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Found 24 papers, 7 papers with code
A Dataset and Model for Realistic License Plate Deblurring
Vehicle license plate recognition is a crucial task in intelligent traffic management systems.
DD-VNB: A Depth-based Dual-Loop Framework for Real-time Visually Navigated Bronchoscopy
Specifically, the relative pose changes are fed into the registration process as the initial guess to boost its accuracy and speed.
Mudjacking: Patching Backdoor Vulnerabilities in Foundation Models
However, foundation models are vulnerable to backdoor attacks and a backdoored foundation model is a single-point-of-failure of the AI ecosystem, e. g., multiple downstream classifiers inherit the backdoor vulnerabilities simultaneously.
Visual Hallucinations of Multi-modal Large Language Models
We find that existing MLLMs such as GPT-4V, LLaVA-1. 5, and MiniGPT-v2 hallucinate for a large fraction of the instances in our benchmark.
BronchoTrack: Airway Lumen Tracking for Branch-Level Bronchoscopic Localization
Localizing the bronchoscope in real time is essential for ensuring intervention quality.
ChemReasoner: Heuristic Search over a Large Language Model's Knowledge Space using Quantum-Chemical Feedback
The discovery of new catalysts is essential for the design of new and more efficient chemical processes in order to transition to a sustainable future.
DaFoEs: Mixing Datasets towards the generalization of vision-state deep-learning Force Estimation in Minimally Invasive Robotic Surgery
In this paper, we present a new vision-haptic dataset (DaFoEs) with variable soft environments for the training of deep neural models.
SurgPLAN: Surgical Phase Localization Network for Phase Recognition
To address these two challenges, we propose a Surgical Phase LocAlization Network, named SurgPLAN, to facilitate a more accurate and stable surgical phase recognition with the principle of temporal detection.
PWISeg: Point-based Weakly-supervised Instance Segmentation for Surgical Instruments
To address this issue, we propose a novel yet effective weakly-supervised surgical instrument instance segmentation approach, named Point-based Weakly-supervised Instance Segmentation (PWISeg).
LEGO: Learning and Graph-Optimized Modular Tracker for Online Multi-Object Tracking with Point Clouds
The state-of-the-art approaches usually employ a tracking-by-detection method, and data association plays a critical role.
PointCert: Point Cloud Classification with Deterministic Certified Robustness Guarantees
Existing certified defenses against adversarial point clouds suffer from a key limitation: their certified robustness guarantees are probabilistic, i. e., they produce an incorrect certified robustness guarantee with some probability.
Pre-trained Encoders in Self-Supervised Learning Improve Secure and Privacy-preserving Supervised Learning
In this work, we perform the first systematic, principled measurement study to understand whether and when a pre-trained encoder can address the limitations of secure or privacy-preserving supervised learning algorithms.
CorruptEncoder: Data Poisoning based Backdoor Attacks to Contrastive Learning
In this work, we take the first step to analyze the limitations of existing backdoor attacks and propose new DPBAs called CorruptEncoder to CL.
Semi-Leak: Membership Inference Attacks Against Semi-supervised Learning
The results show that early stopping can mitigate the membership inference attack, but with the cost of model's utility degradation.
PoisonedEncoder: Poisoning the Unlabeled Pre-training Data in Contrastive Learning
In this work, we propose PoisonedEncoder, a data poisoning attack to contrastive learning.
StolenEncoder: Stealing Pre-trained Encoders in Self-supervised Learning
A pre-trained encoder may be deemed confidential because its training requires lots of data and computation resources as well as its public release may facilitate misuse of AI, e. g., for deepfakes generation.
10 Security and Privacy Problems in Large Foundation Models
A pre-trained foundation model is like an ``operating system'' of the AI ecosystem.
EncoderMI: Membership Inference against Pre-trained Encoders in Contrastive Learning
EncoderMI can be used 1) by a data owner to audit whether its (public) data was used to pre-train an image encoder without its authorization or 2) by an attacker to compromise privacy of the training data when it is private/sensitive.
PointGuard: Provably Robust 3D Point Cloud Classification
Our first major theoretical contribution is that we show PointGuard provably predicts the same label for a 3D point cloud when the number of adversarially modified, added, and/or deleted points is bounded.
Prospects of Quantum Computing for Molecular Sciences
Molecular science is governed by the dynamics of electrons, atomic nuclei, and their interaction with electromagnetic fields.
Quantum Physics
Almost Tight L0-norm Certified Robustness of Top-k Predictions against Adversarial Perturbations
For instance, our method can build a classifier that achieves a certified top-3 accuracy of 69. 2\% on ImageNet when an attacker can arbitrarily perturb 5 pixels of a testing image.
Bounding Boxes Are All We Need: Street View Image Classification via Context Encoding of Detected Buildings
Instead of using visual features of the whole image directly as common image-level models based on convolutional neural networks (CNNs) do, the proposed framework firstly obtains the bounding boxes of buildings in street view images from a detector.
On the Intrinsic Differential Privacy of Bagging
Bagging, a popular ensemble learning framework, randomly creates some subsamples of the training data, trains a base model for each subsample using a base learner, and takes majority vote among the base models when making predictions.
