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Found 23 papers, 8 papers with code
AFLoRA: Adaptive Freezing of Low Rank Adaptation in Parameter Efficient Fine-Tuning of Large Models
While compared in terms of runtime, AFLoRA can yield up to $1. 86\times$ improvement as opposed to similar PEFT alternatives.
Glyph-ByT5: A Customized Text Encoder for Accurate Visual Text Rendering
Visual text rendering poses a fundamental challenge for contemporary text-to-image generation models, with the core problem lying in text encoder deficiencies.
LMUFormer: Low Complexity Yet Powerful Spiking Model With Legendre Memory Units
Moreover, we present a spiking version of this architecture, which introduces the benefit of states within the patch embedding and channel mixer modules while simultaneously reducing the computing complexity.
When Bio-Inspired Computing meets Deep Learning: Low-Latency, Accurate, & Energy-Efficient Spiking Neural Networks from Artificial Neural Networks
However, advanced ANN-to-SNN conversion approaches demonstrate that for lossless conversion, the number of SNN time steps must equal the number of quantization steps in the ANN activation function.
COLE: A Hierarchical Generation Framework for Multi-Layered and Editable Graphic Design
Our COLE system comprises multiple fine-tuned Large Language Models (LLMs), Large Multimodal Models (LMMs), and Diffusion Models (DMs), each specifically tailored for design-aware layer-wise captioning, layout planning, reasoning, and the task of generating images and text.
Spiking Neural Networks with Dynamic Time Steps for Vision Transformers
Recently proposed SNN training algorithms have significantly reduced the number of time steps (down to 1) for improved latency and energy efficiency, however, they target only convolutional neural networks (CNN).
Generating Progressive Images from Pathological Transitions via Diffusion Model
Deep learning is widely applied in computer-aided pathological diagnosis, which alleviates the pathologist workload and provide timely clinical analysis.
CPIA Dataset: A Comprehensive Pathological Image Analysis Dataset for Self-supervised Learning Pre-training
This paper presents the comprehensive pathological image analysis (CPIA) dataset, a large-scale SSL pre-training dataset combining 103 open-source datasets with extensive standardization.
Corporate Credit Rating: A Survey
Corporate credit rating (CCR) plays a very important role in the process of contemporary economic and social development.
Latency-aware Unified Dynamic Networks for Efficient Image Recognition
Dynamic computation has emerged as a promising avenue to enhance the inference efficiency of deep networks.
Dynamic Perceiver for Efficient Visual Recognition
Early exits are placed exclusively within the classification branch, thus eliminating the need for linear separability in low-level features.
Efficient automatic segmentation for multi-level pulmonary arteries: The PARSE challenge
Efficient automatic segmentation of multi-level (i. e. main and branch) pulmonary arteries (PA) in CTPA images plays a significant role in clinical applications.
In-Sensor & Neuromorphic Computing are all you need for Energy Efficient Computer Vision
Although the overhead for the first layer MACs with direct encoding is negligible for deep SNNs and the CV processing is efficient using SNNs, the data transfer between the image sensors and the downstream processing costs significant bandwidth and may dominate the total energy.
Hoyer regularizer is all you need for ultra low-latency spiking neural networks
Spiking Neural networks (SNN) have emerged as an attractive spatio-temporal computing paradigm for a wide range of low-power vision tasks.
Artificial Intelligence Security Competition (AISC)
The security of artificial intelligence (AI) is an important research area towards safe, reliable, and trustworthy AI systems.
FeatureBooster: Boosting Feature Descriptors with a Lightweight Neural Network
We introduce a lightweight network to improve descriptors of keypoints within the same image.
Enabling ISP-less Low-Power Computer Vision
However, direct inference on the raw images degrades the test accuracy due to the difference in covariance of the raw images captured by the image sensors compared to the ISP-processed images used for training.
OpenFHE: Open-Source Fully Homomorphic Encryption Library
Fully Homomorphic Encryption (FHE) is a powerful cryptographic primitive that enables performing computations over encrypted data without having access to the secret key.
A multi view multi stage and multi window framework for pulmonary artery segmentation from CT scans
In addition, in order to improve the segmentation performance, we adopt multi-view and multi-window level method, at the same time we employ a fine-tune strategy to mitigate the impact of inconsistent labeling.
P2M-DeTrack: Processing-in-Pixel-in-Memory for Energy-efficient and Real-Time Multi-Object Detection and Tracking
The designs also reduce the sensor and total energy (obtained from in-house circuit simulations at Globalfoundries 22nm technology node) per frame by 5. 7x and 1. 14x, respectively.
Risk Assessment with Generic Energy Storage under Exogenous and Endogenous Uncertainty
Current risk assessment ignores the stochastic nature of energy storage availability itself and thus lead to potential risk during operation.
StructDepth: Leveraging the structural regularities for self-supervised indoor depth estimation
Inspired by the early works on indoor modeling, we leverage the structural regularities exhibited in indoor scenes, to train a better depth network.
Every Corporation Owns Its Image: Corporate Credit Ratings via Convolutional Neural Networks
Credit rating is an analysis of the credit risks associated with a corporation, which reflect the level of the riskiness and reliability in investing.
