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Found 69 papers, 17 papers with code
Grounding Creativity in Physics: A Brief Survey of Physical Priors in AIGC
By presenting an in-depth analysis of physics-grounded AIGC, this survey aims to bridge the gap between generative models and physical realism, providing insights that inspire future research in physically consistent content generation.
The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions
Dataset distillation, which condenses large-scale datasets into compact synthetic representations, has emerged as a critical solution for training modern deep learning models efficiently.
CLIP-SR: Collaborative Linguistic and Image Processing for Super-Resolution
Our proposed multi-modal collaborative framework enables the production of realistic and high-quality SR images at significant up-scaling factors.
RIGI: Rectifying Image-to-3D Generation Inconsistency via Uncertainty-aware Learning
Given a single image of a target object, image-to-3D generation aims to reconstruct its texture and geometric shape.
MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing
We identify inter-task and intra-task sensitivity issues in current ICL methods for PCP, which we attribute to inflexible sampling strategies lacking context adaptation at the point and prompt levels.
Performance Boundaries and Tradeoffs in Super-Resolution Imaging Technologies for Space Targets
This paper investigates these limits by analyzing the boundaries of super- resolution algorithms for space targets and examines the relationships between key contributing factors.
Fed-piLot: Optimizing LoRA Assignment for Efficient Federated Foundation Model Fine-Tuning
Specifically, Federated FMs (FedFMs) fine-tuning using low-rank adaptation (LoRA) modules instead of the full model over multiple clients can achieve both parameter efficiency and data privacy.
Non-stationary BERT: Exploring Augmented IMU Data For Robust Human Activity Recognition
Human Activity Recognition (HAR) has gained great attention from researchers due to the popularity of mobile devices and the need to observe users' daily activity data for better human-computer interaction.
XRL: An FMM-Accelerated SIE Simulator for Resistance and Inductance Extraction of Complicated 3-D Geometries
A fast multipole method (FMM)-accelerated surface integral equation (SIE) simulator, called XRL, is proposed for broadband resistance/inductance (RL) extraction under the magneto-quasi-static assumption.
Breaking Class Barriers: Efficient Dataset Distillation via Inter-Class Feature Compensator
This leads to inefficient utilization of the distillation budget and oversight of inter-class feature distributions, which ultimately limits the effectiveness and efficiency, as demonstrated in our analysis.
Evolving from Single-modal to Multi-modal Facial Deepfake Detection: A Survey
We provide comprehensive taxonomies of detection techniques, discuss the evolution of generative methods from auto-encoders and GANs to diffusion models, and categorize these technologies by their unique attributes.
DD-RobustBench: An Adversarial Robustness Benchmark for Dataset Distillation
Dataset distillation is an advanced technique aimed at compressing datasets into significantly smaller counterparts, while preserving formidable training performance.
Frequency-Aware Deepfake Detection: Improving Generalizability through Frequency Space Learning
Consequently, these detectors have exhibited a lack of proficiency in learning the frequency domain and tend to overfit to the artifacts present in the training data, leading to suboptimal performance on unseen sources.
Data-Independent Operator: A Training-Free Artifact Representation Extractor for Generalizable Deepfake Detection
Due to its unbias towards both the training and test sources, we define it as Data-Independent Operator (DIO) to achieve appealing improvements on unseen sources.
Learning to Retrieve for Job Matching
Web-scale search systems typically tackle the scalability challenge with a two-step paradigm: retrieval and ranking.
LinkSAGE: Optimizing Job Matching Using Graph Neural Networks
This methodology decouples the training of the GNN model from that of existing Deep Neural Nets (DNN) models, eliminating the need for frequent GNN retraining while maintaining up-to-date graph signals in near realtime, allowing for the effective integration of GNN insights through transfer learning.
LiGNN: Graph Neural Networks at LinkedIn
In this paper, we present LiGNN, a deployed large-scale Graph Neural Networks (GNNs) Framework.
Two Trades is not Baffled: Condensing Graph via Crafting Rational Gradient Matching
Training on large-scale graphs has achieved remarkable results in graph representation learning, but its cost and storage have raised growing concerns.
Unified Diffusion-Based Rigid and Non-Rigid Editing with Text and Image Guidance
Existing text-to-image editing methods tend to excel either in rigid or non-rigid editing but encounter challenges when combining both, resulting in misaligned outputs with the provided text prompts.
Rethinking the Up-Sampling Operations in CNN-based Generative Network for Generalizable Deepfake Detection
Recently, the proliferation of highly realistic synthetic images, facilitated through a variety of GANs and Diffusions, has significantly heightened the susceptibility to misuse.
VehicleGAN: Pair-flexible Pose Guided Image Synthesis for Vehicle Re-identification
Vehicle Re-identification (Re-ID) has been broadly studied in the last decade; however, the different camera view angle leading to confused discrimination in the feature subspace for the vehicles of various poses, is still challenging for the Vehicle Re-ID models in the real world.
CLE Diffusion: Controllable Light Enhancement Diffusion Model
Low light enhancement has gained increasing importance with the rapid development of visual creation and editing.
Generating Reliable Pixel-Level Labels for Source Free Domain Adaptation
Our workflow generates target-like images using the noisy predictions from the original target domain images.
Counterfactual Co-occurring Learning for Bias Mitigation in Weakly-supervised Object Localization
In this paper, we conduct a thorough causal analysis to investigate the origins of biased activation.
Text-guided Eyeglasses Manipulation with Spatial Constraints
To address these limitations, we propose a Text-guided Eyeglasses Manipulation method that allows for control of the eyeglasses shape and style based on a binary mask and text, respectively.
UTSGAN: Unseen Transition Suss GAN for Transition-Aware Image-to-image Translation
Furthermore, we propose the use of transition consistency, defined on the transition variable, to enable regularization of consistency on unobserved translations, which is omitted in previous works.
Dual Stage Stylization Modulation for Domain Generalized Semantic Segmentation
To tackle this challenge, we introduce a dual-stage Feature Transform (dFT) layer within the Adversarial Semantic Hallucination+ (ASH+) framework.
An Operator Theory for Analyzing the Resolution of Multi-illumination Imaging Modalities
We show that the resolution of multi-illumination imaging is approximately determined by the new imaging kernel from our operator theory and better resolution can be realized by sparsity-promoting techniques in practice but only for resolving very sparse sources.
Enhancing Non-line-of-sight Imaging via Learnable Inverse Kernel and Attention Mechanisms
Recovering information from non-line-of-sight (NLOS) imaging is a computationally-intensive inverse problem.
Generative Gradient Inversion via Over-Parameterized Networks in Federated Learning
Federated learning has gained recognitions as a secure approach for safeguarding local private data in collaborative learning.
Super-resolution of positive near-colliding point sources
In this paper, we analyze the capacity of super-resolution of one-dimensional positive sources.
A mathematical theory of super-resolution and two-point resolution
The second crucial contribution of this paper is the theoretical proof of a two-point resolution limit in multi-dimensional spaces.
A mathematical theory of resolution limits for super-resolution of positive sources
A priori information on the positivity of source intensities is ubiquitous in imaging fields and is also important for a multitude of super-resolution and deconvolution algorithms.
Meta Knowledge Condensation for Federated Learning
In this work, we first introduce a meta knowledge representation method that extracts meta knowledge from distributed clients.
Nearly optimal resolution estimate for the two-dimensional super-resolution and a new algorithm for direction of arrival estimation with uniform rectangular array
The stability result exhibits the optimal performance of sparsity promoting in solving such problems; (iii) Our techniques pave the way for improving the estimate for resolution limits in higher-dimensional super-resolutions to nearly optimal; (iv) Inspired by these new techniques, we propose a new coordinate-combination-based model order detection algorithm for two-dimensional DOA estimation and theoretically demonstrate its optimal performance, and (v) we also propose a new coordinate-combination-based MUSIC algorithm for super-resolving sources in two-dimensional DOA estimation.
Dynamic super-resolution in particle tracking problems
This result is derived by an observation that the inherent cut-off frequency for the velocity recovery can be viewed as the total observation time multiplies the cut-off frequency of the imaging system, which may lead to a better resolution limit as compared to the one for each diffraction-limited frame.
A measurement decoupling based fast algorithm for super-resolving point sources with multi-cluster structure
We consider the problem of resolving closely spaced point sources in one dimension from their Fourier data in a bounded domain.
Active Learning for Point Cloud Semantic Segmentation via Spatial-Structural Diversity Reasoning
To deploy SSDR-AL in a more practical scenario, we design a noise-aware iterative labeling strategy to confront the "noisy annotation" problem introduced by the previous "dominant labeling" strategy in superpoints.
Mathematical Foundation of Sparsity-based Multi-snapshot Spectral Estimation
In this paper, we study the spectral estimation problem of estimating the locations of a fixed number of point sources given multiple snapshots of Fourier measurements in a bounded domain.
Onsite Non-Line-of-Sight Imaging via Online Calibrations
There has been an increasing interest in deploying non-line-of-sight (NLOS) imaging systems for recovering objects behind an obstacle.
Unsupervised Domain Adaptive Person Re-Identification via Human Learning Imitation
Unsupervised domain adaptive person re-identification has received significant attention due to its high practical value.
Domain Adaptive Person Re-Identification
Person Re-Identification
SPG-VTON: Semantic Prediction Guidance for Multi-pose Virtual Try-on
Third, a Try-on Synthesis Module (TSM) combines the coarse result and the warped clothes to generate the final virtual try-on image, preserving details of the desired clothes and under the desired pose.
Pro-UIGAN: Progressive Face Hallucination from Occluded Thumbnails
Specifically, we design a novel cross-modal transformer module for facial priors estimation, in which an input face and its landmark features are formulated as queries and keys, respectively.
Automated Deepfake Detection
In this paper, we propose to utilize Automated Machine Learning to adaptively search a neural architecture for deepfake detection.
Adversarial Semantic Hallucination for Domain Generalized Semantic Segmentation
In this work, we propose an adversarial semantic hallucination approach (ASH), which combines a class-conditioned hallucination module and a semantic segmentation module.
Mathematical Theory of Computational Resolution Limit in Multi-dimensions
Our results indicate that there exists a phase transition phenomenon regarding to the super-resolution factor and the signal-to-noise ratio in each of the two recovery problems.
Generative Transition Mechanism to Image-to-Image Translation via Encoded Transformation
To benefit the generalization ability of the translation model, we propose transition encoding to facilitate explicit regularization of these two {kinds} of consistencies on unseen transitions.
Non-line-of-Sight Imaging via Neural Transient Fields
We present a neural modeling framework for Non-Line-of-Sight (NLOS) imaging.
Point Adversarial Self Mining: A Simple Method for Facial Expression Recognition
In this paper, we propose a simple yet effective approach, named Point Adversarial Self Mining (PASM), to improve the recognition accuracy in facial expression recognition.
Omni-supervised Facial Expression Recognition via Distilled Data
However, the performance of the current state-of-the-art facial expression recognition (FER) approaches is directly related to the labeled data for training.
Federated Generative Adversarial Learning
This work studies training generative adversarial networks under the federated learning setting.
Adversarial Style Mining for One-Shot Unsupervised Domain Adaptation
We aim at the problem named One-Shot Unsupervised Domain Adaptation.
domain classification
One-shot Unsupervised Domain Adaptation
+2
Pseudo-Labeling for Small Lesion Detection on Diabetic Retinopathy Images
The first one is that our dataset is not fully labeled, i. e., only a subset of all lesion instances are marked.
CRATOS: Cognition of Reliable Algorithm for Time-series Optimal Solution
Our methods can significantly reduce the cost of development and maintenance of anomaly detection.
Face Hallucination with Finishing Touches
The coarse-level FHnet generates a frontal coarse HR face and then the fine-level FHnet makes use of the facial component appearance prior, i. e., fine-grained facial components, to attain a frontal HR face image with authentic details.
Very Long Natural Scenery Image Prediction by Outpainting
The second challenge is how to maintain high quality in generated results, especially for multi-step generations in which generated regions are spatially far away from the initial input.
Unsupervised Eyeglasses Removal in the Wild
Given two facial images with and without eyeglasses, the proposed model learns to swap the eye area in two faces.
Feature-level and Model-level Audiovisual Fusion for Emotion Recognition in the Wild
In this paper, we proposed two strategies to fuse information extracted from different modalities, i. e., audio and visual.
NULI at SemEval-2019 Task 6: Transfer Learning for Offensive Language Detection using Bidirectional Transformers
Transfer learning and domain adaptive learning have been applied to various fields including computer vision (e. g., image recognition) and natural language processing (e. g., text classification).
Filter Pruning by Switching to Neighboring CNNs with Good Attributes
In addition, when evaluating the filter importance, only the magnitude information of the filters is considered.
Significance-aware Information Bottleneck for Domain Adaptive Semantic Segmentation
For unsupervised domain adaptation problems, the strategy of aligning the two domains in latent feature space through adversarial learning has achieved much progress in image classification, but usually fails in semantic segmentation tasks in which the latent representations are overcomplex.
A Top-down Approach to Articulated Human Pose Estimation and Tracking
Both the tasks of multi-person human pose estimation and pose tracking in videos are quite challenging.
Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration
In this paper, we analyze this norm-based criterion and point out that its effectiveness depends on two requirements that are not always met: (1) the norm deviation of the filters should be large; (2) the minimum norm of the filters should be small.
Every Node Counts: Self-Ensembling Graph Convolutional Networks for Semi-Supervised Learning
To capitalize on the information from unlabeled nodes to boost the training for GCN, we propose a novel framework named Self-Ensembling GCN (SEGCN), which marries GCN with Mean Teacher - another powerful model in semi-supervised learning.
Ranked #4 on
Node Classification
on Cora: fixed 20 node per class
Forecasting the presence and intensity of hostility on Instagram using linguistic and social features
Online antisocial behavior, such as cyberbullying, harassment, and trolling, is a widespread problem that threatens free discussion and has negative physical and mental health consequences for victims and communities.
Computer-aided diagnosis of lung carcinoma using deep learning - a pilot study
Aim: Early detection and correct diagnosis of lung cancer are the most important steps in improving patient outcome.
Optimize transfer learning for lung diseases in bronchoscopy using a new concept: sequential fine-tuning
To help doctors to be more selective on biopsies and provide a second opinion on diagnosis, in this work, we propose a computer-aided diagnosis (CAD) system for lung diseases including cancers and tuberculosis (TB).
Improving Speech Related Facial Action Unit Recognition by Audiovisual Information Fusion
Instead of solely improving visual observations, this paper presents a novel audiovisual fusion framework, which makes the best use of visual and acoustic cues in recognizing speech-related facial AUs.
Facial Expression Recognition via a Boosted Deep Belief Network
A training process for facial expression recognition is usually performed sequentially in three individual stages: feature learning, feature selection, and classifier construction.
Facial Expression Recognition
Facial Expression Recognition (FER)
+1
