Search Results for author:
Found 101 papers, 47 papers with code
Few-Shot Font Generation by Learning Fine-Grained Local Styles
Instead of explicitly disentangling global or component-wise modeling, the cross-attention mechanism can attend to the right local styles in the reference glyphs and aggregate the reference styles into a fine-grained style representation for the given content glyphs.
Results of the NeurIPS'21 Challenge on Billion-Scale Approximate Nearest Neighbor Search
The outcome of the competition was ranked leaderboards of algorithms in each track based on recall at a query throughput threshold.
Few-Shot Head Swapping in the Wild
Particularly, seamless blending is achieved with the help of a Semantic-Guided Color Reference Creation procedure and a Blending UNet.
Human-Object Interaction Detection via Disentangled Transformer
To associate the predictions of disentangled decoders, we first generate a unified representation for HOI triplets with a base decoder, and then utilize it as input feature of each disentangled decoder.
GitNet: Geometric Prior-based Transformation for Birds-Eye-View Segmentation
Birds-eye-view (BEV) semantic segmentation is critical for autonomous driving for its powerful spatial representation ability.
Implicit Sample Extension for Unsupervised Person Re-Identification
Specifically, we generate support samples from actual samples and their neighbouring clusters in the embedding space through a progressive linear interpolation (PLI) strategy.
DaViT: Dual Attention Vision Transformers
We show that these two self-attentions complement each other: (i) since each channel token contains an abstract representation of the entire image, the channel attention naturally captures global interactions and representations by taking all spatial positions into account when computing attention scores between channels; (ii) the spatial attention refines the local representations by performing fine-grained interactions across spatial locations, which in turn helps the global information modeling in channel attention.
Ranked #7 on
Image Classification
on ImageNet
(using extra training data)
ViSTA: Vision and Scene Text Aggregation for Cross-Modal Retrieval
Visual appearance is considered to be the most important cue to understand images for cross-modal retrieval, while sometimes the scene text appearing in images can provide valuable information to understand the visual semantics.
Ranked #3 on
Cross-Modal Retrieval
on Flickr30k
Efficient Video Segmentation Models with Per-frame Inference
To this end, we perform inference at each frame.
Context Autoencoder for Self-Supervised Representation Learning
We present a novel masked image modeling (MIM) approach, context autoencoder (CAE), for self-supervised learning.
HRFormer: High-Resolution Vision Transformer for Dense Predict
We present a High-Resolution Transformer (HRFormer) that learns high-resolution representations for dense prediction tasks, in contrast to the original Vision Transformer that produces low-resolution representations and has high memory and computational cost.
SPANN: Highly-efficient Billion-scale Approximate Nearest Neighbor Search
It stores the centroid points of the posting lists in the memory and the large posting lists in the disk.
Whole Brain Segmentation with Full Volume Neural Network
To address these issues, we propose to adopt a full volume framework, which feeds the full volume brain image into the segmentation network and directly outputs the segmentation result for the whole brain volume.
HRFormer: High-Resolution Transformer for Dense Prediction
We present a High-Resolution Transformer (HRFormer) that learns high-resolution representations for dense prediction tasks, in contrast to the original Vision Transformer that produces low-resolution representations and has high memory and computational cost.
Realistic Image Synthesis with Configurable 3D Scene Layouts
Our approach takes a 3D scene with semantic class labels as input and trains a 3D scene painting network that synthesizes color values for the input 3D scene.
Conditional DETR for Fast Training Convergence
Our approach, named conditional DETR, learns a conditional spatial query from the decoder embedding for decoder multi-head cross-attention.
Content-Aware Convolutional Neural Networks
In practice, the convolutional operation on some of the windows (e. g., smooth windows that contain very similar pixels) can be very redundant and may introduce noises into the computation.
Cross-Modal Attention Consistency for Video-Audio Unsupervised Learning
Cross-modal correlation provides an inherent supervision for video unsupervised representation learning.
On the Connection between Local Attention and Dynamic Depth-wise Convolution
Sparse connectivity: there is no connection across channels, and each position is connected to the positions within a small local window.
Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision
Our approach imposes the consistency on two segmentation networks perturbed with different initialization for the same input image.
SPANN: Highly-efficient Billion-scale Approximate Nearest Neighborhood Search
It stores the centroid points of the posting lists in the memory and the large posting lists in the disk.
Lite-HRNet: A Lightweight High-Resolution Network
We introduce a lightweight unit, conditional channel weighting, to replace costly pointwise (1x1) convolutions in shuffle blocks.
Ranked #22 on
Pose Estimation
on COCO test-dev
Bottom-Up Human Pose Estimation Via Disentangled Keypoint Regression
Our motivation is that regressing keypoint positions accurately needs to learn representations that focus on the keypoint regions.
Learning Versatile Neural Architectures by Propagating Network Codes
(4) Thorough studies of NCP on inter-, cross-, and intra-tasks highlight the importance of cross-task neural architecture design, i. e., multitask neural architectures and architecture transferring between different tasks.
Boosting Adversarial Transferability through Enhanced Momentum
Various momentum iterative gradient-based methods are shown to be effective to improve the adversarial transferability.
Admix: Enhancing the Transferability of Adversarial Attacks
We investigate in this direction and observe that existing transformations are all applied on a single image, which might limit the adversarial transferability.
Consistent Instance Classification for Unsupervised Representation Learning
The proposed consistent instance classification (ConIC) approach simultaneously optimizes the classification loss and an additional consistency loss explicitly penalizing the feature dissimilarity between the augmented views from the same instance.
Improving Person Re-identification with Iterative Impression Aggregation
Not only does such a simple method improve the performance of the baseline models, it also achieves comparable performance with latest advanced re-ranking methods.
Informative Dropout for Robust Representation Learning: A Shape-bias Perspective
Specifically, we discriminate texture from shape based on local self-information in an image, and adopt a Dropout-like algorithm to decorrelate the model output from the local texture.
Distillation Guided Residual Learning for Binary Convolutional Neural Networks
We observe that, this performance gap leads to substantial residuals between intermediate feature maps of BCNN and FCNN.
SegFix: Model-Agnostic Boundary Refinement for Segmentation
We present a model-agnostic post-processing scheme to improve the boundary quality for the segmentation result that is generated by any existing segmentation model.
Point-Set Anchors for Object Detection, Instance Segmentation and Pose Estimation
A recent approach for object detection and human pose estimation is to regress bounding boxes or human keypoints from a central point on the object or person.
Bottom-Up Human Pose Estimation by Ranking Heatmap-Guided Adaptive Keypoint Estimates
The typical bottom-up human pose estimation framework includes two stages, keypoint detection and grouping.
Weakly-Supervised Action Localization by Generative Attention Modeling
By maximizing the conditional probability with respect to the attention, the action and non-action frames are well separated.
Closed-loop Matters: Dual Regression Networks for Single Image Super-Resolution
Extensive experiments with paired training data and unpaired real-world data demonstrate our superiority over existing methods.
Efficient Semantic Video Segmentation with Per-frame Inference
For semantic segmentation, most existing real-time deep models trained with each frame independently may produce inconsistent results for a video sequence.
Ranked #2 on
Video Semantic Segmentation
on CamVid
Segmentation Transformer: Object-Contextual Representations for Semantic Segmentation
We empirically demonstrate that the proposed approach achieves competitive performance on various challenging semantic segmentation benchmarks: Cityscapes, ADE20K, LIP, PASCAL-Context, and COCO-Stuff.
Ranked #2 on
Semantic Segmentation
on Cityscapes test
(using extra training data)
Cross View Fusion for 3D Human Pose Estimation
It consists of two separate steps: (1) estimating the 2D poses in multi-view images and (2) recovering the 3D poses from the multi-view 2D poses.
Ranked #3 on
3D Human Pose Estimation
on Total Capture
Global-Local Temporal Representations For Video Person Re-Identification
The long-term relations are captured by a temporal self-attention model to alleviate the occlusions and noises in video sequences.
HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation
HigherHRNet even surpasses all top-down methods on CrowdPose test (67. 6% AP), suggesting its robustness in crowded scene.
Ranked #2 on
Pose Estimation
on UAV-Human
Deep High-Resolution Representation Learning for Visual Recognition
High-resolution representations are essential for position-sensitive vision problems, such as human pose estimation, semantic segmentation, and object detection.
Ranked #1 on
Object Detection
on COCO test-dev
(Hardware Burden metric)
Interlaced Sparse Self-Attention for Semantic Segmentation
There are two successive attention modules each estimating a sparse affinity matrix.
MMDetection: Open MMLab Detection Toolbox and Benchmark
In this paper, we introduce the various features of this toolbox.
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.
High-Resolution Representations for Labeling Pixels and Regions
The proposed approach achieves superior results to existing single-model networks on COCO object detection.
Ranked #4 on
Semantic Segmentation
on LIP val
Structured Knowledge Distillation for Dense Prediction
Here we propose to distill structured knowledge from large networks to compact networks, taking into account the fact that dense prediction is a structured prediction problem.
Deep High-Resolution Representation Learning for Human Pose Estimation
We start from a high-resolution subnetwork as the first stage, gradually add high-to-low resolution subnetworks one by one to form more stages, and connect the mutli-resolution subnetworks in parallel.
Ranked #1 on
Keypoint Detection
on COCO test-dev
Supervised Quantization for Similarity Search
In this paper, we address the problem of searching for semantically similar images from a large database.
Collaborative Quantization for Cross-Modal Similarity Search
Cross-modal similarity search is a problem about designing a search system supporting querying across content modalities, e. g., using an image to search for texts or using a text to search for images.
Deep Triplet Quantization
We propose Deep Triplet Quantization (DTQ), a novel approach to learning deep quantization models from the similarity triplets.
Ranked #1 on
Image Retrieval
on NUS-WIDE
Disparity-preserved Deep Cross-platform Association for Cross-platform Video Recommendation
However, there remain two unsolved challenges: i) there exist inconsistencies in cross-platform association due to platform-specific disparity, and ii) data from distinct platforms may have different semantic granularities.
Weakly Supervised Dense Event Captioning in Videos
Dense event captioning aims to detect and describe all events of interest contained in a video.
Accelerating Deep Neural Networks with Spatial Bottleneck Modules
This paper presents an efficient module named spatial bottleneck for accelerating the convolutional layers in deep neural networks.
OCNet: Object Context Network for Scene Parsing
To capture richer context information, we further combine our interlaced sparse self-attention scheme with the conventional multi-scale context schemes including pyramid pooling~\citep{zhao2017pyramid} and atrous spatial pyramid pooling~\citep{chen2018deeplab}.
Ranked #9 on
Semantic Segmentation
on Trans10K
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.
IGCV3: Interleaved Low-Rank Group Convolutions for Efficient Deep Neural Networks
In this paper, we are interested in building lightweight and efficient convolutional neural networks.
Weakly-Supervised Semantic Segmentation Network With Deep Seeded Region Growing
Inspired by the traditional image segmentation methods of seeded region growing, we propose to train a semantic segmentation network starting from the discriminative regions and progressively increase the pixel-level supervision using by seeded region growing.
Ranked #21 on
Weakly-Supervised Semantic Segmentation
on COCO 2014 val
(using extra training data)
Part-Aligned Bilinear Representations for Person Re-identification
We propose a novel network that learns a part-aligned representation for person re-identification.
Ranked #4 on
Person Re-Identification
on UAV-Human
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.
Object Detection in Videos by High Quality Object Linking
In particular, our method improves results by 8. 8% over the static image detector for fast moving objects.
LVreID: Person Re-Identification with Long Sequence Videos
This paper mainly establishes a large-scale Long sequence Video database for person re-IDentification (LVreID).
S4Net: Single Stage Salient-Instance Segmentation
Taking into account the category-independent property of each target, we design a single stage salient instance segmentation framework, with a novel segmentation branch.
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.
Ensemble Diffusion for Retrieval
This stimulates a great research interest of considering similarity fusion in the framework of diffusion process (i. e., fusion with diffusion) for robust retrieval.
Human Pose Estimation using Global and Local Normalization
We present a two-stage normalization scheme, human body normalization and limb normalization, to make the distribution of the relative joint locations compact, resulting in easier learning of convolutional spatial models and more accurate pose estimation.
Rethink ReLU to Training Better CNNs
Most of convolutional neural networks share the same characteristic: each convolutional layer is followed by a nonlinear activation layer where Rectified Linear Unit (ReLU) is the most widely used.
Deeply-Learned Part-Aligned Representations for Person Re-Identification
In this paper, we address the problem of person re-identification, which refers to associating the persons captured from different cameras.
Ranked #87 on
Person Re-Identification
on Market-1501
Orthogonal and Idempotent Transformations for Learning Deep Neural Networks
Identity transformations, used as skip-connections in residual networks, directly connect convolutional layers close to the input and those close to the output in deep neural networks, improving information flow and thus easing the training.
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.
Learning Correspondence Structures for Person Re-identification
We first introduce a boosting-based approach to learn a correspondence structure which indicates the patch-wise matching probabilities between images from a target camera pair.
Deep Convolutional Neural Networks with Merge-and-Run Mappings
A deep residual network, built by stacking a sequence of residual blocks, is easy to train, because identity mappings skip residual branches and thus improve information flow.
Geometric Neural Phrase Pooling: Modeling the Spatial Co-occurrence of Neurons
For this, we consider the neurons in the hidden layer as neural words, and construct a set of geometric neural phrases on top of them.
A Survey on Learning to Hash
In this paper, we present a comprehensive survey of the learning to hash algorithms, categorize them according to the manners of preserving the similarities into: pairwise similarity preserving, multiwise similarity preserving, implicit similarity preserving, as well as quantization, and discuss their relations.
Deeply-Fused Nets
Second, in our suggested fused net formed by one deep and one shallow base networks, the flows of the information from the earlier intermediate layer of the deep base network to the output and from the input to the later intermediate layer of the deep base network are both improved.
InterActive: Inter-Layer Activeness Propagation
An increasing number of computer vision tasks can be tackled with deep features, which are the intermediate outputs of a pre-trained Convolutional Neural Network.
DisturbLabel: Regularizing CNN on the Loss Layer
During a long period of time we are combating over-fitting in the CNN training process with model regularization, including weight decay, model averaging, data augmentation, etc.
Good Practice in CNN Feature Transfer
The objective of this paper is the effective transfer of the Convolutional Neural Network (CNN) feature in image search and classification.
A diffusion and clustering-based approach for finding coherent motions and understanding crowd scenes
These semantic regions can be used to recognize pre-defined activities in crowd scenes.
RIDE: Reversal Invariant Descriptor Enhancement
In many fine-grained object recognition datasets, image orientation (left/right) might vary from sample to sample.
Scalable Person Re-Identification: A Benchmark
As a minor contribution, inspired by recent advances in large-scale image search, this paper proposes an unsupervised Bag-of-Words descriptor.
Ranked #82 on
Person Re-Identification
on DukeMTMC-reID
DeepSaliency: Multi-Task Deep Neural Network Model for Salient Object Detection
A key problem in salient object detection is how to effectively model the semantic properties of salient objects in a data-driven manner.
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.
Similarity Learning on an Explicit Polynomial Kernel Feature Map for Person Re-Identification
We follow the learning-to-rank methodology and learn a similarity function to maximize the difference between the similarity scores of matched and unmatched images for a same person.
Co-Saliency Detection via Looking Deep and Wide
In the proposed framework, the wide and deep information are explored for the object proposal windows extracted in each image, and the co-saliency scores are calculated by integrating the intra-image contrast and intra group consistency via a principled Bayesian formulation.
Person Re-identification with Correspondence Structure Learning
This paper addresses the problem of handling spatial misalignments due to camera-view changes or human-pose variations in person re-identification.
Group $K$-Means
We study how to learn multiple dictionaries from a dataset, and approximate any data point by the sum of the codewords each chosen from the corresponding dictionary.
Salient Object Detection: A Discriminative Regional Feature Integration Approach
Our method, which is based on multi-level image segmentation, utilizes the supervised learning approach to map the regional feature vector to a saliency score.
Deep Regression for Face Alignment
In this paper, we present a deep regression approach for face alignment.
Hashing for Similarity Search: A Survey
Similarity search (nearest neighbor search) is a problem of pursuing the data items whose distances to a query item are the smallest from a large database.
Low-rank SIFT: An Affine Invariant Feature for Place Recognition
As an extension of SIFT, our method seeks to add prior to solve the ill-posed affine parameter estimation problem and normalizes them directly, and is applicable to objects with regular structures.
Inner Product Similarity Search using Compositional Codes
This paper addresses the nearest neighbor search problem under inner product similarity and introduces a compact code-based approach.
Orientational Pyramid Matching for Recognizing Indoor Scenes
The novelty lies in that OPM uses the 3D orientations to form the pyramid and produce the pooling regions, which is unlike SPM that uses the spatial positions to form the pyramid.
Optimized Cartesian $K$-Means
In OCKM, multiple sub codewords are used to encode the subvector of a data point in a subspace.
Fast Approximate $K$-Means via Cluster Closures
Traditional $k$-means is an iterative algorithm---in each iteration new cluster centers are computed and each data point is re-assigned to its nearest center.
Fast Neighborhood Graph Search using Cartesian Concatenation
This structure augments the neighborhood graph with a bridge graph.
Scalable $k$-NN graph construction
The $k$-NN graph has played a central role in increasingly popular data-driven techniques for various learning and vision tasks; yet, finding an efficient and effective way to construct $k$-NN graphs remains a challenge, especially for large-scale high-dimensional data.
Hybrid Affinity Propagation
We formulate this problem as finding a few image exemplars to represent the image set semantically and visually, and solve it in a hybrid way by exploiting both visual and textual information associated with images.
Supervised Kernel Descriptors for Visual Recognition
In visual recognition tasks, the design of low level image feature representation is fundamental.
