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Found 149 papers, 53 papers with code
Rethinking Classification and Localization for Object Detection
Two head structures (i. e. fully connected head and convolution head) have been widely used in R-CNN based detectors for classification and localization tasks.
MMDetection: Open MMLab Detection Toolbox and Benchmark
In this paper, we introduce the various features of this toolbox.
PixArt-$α$: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis
We hope PIXART-$\alpha$ will provide new insights to the AIGC community and startups to accelerate building their own high-quality yet low-cost generative models from scratch.
PIXART-δ: Fast and Controllable Image Generation with Latent Consistency Models
As a state-of-the-art, open-source image generation model, PIXART-{\delta} offers a promising alternative to the Stable Diffusion family of models, contributing significantly to text-to-image synthesis.
Large Scale Incremental Learning
We believe this is because of the combination of two factors: (a) the data imbalance between the old and new classes, and (b) the increasing number of visually similar classes.
A Survey of Reasoning with Foundation Models
Reasoning, a crucial ability for complex problem-solving, plays a pivotal role in various real-world settings such as negotiation, medical diagnosis, and criminal investigation.
ManTra-Net: Manipulation Tracing Network for Detection and Localization of Image Forgeries With Anomalous Features
To fight against real-life image forgery, which commonly involves different types and combined manipulations, we propose a unified deep neural architecture called ManTra-Net.
Ranked #4 on
Image Manipulation Localization
on CocoGlide
QATM: Quality-Aware Template Matching For Deep Learning
Finding a template in a search image is one of the core problems many computer vision, such as semantic image semantic, image-to-GPS verification \etc.
MRI Tumor Segmentation with Densely Connected 3D CNN
The major difficulty of our segmentation model comes with the fact that the location, structure, and shape of gliomas vary significantly among different patients.
Prompt-aligned Gradient for Prompt Tuning
Thanks to the large pre-trained vision-language models (VLMs) like CLIP, we can craft a zero-shot classifier by "prompt", e. g., the confidence score of an image being "[CLASS]" can be obtained by using the VLM provided similarity measure between the image and the prompt sentence "a photo of a [CLASS]".
SOL: A Library for Scalable Online Learning Algorithms
SOL is an open-source library for scalable online learning algorithms, and is particularly suitable for learning with high-dimensional data.
SmartPlay: A Benchmark for LLMs as Intelligent Agents
We introduce SmartPlay: both a challenging benchmark and a methodology for evaluating LLMs as agents.
MiM-ISTD: Mamba-in-Mamba for Efficient Infrared Small Target Detection
Inspired by the recent basic model with linear complexity for long-distance modeling, called Mamba, we explore the potential of this state space model for ISTD task in terms of effectiveness and efficiency in the paper.
BusterNet: Detecting Copy-Move Image Forgery with Source/Target Localization
We introduce a novel deep neural architecture for image copy-move forgery detection (CMFD), code-named BusterNet.
VelocityGAN: Data-Driven Full-Waveform Inversion Using Conditional Adversarial Networks
Acoustic- and elastic-waveform inversion is an important and widely used method to reconstruct subsurface velocity image.
Future Video Synthesis with Object Motion Prediction
We present an approach to predict future video frames given a sequence of continuous video frames in the past.
Ranked #2 on
Video Prediction
on Cityscapes
(using extra training data)
AniFaceGAN: Animatable 3D-Aware Face Image Generation for Video Avatars
To achieve meaningful control over facial expressions via deformation, we propose a 3D-level imitative learning scheme between the generator and a parametric 3D face model during adversarial training of the 3D-aware GAN.
Optimizing Video Prediction via Video Frame Interpolation
Extensive experiments on the Cityscapes, KITTI, DAVIS, Middlebury, and Vimeo90K datasets show that our video prediction results are robust in general scenarios, and our approach outperforms other video prediction methods that require a large amount of training data or extra semantic information.
DNA-GPT: Divergent N-Gram Analysis for Training-Free Detection of GPT-Generated Text
However, this progress also presents a significant challenge in detecting the origin of a given text, and current research on detection methods lags behind the rapid evolution of LLMs.
Reliable Conflictive Multi-View Learning
To solve this, we point out a new Reliable Conflictive Multi-view Learning (RCML) problem, which requires the model to provide decision results and attached reliabilities for conflictive multi-view data.
Self-supervised Learning from a Multi-view Perspective
In particular, we propose a composite objective that bridges the gap between prior contrastive and predictive learning objectives, and introduce an additional objective term to discard task-irrelevant information.
Video Waterdrop Removal via Spatio-Temporal Fusion in Driving Scenes
The waterdrops on windshields during driving can cause severe visual obstructions, which may lead to car accidents.
Uncertainty Weighted Actor-Critic for Offline Reinforcement Learning
Offline Reinforcement Learning promises to learn effective policies from previously-collected, static datasets without the need for exploration.
Improving GAN Training with Probability Ratio Clipping and Sample Reweighting
Despite success on a wide range of problems related to vision, generative adversarial networks (GANs) often suffer from inferior performance due to unstable training, especially for text generation.
Ranked #2 on
Text Generation
on EMNLP2017 WMT
SauvolaNet: Learning Adaptive Sauvola Network for Degraded Document Binarization
The AST module further consolidates the outputs from MWS and PWA and predicts the final adaptive threshold for each pixel location.
M3SOT: Multi-frame, Multi-field, Multi-space 3D Single Object Tracking
3D Single Object Tracking (SOT) stands a forefront task of computer vision, proving essential for applications like autonomous driving.
Towards Understanding Learning Representations: To What Extent Do Different Neural Networks Learn the Same Representation
The theory gives a complete characterization of the structure of neuron activation subspace matches, where the core concepts are maximum match and simple match which describe the overall and the finest similarity between sets of neurons in two networks respectively.
Personalized Federated Learning under Mixture of Distributions
The recent trend towards Personalized Federated Learning (PFL) has garnered significant attention as it allows for the training of models that are tailored to each client while maintaining data privacy.
LineCounter: Learning Handwritten Text Line Segmentation by Counting
In this paper, we propose a novel Line Counting formulation for HTLS -- that involves counting the number of text lines from the top at every pixel location.
Avoiding spurious correlations via logit correction
Empirical studies suggest that machine learning models trained with empirical risk minimization (ERM) often rely on attributes that may be spuriously correlated with the class labels.
Preserving Commonsense Knowledge from Pre-trained Language Models via Causal Inference
Intriguingly, the unified objective can be seen as the sum of the vanilla fine-tuning objective, which learns new knowledge from target data, and the causal objective, which preserves old knowledge from PLMs.
Graph Generative Model for Benchmarking Graph Neural Networks
As the field of Graph Neural Networks (GNN) continues to grow, it experiences a corresponding increase in the need for large, real-world datasets to train and test new GNN models on challenging, realistic problems.
Towards Understanding Mixture of Experts in Deep Learning
To our knowledge, this is the first result towards formally understanding the mechanism of the MoE layer for deep learning.
SKFAC: Training Neural Networks With Faster Kronecker-Factored Approximate Curvature
For the fully connected layers, by utilizing the low-rank property of Kronecker factors of Fisher information matrix, our method only requires inverting a small matrix to approximate the curvature with desirable accuracy.
Embedding Novel Views in a Single JPEG Image
We propose a novel approach for embedding novel views in a single JPEG image while preserving the perceptual fidelity of the modified JPEG image and the restored novel views.
SKFAC:Training Neural Networks with Faster Kronecker-Factored Approximate Curvature
For the fully connected layers, by utilizing the low-rank property of Kronecker factors of Fisher information matrix, our method only requires inverting a small matrix to approximate the curvature with desirable accuracy.
CapsuleGAN: Generative Adversarial Capsule Network
We provide guidelines for designing CapsNet discriminators and the updated GAN objective function, which incorporates the CapsNet margin loss, for training CapsuleGAN models.
Deep Multimodal Image-Repurposing Detection
Nefarious actors on social media and other platforms often spread rumors and falsehoods through images whose metadata (e. g., captions) have been modified to provide visual substantiation of the rumor/falsehood.
Spatially Constrained GAN for Face and Fashion Synthesis
Besides, a segmentor network is constructed to impose spatial constraints on the generator.
Editing Massive Concepts in Text-to-Image Diffusion Models
We further propose a comprehensive benchmark, named ImageNet Concept Editing Benchmark (ICEB), for evaluating massive concept editing for T2I models with two subtasks, free-form prompts, massive concept categories, and extensive evaluation metrics.
AIRD: Adversarial Learning Framework for Image Repurposing Detection
Image repurposing is a commonly used method for spreading misinformation on social media and online forums, which involves publishing untampered images with modified metadata to create rumors and further propaganda.
Style-Aware Normalized Loss for Improving Arbitrary Style Transfer
Neural Style Transfer (NST) has quickly evolved from single-style to infinite-style models, also known as Arbitrary Style Transfer (AST).
Multi-spectral Class Center Network for Face Manipulation Detection and Localization
Next, a novel Multi-Spectral Class Center Network (MSCCNet) is proposed for face manipulation detection and localization.
SPRING: Studying the Paper and Reasoning to Play Games
Finally, we show the potential of games as a test bed for LLMs.
Self-Organized Text Detection With Minimal Post-Processing via Border Learning
In this paper we propose a new solution to the text detection problem via border learning.
Maximizing Mutual Information Across Feature and Topology Views for Learning Graph Representations
Specifically, we first utilize a multi-view representation learning module to better capture both local and global information content across feature and topology views on graphs.
Planimation
Planimation is a modular and extensible open source framework to visualise sequential solutions of planning problems specified in PDDL.
Forecasting Future Humphrey Visual Fields Using Deep Learning
Methods: All datapoints from consecutive 24-2 HVFs from 1998 to 2018 were extracted from a University of Washington database.
Towards Consistency and Complementarity: A Multiview Graph Information Bottleneck Approach
In this paper, we propose a novel Multiview Variational Graph Information Bottleneck (MVGIB) principle to maximize the agreement for common representations and the disagreement for view-specific representations.
NODE-ImgNet: a PDE-informed effective and robust model for image denoising
Inspired by the traditional partial differential equation (PDE) approach for image denoising, we propose a novel neural network architecture, referred as NODE-ImgNet, that combines neural ordinary differential equations (NODEs) with convolutional neural network (CNN) blocks.
Predictive Local Smoothness for Stochastic Gradient Methods
To address this problem, we propose a simple yet effective method for improving stochastic gradient methods named predictive local smoothness (PLS).
Facial Landmark Detection: a Literature Survey
The regression-based methods implicitly capture facial shape and appearance information.
Machine Learning for Exam Triage
In this project, we extend the state-of-the-art CheXNet (Rajpurkar et al. [2017]) by making use of the additional non-image features in the dataset.
Bidirectional Conditional Generative Adversarial Networks
Conditional Generative Adversarial Networks (cGANs) are generative models that can produce data samples ($x$) conditioned on both latent variables ($z$) and known auxiliary information ($c$).
Generating retinal flow maps from structural optical coherence tomography with artificial intelligence
Despite significant advances in artificial intelligence (AI) for computer vision, its application in medical imaging has been limited by the burden and limits of expert-generated labels.
Incremental Classifier Learning with Generative Adversarial Networks
To address these problems, we propose (a) a new loss function to combine the cross-entropy loss and distillation loss, (b) a simple way to estimate and remove the unbalance between the old and new classes , and (c) using Generative Adversarial Networks (GANs) to generate historical data and select representative exemplars during generation.
Seismic-Net: A Deep Densely Connected Neural Network to Detect Seismic Events
In particular, we demonstrate the efficacy of our Seismic-Net by formulating our detection problem as an event detection problem with time series data.
Clustering with Outlier Removal
Then an objective function based Holoentropy is designed to enhance the compactness of each cluster with a few outliers removed.
Cascaded Region-based Densely Connected Network for Event Detection: A Seismic Application
Because of the fact that some positive events are not correctly annotated, we further formulate the detection problem as a learning-from-noise problem.
Simultaneous Facial Landmark Detection, Pose and Deformation Estimation under Facial Occlusion
Facial landmark detection, head pose estimation, and facial deformation analysis are typical facial behavior analysis tasks in computer vision.
Constrained Joint Cascade Regression Framework for Simultaneous Facial Action Unit Recognition and Facial Landmark Detection
Experimental results demonstrate that the intertwined relationships of facial action units and face shapes boost the performances of both facial action unit recognition and facial landmark detection.
Constrained Deep Transfer Feature Learning and its Applications
Furthermore, we propose to exploit the target domain knowledge and incorporate such prior knowledge as a constraint during transfer learning to ensure that the transferred data satisfies certain properties of the target domain.
Facial Expression Recognition
Facial Expression Recognition (FER)
+1
Robust Facial Landmark Detection under Significant Head Poses and Occlusion
In this work, we propose a unified robust cascade regression framework that can handle both images with severe occlusion and images with large head poses.
A Hierarchical Probabilistic Model for Facial Feature Detection
Facial feature detection from facial images has attracted great attention in the field of computer vision.
Facial Feature Tracking under Varying Facial Expressions and Face Poses based on Restricted Boltzmann Machines
To handle pose variations, the frontal face shape prior model is incorporated into a 3-way RBM model that could capture the relationship between frontal face shapes and non-frontal face shapes.
Deep Matching and Validation Network -- An End-to-End Solution to Constrained Image Splicing Localization and Detection
Here the task is to estimate the probability that the donor image has been used to splice the query image, and obtain the splicing masks for both the query and donor images.
Families in the Wild (FIW): Large-Scale Kinship Image Database and Benchmarks
Motivated by the lack of a single, unified dataset for kinship recognition, we aim to provide a dataset that captivates the interest of the research community.
Facial Landmark Detection with Tweaked Convolutional Neural Networks
We present a novel convolutional neural network (CNN) design for facial landmark coordinate regression.
Large-scale Online Feature Selection for Ultra-high Dimensional Sparse Data
However, unlike many second-order learning methods that often suffer from extra high computational cost, we devise a novel smart algorithm for second-order online feature selection using a MaxHeap-based approach, which is not only more effective than the existing first-order approaches, but also significantly more efficient and scalable for large-scale feature selection with ultra-high dimensional sparse data, as validated from our extensive experiments.
LOGO-Net: Large-scale Deep Logo Detection and Brand Recognition with Deep Region-based Convolutional Networks
In this paper, we introduce "LOGO-Net", a large-scale logo image database for logo detection and brand recognition from real-world product images.
Learning Document Image Binarization from Data
In this paper we present a fully trainable binarization solution for degraded document images.
Gaussian Process Volatility Model
A Gaussian Process (GP) defines a distribution over functions, which allows us to capture highly flexible functional relationships for the variances.
Dynamic Covariance Models for Multivariate Financial Time Series
The accurate prediction of time-changing covariances is an important problem in the modeling of multivariate financial data.
Blockwise SURE Shrinkage for Non-Local Means
In this letter, we investigate the shrinkage problem for the non-local means (NLM) image denoising.
Unsupervised Adversarial Invariance
Data representations that contain all the information about target variables but are invariant to nuisance factors benefit supervised learning algorithms by preventing them from learning associations between these factors and the targets, thus reducing overfitting.
Image-to-GPS Verification Through A Bottom-Up Pattern Matching Network
The image-to-GPS verification problem asks whether a given image is taken at a claimed GPS location.
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.
Relaxing From Vocabulary: Robust Weakly-Supervised Deep Learning for Vocabulary-Free Image Tagging
The development of deep learning has empowered machines with comparable capability of recognizing limited image categories to human beings.
Unified Adversarial Invariance
We present a unified invariance framework for supervised neural networks that can induce independence to nuisance factors of data without using any nuisance annotations, but can additionally use labeled information about biasing factors to force their removal from the latent embedding for making fair predictions.
Coverage-based Outlier Explanation
Existing algorithms are primarily focused on detection, that is the identification of outliers in a given dataset.
Towards Understanding the Spectral Bias of Deep Learning
An intriguing phenomenon observed during training neural networks is the spectral bias, which states that neural networks are biased towards learning less complex functions.
Cross-modality Person re-identification with Shared-Specific Feature Transfer
In this paper, we tackle the above limitation by proposing a novel cross-modality shared-specific feature transfer algorithm (termed cm-SSFT) to explore the potential of both the modality-shared information and the modality-specific characteristics to boost the re-identification performance.
Cross-Modality Person Re-identification
Person Re-Identification
Signature features with the visibility transformation
In this paper we put the visibility transformation on a clear theoretical footing and show that this transform is able to embed the effect of the absolute position of the data stream into signature features in a unified and efficient way.
A Finite Time Analysis of Two Time-Scale Actor Critic Methods
In this work, we provide a non-asymptotic analysis for two time-scale actor-critic methods under non-i. i. d.
Learning to Detect Bipolar Disorder and Borderline Personality Disorder with Language and Speech in Non-Clinical Interviews
Bipolar disorder (BD) and borderline personality disorder (BPD) are both chronic psychiatric disorders.
VideoFlow: A Framework for Building Visual Analysis Pipelines
The past years have witnessed an explosion of deep learning frameworks like PyTorch and TensorFlow since the success of deep neural networks.
Uncertainty Weighted Offline Reinforcement Learning
Offline Reinforcement Learning promises to learn effective policies from previously-collected, static datasets without the need for exploration.
Claw U-Net: A Unet-based Network with Deep Feature Concatenation for Scleral Blood Vessel Segmentation
Sturge-Weber syndrome (SWS) is a vascular malformation disease, and it may cause blindness if the patient's condition is severe.
Unsupervised Domain Adaptation for Visual Navigation
We further show that our method can be used to transfer the navigation policies learned in simulation to the real world.
A Finite-Time Analysis of Two Time-Scale Actor-Critic Methods
In this work, we provide a non-asymptotic analysis for two time-scale actor-critic methods under non-i. i. d.
Class-agnostic Object Detection
Finally, we propose (1) baseline methods and (2) a new adversarial learning framework for class-agnostic detection that forces the model to exclude class-specific information from features used for predictions.
Ranked #100 on
Image Classification
on ObjectNet
(using extra training data)
Game Theoretic Consequences of Resident Matching
The resident matching algorithm, Gale-Shapley, currently used by SF Match and the National Residency Match Program (NRMP), has been in use for over 50 years without fundamental alteration.
Nearly Minimax Optimal Regret for Learning Infinite-horizon Average-reward MDPs with Linear Function Approximation
We study reinforcement learning in an infinite-horizon average-reward setting with linear function approximation, where the transition probability function of the underlying Markov Decision Process (MDP) admits a linear form over a feature mapping of the current state, action, and next state.
Modelling Paralinguistic Properties in Conversational Speech to Detect Bipolar Disorder and Borderline Personality Disorder
Bipolar disorder (BD) and borderline personality disorder (BPD) are two chronic mental health conditions that clinicians find challenging to distinguish based on clinical interviews, due to their overlapping symptoms.
CRT-Net: A Generalized and Scalable Framework for the Computer-Aided Diagnosis of Electrocardiogram Signals
Subsequently, a novel deep neural network, namely CRT-Net, is designed for the fine-grained and comprehensive representation and recognition of 1-D ECG signals.
Feature Flow Regularization: Improving Structured Sparsity in Deep Neural Networks
Pruning is a model compression method that removes redundant parameters in deep neural networks (DNNs) while maintaining accuracy.
Video Super-Resolution with Long-Term Self-Exemplars
Existing video super-resolution methods often utilize a few neighboring frames to generate a higher-resolution image for each frame.
Demonstration-Guided Reinforcement Learning with Learned Skills
Prior approaches for demonstration-guided RL treat every new task as an independent learning problem and attempt to follow the provided demonstrations step-by-step, akin to a human trying to imitate a completely unseen behavior by following the demonstrator's exact muscle movements.
Towards Photorealistic Colorization by Imagination
We present a novel approach to automatic image colorization by imitating the imagination process of human experts.
Adaptive Sampling for Heterogeneous Rank Aggregation from Noisy Pairwise Comparisons
In heterogeneous rank aggregation problems, users often exhibit various accuracy levels when comparing pairs of items.
Causal Triple Attention Time Series Forecasting
Time series forecasting has historically been a key area of academic research and industrial applications.
ADDS: Adaptive Differentiable Sampling for Robust Multi-Party Learning
Inspired by the idea of dropout in neural networks, we introduce a network sampling strategy in the multi-party setting, which distributes different subnets of the central model to clients for updating, and the differentiable sampling rates allow each client to extract optimal local architecture from the supernet according to its private data distribution.
Spatially Self-Paced Convolutional Networks for Change Detection in Heterogeneous Images
Change detection in heterogeneous remote sensing images is a challenging problem because it is hard to make a direct comparison in the original observation spaces, and most methods rely on a set of manually labeled samples.
Unsupervised cross domain learning with applications to 7 layer segmentation of OCTs
Unsupervised cross domain adaptation for OCT 7 layer segmentation and other medical applications where labeled training data is only available in a source domain and unavailable in the target domain.
The China Trade Shock and the ESG Performances of US firms
Exploiting a trade policy in which US congress granted China the Permanent Normal Trade Relations and the resulting change in expected tariff rates on Chinese imports, we find that greater import competition from China leads to an increase in the US company's ESG performance.
Real-time Online Multi-Object Tracking in Compressed Domain
Recent online Multi-Object Tracking (MOT) methods have achieved desirable tracking performance.
Lesion Localization in OCT by Semi-Supervised Object Detection
Over 300 million people worldwide are affected by various retinal diseases.
Multi-view Point Cloud Registration based on Evolutionary Multitasking with Bi-Channel Knowledge Sharing Mechanism
The modeling of multi-view point cloud registration as multi-task optimization are twofold.
FashionVLP: Vision Language Transformer for Fashion Retrieval With Feedback
Fashion image retrieval based on a query pair of reference image and natural language feedback is a challenging task that requires models to assess fashion related information from visual and textual modalities simultaneously.
Geometric Policy Iteration for Markov Decision Processes
GPI updates the policy of a single state by switching to an action that is mapped to the boundary of the value function polytope, followed by an immediate update of the value function.
Turning Mathematics Problems into Games: Reinforcement Learning and Gröbner bases together solve Integer Feasibility Problems
The Gr\"obner basis can be seen as a set of connecting moves (actions) of the game.
Sybil-Proof Diffusion Auction in Social Networks
A diffusion auction is a market to sell commodities over a social network, where the challenge is to incentivize existing buyers to invite their neighbors in the network to join the market.
Bayesian Layer Graph Convolutioanl Network for Hyperspetral Image Classification
In recent years, research on hyperspectral image (HSI) classification has continuous progress on introducing deep network models, and recently the graph convolutional network (GCN) based models have shown impressive performance.
How to Describe Images in a More Funny Way? Towards a Modular Approach to Cross-Modal Sarcasm Generation
Sarcasm generation has been investigated in previous studies by considering it as a text-to-text generation problem, i. e., generating a sarcastic sentence for an input sentence.
Borda Regret Minimization for Generalized Linear Dueling Bandits
To attain this lower bound, we propose an explore-then-commit type algorithm for the stochastic setting, which has a nearly matching regret upper bound $\tilde{O}(d^{2/3} T^{2/3})$.
Elastic Interaction Energy-Based Generative Model: Approximation in Feature Space
We adopt the GAN framework and replace the discriminator with a feature transformation network to map the data into a latent space.
Plan, Eliminate, and Track -- Language Models are Good Teachers for Embodied Agents
We propose the Plan, Eliminate, and Track (PET) framework.
Score-based Transport Modeling for Mean-Field Fokker-Planck Equations
We use the score-based transport modeling method to solve the mean-field Fokker-Planck equations, which we call MSBTM.
Uniform-PAC Guarantees for Model-Based RL with Bounded Eluder Dimension
Recently, there has been remarkable progress in reinforcement learning (RL) with general function approximation.
A Neural RDE-based model for solving path-dependent PDEs
The concept of the path-dependent partial differential equation (PPDE) was first introduced in the context of path-dependent derivatives in financial markets.
Exploring the Application of Large-scale Pre-trained Models on Adverse Weather Removal
Inspired by the various successful applications of large-scale pre-trained models (e. g, CLIP), in this paper, we explore the potential benefits of them for this task through both spatial feature representation learning and semantic information embedding aspects: 1) for spatial feature representation learning, we design a Spatially-Adaptive Residual (\textbf{SAR}) Encoder to extract degraded areas adaptively.
Stability Analysis Framework for Particle-based Distance GANs with Wasserstein Gradient Flow
In this paper, we analyze the stability of the training process of these GANs from the perspective of probability density dynamics.
Emotional Intelligence of Large Language Models
Specifically, we first developed a novel psychometric assessment focusing on Emotion Understanding (EU), a core component of EI, suitable for both humans and LLMs.
One-Nearest Neighborhood Guides Inlier Estimation for Unsupervised Point Cloud Registration
In this paper, we propose an effective inlier estimation method for unsupervised point cloud registration by capturing geometric structure consistency between the source point cloud and its corresponding reference point cloud copy.
Optimal battery thermal management for electric vehicles with battery degradation minimization
A control-oriented onboard BTMS model is proposed and verified under different speed profiles and temperatures.
FashionNTM: Multi-turn Fashion Image Retrieval via Cascaded Memory
Extensive evaluation results show that our proposed method outperforms the previous state-of-the-art algorithm by 50. 5%, on Multi-turn FashionIQ -- the only existing multi-turn fashion dataset currently, in addition to having a relative improvement of 12. 6% on Multi-turn Shoes -- an extension of the single-turn Shoes dataset that we created in this work.
AniPortraitGAN: Animatable 3D Portrait Generation from 2D Image Collections
For the new task, we base our method on the generative radiance manifold representation and equip it with learnable facial and head-shoulder deformations.
Variance-Aware Regret Bounds for Stochastic Contextual Dueling Bandits
Dueling bandits is a prominent framework for decision-making involving preferential feedback, a valuable feature that fits various applications involving human interaction, such as ranking, information retrieval, and recommendation systems.
Large Language Models Can Be Good Privacy Protection Learners
To address this challenge, we introduce Privacy Protection Language Models (PPLM), a novel paradigm for fine-tuning LLMs that effectively injects domain-specific knowledge while safeguarding data privacy.
Open-Ended Instructable Embodied Agents with Memory-Augmented Large Language Models
Pre-trained and frozen large language models (LLMs) can effectively map simple scene rearrangement instructions to programs over a robot's visuomotor functions through appropriate few-shot example prompting.
Language Grounded QFormer for Efficient Vision Language Understanding
Large-scale pretraining and instruction tuning have been successful for training general-purpose language models with broad competencies.
Drag-A-Video: Non-rigid Video Editing with Point-based Interaction
In this paper, we propose a new diffusion-based method for interactive point-based video manipulation, called Drag-A-Video.
PCRDiffusion: Diffusion Probabilistic Models for Point Cloud Registration
We propose a new framework that formulates point cloud registration as a denoising diffusion process from noisy transformation to object transformation.
Fast Training of Diffusion Transformer with Extreme Masking for 3D Point Clouds Generation
Motivated by the inherent redundancy of 3D compared to 2D, we propose FastDiT-3D, a novel masked diffusion transformer tailored for efficient 3D point cloud generation, which greatly reduces training costs.
SimAC: A Simple Anti-Customization Method against Text-to-Image Synthesis of Diffusion Models
Despite the success of diffusion-based customization methods on visual content creation, increasing concerns have been raised about such techniques from both privacy and political perspectives.
Neural Gaussian Similarity Modeling for Differential Graph Structure Learning
Graph Structure Learning (GSL) has demonstrated considerable potential in the analysis of graph-unknown non-Euclidean data across a wide range of domains.
MoMA: Model-based Mirror Ascent for Offline Reinforcement Learning
Model-based offline reinforcement learning methods (RL) have achieved state-of-the-art performance in many decision-making problems thanks to their sample efficiency and generalizability.
Bootstrapping Audio-Visual Segmentation by Strengthening Audio Cues
This bidirectional interaction narrows the modality imbalance, facilitating more effective learning of integrated audio-visual representations.
TCI-Former: Thermal Conduction-Inspired Transformer for Infrared Small Target Detection
We abstract this process as the directional movement of feature map pixels to target areas through convolution, pooling and interactions with surrounding pixels, which can be analogous to the movement of thermal particles constrained by surrounding variables and particles.
INSIDE: LLMs' Internal States Retain the Power of Hallucination Detection
Knowledge hallucination have raised widespread concerns for the security and reliability of deployed LLMs.
The practice of qualitative parameterisation in the development of Bayesian networks
Since the practice focuses on qualitative issues, despite being quantitative in nature, we call this step qualitative parameterisation and provide an outline of its role in the BN development process.
GCOF: Self-iterative Text Generation for Copywriting Using Large Language Model
Large language models(LLM) such as ChatGPT have substantially simplified the generation of marketing copy, yet producing content satisfying domain specific requirements, such as effectively engaging customers, remains a significant challenge.
EyeGPT: Ophthalmic Assistant with Large Language Models
Artificial intelligence (AI) has gained significant attention in healthcare consultation due to its potential to improve clinical workflow and enhance medical communication.
PixArt-Σ: Weak-to-Strong Training of Diffusion Transformer for 4K Text-to-Image Generation
In this paper, we introduce PixArt-\Sigma, a Diffusion Transformer model~(DiT) capable of directly generating images at 4K resolution.
Causal knowledge engineering: A case study from COVID-19
The unique challenges of the setting lead to experiments with the elicitation approach, and what emerged was a knowledge engineering method we call Causal Knowledge Engineering (CKE).
Protein Conformation Generation via Force-Guided SE(3) Diffusion Models
The conformational landscape of proteins is crucial to understanding their functionality in complex biological processes.
Robust Preference Optimization with Provable Noise Tolerance for LLMs
The preference alignment aims to enable large language models (LLMs) to generate responses that conform to human values, which is essential for developing general AI systems.
Automatic Controllable Colorization via Imagination
Unlike most previous end-to-end automatic colorization algorithms, our framework allows for iterative and localized modifications of the colorization results because we explicitly model the coloring samples.
AgentKit: Flow Engineering with Graphs, not Coding
The chains of nodes can be designed to explicitly enforce a naturally structured "thought process".
