
Understanding the Robustness of 3D Object Detection with Bird's-Eye-View Representations in Autonomous Driving
However, there still lacks a systematic understanding of the robustness of these vision-dependent BEV models, which is closely related to the safety of autonomous driving systems.
To Make Yourself Invisible with Adversarial Semantic Contours
In this paper, we propose Adversarial Semantic Contour (ASC), an MAP estimate of a Bayesian formulation of sparse attack with a deceived prior of object contour.
Improving Model Generalization by On-manifold Adversarial Augmentation in the Frequency Domain
To address this issue, we proposed a novel method of Augmenting data with Adversarial examples via a Wavelet module (AdvWavAug), an on-manifold adversarial data augmentation technique that is simple to implement.
A Comprehensive Study on Robustness of Image Classification Models: Benchmarking and Rethinking
In our benchmark, we evaluate the robustness of 55 typical deep learning models on ImageNet with diverse architectures (e. g., CNNs, Transformers) and learning algorithms (e. g., normal supervised training, pre-training, adversarial training) under numerous adversarial attacks and out-of-distribution (OOD) datasets.
Seq-Masks: Bridging the gap between appearance and gait modeling for video-based person re-identification
Validations on the gait recognition metric CASIA-B dataset further demonstrated the capability of our hybrid model.
Subtask-dominated Transfer Learning for Long-tail Person Search
One major challenge comes from the imbalanced long-tail person identity distributions, which prevents the one-step person search model from learning discriminative person features for the final re-identification.
You Cannot Easily Catch Me: A Low-Detectable Adversarial Patch for Object Detectors
Fortunately, most existing adversarial patches can be outwitted, disabled and rejected by a simple classification network called an adversarial patch detector, which distinguishes adversarial patches from original images.
Improving the Robustness of Adversarial Attacks Using an Affine-Invariant Gradient Estimator
As designers of artificial intelligence try to outwit hackers, both sides continue to hone in on AI's inherent vulnerabilities.
Improving Visual Quality of Unrestricted Adversarial Examples with Wavelet-VAE
Traditional adversarial examples are typically generated by adding perturbation noise to the input image within a small matrix norm.
Making Person Search Enjoy the Merits of Person Re-identification
In the proposed TDN, for better knowledge transfer from the Re-ID teacher model to the one-step person search model, we design a strong one-step person search base framework by partially disentangling the two subtasks.
Distribution Context Aware Loss for Person Re-identification
To learn the optimal similarity function between probe and gallery images in Person re-identification, effective deep metric learning methods have been extensively explored to obtain discriminative feature embedding.
Learning to Self-Train for Semi-Supervised Few-Shot Classification
On each task, we train a few-shot model to predict pseudo labels for unlabeled data, and then iterate the self-training steps on labeled and pseudo-labeled data with each step followed by fine-tuning.
Robust and Efficient Graph Correspondence Transfer for Person Re-identification
To address this problem, in this paper, we present a robust and efficient graph correspondence transfer (REGCT) approach for explicit spatial alignment in Re-ID.
Graph Correspondence Transfer for Person Re-identification
In this paper, we propose a graph correspondence transfer (GCT) approach for person re-identification.
Weighted Bilinear Coding over Salient Body Parts for Person Re-identification
Meanwhile, a weighting scheme is applied on the bilinear coding to adaptively adjust the weights of local features at different locations based on their importance in recognition, further improving the discriminability of feature aggregation.
