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Found 29 papers, 9 papers with code
DroidAnalytics: A Signature Based Analytic System to Collect, Extract, Analyze and Associate Android Malware
In this paper, we present the design and implementation of DroidAnalytics, a signature based analytic system to automatically collect, manage, analyze and extract android malware.
Cryptography and Security
Pyramid Recurrent Neural Networks for Multi-Scale Change-Point Detection
To address this, we show how changepoint detection can be treated as a supervised learning problem, and propose a new deep neural network architecture that can efficiently identify both abrupt and gradual changes at multiple scales.
Deep Learning for Multi-Scale Changepoint Detection in Multivariate Time Series
Many real-world time series, such as in health, have changepoints where the system's structure or parameters change.
Linear Context Transform Block
In this work, we first revisit the SE block, and then present a detailed empirical study of the relationship between global context and attention distribution, based on which we propose a simple yet effective module, called Linear Context Transform (LCT) block.
Progressive Automatic Design of Search Space for One-Shot Neural Architecture Search
In this way, PAD-NAS can automatically design the operations for each layer and achieve a trade-off between search space quality and model diversity.
Gaussian Context Transformer
These approaches commonly learn the relationship between global contexts and attention activations by using fully-connected layers or linear transformations.
AlphaFold Accelerates Artificial Intelligence Powered Drug Discovery: Efficient Discovery of a Novel Cyclin-dependent Kinase 20 (CDK20) Small Molecule Inhibitor
The AlphaFold computer program predicted protein structures for the whole human genome, which has been considered as a remarkable breakthrough both in artificial intelligence (AI) application and structural biology.
ScalableViT: Rethinking the Context-oriented Generalization of Vision Transformer
The vanilla self-attention mechanism inherently relies on pre-defined and steadfast computational dimensions.
SepViT: Separable Vision Transformer
Vision Transformers have witnessed prevailing success in a series of vision tasks.
Dressing in the Wild by Watching Dance Videos
While significant progress has been made in garment transfer, one of the most applicable directions of human-centric image generation, existing works overlook the in-the-wild imagery, presenting severe garment-person misalignment as well as noticeable degradation in fine texture details.
TRT-ViT: TensorRT-oriented Vision Transformer
We revisit the existing excellent Transformers from the perspective of practical application.
MoCoViT: Mobile Convolutional Vision Transformer
Recently, Transformer networks have achieved impressive results on a variety of vision tasks.
Parallel Pre-trained Transformers (PPT) for Synthetic Data-based Instance Segmentation
Recently, Synthetic data-based Instance Segmentation has become an exceedingly favorable optimization paradigm since it leverages simulation rendering and physics to generate high-quality image-annotation pairs.
Next-ViT: Next Generation Vision Transformer for Efficient Deployment in Realistic Industrial Scenarios
Then, Next Hybrid Strategy (NHS) is designed to stack NCB and NTB in an efficient hybrid paradigm, which boosts performance in various downstream tasks.
Ranked #281 on
Image Classification
on ImageNet
Multi-Granularity Distillation Scheme Towards Lightweight Semi-Supervised Semantic Segmentation
Consequently, we offer the first attempt to provide lightweight SSSS models via a novel multi-granularity distillation (MGD) scheme, where multi-granularity is captured from three aspects: i) complementary teacher structure; ii) labeled-unlabeled data cooperative distillation; iii) hierarchical and multi-levels loss setting.
Knowledge Distillation
Semi-Supervised Semantic Segmentation
Local and Global Logit Adjustments for Long-Tailed Learning
LGLA consists of two core components: a Class-aware Logit Adjustment (CLA) strategy and an Adaptive Angular Weighted (AAW) loss.
SwiftAvatar: Efficient Auto-Creation of Parameterized Stylized Character on Arbitrary Avatar Engines
To this end, we propose SwiftAvatar, a novel avatar auto-creation framework that is evidently superior to previous works.
AlignDet: Aligning Pre-training and Fine-tuning in Object Detection
To this end, we propose AlignDet, a unified pre-training framework that can be adapted to various existing detectors to alleviate the discrepancies.
HandMIM: Pose-Aware Self-Supervised Learning for 3D Hand Mesh Estimation
Our approach involves a unified and multi-granularity strategy that includes a pseudo keypoint alignment module in the teacher-student framework for learning pose-aware semantic class tokens.
AvatarVerse: High-quality & Stable 3D Avatar Creation from Text and Pose
We present AvatarVerse, a stable pipeline for generating expressive high-quality 3D avatars from nothing but text descriptions and pose guidance.
DLIP: Distilling Language-Image Pre-training
Furthermore, DLIP succeeds in retaining more than 95% of the performance with 22. 4% parameters and 24. 8% FLOPs compared to the teacher model and accelerates inference speed by 2. 7x.
DiffusionEngine: Diffusion Model is Scalable Data Engine for Object Detection
This paper reveals that the recently developed Diffusion Model is a scalable data engine for object detection.
PhotoVerse: Tuning-Free Image Customization with Text-to-Image Diffusion Models
Personalized text-to-image generation has emerged as a powerful and sought-after tool, empowering users to create customized images based on their specific concepts and prompts.
UGC: Unified GAN Compression for Efficient Image-to-Image Translation
Recent years have witnessed the prevailing progress of Generative Adversarial Networks (GANs) in image-to-image translation.
AutoDiffusion: Training-Free Optimization of Time Steps and Architectures for Automated Diffusion Model Acceleration
Therefore, we propose to search the optimal time steps sequence and compressed model architecture in a unified framework to achieve effective image generation for diffusion models without any further training.
ResAdapter: Domain Consistent Resolution Adapter for Diffusion Models
Especially, after learning a deep understanding of pure resolution priors, ResAdapter trained on the general dataset, generates resolution-free images with personalized diffusion models while preserving their original style domain.
ByteEdit: Boost, Comply and Accelerate Generative Image Editing
Recent advancements in diffusion-based generative image editing have sparked a profound revolution, reshaping the landscape of image outpainting and inpainting tasks.
Wi-Fi-based Personnel Identity Recognition: Addressing Dataset Imbalance with C-DDPMs
Wireless sensing technologies become increasingly prevalent due to the ubiquitous nature of wireless signals and their inherent privacy-friendly characteristics.
UniFL: Improve Stable Diffusion via Unified Feedback Learning
Diffusion models have revolutionized the field of image generation, leading to the proliferation of high-quality models and diverse downstream applications.
