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Found 78 papers, 29 papers with code
Network Pruning by Greedy Subnetwork Selection
Theoretically, we show that the small networks pruned using our method achieve provably lower loss than small networks trained from scratch with the same size.
High Dynamic Range Novel View Synthesis with Single Exposure
Typically, multiple-exposure LDR images are employed to capture a wider range of brightness levels in a scene, as a single LDR image cannot represent both the brightest and darkest regions simultaneously.
self-prompting analogical reasoning for uav object detection
While for analogical reasoningmodule, graph nodes consist of category-level prompt nodes and pixel-level image feature nodes. Analogical inference is based on graph convolution.
Tumor-associated CD19$^+$ macrophages induce immunosuppressive microenvironment in hepatocellular carcinoma
The CD19$^+$ macrophages exhibit increased levels of PD-L1 and CD73, enhanced mitochondrial oxidation, and compromised phagocytosis, indicating their immunosuppressive functions.
Bayesian Test-Time Adaptation for Vision-Language Models
Test-time adaptation with pre-trained vision-language models, such as CLIP, aims to adapt the model to new, potentially out-of-distribution test data.
Generative Data Mining with Longtail-Guided Diffusion
It is difficult to anticipate the myriad challenges that a predictive model will encounter once deployed.
Cloud Object Detector Adaptation by Integrating Different Source Knowledge
Knowledge dissemination combines knowledge from cloud detector and CLIP model to initialize a target detector and a CLIP detector in target domain.
Is Foreground Prototype Sufficient? Few-Shot Medical Image Segmentation with Background-Fused Prototype
However, this paradigm is not applicable to medical images where the foreground and background share numerous visual features, necessitating a more detailed description for background.
Domain Adaptive Diabetic Retinopathy Grading with Model Absence and Flowing Data
Specifically, we first theoretically reformulate conventional perturbation optimization in a generative way--learning a perturbation generation function with a latent input variable.
OpenHumanVid: A Large-Scale High-Quality Dataset for Enhancing Human-Centric Video Generation
Recent advancements in visual generation technologies have markedly increased the scale and availability of video datasets, which are crucial for training effective video generation models.
FreeCap: Hybrid Calibration-Free Motion Capture in Open Environments
We propose a novel hybrid calibration-free method FreeCap to accurately capture global multi-person motions in open environments.
LGU-SLAM: Learnable Gaussian Uncertainty Matching with Deformable Correlation Sampling for Deep Visual SLAM
In general, they heavily rely on global visual similarity matching.
Queryable Prototype Multiple Instance Learning with Vision-Language Models for Incremental Whole Slide Image Classification
Whole Slide Image (WSI) classification has very significant applications in clinical pathology, e. g., tumor identification and cancer diagnosis.
Rethinking Weak-to-Strong Augmentation in Source-Free Domain Adaptive Object Detection
The core idea is to distill semantics lossless knowledge in the weak features (from the weak/teacher branch) to guide the representation learning upon the strong features (from the strong/student branch).
Uncertainty-Guided Enhancement on Driving Perception System via Foundation Models
Multimodal foundation models offer promising advancements for enhancing driving perception systems, but their high computational and financial costs pose challenges.
Fine-Grained Gradient Restriction: A Simple Approach for Mitigating Catastrophic Forgetting
A fundamental challenge in continual learning is to balance the trade-off between learning new tasks and remembering the previously acquired knowledge.
VLMine: Long-Tail Data Mining with Vision Language Models
We propose a simple and scalable data mining approach that leverages the knowledge contained within a large vision language model (VLM).
Interpretable Vision-Language Survival Analysis with Ordinal Inductive Bias for Computational Pathology
Our VLSA could pave a new way for SA in CPATH by offering weakly-supervised MIL an effective means to learn valuable prognostic clues from gigapixel WSIs.
Approximately Invertible Neural Network for Learned Image Compression
The analysis transform and synthesis transform are used to encode an image to latent feature and decode the quantized feature to reconstruct the image, and can be regarded as coupled transforms.
Deformable Feature Alignment and Refinement for Moving Infrared Dim-small Target Detection
Specifically, a Temporal Deformable Alignment (TDA) module based on the designed Dilated Convolution Attention Fusion (DCAF) block is developed to explicitly align the adjacent frames with the current frame at the feature level.
Few-Shot Medical Image Segmentation with High-Fidelity Prototypes
Few-shot Semantic Segmentation (FSS) aims to adapt a pretrained model to new classes with as few as a single labelled training sample per class.
Triple-domain Feature Learning with Frequency-aware Memory Enhancement for Moving Infrared Small Target Detection
To extend feature source domains and enhance feature representation, we propose a new Triple-domain Strategy (Tridos) with the frequency-aware memory enhancement on spatio-temporal domain for infrared small target detection.
Proxy Denoising for Source-Free Domain Adaptation
We design a proxy denoising mechanism to correct ViL's predictions, grounded on a proxy confidence theory that models the dynamic effect of proxy's divergence against the domain-invariant space during adaptation.
Ranked #9 on
Unsupervised Domain Adaptation
on Office-Home
Unified Source-Free Domain Adaptation
To tackle this unified SFDA problem, we propose a novel approach called Latent Causal Factors Discovery (LCFD).
Source-Free Domain Adaptation with Frozen Multimodal Foundation Model
We find that directly applying the ViL model to the target domain in a zero-shot fashion is unsatisfactory, as it is not specialized for this particular task but largely generic.
Spatial-Temporal Transformer based Video Compression Framework
Similar as the traditional video coding, LVC inherits motion estimation/compensation, residual coding and other modules, all of which are implemented with neural networks (NNs).
MSMix:An Interpolation-Based Text Data Augmentation Method Manifold Swap Mixup
To solve the problem of poor performance of deep neural network models due to insufficient data, a simple yet effective interpolation-based data augmentation method is proposed: MSMix (Manifold Swap Mixup).
Independent Feature Decomposition and Instance Alignment for Unsupervised Domain Adaptation
Existing Unsupervised Domain Adaptation (UDA) methods typically attempt to perform knowledge transfer in a domain-invariant space explicitly or implicitly.
Efficient Transformer-based 3D Object Detection with Dynamic Token Halting
Although halting a token is a non-differentiable operation, our method allows for differentiable end-to-end learning by leveraging an equivalent differentiable forward-pass.
Homeomorphism Alignment for Unsupervised Domain Adaptation
With distribution alignment, it is challenging to acquire a common space which maintains fully the discriminative structure of both domains.
Bit Allocation using Optimization
In this paper, we consider the problem of bit allocation in Neural Video Compression (NVC).
BOME! Bilevel Optimization Made Easy: A Simple First-Order Approach
Bilevel optimization (BO) is useful for solving a variety of important machine learning problems including but not limited to hyperparameter optimization, meta-learning, continual learning, and reinforcement learning.
Diffusion-based Molecule Generation with Informative Prior Bridges
AI-based molecule generation provides a promising approach to a large area of biomedical sciences and engineering, such as antibody design, hydrolase engineering, or vaccine development.
First Hitting Diffusion Models for Generating Manifold, Graph and Categorical Data
We propose a family of First Hitting Diffusion Models (FHDM), deep generative models that generate data with a diffusion process that terminates at a random first hitting time.
Future Gradient Descent for Adapting the Temporal Shifting Data Distribution in Online Recommendation Systems
One of the key challenges of learning an online recommendation model is the temporal domain shift, which causes the mismatch between the training and testing data distribution and hence domain generalization error.
Let us Build Bridges: Understanding and Extending Diffusion Generative Models
Diffusion-based generative models have achieved promising results recently, but raise an array of open questions in terms of conceptual understanding, theoretical analysis, algorithm improvement and extensions to discrete, structured, non-Euclidean domains.
AIM 2022 Challenge on Super-Resolution of Compressed Image and Video: Dataset, Methods and Results
The homepage of this challenge is at https://github. com/RenYang-home/AIM22_CompressSR.
KUNet: Imaging Knowledge-Inspired Single HDR Image Reconstruction
Recently, with the rise of high dynamic range (HDR) display devices, there is a great demand to transfer traditional low dynamic range (LDR) images into HDR versions.
Multi-Class 3D Object Detection with Single-Class Supervision
While multi-class 3D detectors are needed in many robotics applications, training them with fully labeled datasets can be expensive in labeling cost.
The scope for AI-augmented interpretation of building blueprints in commercial and industrial property insurance
This report, commissioned by the WTW research network, investigates the use of AI in property risk assessment.
NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video: Dataset, Methods and Results
This challenge includes three tracks.
Source-Free Object Detection by Learning To Overlook Domain Style
This approach suffers from both unsatisfactory accuracy of pseudo labels due to the presence of domain shift and limited use of target domain training data.
argmax centroid
We propose a general method to construct centroid approximation for the distribution of maximum points of a random function (a. k. a.
Centroid Approximation for Bootstrap: Improving Particle Quality at Inference
In this work, we propose an efficient method to explicitly \emph{optimize} a small set of high quality ``centroid'' points to better approximate the ideal bootstrap distribution.
Pareto Navigation Gradient Descent: a First-Order Algorithm for Optimization in Pareto Set
The notion of the Pareto set allows us to focus on the set of (often infinite number of) models that cannot be strictly improved.
Pareto Navigation Gradient Descent: a First Order Algorithm for Optimization in Pareto Set
The notion of the Pareto set allows us to focus on the set of (often infinite number of) models that cannot be strictly improved.
MaxUp: Lightweight Adversarial Training With Data Augmentation Improves Neural Network Training
The idea is to generate a set of augmented data with some random perturbations or transforms, and minimize the maximum, or worst case loss over the augmented data.
VCNet and Functional Targeted Regularization For Learning Causal Effects of Continuous Treatments
Motivated by the rising abundance of observational data with continuous treatments, we investigate the problem of estimating the average dose-response curve (ADRF).
QoS-aware Link Scheduling Strategy for Data Transmission in SDVN
The original contributions of this paper are summarized as follows: (1) Model the packets collision probability of broadcast or NACK transmission in VANET with the combination theory and investigate the potential influence of miss my packets (MMP) problem.
Networking and Internet Architecture
Varying Coefficient Neural Network with Functional Targeted Regularization for Estimating Continuous Treatment Effects
With the rising abundance of observational data with continuous treatments, we investigate the problem of estimating average dose-response curve (ADRF).
Greedy Optimization Provably Wins the Lottery: Logarithmic Number of Winning Tickets is Enough
Despite the great success of deep learning, recent works show that large deep neural networks are often highly redundant and can be significantly reduced in size.
Adaptive Dense-to-Sparse Paradigm for Pruning Online Recommendation System with Non-Stationary Data
To the best of our knowledge, this is the first work to provide in-depth analysis and discussion of applying pruning to online recommendation systems with non-stationary data distribution.
Go Wide, Then Narrow: Efficient Training of Deep Thin Networks
This is achieved by layerwise imitation, that is, forcing the thin network to mimic the intermediate outputs of the wide network from layer to layer.
SAFER: A Structure-free Approach for Certified Robustness to Adversarial Word Substitutions
For security reasons, it is of critical importance to develop models with certified robustness that can provably guarantee that the prediction is can not be altered by any possible synonymous word substitution.
Unsupervised Feature Selection via Multi-step Markov Transition Probability
Our main contributions are a novel feature section approach which uses multi-step transition probability to characterize the data structure, and three algorithms proposed from the positive and negative aspects for keeping data structure.
Learning Various Length Dependence by Dual Recurrent Neural Networks
Many variants of RNN have been proposed to solve the gradient problems of training RNNs and process long sequences.
Disentanglement Then Reconstruction: Learning Compact Features for Unsupervised Domain Adaptation
By this reconstructor, we can construct prototypes for the original features using class prototypes and domain prototypes correspondingly.
Steepest Descent Neural Architecture Optimization: Escaping Local Optimum with Signed Neural Splitting
Recently, Liu et al.[19] proposed a splitting steepest descent (S2D) method that jointly optimizes the neural parameters and architectures based on progressively growing network structures by splitting neurons into multiple copies in a steepest descent fashion.
Joint COCO and Mapillary Workshop at ICCV 2019 Keypoint Detection Challenge Track Technical Report: Distribution-Aware Coordinate Representation for Human Pose Estimation
Taking them together, we formulate a novel Distribution-Aware coordinate Representation for Keypoint (DARK) method.
Good Subnetworks Provably Exist: Pruning via Greedy Forward Selection
This differs from the existing methods based on backward elimination, which remove redundant neurons from the large network.
Black-Box Certification with Randomized Smoothing: A Functional Optimization Based Framework
Randomized classifiers have been shown to provide a promising approach for achieving certified robustness against adversarial attacks in deep learning.
Stein Self-Repulsive Dynamics: Benefits From Past Samples
Our idea is to introduce Stein variational gradient as a repulsive force to push the samples of Langevin dynamics away from the past trajectories.
MaxUp: A Simple Way to Improve Generalization of Neural Network Training
The idea is to generate a set of augmented data with some random perturbations or transforms and minimize the maximum, or worst case loss over the augmented data.
Ranked #1 on
Image Classification
on ImageNet
(Hardware Burden metric)
Post-training Quantization with Multiple Points: Mixed Precision without Mixed Precision
We propose multipoint quantization, a quantization method that approximates a full-precision weight vector using a linear combination of multiple vectors of low-bit numbers; this is in contrast to typical quantization methods that approximate each weight using a single low precision number.
Extended Stochastic Gradient MCMC for Large-Scale Bayesian Variable Selection
Stochastic gradient Markov chain Monte Carlo (MCMC) algorithms have received much attention in Bayesian computing for big data problems, but they are only applicable to a small class of problems for which the parameter space has a fixed dimension and the log-posterior density is differentiable with respect to the parameters.
Distribution-Aware Coordinate Representation for Human Pose Estimation
Interestingly, we found that the process of decoding the predicted heatmaps into the final joint coordinates in the original image space is surprisingly significant for human pose estimation performance, which nevertheless was not recognised before.
Ranked #2 on
Multi-Person Pose Estimation
on MS COCO
(using extra training data)
Strain engineering of epitaxial oxide heterostructures beyond substrate limitations
The limitation of commercially available single-crystal substrates and the lack of continuous strain tunability preclude the ability to take full advantage of strain engineering for further exploring novel properties and exhaustively studying fundamental physics in complex oxides.
Materials Science
Fast Human Pose Estimation
In this work, we investigate the under-studied but practically critical pose model efficiency problem.
Ranked #9 on
Pose Estimation
on Leeds Sports Poses
Stein Neural Sampler
We propose two novel samplers to generate high-quality samples from a given (un-normalized) probability density.
Variable Selection via Penalized Neural Network: a Drop-Out-One Loss Approach
We propose a variable selection method for high dimensional regression models, which allows for complex, nonlinear, and high-order interactions among variables.
Do Convolutional Neural Networks Learn Class Hierarchy?
We present visual-analytics methods to reveal and analyze this hierarchy of similar classes in relation with CNN-internal data.
Pose-Invariant Face Alignment with a Single CNN
Face alignment has witnessed substantial progress in the last decade.
Ranked #13 on
Facial Landmark Detection
on 300W
3D Reconstruction in the Presence of Glasses by Acoustic and Stereo Fusion
We present a novel sensor fusion algorithm that first segments the depth map into different categories such as opaque/transparent/infinity (e. g., too far to measure) and then updates the depth map based on the segmentation outcome.
Real-time Simultaneous Pose and Shape Estimation for Articulated Objects Using a Single Depth Camera
In this paper we present a novel real-time algorithm for simultaneous pose and shape estimation for articulated objects, such as human beings and animals.
Data-driven Flower Petal Modeling with Botany Priors
Each segmented petal is then fitted with a scale-invariant morphable petal shape model, which is constructed from individually scanned exemplar petals.
Quality Dynamic Human Body Modeling Using a Single Low-cost Depth Camera
In this paper we present a novel autonomous pipeline to build a personalized parametric model (pose-driven avatar) using a single depth sensor.
Video Enhancement of People Wearing Polarized Glasses: Darkening Reversal and Reflection Reduction
With the wide-spread of consumer 3D-TV technology, stereoscopic videoconferencing systems are emerging.
Exploring Social Influence for Recommendation - A Probabilistic Generative Model Approach
The experimental results also confirm that our social influence based group recommendation algorithm outperforms the state-of-the-art algorithms for group recommendation.
