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Found 309 papers, 127 papers with code
Understanding Generalization and Optimization Performance of Deep CNNs
Besides, we prove that for an arbitrary gradient descent algorithm, the computed approximate stationary point by minimizing empirical risk is also an approximate stationary point to the population risk.
Object Region Mining with Adversarial Erasing: A Simple Classification to Semantic Segmentation Approach
We investigate a principle way to progressively mine discriminative object regions using classification networks to address the weakly-supervised semantic segmentation problems.
Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi- Supervised Semantic Segmentation
It can produce dense and reliable object localization maps and effectively benefit both weakly- and semi- supervised semantic segmentation.
Tensor Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Tensors via Convex Optimization
In this work, we prove that under certain suitable assumptions, we can recover both the low-rank and the sparse components exactly by simply solving a convex program whose objective is a weighted combination of the tensor nuclear norm and the $\ell_1$-norm, i. e., $\min_{{\mathcal{L}},\ {\mathcal{E}}} \ \|{{\mathcal{L}}}\|_*+\lambda\|{{\mathcal{E}}}\|_1, \ \text{s. t.}
Subspace Clustering by Block Diagonal Representation
Second, we observe that many existing methods approximate the block diagonal representation matrix by using different structure priors, e. g., sparsity and low-rankness, which are indirect.
WSNet: Compact and Efficient Networks Through Weight Sampling
We present a new approach and a novel architecture, termed WSNet, for learning compact and efficient deep neural networks.
Learning Markov Clustering Networks for Scene Text Detection
MCN predicts instance-level bounding boxes by firstly converting an image into a Stochastic Flow Graph (SFG) and then performing Markov Clustering on this graph.
Learning Pixel-wise Labeling from the Internet without Human Interaction
Collecting sufficient annotated data is very expensive in many applications, especially for pixel-level prediction tasks such as semantic segmentation.
Transferable Semi-supervised Semantic Segmentation
The performance of deep learning based semantic segmentation models heavily depends on sufficient data with careful annotations.
Zigzag Learning for Weakly Supervised Object Detection
Unlike them, we propose a zigzag learning strategy to simultaneously discover reliable object instances and prevent the model from overfitting initial seeds.
Left-Right Comparative Recurrent Model for Stereo Matching
Left-right consistency check is an effective way to enhance the disparity estimation by referring to the information from the opposite view.
Multi-View Image Generation from a Single-View
This paper addresses a challenging problem -- how to generate multi-view cloth images from only a single view input.
Stochastic Primal-Dual Proximal ExtraGradient Descent for Compositely Regularized Optimization
This optimization model abstracts a number of important applications in artificial intelligence and machine learning, such as fused Lasso, fused logistic regression, and a class of graph-guided regularized minimization.
Cross-domain Human Parsing via Adversarial Feature and Label Adaptation
Our proposed model explicitly learns a feature compensation network, which is specialized for mitigating the cross-domain differences.
Weaving Multi-scale Context for Single Shot Detector
For 320x320 input of batch size = 8, WeaveNet reaches 79. 5% mAP on PASCAL VOC 2007 test in 101 fps with only 4 fps extra cost, and further improves to 79. 7% mAP with more iterations.
Nonconvex Sparse Spectral Clustering by Alternating Direction Method of Multipliers and Its Convergence Analysis
Experimental analysis on several real data sets verifies the effectiveness of our method.
Personalized and Occupational-aware Age Progression by Generative Adversarial Networks
Second, we propose a new occupational-aware adversarial face aging network, which learns human aging process under different occupations.
HashGAN:Attention-aware Deep Adversarial Hashing for Cross Modal Retrieval
The proposed new adversarial network, HashGAN, consists of three building blocks: 1) the feature learning module to obtain feature representations, 2) the generative attention module to generate an attention mask, which is used to obtain the attended (foreground) and the unattended (background) feature representations, 3) the discriminative hash coding module to learn hash functions that preserve the similarities between different modalities.
Integrated Face Analytics Networks through Cross-Dataset Hybrid Training
Specifically, iFAN achieves an overall F-score of 91. 15% on the Helen dataset for face parsing, a normalized mean error of 5. 81% on the MTFL dataset for facial landmark localization and an accuracy of 45. 73% on the BNU dataset for emotion recognition with a single model.
Predicting Scene Parsing and Motion Dynamics in the Future
The ability of predicting the future is important for intelligent systems, e. g. autonomous vehicles and robots to plan early and make decisions accordingly.
Ensemble Robustness and Generalization of Stochastic Deep Learning Algorithms
As most deep learning algorithms are stochastic (e. g., Stochastic Gradient Descent, Dropout, and Bayes-by-backprop), we revisit the robustness arguments of Xu & Mannor, and introduce a new approach, ensemble robustness, that concerns the robustness of a population of hypotheses.
Deep Sparse Subspace Clustering
In this paper, we present a deep extension of Sparse Subspace Clustering, termed Deep Sparse Subspace Clustering (DSSC).
Discriminative Similarity for Clustering and Semi-Supervised Learning
By generalization analysis via Rademacher complexity, the generalization error bound for the kernel classifier learned from hypothetical labeling is expressed as the sum of pairwise similarity between the data from different classes, parameterized by the weights of the kernel classifier.
On the Suboptimality of Proximal Gradient Descent for $\ell^{0}$ Sparse Approximation
We study the proximal gradient descent (PGD) method for $\ell^{0}$ sparse approximation problem as well as its accelerated optimization with randomized algorithms in this paper.
Self-explanatory Deep Salient Object Detection
Through visualizing the differences, we can interpret the capability of different deep neural networks based saliency detection models and demonstrate that our proposed model indeed uses more reasonable structure for salient object detection.
Training Group Orthogonal Neural Networks with Privileged Information
Learning rich and diverse representations is critical for the performance of deep convolutional neural networks (CNNs).
Learning with Rethinking: Recurrently Improving Convolutional Neural Networks through Feedback
However, most of the existing CNN models only learn features through a feedforward structure and no feedback information from top to bottom layers is exploited to enable the networks to refine themselves.
FoveaNet: Perspective-aware Urban Scene Parsing
Thus, they suffer from heterogeneous object scales caused by perspective projection of cameras on actual scenes and inevitably encounter parsing failures on distant objects as well as other boundary and recognition errors.
The Landscape of Deep Learning Algorithms
For an $l$-layer linear neural network, we prove its empirical risk uniformly converges to its population risk at the rate of $\mathcal{O}(r^{2l}\sqrt{d\log(l)}/\sqrt{n})$ with training sample size of $n$, the total weight dimension of $d$ and the magnitude bound $r$ of weight of each layer.
Accelerated Randomized Mirror Descent Algorithms For Composite Non-strongly Convex Optimization
Avoiding this devise, we propose an accelerated randomized mirror descent method for solving this problem without the strongly convex assumption.
Neural Person Search Machines
We investigate the problem of person search in the wild in this work.
Ranked #4 on
Person Re-Identification
on CUHK-SYSU
Perceptual Generative Adversarial Networks for Small Object Detection
In this work, we address the small object detection problem by developing a single architecture that internally lifts representations of small objects to "super-resolved" ones, achieving similar characteristics as large objects and thus more discriminative for detection.
Video-based Person Re-identification with Accumulative Motion Context
Video based person re-identification plays a central role in realistic security and video surveillance.
A Unified Framework for Stochastic Matrix Factorization via Variance Reduction
We propose a unified framework to speed up the existing stochastic matrix factorization (SMF) algorithms via variance reduction.
Diversified Visual Attention Networks for Fine-Grained Object Classification
Fine-grained object classification is a challenging task due to the subtle inter-class difference and large intra-class variation.
IAN: The Individual Aggregation Network for Person Search
In addition, to relieve the negative effect caused by varying visual appearances of the same individual, IAN introduces a novel center loss that can increase the intra-class compactness of feature representations.
Deep Self-Taught Learning for Weakly Supervised Object Localization
To overcome this issue, we propose a deep self-taught learning approach, which makes the detector learn the object-level features reliable for acquiring tight positive samples and afterwards re-train itself based on them.
End-to-End Comparative Attention Networks for Person Re-identification
The CAN model is able to learn which parts of images are relevant for discerning persons and automatically integrates information from different parts to determine whether a pair of images belongs to the same person.
On Fundamental Limits of Robust Learning
We consider the problems of robust PAC learning from distributed and streaming data, which may contain malicious errors and outliers, and analyze their fundamental complexity questions.
Interpretable Structure-Evolving LSTM
Instead of learning LSTM models over the pre-fixed structures, we propose to further learn the intermediate interpretable multi-level graph structures in a progressive and stochastic way from data during the LSTM network optimization.
Tree-Structured Reinforcement Learning for Sequential Object Localization
Therefore, Tree-RL can better cover different objects with various scales which is quite appealing in the context of object proposal.
Efficient Hyperparameter Optimization of Deep Learning Algorithms Using Deterministic RBF Surrogates
Those methods adopt probabilistic surrogate models like Gaussian processes to approximate and minimize the validation error function of hyperparameter values.
Outlier Robust Online Learning
We consider the problem of learning from noisy data in practical settings where the size of data is too large to store on a single machine.
Robust LSTM-Autoencoders for Face De-Occlusion in the Wild
The first one, named multi-scale spatial LSTM encoder, reads facial patches of various scales sequentially to output a latent representation, and occlusion-robustness is achieved owing to the fact that the influence of occlusion is only upon some of the patches.
Video Scene Parsing with Predictive Feature Learning
In this way, the network can effectively learn to capture video dynamics and temporal context, which are critical clues for video scene parsing, without requiring extra manual annotations.
Multi-Path Feedback Recurrent Neural Network for Scene Parsing
In this paper, we consider the scene parsing problem and propose a novel Multi-Path Feedback recurrent neural network (MPF-RNN) for parsing scene images.
Deep Recurrent Regression for Facial Landmark Detection
We propose a novel end-to-end deep architecture for face landmark detection, based on a deep convolutional and deconvolutional network followed by carefully designed recurrent network structures.
Multi-stage Object Detection with Group Recursive Learning
Most of existing detection pipelines treat object proposals independently and predict bounding box locations and classification scores over them separately.
Hyperparameter Transfer Learning through Surrogate Alignment for Efficient Deep Neural Network Training
Recently, several optimization methods have been successfully applied to the hyperparameter optimization of deep neural networks (DNNs).
Scale-aware Pixel-wise Object Proposal Networks
In particular, in order to improve the localization accuracy, a fully convolutional network is employed which predicts locations of object proposals for each pixel.
Collaborative Layer-wise Discriminative Learning in Deep Neural Networks
In this paper, we propose a layer-wise discriminative learning method to enhance the discriminative capability of a deep network by allowing its layers to work collaboratively for classification.
Training Skinny Deep Neural Networks with Iterative Hard Thresholding Methods
In this paper, we propose an iterative hard thresholding (IHT) approach to train Skinny Deep Neural Networks (SDNNs).
Deep Edge Guided Recurrent Residual Learning for Image Super-Resolution
To address this essentially ill-posed problem, we introduce a Deep Edge Guided REcurrent rEsidual~(DEGREE) network to progressively recover the high-frequency details.
Scale-aware Fast R-CNN for Pedestrian Detection
Taking pedestrian detection as an example, we illustrate how we can leverage this philosophy to develop a Scale-Aware Fast R-CNN (SAF R-CNN) framework.
Ranked #23 on
Pedestrian Detection
on Caltech
A Focused Dynamic Attention Model for Visual Question Answering
Solving VQA problems requires techniques from both computer vision for understanding the visual contents of a presented image or video, as well as the ones from natural language processing for understanding semantics of the question and generating the answers.
Attentive Contexts for Object Detection
We provide preliminary answers to these questions through developing a novel Attention to Context Convolution Neural Network (AC-CNN) based object detection model.
Semantic Object Parsing with Graph LSTM
By taking the semantic object parsing task as an exemplar application scenario, we propose the Graph Long Short-Term Memory (Graph LSTM) network, which is the generalization of LSTM from sequential data or multi-dimensional data to general graph-structured data.
Auxiliary Image Regularization for Deep CNNs with Noisy Labels
Precisely-labeled data sets with sufficient amount of samples are very important for training deep convolutional neural networks (CNNs).
Learning with $\ell^{0}$-Graph: $\ell^{0}$-Induced Sparse Subspace Clustering
Sparse subspace clustering methods, such as Sparse Subspace Clustering (SSC) \cite{ElhamifarV13} and $\ell^{1}$-graph \cite{YanW09, ChengYYFH10}, are effective in partitioning the data that lie in a union of subspaces.
Semantic Object Parsing with Local-Global Long Short-Term Memory
The long chains of sequential computation by stacked LG-LSTM layers also enable each pixel to sense a much larger region for inference benefiting from the memorization of previous dependencies in all positions along all dimensions.
Sense Beyond Expressions: Cuteness
With the development of Internet culture, cuteness has become a popular concept.
Modality-dependent Cross-media Retrieval
Specifically, by jointly optimizing the correlation between images and text and the linear regression from one modal space (image or text) to the semantic space, two couples of mappings are learned to project images and text from their original feature spaces into two common latent subspaces (one for I2T and the other for T2I).
Distributed Robust Learning
We propose a framework for distributed robust statistical learning on {\em big contaminated data}.
Correlation Adaptive Subspace Segmentation by Trace Lasso
In this work, we argue that both sparsity and the grouping effect are important for subspace segmentation.
TS2C: Tight Box Mining with Surrounding Segmentation Context for Weakly Supervised Object Detection
This work provides a simple approach to discover tight object bounding boxes with only image-level supervision, called Tight box mining with Surrounding Segmentation Context (TS2C).
Object Relation Detection Based on One-shot Learning
Detecting the relations among objects, such as "cat on sofa" and "person ride horse", is a crucial task in image understanding, and beneficial to bridging the semantic gap between images and natural language.
Multi-Fiber Networks for Video Recognition
In this paper, we aim to reduce the computational cost of spatio-temporal deep neural networks, making them run as fast as their 2D counterparts while preserving state-of-the-art accuracy on video recognition benchmarks.
Ranked #36 on
Action Recognition
on UCF101
(using extra training data)
$A^2$-Nets: Double Attention Networks
Learning to capture long-range relations is fundamental to image/video recognition.
Ranked #35 on
Action Recognition
on UCF101
New Insight into Hybrid Stochastic Gradient Descent: Beyond With-Replacement Sampling and Convexity
In this paper, we affirmatively answer this open question by showing that under WoRS and for both convex and non-convex problems, it is still possible for HSGD (with constant step-size) to match full gradient descent in rate of convergence, while maintaining comparable sample-size-independent incremental first-order oracle complexity to stochastic gradient descent.
Efficient Stochastic Gradient Hard Thresholding
To address these deficiencies, we propose an efficient hybrid stochastic gradient hard thresholding (HSG-HT) method that can be provably shown to have sample-size-independent gradient evaluation and hard thresholding complexity bounds.
Dual-Agent GANs for Photorealistic and Identity Preserving Profile Face Synthesis
In particular, we employ an off-the-shelf 3D face model as a simulator to generate profile face images with varying poses.
Ranked #1 on
Face Verification
on IJB-A
Multimodal Learning and Reasoning for Visual Question Answering
In this work we introduce a modular neural network model that learns a multimodal and multifaceted representation of the image and the question.
Robust Logistic Regression and Classification
We consider logistic regression with arbitrary outliers in the covariate matrix.
Online Robust PCA via Stochastic Optimization
Robust PCA methods are typically based on batch optimization and have to load all the samples into memory.
Online PCA for Contaminated Data
We consider the online Principal Component Analysis (PCA) for contaminated samples (containing outliers) which are revealed sequentially to the Principal Components (PCs) estimator.
MoNet: Deep Motion Exploitation for Video Object Segmentation
In this paper, we propose a novel MoNet model to deeply exploit motion cues for boosting video object segmentation performance from two aspects, i. e., frame representation learning and segmentation refinement.
Deep Adversarial Subspace Clustering
To solve this issue, we propose a novel deep adversarial subspace clustering (DASC) model, which learns more favorable sample representations by deep learning for subspace clustering, and more importantly introduces adversarial learning to supervise sample representation learning and subspace clustering.
Ranked #2 on
Image Clustering
on coil-40
Human Pose Estimation With Parsing Induced Learner
Comprehensive experiments on benchmarks LIP and extended PASCAL-Person-Part show that the proposed Parsing Induced Learner can improve performance of both single- and multi-person pose estimation to new state-of-the-art.
Towards Pose Invariant Face Recognition in the Wild
To this end, we propose a Pose Invariant Model (PIM) for face recognition in the wild, with three distinct novelties.
Weakly Supervised Phrase Localization With Multi-Scale Anchored Transformer Network
In this paper, we propose a novel weakly supervised model, Multi-scale Anchored Transformer Network (MATN), to accurately localize free-form textual phrases with only image-level supervision.
Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi-Supervised Semantic Segmentation
Despite remarkable progress, weakly supervised segmentation methods are still inferior to their fully supervised counterparts.
Dynamic Conditional Networks for Few-Shot Learning
This paper proposes a novel Dynamic Conditional Convolutional Network (DCCN) to handle conditional few-shot learning, i. e, only a few training samples are available for each condition.
ML-LocNet: Improving Object Localization with Multi-view Learning Network
This paper addresses Weakly Supervised Object Localization (WSOL) with only image-level supervision.
Mutual Learning to Adapt for Joint Human Parsing and Pose Estimation
This paper presents a novel Mutual Learning to Adapt model (MuLA) for joint human parsing and pose estimation.
Ranked #11 on
Semantic Segmentation
on LIP val
Pose Partition Networks for Multi-Person Pose Estimation
This paper proposes a novel Pose Partition Network (PPN) to address the challenging multi-person pose estimation problem.
Policy Optimization with Demonstrations
Exploration remains a significant challenge to reinforcement learning methods, especially in environments where reward signals are sparse.
Empirical Risk Landscape Analysis for Understanding Deep Neural Networks
This work aims to provide comprehensive landscape analysis of empirical risk in deep neural networks (DNNs), including the convergence behavior of its gradient, its stationary points and the empirical risk itself to their corresponding population counterparts, which reveals how various network parameters determine the convergence performance.
Egocentric Spatial Memory Network
During the exploration, our proposed ESM network model updates belief of the global map based on local observations using a recurrent neural network.
Interpreting Deep Classification Models With Bayesian Inference
The results demonstrate that the proposed interpreter successfully finds the core hidden units most responsible for prediction making.
WSNet: Learning Compact and Efficient Networks with Weight Sampling
We present a new approach and a novel architecture, termed WSNet, for learning compact and efficient deep neural networks.
Similarity R-C3D for Few-shot Temporal Activity Detection
In this paper, we present a conceptually simple and general yet novel framework for few-shot temporal activity detection which detects the start and end time of the few-shot input activities in an untrimmed video.
Better Guider Predicts Future Better: Difference Guided Generative Adversarial Networks
In this paper, we propose a novel GAN based on inter-frame difference to circumvent the difficulties.
Deep Reasoning with Multi-Scale Context for Salient Object Detection
However, the saliency inference module that performs saliency prediction from the fused features receives much less attention on its architecture design and typically adopts only a few fully convolutional layers.
Learning Scalable Discriminative Dictionary with Sample Relatedness
We use sample relatedness information to improve the generalization of the learned dictionary.
Robust Subspace Segmentation with Block-diagonal Prior
Most current state-of-the-art subspace segmentation methods (such as SSC and LRR) resort to alternative structural priors (such as sparseness and low-rankness) to construct the affinity matrix.
Recurrently Target-Attending Tracking
The produced confidence maps from the RNNs are employed to adaptively regularize the learning of discriminative correlation filters by suppressing clutter background noises while making full use of the information from reliable parts.
Recurrent Face Aging
Modeling the aging process of human face is important for cross-age face verification and recognition.
Highway Vehicle Counting in Compressed Domain
HCR hierarchically divides the traffic scenes into different cases according to vehicle density, such that the broad-variation characteristics of traffic scenes can be better approximated.
Memory-Augmented Attribute Manipulation Networks for Interactive Fashion Search
We introduce a new fashion search protocol where attribute manipulation is allowed within the interaction between users and search engines, e. g. manipulating the color attribute of the clothing from red to blue.
Outlier-Robust Tensor PCA
Low-rank tensor analysis is important for various real applications in computer vision.
Learning The Structure of Deep Convolutional Networks
In this work, we develop a novel method for automatically learning aspects of the structure of a deep model, in order to improve its performance, especially when labeled training data are scarce.
Recurrent 3D-2D Dual Learning for Large-Pose Facial Landmark Detection
To address these challenging issues, we introduce a novel recurrent 3D-2D dual learning model that alternatively performs 2D-based 3D face model refinement and 3D-to-2D projection based 2D landmark refinement to reliably reason about self-occluded landmarks, precisely capture the subtle landmark displacement and accurately detect landmarks even in presence of extremely large poses.
Regional Interactive Image Segmentation Networks
The interactive image segmentation model allows users to iteratively add new inputs for refinement until a satisfactory result is finally obtained.
Ranked #10 on
Interactive Segmentation
on SBD
(NoC@85 metric)
Multi-Prototype Networks for Unconstrained Set-based Face Recognition
In this paper, we study the challenging unconstrained set-based face recognition problem where each subject face is instantiated by a set of media (images and videos) instead of a single image.
Few-shot Adaptive Faster R-CNN
To address these challenges, we first introduce a pairing mechanism over source and target features to alleviate the issue of insufficient target domain samples.
Foreground-aware Pyramid Reconstruction for Alignment-free Occluded Person Re-identification
FPR uses the error from robust reconstruction over spatial pyramid features to measure similarities between two persons.
Cycle-SUM: Cycle-consistent Adversarial LSTM Networks for Unsupervised Video Summarization
The evaluator defines a learnable information preserving metric between original video and summary video and "supervises" the selector to identify the most informative frames to form the summary video.
Ranked #7 on
Unsupervised Video Summarization
on TvSum
Hierarchical Meta Learning
Extensive experiments on few-shot classification and regression problems clearly demonstrate the superiority of HML over fine-tuning and state-of-the-art meta learning approaches in terms of generalization across heterogeneous tasks.
Cross-Resolution Face Recognition via Prior-Aided Face Hallucination and Residual Knowledge Distillation
Recent deep learning based face recognition methods have achieved great performance, but it still remains challenging to recognize very low-resolution query face like 28x28 pixels when CCTV camera is far from the captured subject.
Panoptic Edge Detection
In this work, we address a new finer-grained task, termed panoptic edge detection (PED), which aims at predicting semantic-level boundaries for stuff categories and instance-level boundaries for instance categories, in order to provide more comprehensive and unified scene understanding from the perspective of edges. We then propose a versatile framework, Panoptic Edge Network (PEN), which aggregates different tasks of object detection, semantic and instance edge detection into a single holistic network with multiple branches.
Deep Face Recognition Model Compression via Knowledge Transfer and Distillation
However, deploying such high performing models to resource constraint devices or real-time applications is challenging.
Understanding Adversarial Behavior of DNNs by Disentangling Non-Robust and Robust Components in Performance Metric
Despite many previous works studying the reason behind such adversarial behavior, the relationship between the generalization performance and adversarial behavior of DNNs is still little understood.
Unsupervised Image Noise Modeling with Self-Consistent GAN
They jointly facilitate unsupervised learning of a noise model for various noise types.
Delving into 3D Action Anticipation from Streaming Videos
Action anticipation, which aims to recognize the action with a partial observation, becomes increasingly popular due to a wide range of applications.
Neural Epitome Search for Architecture-Agnostic Network Compression
The recent WSNet [1] is a new model compression method through sampling filterweights from a compact set and has demonstrated to be effective for 1D convolutionneural networks (CNNs).
Dynamic Kernel Distillation for Efficient Pose Estimation in Videos
Existing video-based human pose estimation methods extensively apply large networks onto every frame in the video to localize body joints, which suffer high computational cost and hardly meet the low-latency requirement in realistic applications.
Ranked #3 on
2D Human Pose Estimation
on JHMDB (2D poses only)
Hierarchic Neighbors Embedding
Manifold learning now plays a very important role in machine learning and many relevant applications.
Efficient Meta Learning via Minibatch Proximal Update
We address the problem of meta-learning which learns a prior over hypothesis from a sample of meta-training tasks for fast adaptation on meta-testing tasks.
Efficient Differentiable Neural Architecture Search with Meta Kernels
We demonstrate the effectiveness of our search strategy by conducting extensive experiments.
Zoom in to where it matters: a hierarchical graph based model for mammogram analysis
In clinical practice, human radiologists actually review medical images with high resolution monitors and zoom into region of interests (ROIs) for a close-up examination.
RC-DARTS: Resource Constrained Differentiable Architecture Search
In this paper, we propose the resource constrained differentiable architecture search (RC-DARTS) method to learn architectures that are significantly smaller and faster while achieving comparable accuracy.
MetaSelector: Meta-Learning for Recommendation with User-Level Adaptive Model Selection
Recommender systems often face heterogeneous datasets containing highly personalized historical data of users, where no single model could give the best recommendation for every user.
Cross-layer Feature Pyramid Network for Salient Object Detection
Feature pyramid network (FPN) based models, which fuse the semantics and salient details in a progressive manner, have been proven highly effective in salient object detection.
Semantic Domain Adversarial Networks for Unsupervised Domain Adaptation
To successfully align the multi-modal data structures across domains, the following works exploit discriminative information in the adversarial training process, e. g., using multiple class-wise discriminators and introducing conditional information in input or output of the domain discriminator.
Compressed Video Action Recognition with Refined Motion Vector
Although CNN has reached satisfactory performance in image-related tasks, using CNN to process videos is much more challenging due to the enormous size of raw video streams.
Boosting Few-Shot Learning With Adaptive Margin Loss
Few-shot learning (FSL) has attracted increasing attention in recent years but remains challenging, due to the intrinsic difficulty in learning to generalize from a few examples.
Ranked #1 on
Few-Shot Image Classification
on ImageNet (1-shot)
Multi-Miner: Object-Adaptive Region Mining for Weakly-Supervised Semantic Segmentation
In this paper, we propose a novel multi-miner framework to perform a region mining process that adapts to diverse object sizes and is thus able to mine more integral and finer object regions.
Inference Stage Optimization for Cross-scenario 3D Human Pose Estimation
In this work, we propose a novel framework, Inference Stage Optimization (ISO), for improving the generalizability of 3D pose models when source and target data come from different pose distributions.
Ranked #118 on
3D Human Pose Estimation
on 3DPW
(PA-MPJPE metric)
Local Grid Rendering Networks for 3D Object Detection in Point Clouds
The performance of 3D object detection models over point clouds highly depends on their capability of modeling local geometric patterns.
Adversarial Self-Supervised Learning for Semi-Supervised 3D Action Recognition
Self-supervised learning (SSL) has been proved very effective at learning representations from unlabeled data in the image domain.
Dual Adversarial Auto-Encoders for Clustering
By performing variational inference on the objective function of Dual-AAE, we derive a new reconstruction loss which can be optimized by training a pair of Auto-encoders.
Exploring Balanced Feature Spaces for Representation Learning
Motivated by this question, we conduct a series of studies on the performance of self-supervised contrastive learning and supervised learning methods over multiple datasets where training instance distributions vary from a balanced one to a long-tailed one.
Ranked #40 on
Long-tail Learning
on CIFAR-10-LT (ρ=10)
Learning Safe Policies with Cost-sensitive Advantage Estimation
Reinforcement Learning (RL) with safety guarantee is critical for agents performing tasks in risky environments.
DiffAutoML: Differentiable Joint Optimization for Efficient End-to-End Automated Machine Learning
Our method performs co-optimization of the neural architectures, training hyper-parameters and data augmentation policies in an end-to-end fashion without the need of model retraining.
Towards Theoretically Understanding Why SGD Generalizes Better Than ADAM in Deep Learning
The result shows that (1) the escaping time of both SGD and ADAM~depends on the Radon measure of the basin positively and the heaviness of gradient noise negatively; (2) for the same basin, SGD enjoys smaller escaping time than ADAM, mainly because (a) the geometry adaptation in ADAM~via adaptively scaling each gradient coordinate well diminishes the anisotropic structure in gradient noise and results in larger Radon measure of a basin; (b) the exponential gradient average in ADAM~smooths its gradient and leads to lighter gradient noise tails than SGD.
Toward Accurate Person-level Action Recognition in Videos of Crowded Scenes
Prior works always fail to deal with this problem in two aspects: (1) lacking utilizing information of the scenes; (2) lacking training data in the crowd and complex scenes.
A Simple Baseline for Pose Tracking in Videos of Crowded Scenes
This paper presents our solution to ACM MM challenge: Large-scale Human-centric Video Analysis in Complex Events\cite{lin2020human}; specifically, here we focus on Track3: Crowd Pose Tracking in Complex Events.
Towards Accurate Human Pose Estimation in Videos of Crowded Scenes
In this paper, we focus on improving human pose estimation in videos of crowded scenes from the perspectives of exploiting temporal context and collecting new data.
Fooling the primate brain with minimal, targeted image manipulation
Here we propose an array of methods for creating minimal, targeted image perturbations that lead to changes in both neuronal activity and perception as reflected in behavior.
ORDNet: Capturing Omni-Range Dependencies for Scene Parsing
Our ORDNet is able to extract more comprehensive context information and well adapt to complex spatial variance in scene images.
Augmented Transformer with Adaptive Graph for Temporal Action Proposal Generation
Temporal action proposal generation (TAPG) is a fundamental and challenging task in video understanding, especially in temporal action detection.
How Well Does Self-Supervised Pre-Training Perform with Streaming Data?
Prior works on self-supervised pre-training focus on the joint training scenario, where massive unlabeled data are assumed to be given as input all at once, and only then is a learner trained.
Joint Face Image Restoration and Frontalization for Recognition
First, MDFR is a well-designed encoder-decoder architecture which extracts feature representation from an input face image with arbitrary low-quality factors and restores it to a high-quality counterpart.
Image-to-Video Generation via 3D Facial Dynamics
Video generation from a single face image is an interesting problem and usually tackled by utilizing Generative Adversarial Networks (GANs) to integrate information from the input face image and a sequence of sparse facial landmarks.
Triplet Contrastive Learning for Brain Tumor Classification
Brain tumor is a common and fatal form of cancer which affects both adults and children.
DHA: End-to-End Joint Optimization of Data Augmentation Policy, Hyper-parameter and Architecture
In view of these, we propose DHA, which achieves joint optimization of Data augmentation policy, Hyper-parameter and Architecture.
Sample Efficient Deep Neuroevolution in Low Dimensional Latent Space
Current deep neuroevolution models are usually trained in a large parameter search space for complex learning tasks, e. g. playing video games, which needs billions of samples and thousands of search steps to obtain significant performance.
PROTOTYPE-ASSISTED ADVERSARIAL LEARNING FOR UNSUPERVISED DOMAIN ADAPTATION
Previous adversarial learning methods condition domain alignment only on pseudo labels, but noisy and inaccurate pseudo labels may perturb the multi-class distribution embedded in probabilistic predictions, hence bringing insufficient alleviation to the latent mismatch problem.
Prototype Recalls for Continual Learning
Without tampering with the performance on initial tasks, our method learns novel concepts given a few training examples of each class in new tasks.
Towards Disentangling Non-Robust and Robust Components in Performance Metric
Then, we show by experiments that DNNs under standard training rely heavily on optimizing the non-robust component in achieving decent performance.
How Well Does Self-Supervised Pre-Training Perform with Streaming ImageNet?
Prior works on self-supervised pre-training focus on the joint training scenario, where massive unlabeled data are assumed to be given as input all at once, and only then is a learner trained.
Geometry-Guided Progressive NeRF for Generalizable and Efficient Neural Human Rendering
Meanwhile, for achieving higher rendering efficiency, we introduce a progressive rendering pipeline through geometry guidance, which leverages the geometric feature volume and the predicted density values to progressively reduce the number of sampling points and speed up the rendering process.
UMAD: Universal Model Adaptation under Domain and Category Shift
To achieve bilateral adaptation in the target domain, we further maximize localized mutual information to align known samples with the source classifier and employ an entropic loss to push unknown samples far away from the source classification boundary, respectively.
Ranked #6 on
Universal Domain Adaptation
on VisDA2017
Towards Adversarially Robust Deep Image Denoising
Secondly, to robustify DIDs, we propose an adversarial training strategy, hybrid adversarial training ({\sc HAT}), that jointly trains DIDs with adversarial and non-adversarial noisy data to ensure that the reconstruction quality is high and the denoisers around non-adversarial data are locally smooth.
The Geometry of Robust Value Functions
The key of this perspective is to decompose the value space, in a state-wise manner, into unions of hypersurfaces.
Tyger: Task-Type-Generic Active Learning for Molecular Property Prediction
However, existing best deep AL methods are mostly developed for a single type of learning task (e. g., single-label classification), and hence may not perform well in molecular property prediction that involves various task types.
Reachability-Aware Laplacian Representation in Reinforcement Learning
In Reinforcement Learning (RL), Laplacian Representation (LapRep) is a task-agnostic state representation that encodes the geometry of the environment.
MagicVideo: Efficient Video Generation With Latent Diffusion Models
In specific, unlike existing works that directly train video models in the RGB space, we use a pre-trained VAE to map video clips into a low-dimensional latent space and learn the distribution of videos' latent codes via a diffusion model.
Ranked #10 on
Text-to-Video Generation
on MSR-VTT
Diffusion Probabilistic Model Made Slim
Despite the recent visually-pleasing results achieved, the massive computational cost has been a long-standing flaw for diffusion probabilistic models (DPMs), which, in turn, greatly limits their applications on resource-limited platforms.
PV3D: A 3D Generative Model for Portrait Video Generation
While some prior works have applied such image GANs to unconditional 2D portrait video generation and static 3D portrait synthesis, there are few works successfully extending GANs for generating 3D-aware portrait videos.
CMAE-V: Contrastive Masked Autoencoders for Video Action Recognition
Contrastive Masked Autoencoder (CMAE), as a new self-supervised framework, has shown its potential of learning expressive feature representations in visual image recognition.
Temporal Perceiving Video-Language Pre-training
Specifically, text-video localization consists of moment retrieval, which predicts start and end boundaries in videos given the text description, and text localization which matches the subset of texts with the video features.
MV-Adapter: Multimodal Video Transfer Learning for Video Text Retrieval
The first is a Temporal Adaptation Module that is incorporated in the video branch to introduce global and local temporal contexts.
AgileGAN3D: Few-Shot 3D Portrait Stylization by Augmented Transfer Learning
Capitalizing on the recent advancement of 3D-aware GAN models, we perform \emph{guided transfer learning} on a pretrained 3D GAN generator to produce multi-view-consistent stylized renderings.
OmniAvatar: Geometry-Guided Controllable 3D Head Synthesis
We present OmniAvatar, a novel geometry-guided 3D head synthesis model trained from in-the-wild unstructured images that is capable of synthesizing diverse identity-preserved 3D heads with compelling dynamic details under full disentangled control over camera poses, facial expressions, head shapes, articulated neck and jaw poses.
Associating Spatially-Consistent Grouping with Text-supervised Semantic Segmentation
In this work, we investigate performing semantic segmentation solely through the training on image-sentence pairs.
DOAD: Decoupled One Stage Action Detection Network
Specifically, one branch focuses on detection representation for actor detection, and the other one for action recognition.
VLAB: Enhancing Video Language Pre-training by Feature Adapting and Blending
Towards this goal, we propose a novel video-text pre-training method dubbed VLAB: Video Language pre-training by feature Adapting and Blending, which transfers CLIP representations to video pre-training tasks and develops unified video multimodal models for a wide range of video-text tasks.
Ranked #1 on
Visual Question Answering (VQA)
on MSVD-QA
(using extra training data)
Delving Deeper into Data Scaling in Masked Image Modeling
The study reveals that: 1) MIM can be viewed as an effective method to improve the model capacity when the scale of the training data is relatively small; 2) Strong reconstruction targets can endow the models with increased capacities on downstream tasks; 3) MIM pre-training is data-agnostic under most scenarios, which means that the strategy of sampling pre-training data is non-critical.
AdjointDPM: Adjoint Sensitivity Method for Gradient Backpropagation of Diffusion Probabilistic Models
Existing customization methods require access to multiple reference examples to align pre-trained diffusion probabilistic models (DPMs) with user-provided concepts.
MagicEdit: High-Fidelity and Temporally Coherent Video Editing
In this report, we present MagicEdit, a surprisingly simple yet effective solution to the text-guided video editing task.
MagicAvatar: Multimodal Avatar Generation and Animation
This report presents MagicAvatar, a framework for multimodal video generation and animation of human avatars.
MagicProp: Diffusion-based Video Editing via Motion-aware Appearance Propagation
The flexibility of these techniques enables the editing of arbitrary regions within the frame.
MaskDiffusion: Boosting Text-to-Image Consistency with Conditional Mask
In this work, we identify that a crucial factor leading to the text-image mismatch issue is the inadequate cross-modality relation learning between the prompt and the output image.
GETAvatar: Generative Textured Meshes for Animatable Human Avatars
We study the problem of 3D-aware full-body human generation, aiming at creating animatable human avatars with high-quality textures and geometries.
Low-Resolution Self-Attention for Semantic Segmentation
Semantic segmentation tasks naturally require high-resolution information for pixel-wise segmentation and global context information for class prediction.
ChatAnything: Facetime Chat with LLM-Enhanced Personas
For MoV, we utilize the text-to-speech (TTS) algorithms with a variety of pre-defined tones and select the most matching one based on the user-provided text description automatically.
EPIM: Efficient Processing-In-Memory Accelerators based on Epitome
On the hardware side, we modify the datapath of current PIM accelerators to accommodate epitomes and implement a feature map reuse technique to reduce computation cost.
AvatarStudio: High-fidelity and Animatable 3D Avatar Creation from Text
We study the problem of creating high-fidelity and animatable 3D avatars from only textual descriptions.
PixelLM: Pixel Reasoning with Large Multimodal Model
PixelLM excels across various pixel-level image reasoning and understanding tasks, outperforming well-established methods in multiple benchmarks, including MUSE, single- and multi-referring segmentation.
Vista-LLaMA: Reliable Video Narrator via Equal Distance to Visual Tokens
This amplifies the effect of visual tokens on text generation, especially when the relative distance is longer between visual and text tokens.
Ranked #6 on
Zero-Shot Video Question Answer
on MSRVTT-QA
Towards Accurate Guided Diffusion Sampling through Symplectic Adjoint Method
However, since the off-the-shelf pre-trained networks are trained on clean images, the one-step estimation procedure of the clean image may be inaccurate, especially in the early stages of the generation process in diffusion models.
DREAM-Talk: Diffusion-based Realistic Emotional Audio-driven Method for Single Image Talking Face Generation
The generation of emotional talking faces from a single portrait image remains a significant challenge.
MagicVideo-V2: Multi-Stage High-Aesthetic Video Generation
The growing demand for high-fidelity video generation from textual descriptions has catalyzed significant research in this field.
Magic-Boost: Boost 3D Generation with Mutli-View Conditioned Diffusion
Benefiting from the rapid development of 2D diffusion models, 3D content creation has made significant progress recently.
RAIN: A Simple Approach for Robust and Accurate Image Classification Networks
However, similar to existing preprocessing-based methods, the randomized process will degrade the prediction accuracy.
Class Prototype-based Cleaner for Label Noise Learning
Semi-supervised learning based methods are current SOTA solutions to the noisy-label learning problem, which rely on learning an unsupervised label cleaner first to divide the training samples into a labeled set for clean data and an unlabeled set for noise data.
Ranked #3 on
Image Classification
on Clothing1M
(using extra training data)
Residual Distillation: Towards Portable Deep Neural Networks without Shortcuts
In particular, we propose a novel joint-training framework to train plain CNN by leveraging the gradients of the ResNet counterpart.
Towards Understanding Why Lookahead Generalizes Better Than SGD and Beyond
Specifically, we prove that lookahead using SGD as its inner-loop optimizer can better balance the optimization error and generalization error to achieve smaller excess risk error than vanilla SGD on (strongly) convex problems and nonconvex problems with Polyak-{\L}ojasiewicz condition which has been observed/proved in neural networks.
Deep Learning with S-shaped Rectified Linear Activation Units
Rectified linear activation units are important components for state-of-the-art deep convolutional networks.
Video-based Person Re-identification with Accumulative Motion Context
Video based person re-identification plays a central role in realistic security and video surveillance.
Adaptive ROI Generation for Video Object Segmentation Using Reinforcement Learning
In this paper, we aim to tackle the task of semi-supervised video object segmentation across a sequence of frames where only the ground-truth segmentation of the first frame is provided.
AutoSpace: Neural Architecture Search with Less Human Interference
Current neural architecture search (NAS) algorithms still require expert knowledge and effort to design a search space for network construction.
Learning Generalizable and Identity-Discriminative Representations for Face Anti-Spoofing
Face anti-spoofing (a. k. a presentation attack detection) has drawn growing attention due to the high-security demand in face authentication systems.
Ranked #2 on
Face Anti-Spoofing
on MSU-MFSD
A Good Practice Towards Top Performance of Face Recognition: Transferred Deep Feature Fusion
In this paper, we propose a unified learning framework named Transferred Deep Feature Fusion (TDFF) targeting at the new IARPA Janus Benchmark A (IJB-A) face recognition dataset released by NIST face challenge.
Variational Prototype Replays for Continual Learning
In each classification task, our method learns a set of variational prototypes with their means and variances, where embedding of the samples from the same class can be represented in a prototypical distribution and class-representative prototypes are separated apart.
Egocentric Spatial Memory
Egocentric spatial memory (ESM) defines a memory system with encoding, storing, recognizing and recalling the spatial information about the environment from an egocentric perspective.
On Robustness of Neural Ordinary Differential Equations
We then provide an insightful understanding of this phenomenon by exploiting a certain desirable property of the flow of a continuous-time ODE, namely that integral curves are non-intersecting.
Global Knowledge Calibration for Fast Open-Vocabulary Segmentation
Recent advancements in pre-trained vision-language models, such as CLIP, have enabled the segmentation of arbitrary concepts solely from textual inputs, a process commonly referred to as open-vocabulary semantic segmentation (OVS).
Knowledge Distillation
Open Vocabulary Semantic Segmentation
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