Search Results for author:
Found 76 papers, 27 papers with code
PoseAnimate: Zero-shot high fidelity pose controllable character animation
Image-to-video(I2V) generation aims to create a video sequence from a single image, which requires high temporal coherence and visual fidelity with the source image. However, existing approaches suffer from character appearance inconsistency and poor preservation of fine details.
BARET : Balanced Attention based Real image Editing driven by Target-text Inversion
Image editing approaches with diffusion models have been rapidly developed, yet their applicability are subject to requirements such as specific editing types (e. g., foreground or background object editing, style transfer), multiple conditions (e. g., mask, sketch, caption), and time consuming fine-tuning of diffusion models.
OmniMotionGPT: Animal Motion Generation with Limited Data
Our paper aims to generate diverse and realistic animal motion sequences from textual descriptions, without a large-scale animal text-motion dataset.
Exploring the Robustness of Human Parsers Towards Common Corruptions
The experimental results show that the proposed method demonstrates good universality which can improve the robustness of the human parsing models and even the semantic segmentation models when facing various image common corruptions.
High-Fidelity Clothed Avatar Reconstruction from a Single Image
This paper presents a framework for efficient 3D clothed avatar reconstruction.
Monocular 3D Object Detection with Bounding Box Denoising in 3D by Perceiver
Motivated by a new and strong observation that this challenge can be remedied by a 3D-space local-grid search scheme in an ideal case, we propose a stage-wise approach, which combines the information flow from 2D-to-3D (3D bounding box proposal generation with a single 2D image) and 3D-to-2D (proposal verification by denoising with 3D-to-2D contexts) in a top-down manner.
POTTER: Pooling Attention Transformer for Efficient Human Mesh Recovery
In this paper, we propose a pure transformer architecture named POoling aTtention TransformER (POTTER) for the HMR task from single images.
Ranked #33 on
3D Human Pose Estimation
on 3DPW
DiffMesh: A Motion-aware Diffusion-like Framework for Human Mesh Recovery from Videos
While image-based HMR methods have achieved impressive results, they often struggle to recover humans in dynamic scenarios, leading to temporal inconsistencies and non-smooth 3D motion predictions due to the absence of human motion.
Ranked #56 on
3D Human Pose Estimation
on 3DPW
AdPE: Adversarial Positional Embeddings for Pretraining Vision Transformers via MAE+
A criterion in unsupervised pretraining is the pretext task needs to be sufficiently hard to prevent the transformer encoder from learning trivial low-level features not generalizable well to downstream tasks.
Self-similarity Driven Scale-invariant Learning for Weakly Supervised Person Search
On the other hand, the similarity of cross-scale images is often smaller than that of images with the same scale for a person, which will increase the difficulty of matching.
MorphGANFormer: Transformer-based Face Morphing and De-Morphing
Semantic face image manipulation has received increasing attention in recent years.
Efficient Image Super-Resolution with Feature Interaction Weighted Hybrid Network
Recently, great progress has been made in single-image super-resolution (SISR) based on deep learning technology.
Adversarial Pretraining of Self-Supervised Deep Networks: Past, Present and Future
In this paper, we review adversarial pretraining of self-supervised deep networks including both convolutional neural networks and vision transformers.
Spatial-Temporal Pyramid Graph Reasoning for Action Recognition
Spatial-temporal relation reasoning is a significant yet challenging problem for video action recognition.
Ranked #35 on
Action Recognition
on Something-Something V1
Exploring Resolution and Degradation Clues as Self-supervised Signal for Low Quality Object Detection
Image restoration algorithms such as super resolution (SR) are indispensable pre-processing modules for object detection in low quality images.
AIParsing: Anchor-free Instance-level Human Parsing
Most state-of-the-art instance-level human parsing models adopt two-stage anchor-based detectors and, therefore, cannot avoid the heuristic anchor box design and the lack of analysis on a pixel level.
Cross-receptive Focused Inference Network for Lightweight Image Super-Resolution
Recently, Transformer-based methods have shown impressive performance in single image super-resolution (SISR) tasks due to the ability of global feature extraction.
FeatER: An Efficient Network for Human Reconstruction via Feature Map-Based TransformER
Recently, vision transformers have shown great success in a set of human reconstruction tasks such as 2D human pose estimation (2D HPE), 3D human pose estimation (3D HPE), and human mesh reconstruction (HMR) tasks.
Ranked #29 on
3D Human Pose Estimation
on 3DPW
Multitask AET with Orthogonal Tangent Regularity for Dark Object Detection
To enhance object detection in a dark environment, we propose a novel multitask auto encoding transformation (MAET) model which is able to explore the intrinsic pattern behind illumination translation.
Ranked #1 on
2D Object Detection
on ExDark
CTCNet: A CNN-Transformer Cooperation Network for Face Image Super-Resolution
Then, we design an efficient Feature Refinement Module (FRM) to enhance the encoded features.
CaCo: Both Positive and Negative Samples are Directly Learnable via Cooperative-adversarial Contrastive Learning
It trains an encoder by distinguishing positive samples from negative ones given query anchors.
Ranked #65 on
Self-Supervised Image Classification
on ImageNet
Dual-Flattening Transformers through Decomposed Row and Column Queries for Semantic Segmentation
We propose a Dual-Flattening Transformer (DFlatFormer) to enable high-resolution output by reducing complexity to $\mathcal{O}(hw(H+W))$ that is multiple orders of magnitude smaller than the naive dense transformer.
RestoreDet: Degradation Equivariant Representation for Object Detection in Low Resolution Images
Image restoration algorithms such as super resolution (SR) are indispensable pre-processing modules for object detection in degraded images.
Cross-domain Contrastive Learning for Unsupervised Domain Adaptation
Unsupervised domain adaptation (UDA) aims to transfer knowledge learned from a fully-labeled source domain to a different unlabeled target domain.
Self-Supervised Graph Representation Learning via Topology Transformations
We formalize the proposed model from an information-theoretic perspective, by maximizing the mutual information between topology transformations and node representations before and after the transformations.
Contrastive Learning with Stronger Augmentations
Thus, we propose a general framework called Contrastive Learning with Stronger Augmentations~(CLSA) to complement current contrastive learning approaches.
Hierarchical Deep CNN Feature Set-Based Representation Learning for Robust Cross-Resolution Face Recognition
(i) To learn more robust and discriminative features, we desire to adaptively fuse the contextual features from different layers.
Self-Supervised Multi-View Learning via Auto-Encoding 3D Transformations
Then, we self-train a representation to capture the intrinsic 3D object representation by decoding 3D transformation parameters from the fused feature representations of multiple views before and after the transformation.
A Bayesian Federated Learning Framework with Online Laplace Approximation
On the client side, a prior loss that uses the global posterior probabilistic parameters delivered from the server is designed to guide the local training.
TopoTER: Unsupervised Learning of Topology Transformation Equivariant Representations
We formalize the TopoTER from an information-theoretic perspective, by maximizing the mutual information between topology transformations and node representations before and after the transformations.
AdCo: Adversarial Contrast for Efficient Learning of Unsupervised Representations from Self-Trained Negative Adversaries
Contrastive learning relies on constructing a collection of negative examples that are sufficiently hard to discriminate against positive queries when their representations are self-trained.
Knowledge-Enriched Distributional Model Inversion Attacks
We present a novel inversion-specific GAN that can better distill knowledge useful for performing attacks on private models from public data.
K-Shot Contrastive Learning of Visual Features with Multiple Instance Augmentations
In this paper, we propose the $K$-Shot Contrastive Learning (KSCL) of visual features by applying multiple augmentations to investigate the sample variations within individual instances.
Human in Events: A Large-Scale Benchmark for Human-centric Video Analysis in Complex Events
To this end, we present a new large-scale dataset with comprehensive annotations, named Human-in-Events or HiEve (Human-centric video analysis in complex Events), for the understanding of human motions, poses, and actions in a variety of realistic events, especially in crowd & complex events.
Spatial-Temporal Transformer Networks for Traffic Flow Forecasting
In this paper, we propose a novel paradigm of Spatial-Temporal Transformer Networks (STTNs) that leverages dynamical directed spatial dependencies and long-range temporal dependencies to improve the accuracy of long-term traffic forecasting.
FLAT: Few-Shot Learning via Autoencoding Transformation Regularizers
To this end, we present a novel regularization mechanism by learning the change of feature representations induced by a distribution of transformations without using the labels of data examples.
EnAET: A Self-Trained framework for Semi-Supervised and Supervised Learning with Ensemble Transformations
In this study, we propose a new EnAET framework to further improve existing semi-supervised methods with self-supervised information.
Ranked #1 on
Semi-Supervised Image Classification
on STL-10
GraphTER: Unsupervised Learning of Graph Transformation Equivariant Representations via Auto-Encoding Node-wise Transformations
Recent advances in Graph Convolutional Neural Networks (GCNNs) have shown their efficiency for non-Euclidean data on graphs, which often require a large amount of labeled data with high cost.
AETv2: AutoEncoding Transformations for Self-Supervised Representation Learning by Minimizing Geodesic Distances in Lie Groups
For this reason, we should use the geodesic to characterize how an image transform along the manifold of a transformation group, and adopt its length to measure the deviation between transformations.
An End-to-End Foreground-Aware Network for Person Re-Identification
By presenting a target attention loss, the pedestrian features extracted from the foreground branch become more insensitive to the backgrounds, which greatly reduces the negative impacts of changing backgrounds on matching an identical across different camera views.
Progressive Unsupervised Person Re-identification by Tracklet Association with Spatio-Temporal Regularization
In our approach, we first collect tracklet data within each camera by automatic person detection and tracking.
Spatiotemporal Co-attention Recurrent Neural Networks for Human-Skeleton Motion Prediction
To this end, we propose a novel Skeleton-joint Co-attention Recurrent Neural Networks (SC-RNN) to capture the spatial coherence among joints, and the temporal evolution among skeletons simultaneously on a skeleton-joint co-attention feature map in spatiotemporal space.
PC-DARTS: Partial Channel Connections for Memory-Efficient Architecture Search
Differentiable architecture search (DARTS) provided a fast solution in finding effective network architectures, but suffered from large memory and computing overheads in jointly training a super-network and searching for an optimal architecture.
Ranked #20 on
Neural Architecture Search
on CIFAR-10
Learning Generalized Transformation Equivariant Representations via Autoencoding Transformations
Transformation Equivariant Representations (TERs) aim to capture the intrinsic visual structures that equivary to various transformations by expanding the notion of {\em translation} equivariance underlying the success of Convolutional Neural Networks (CNNs).
Differential Recurrent Neural Network and its Application for Human Activity Recognition
The Long Short-Term Memory (LSTM) recurrent neural network is capable of processing complex sequential information since it utilizes special gating schemes for learning representations from long input sequences.
Small Data Challenges in Big Data Era: A Survey of Recent Progress on Unsupervised and Semi-Supervised Methods
Representation learning with small labeled data have emerged in many problems, since the success of deep neural networks often relies on the availability of a huge amount of labeled data that is expensive to collect.
AVT: Unsupervised Learning of Transformation Equivariant Representations by Autoencoding Variational Transformations
This ensures the resultant TERs of individual images contain the {\em intrinsic} information about their visual structures that would equivary {\em extricably} under various transformations in a generalized {\em nonlinear} case.
Learning to Adaptively Scale Recurrent Neural Networks
Recent advancements in recurrent neural network (RNN) research have demonstrated the superiority of utilizing multiscale structures in learning temporal representations of time series.
AET vs. AED: Unsupervised Representation Learning by Auto-Encoding Transformations rather than Data
The success of deep neural networks often relies on a large amount of labeled examples, which can be difficult to obtain in many real scenarios.
Prior-Knowledge and Attention-based Meta-Learning for Few-Shot Learning
In this paper, inspired by the human cognition process which utilizes both prior-knowledge and vision attention in learning new knowledge, we present a novel paradigm of meta-learning approach with three developments to introduce attention mechanism and prior-knowledge for meta-learning.
Exploiting temporal and depth information for multi-frame face anti-spoofing
Face anti-spoofing is significant to the security of face recognition systems.
Hierarchical Long Short-Term Concurrent Memory for Human Interaction Recognition
In a Co-LSTM unit, each sub-memory unit stores individual motion information, while this Co-LSTM unit selectively integrates and stores inter-related motion information between multiple interacting persons from multiple sub-memory units via the cell gate and co-memory cell, respectively.
Ranked #1 on
Human Interaction Recognition
on UT
An Adversarial Approach to Hard Triplet Generation
While deep neural networks have demonstrated competitive results for many visual recognition and image retrieval tasks, the major challenge lies in distinguishing similar images from different categories (i. e., hard negative examples) while clustering images with large variations from the same category (i. e., hard positive examples).
Generalized Loss-Sensitive Adversarial Learning with Manifold Margins
Such a manifold assumption suggests the distance over the manifold should be a better measure to characterize the distinct between real and fake sam- ples.
Large-scale Bisample Learning on ID Versus Spot Face Recognition
In this paper, we propose a deep learning based large-scale bisample learning (LBL) method for IvS face recognition.
Interleaved Structured Sparse Convolutional Neural Networks
In this paper, we study the problem of designing efficient convolutional neural network architectures with the interest in eliminating the redundancy in convolution kernels.
A Study of Question Effectiveness Using Reddit "Ask Me Anything" Threads
Asking effective questions is a powerful social skill.
Task-Agnostic Meta-Learning for Few-shot Learning
Meta-learning approaches have been proposed to tackle the few-shot learning problem. Typically, a meta-learner is trained on a variety of tasks in the hopes of being generalizable to new tasks.
CapProNet: Deep Feature Learning via Orthogonal Projections onto Capsule Subspaces
In this paper, we formalize the idea behind capsule nets of using a capsule vector rather than a neuron activation to predict the label of samples.
Deep Ordinal Hashing with Spatial Attention
However, most existing deep hashing methods directly learn the hash functions by encoding the global semantic information, while ignoring the local spatial information of images.
Sharp Attention Network via Adaptive Sampling for Person Re-identification
In this paper, we present novel sharp attention networks by adaptively sampling feature maps from convolutional neural networks (CNNs) for person re-identification (re-ID) problem.
IGCV$2$: Interleaved Structured Sparse Convolutional Neural Networks
In this paper, we study the problem of designing efficient convolutional neural network architectures with the interest in eliminating the redundancy in convolution kernels.
Deep Differential Recurrent Neural Networks
The proposed model progressively builds up the ability of the LSTM gates to detect salient dynamical patterns in deeper stacked layers modeling higher orders of DoS, and thus the proposed LSTM model is termed deep differential Recurrent Neural Network (d2RNN).
Global versus Localized Generative Adversarial Nets
In this paper, we present a novel localized Generative Adversarial Net (GAN) to learn on the manifold of real data.
Interleaved Group Convolutions
The main point lies in a novel building block, a pair of two successive interleaved group convolutions: primary group convolution and secondary group convolution.
Stock Price Prediction via Discovering Multi-Frequency Trading Patterns
Then the future stock prices are predicted as a nonlinear mapping of the combination of these components in an Inverse Fourier Transform (IFT) fashion.
State-Frequency Memory Recurrent Neural Networks
Modeling temporal sequences plays a fundamental role in various modern applications and has drawn more and more attentions in the machine learning community.
Interleaved Group Convolutions for Deep Neural Networks
The main point lies in a novel building block, a pair of two successive interleaved group convolutions: primary group convolution and secondary group convolution.
Concurrence-Aware Long Short-Term Sub-Memories for Person-Person Action Recognition
To this end, we propose a novel Concurrence-Aware Long Short-Term Sub-Memories (Co-LSTSM) to model the long-term inter-related dynamics between two interacting people on the bounding boxes covering people.
Ranked #2 on
Human Interaction Recognition
on BIT
Joint Intermodal and Intramodal Label Transfers for Extremely Rare or Unseen Classes
One of our goals in this paper is to develop a model for revealing the functional relationships between text and image features as to directly transfer intermodal and intramodal labels to annotate the images.
Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities
In this paper, we present the Lipschitz regularization theory and algorithms for a novel Loss-Sensitive Generative Adversarial Network (LS-GAN).
Ranked #43 on
Image Classification
on SVHN
Hierarchically Gated Deep Networks for Semantic Segmentation
While image structures usually have various scales, it is difficult to use a single scale to model the spatial contexts for all individual pixels.
First-Take-All: Temporal Order-Preserving Hashing for 3D Action Videos
With the prevalence of the commodity depth cameras, the new paradigm of user interfaces based on 3D motion capturing and recognition have dramatically changed the way of interactions between human and computers.
Sparse Composite Quantization
The benefit is that the distance evaluation between the query and the dictionary element (a sparse vector) is accelerated using the efficient sparse vector operation, and thus the cost of distance table computation is reduced a lot.
Differential Recurrent Neural Networks for Action Recognition
This change in information gain is quantified by Derivative of States (DoS), and thus the proposed LSTM model is termed as differential Recurrent Neural Network (dRNN).
Rank Subspace Learning for Compact Hash Codes
In this work, we propose a novel hash learning framework that encodes feature's rank orders instead of numeric values in a number of optimal low-dimensional ranking subspaces.
