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Found 23 papers, 6 papers with code
Subtoxic Questions: Dive Into Attitude Change of LLM's Response in Jailbreak Attempts
As Large Language Models (LLMs) of Prompt Jailbreaking are getting more and more attention, it is of great significance to raise a generalized research paradigm to evaluate attack strengths and a basic model to conduct subtler experiments.
Powerful Lossy Compression for Noisy Images
Image compression and denoising represent fundamental challenges in image processing with many real-world applications.
Dual-Space Optimization: Improved Molecule Sequence Design by Latent Prompt Transformer
Designing molecules with desirable properties, such as drug-likeliness and high binding affinities towards protein targets, is a challenging problem.
A Grammatical Compositional Model for Video Action Detection
Analysis of human actions in videos demands understanding complex human dynamics, as well as the interaction between actors and context.
Make Lossy Compression Meaningful for Low-Light Images
Experimental results show that our proposed joint solution achieves a significant improvement over different combinations of existing state-of-the-art sequential ``Compress before Enhance'' or ``Enhance before Compress'' solutions for low-light images, which would make lossy low-light image compression more meaningful.
CHSEL: Producing Diverse Plausible Pose Estimates from Contact and Free Space Data
Rather than attempting to estimate the true pose of the object, which is not tractable without a large number of contacts, we seek to estimate a plausible set of poses which obey the constraints imposed by the sensor data.
Secure Split Learning against Property Inference, Data Reconstruction, and Feature Space Hijacking Attacks
Split learning of deep neural networks (SplitNN) has provided a promising solution to learning jointly for the mutual interest of a guest and a host, which may come from different backgrounds, holding features partitioned vertically.
High-Fidelity Variable-Rate Image Compression via Invertible Activation Transformation
We implement the IAT in a mathematical invertible manner on a single rate Invertible Neural Network (INN) based model and the quality level (QLevel) would be fed into the IAT to generate scaling and bias tensors.
Towards Practical and Efficient Long Video Summary
Recently, video summarization (VS) techniques are widely used to alleviate huge processing pressure brought by numerous long videos.
Category-Aware Transformer Network for Better Human-Object Interaction Detection
Specifically, the Object Query would be initialized via category priors represented by an external object detection model to yield better performance.
Effective Actor-centric Human-object Interaction Detection
Moreover, we also use an actor branch to get interaction prediction of the actor and propose a novel composition strategy based on center-point indexing to generate the final HOI prediction.
Improving Human-Object Interaction Detection via Phrase Learning and Label Composition
Human-Object Interaction (HOI) detection is a fundamental task in high-level human-centric scene understanding.
Learning Oriented Remote Sensing Object Detection via Naive Geometric Computing
Detecting oriented objects along with estimating their rotation information is one crucial step for analyzing remote sensing images.
Self-supervised Incremental Deep Graph Learning for Ethereum Phishing Scam Detection
In our model, two pretext tasks designed from spatial and temporal perspectives help us effectively learn useful node embedding from the huge amount of unlabelled transaction data.
LEAP: TrustZone Based Developer-Friendly TEE for Intelligent Mobile Apps
ARM TrustZone is widely deployed on commercial-off-the-shelf mobile devices for secure execution.
Cryptography and Security
TAMPC: A Controller for Escaping Traps in Novel Environments
We propose an approach to online model adaptation and control in the challenging case of hybrid and discontinuous dynamics where actions may lead to difficult-to-escape "trap" states, under a given controller.
Graph-to-Tree Neural Networks for Learning Structured Input-Output Translation with Applications to Semantic Parsing and Math Word Problem
In particular, we investigated our model for solving two problems, neural semantic parsing and math word problem.
Optimizing Generative Adversarial Networks for Image Super Resolution via Latent Space Regularization
But the results of existing methods still exhibit many unpleasant artifacts and distortions even for the cases where the desired ground truth target images are available for supervised learning such as in single image super resolution (SISR).
Discussion among Different Methods of Updating Model Filter in Object Tracking
Discriminative correlation filters (DCF) have recently shown excellent performance in visual object tracking area.
Robust Object Tracking Based on Self-adaptive Search Area
We change the search area according to the prediction of target motion.
Transformed Low-Rank Model for Line Pattern Noise Removal
This paper addresses the problem of line pattern noise removal from a single image, such as rain streak, hyperspectral stripe and so on.
Weighted Low-rank Tensor Recovery for Hyperspectral Image Restoration
To overcome these limitations, in this work, we propose a unified low-rank tensor recovery model for comprehensive HSI restoration tasks, in which non-local similarity between spectral-spatial cubic and spectral correlation are simultaneously captured by 3-order tensors.
Ranked #12 on
Hyperspectral Image Denoising
on ICVL-HSI-Gaussian50
Hyper-Laplacian Regularized Unidirectional Low-Rank Tensor Recovery for Multispectral Image Denoising
Recent low-rank based matrix/tensor recovery methods have been widely explored in multispectral images (MSI) denoising.
