Search Results for author:
Found 27 papers, 7 papers with code
Improving Diffusion Models for Inverse Problems Using Optimal Posterior Covariance
In this paper, we propose the first unified interpretation for existing zero-shot methods from the perspective of approximating the conditional posterior mean for the reverse diffusion process of conditional sampling.
UMG-CLIP: A Unified Multi-Granularity Vision Generalist for Open-World Understanding
Vision-language foundation models, represented by Contrastive language-image pre-training (CLIP), have gained increasing attention for jointly understanding both vision and textual tasks.
scBiGNN: Bilevel Graph Representation Learning for Cell Type Classification from Single-cell RNA Sequencing Data
A gene-level GNN is established to adaptively learn gene-gene interactions and cell representations via the self-attention mechanism, and a cell-level GNN builds on the cell-cell graph that is constructed from the cell representations generated by the gene-level GNN.
Frequency-Aware Transformer for Learned Image Compression
Learned image compression (LIC) has gained traction as an effective solution for image storage and transmission in recent years.
Hybrid Distillation: Connecting Masked Autoencoders with Contrastive Learners
In order to better obtain both discrimination and diversity, we propose a simple but effective Hybrid Distillation strategy, which utilizes both the supervised/CL teacher and the MIM teacher to jointly guide the student model.
Learned Lossless Compression for JPEG via Frequency-Domain Prediction
In this paper, we propose a novel framework for learned lossless compression of JPEG images that achieves end-to-end optimized prediction of the distribution of decoded DCT coefficients.
Pose-Oriented Transformer with Uncertainty-Guided Refinement for 2D-to-3D Human Pose Estimation
There has been a recent surge of interest in introducing transformers to 3D human pose estimation (HPE) due to their powerful capabilities in modeling long-term dependencies.
Promoting Semantic Connectivity: Dual Nearest Neighbors Contrastive Learning for Unsupervised Domain Generalization
Then, we propose a novel unsupervised domain generalization approach, namely Dual Nearest Neighbors contrastive learning with strong Augmentation (DN^2A).
Optimization-based Block Coordinate Gradient Coding for Mitigating Partial Stragglers in Distributed Learning
To reduce computational complexity, we first transform each to an equivalent but much simpler discrete problem with N\llL variables representing the partition of the L coordinates into N blocks, each with identical redundancy.
Hierarchical Spherical CNNs with Lifting-based Adaptive Wavelets for Pooling and Unpooling
Specifically, adaptive spherical wavelets are learned with a lifting structure that consists of trainable lifting operators (i. e., update and predict operators).
LiftPool: Lifting-based Graph Pooling for Hierarchical Graph Representation Learning
Subsequently, this local information is aligned and propagated to the preserved nodes to alleviate information loss in graph coarsening.
Hybrid ISTA: Unfolding ISTA With Convergence Guarantees Using Free-Form Deep Neural Networks
This framework is general to endow arbitrary DNNs for solving linear inverse problems with convergence guarantees.
Hierarchical Graph Networks for 3D Human Pose Estimation
Recent 2D-to-3D human pose estimation works tend to utilize the graph structure formed by the topology of the human skeleton.
Ranked #42 on
3D Human Pose Estimation
on MPI-INF-3DHP
(AUC metric)
Variance Reduced Domain Randomization for Policy Gradient
In this paper, we theoretically derive a bias-free and state/environment-dependent optimal baseline for DR, and analytically show its ability to achieve further variance reduction over the standard constant and state-dependent baselines for DR. We further propose a variance reduced domain randomization (VRDR) approach for policy gradient methods, to strike a tradeoff between the variance reduction and computational complexity in practice.
Graph Convolutional Networks via Adaptive Filter Banks
Graph convolutional networks have been a powerful tool in representation learning of networked data.
Graph Neural Networks With Lifting-based Adaptive Graph Wavelets
To ensure that the learned graph representations are invariant to node permutations, a layer is employed at the input of the networks to reorder the nodes according to their local topology information.
Message Passing in Graph Convolution Networks via Adaptive Filter Banks
Furthermore, each filter in the spectral domain corresponds to a message passing scheme, and diverse schemes are implemented via the filter bank.
Multi-dataset Pretraining: A Unified Model for Semantic Segmentation
This is achieved by first pretraining the network via the proposed pixel-to-prototype contrastive loss over multiple datasets regardless of their taxonomy labels, and followed by fine-tuning the pretrained model over specific dataset as usual.
Monotonic Robust Policy Optimization with Model Discrepancy
To mitigate the model discrepancy between training and target (testing) environments, domain randomization (DR) can generate plenty of environments with a sufficient diversity by randomly sampling environment parameters in simulator.
Learning Latent Architectural Distribution in Differentiable Neural Architecture Search via Variational Information Maximization
Existing differentiable neural architecture search approaches simply assume the architectural distribution on each edge is independent of each other, which conflicts with the intrinsic properties of architecture.
VEM-GCN: Topology Optimization with Variational EM for Graph Convolutional Networks
In the variational E-step, graph topology is optimized by approximating the posterior probability distribution of the latent adjacency matrix with a neural network learned from node embeddings.
PAC-Bayesian Randomized Value Function with Informative Prior
To address this, in this paper, we propose a Bayesian linear regression with informative prior (IP-BLR) operator to leverage the data-dependent prior in the learning process of randomized value function, which can leverage the statistics of training results from previous iterations.
NCGNN: Node-Level Capsule Graph Neural Network for Semisupervised Classification
Therefore, it can relieve the over-smoothing issue and learn effective node representations over graphs with homophily or heterophily.
MimicNorm: Weight Mean and Last BN Layer Mimic the Dynamic of Batch Normalization
We leverage the neural tangent kernel (NTK) theory to prove that our weight mean operation whitens activations and transits network into the chaotic regime like BN layer, and consequently, leads to an enhanced convergence.
AP-Loss for Accurate One-Stage Object Detection
This paper alleviates this issue by proposing a novel framework to replace the classification task in one-stage detectors with a ranking task, and adopting the Average-Precision loss (AP-loss) for the ranking problem.
Group Re-Identification with Multi-grained Matching and Integration
The task of re-identifying groups of people underdifferent camera views is an important yet less-studied problem. Group re-identification (Re-ID) is a very challenging task sinceit is not only adversely affected by common issues in traditionalsingle object Re-ID problems such as viewpoint and human posevariations, but it also suffers from changes in group layout andgroup membership.
Towards Accurate One-Stage Object Detection with AP-Loss
For this purpose, we develop a novel optimization algorithm, which seamlessly combines the error-driven update scheme in perceptron learning and backpropagation algorithm in deep networks.
