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Found 31 papers, 11 papers with code
AnimeDL-2M: Million-Scale AI-Generated Anime Image Detection and Localization in Diffusion Era
Recent advances in image generation, particularly diffusion models, have significantly lowered the barrier for creating sophisticated forgeries, making image manipulation detection and localization (IMDL) increasingly challenging.
Doing Less for More: Consumer Search and Undertreatment in Credence Service Markets
This would arise when the cost of revisiting the initial provider is lower than that of searching for a new one.
MQADet: A Plug-and-Play Paradigm for Enhancing Open-Vocabulary Object Detection via Multimodal Question Answering
Open-vocabulary detection (OVD) is a challenging task to detect and classify objects from an unrestricted set of categories, including those unseen during training.
Skeleton-Guided-Translation: A Benchmarking Framework for Code Repository Translation with Fine-Grained Quality Evaluation
The advancement of large language models has intensified the need to modernize enterprise applications and migrate legacy systems to secure, versatile languages.
Efficient Adaptation of Pre-trained Vision Transformer via Householder Transformation
A common strategy for Parameter-Efficient Fine-Tuning (PEFT) of pre-trained Vision Transformers (ViTs) involves adapting the model to downstream tasks by learning a low-rank adaptation matrix.
AutoTVG: A New Vision-language Pre-training Paradigm for Temporal Video Grounding
Temporal Video Grounding (TVG) aims to localize a moment from an untrimmed video given the language description.
Logic Synthesis with Generative Deep Neural Networks
While deep learning has achieved significant success in various domains, its application to logic circuit design has been limited due to complex constraints and strict feasibility requirement.
CourseGPT-zh: an Educational Large Language Model Based on Knowledge Distillation Incorporating Prompt Optimization
Moreover, considering the alignment of LLM responses with user needs, a novel method for discrete prompt optimization based on LLM-as-Judge is introduced.
Low-Rank Rescaled Vision Transformer Fine-Tuning: A Residual Design Approach
Parameter-efficient fine-tuning for pre-trained Vision Transformers aims to adeptly tailor a model to downstream tasks by learning a minimal set of new adaptation parameters while preserving the frozen majority of pre-trained parameters.
Circuit Transformer: A Transformer That Preserves Logical Equivalence
In this study, we introduce a generative neural model, the "Circuit Transformer", which eliminates such wrong predictions and produces logic circuits strictly equivalent to given Boolean functions.
Computation Rate Maximization for Wireless Powered Edge Computing With Multi-User Cooperation
Simulation results show that the performance of the proposed algorithms is comparable to that of the exhaustive search method, and the deep learning-based algorithm significantly reduces the execution time of the algorithm.
MagDiff: Multi-Alignment Diffusion for High-Fidelity Video Generation and Editing
The high-fidelity alignment is developed to further enhance the fidelity of both video generation and editing by taking the subject image as an additional model input.
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Communication Efficiency Optimization of Federated Learning for Computing and Network Convergence of 6G Networks
In this paper, to improve the communication efficiency of federated learning in complex networks, we study the communication efficiency optimization of federated learning for computing and network convergence of 6G networks, methods that gives decisions on its training process for different network conditions and arithmetic power of participating devices in federated learning.
Fuse Your Latents: Video Editing with Multi-source Latent Diffusion Models
This paper is the first to reveal that T2I and T2V LDMs can complement each other in terms of structure and temporal consistency, ultimately generating high-quality videos.
Reuse and Diffuse: Iterative Denoising for Text-to-Video Generation
Conditioned on an initial video clip with a small number of frames, additional frames are iteratively generated by reusing the original latent features and following the previous diffusion process.
DocDiff: Document Enhancement via Residual Diffusion Models
Removing degradation from document images not only improves their visual quality and readability, but also enhances the performance of numerous automated document analysis and recognition tasks.
Hardware Implementation of Task-based Quantization in Multi-user Signal Recovery
In this work, we apply task-based quantization to multi-user signal recovery and present a hardware prototype implementation.
Near-Field Sparse Channel Representation and Estimation in 6G Wireless Communications
In this case, the spherical wave assumption which takes into account both the user angle and distance is more accurate than the conventional planar one that is only related to the user angle.
Toward Robust Diagnosis: A Contour Attention Preserving Adversarial Defense for COVID-19 Detection
As the COVID-19 pandemic puts pressure on healthcare systems worldwide, the computed tomography image based AI diagnostic system has become a sustainable solution for early diagnosis.
BiTAT: Neural Network Binarization with Task-dependent Aggregated Transformation
Neural network quantization aims to transform high-precision weights and activations of a given neural network into low-precision weights/activations for reduced memory usage and computation, while preserving the performance of the original model.
On Effective Scheduling of Model-based Reinforcement Learning
Model-based reinforcement learning has attracted wide attention due to its superior sample efficiency.
Nonlinear ICA Using Volume-Preserving Transformations
Nonlinear ICA is a fundamental problem in machine learning, aiming to identify the underlying independent components (sources) from data which is assumed to be a nonlinear function (mixing function) of these sources.
VideoLT: Large-scale Long-tailed Video Recognition
In this paper, we introduce VideoLT, a large-scale long-tailed video recognition dataset, as a step toward real-world video recognition.
Hierarchical Neural Architecture Search via Operator Clustering
Recently, the efficiency of automatic neural architecture design has been significantly improved by gradient-based search methods such as DARTS.
An Improvement of PAA on Trend-Based Approximation for Time Series
In this paper, we propose two new approaches for time series that utilize approximate trend feature information.
Formulating Camera-Adaptive Color Constancy as a Few-shot Meta-Learning Problem
In this work, we propose a new approach that affords fast adaptation to previously unseen cameras, and robustness to changes in capture device by leveraging annotated samples across different cameras and datasets.
Non-local NetVLAD Encoding for Video Classification
This paper describes our solution for the 2$^\text{nd}$ YouTube-8M video understanding challenge organized by Google AI.
Extracting Actionability from Machine Learning Models by Sub-optimal Deterministic Planning
Our work represents a new application of automated planning on an emerging and challenging machine learning paradigm.
Social Computing for Mobile Big Data in Wireless Networks
Mobile big data contains vast statistical features in various dimensions, including spatial, temporal, and the underlying social domain.
Multimodal Spontaneous Emotion Corpus for Human Behavior Analysis
The corpus further includes derived features from 3D, 2D, and IR (infrared) sensors and baseline results for facial expression and action unit detection.
Using Projection Kurtosis Concentration Of Natural Images For Blind Noise Covariance Matrix Estimation
Kurtosis of 1D projections provides important statistical characteristics of natural images.
