Search Results for author:
Found 353 papers, 176 papers with code
Visual Tracking via Locality Sensitive Histograms
A robust tracking framework based on the locality sensitive histograms is proposed, which consists of two main components: a new feature for tracking that is robust to illumination changes and a novel multi-region tracking algorithm that runs in realtime even with hundreds of regions.
Online Object Tracking: A Benchmark
Object tracking is one of the most important components in numerous applications of computer vision.
Structured Face Hallucination
Each face image is represented in terms of facial components, contours and smooth regions.
Saliency Detection via Graph-Based Manifold Ranking
The saliency of the image elements is defined based on their relevances to the given seeds or queries.
Least Soft-Threshold Squares Tracking
In this paper, we propose a generative tracking method based on a novel robust linear regression algorithm.
Parallel Coordinate Descent Newton Method for Efficient $\ell_1$-Regularized Minimization
We show that: (1) PCDN is guaranteed to converge globally despite increasing parallelism; (2) PCDN converges to the specified accuracy $\epsilon$ within the limited iteration number of $T_\epsilon$, and $T_\epsilon$ decreases with increasing parallelism (bundle size $P$).
Fast Tracking via Spatio-Temporal Context Learning
In this paper, we present a simple yet fast and robust algorithm which exploits the spatio-temporal context for visual tracking.
Max-Margin Boltzmann Machines for Object Segmentation
We present Max-Margin Boltzmann Machines (MMBMs) for object segmentation.
Deblurring Text Images via L0-Regularized Intensity and Gradient Prior
We propose a simple yet effective L_0-regularized prior based on intensity and gradient for text image deblurring.
Joint Depth Estimation and Camera Shake Removal from Single Blurry Image
The non-uniform blur effect is not only caused by the camera motion, but also the depth variation of the scene.
Context Driven Scene Parsing with Attention to Rare Classes
This paper presents a scalable scene parsing algorithm based on image retrieval and superpixel matching.
Deblurring Low-light Images with Light Streaks
Images taken in low-light conditions with handheld cameras are often blurry due to the required long exposure time.
Ranked #11 on
Deblurring
on RealBlur-R (trained on GoPro)
Robust Visual Tracking via Convolutional Networks
In this paper we present that, even without offline training with a large amount of auxiliary data, simple two-layer convolutional networks can be powerful enough to develop a robust representation for visual tracking.
Multi-Objective Convolutional Learning for Face Labeling
This paper formulates face labeling as a conditional random field with unary and pairwise classifiers.
Multi-Instance Object Segmentation With Occlusion Handling
We present a multi-instance object segmentation algorithm to tackle occlusions.
Adaptive Region Pooling for Object Detection
Learning models for object detection is a challenging problem due to the large intra-class variability of objects in appearance, viewpoints, and rigidity.
PatchCut: Data-Driven Object Segmentation via Local Shape Transfer
The transferred local shape masks constitute a patch-level segmentation solution space and we thus develop a novel cascade algorithm, PatchCut, for coarse-to-fine object segmentation.
Long-Term Correlation Tracking
In this paper, we address the problem of long-term visual tracking where the target objects undergo significant appearance variation due to deformation, abrupt motion, heavy occlusion and out-of-the-view.
Deep Networks for Saliency Detection via Local Estimation and Global Search
In the global search stage, the local saliency map together with global contrast and geometric information are used as global features to describe a set of object candidate regions.
Structural Sparse Tracking
Sparse representation has been applied to visual tracking by finding the best target candidate with minimal reconstruction error by use of target templates.
JOTS: Joint Online Tracking and Segmentation
We present a novel Joint Online Tracking and Segmentation (JOTS) algorithm which integrates the multi-part tracking and segmentation into a unified energy optimization framework to handle the video segmentation task.
Salient Object Detection via Bootstrap Learning
Furthermore, we show that the proposed bootstrap learning approach can be easily applied to other bottom-up saliency models for significant improvement.
To Know Where We Are: Vision-Based Positioning in Outdoor Environments
Augmented reality (AR) displays become more and more popular recently, because of its high intuitiveness for humans and high-quality head-mounted display have rapidly developed.
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.
UA-DETRAC: A New Benchmark and Protocol for Multi-Object Detection and Tracking
In this work, we perform a comprehensive quantitative study on the effects of object detection accuracy to the overall MOT performance, using the new large-scale University at Albany DETection and tRACking (UA-DETRAC) benchmark dataset.
Fast and Accurate Head Pose Estimation via Random Projection Forests
In this paper, we consider the problem of estimating the gaze direction of a person from a low-resolution image.
What Makes an Object Memorable?
We augment both the images and object segmentations from the PASCAL-S dataset with ground truth memorability scores and shed light on the various factors and properties that make an object memorable (or forgettable) to humans.
Hierarchical Convolutional Features for Visual Tracking
The outputs of the last convolutional layers encode the semantic information of targets and such representations are robust to significant appearance variations.
Weakly-supervised Disentangling with Recurrent Transformations for 3D View Synthesis
An important problem for both graphics and vision is to synthesize novel views of a 3D object from a single image.
Learning Support Correlation Filters for Visual Tracking
Sampling and budgeting training examples are two essential factors in tracking algorithms based on support vector machines (SVMs) as a trade-off between accuracy and efficiency.
Object Contour Detection with a Fully Convolutional Encoder-Decoder Network
We develop a deep learning algorithm for contour detection with a fully convolutional encoder-decoder network.
Superpixel Hierarchy
Quantitative and qualitative evaluation on a number of computer vision applications was conducted, demonstrating that the proposed method is the top performer.
Soft-Segmentation Guided Object Motion Deblurring
To address these problems, we propose a novel model for object motion deblurring.
Online Multi-Object Tracking via Structural Constraint Event Aggregation
In addition, to further improve the robustness of data association against mis-detections and clutters, a novel event aggregation approach is developed to integrate structural constraints in assignment costs for online MOT.
Ranked #21 on
Multiple Object Tracking
on KITTI Tracking test
Object Tracking via Dual Linear Structured SVM and Explicit Feature Map
Structured support vector machine (SSVM) based methods has demonstrated encouraging performance in recent object tracking benchmarks.
Hedged Deep Tracking
In recent years, several methods have been developed to utilize hierarchical features learned from a deep convolutional neural network (CNN) for visual tracking.
Image Deblurring Using Smartphone Inertial Sensors
Removing image blur caused by camera shake is an ill-posed problem, as both the latent image and the point spread function (PSF) are unknown.
Blind Image Deblurring Using Dark Channel Prior
Therefore, enforcing the sparsity of the dark channel helps blind deblurring on various scenarios, including natural, face, text, and low-illumination images.
Ranked #10 on
Deblurring
on RealBlur-R (trained on GoPro)
A Comparative Study for Single Image Blind Deblurring
Using these datasets, we conduct a large-scale user study to quantify the performance of several representative state-of-the-art blind deblurring algorithms.
Weakly Supervised Object Localization With Progressive Domain Adaptation
In this paper, we address this problem by progressive domain adaptation with two main steps: classification adaptation and detection adaptation.
Video Segmentation via Object Flow
Video object segmentation is challenging due to fast moving objects, deforming shapes, and cluttered backgrounds.
Ranked #74 on
Semi-Supervised Video Object Segmentation
on DAVIS 2016
(using extra training data)
Robust Kernel Estimation With Outliers Handling for Image Deblurring
Estimating blur kernels from real world images is a challenging problem as the linear image formation assumption does not hold when significant outliers, such as saturated pixels and non-Gaussian noise, are present.
Visual Tracking via Boolean Map Representations
In this paper, we present a simple yet effective Boolean map based representation that exploits connectivity cues for visual tracking.
Learning Fully Convolutional Networks for Iterative Non-blind Deconvolution
In this paper, we propose a fully convolutional networks for iterative non-blind deconvolution We decompose the non-blind deconvolution problem into image denoising and image deconvolution.
Learning a No-Reference Quality Metric for Single-Image Super-Resolution
Numerous single-image super-resolution algorithms have been proposed in the literature, but few studies address the problem of performance evaluation based on visual perception.
Ranked #7 on
Video Quality Assessment
on MSU SR-QA Dataset
Dual Deep Network for Visual Tracking
In this paper, we propose a dual network to better utilize features among layers for visual tracking.
Online multi-object tracking via robust collaborative model and sample selection
For each frame, we construct an association between detections and trackers, and treat each detected image region as a key sample, for online update, if it is associated to a tracker.
Ranked #1 on
Online Multi-Object Tracking
on Oxford Town Center
Diversified Texture Synthesis with Feed-forward Networks
Recent progresses on deep discriminative and generative modeling have shown promising results on texture synthesis.
Unsupervised Holistic Image Generation from Key Local Patches
We introduce a new problem of generating an image based on a small number of key local patches without any geometric prior.
Semantic-driven Generation of Hyperlapse from $360^\circ$ Video
We present a system for converting a fully panoramic ($360^\circ$) video into a normal field-of-view (NFOV) hyperlapse for an optimal viewing experience.
Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution
Convolutional neural networks have recently demonstrated high-quality reconstruction for single-image super-resolution.
Ranked #40 on
Image Super-Resolution
on BSD100 - 4x upscaling
Generative Face Completion
In this paper, we propose an effective face completion algorithm using a deep generative model.
Universal Style Transfer via Feature Transforms
The whitening and coloring transforms reflect a direct matching of feature covariance of the content image to a given style image, which shares similar spirits with the optimization of Gram matrix based cost in neural style transfer.
Learning Structured Semantic Embeddings for Visual Recognition
First, we exploit the discriminative constraints to capture the intra- and inter-class relationships of image embeddings.
Learning Spatial-Aware Regressions for Visual Tracking
Second, we propose a fully convolutional neural network with spatially regularized kernels, through which the filter kernel corresponding to each output channel is forced to focus on a specific region of the target.
Ranked #12 on
Visual Object Tracking
on VOT2017/18
Multi-Task Correlation Particle Filter for Robust Object Tracking
In this paper, we propose a multi-task correlation particle filter (MCPF) for robust visual tracking.
Adaptive Correlation Filters with Long-Term and Short-Term Memory for Object Tracking
Second, we learn a correlation filter over a feature pyramid centered at the estimated target position for predicting scale changes.
Robust Visual Tracking via Hierarchical Convolutional Features
Specifically, we learn adaptive correlation filters on the outputs from each convolutional layer to encode the target appearance.
CREST: Convolutional Residual Learning for Visual Tracking
Our method integrates feature extraction, response map generation as well as model update into the neural networks for an end-to-end training.
Unsupervised Representation Learning by Sorting Sequences
We present an unsupervised representation learning approach using videos without semantic labels.
Ranked #46 on
Self-Supervised Action Recognition
on HMDB51
Face Parsing via Recurrent Propagation
Face parsing is an important problem in computer vision that finds numerous applications including recognition and editing.
Unsupervised Domain Adaptation for Face Recognition in Unlabeled Videos
Despite rapid advances in face recognition, there remains a clear gap between the performance of still image-based face recognition and video-based face recognition, due to the vast difference in visual quality between the domains and the difficulty of curating diverse large-scale video datasets.
Video Deblurring via Semantic Segmentation and Pixel-Wise Non-Linear Kernel
We analyze the relationship between motion blur trajectory and optical flow, and present a novel pixel-wise non-linear kernel model to account for motion blur.
PiCANet: Learning Pixel-wise Contextual Attention for Saliency Detection
We formulate the proposed PiCANet in both global and local forms to attend to global and local contexts, respectively.
Ranked #7 on
RGB Salient Object Detection
on SOC
Stylizing Face Images via Multiple Exemplars
We address the problem of transferring the style of a headshot photo to face images.
Learning to Segment Instances in Videos with Spatial Propagation Network
In addition, we apply a filter on the refined score map that aims to recognize the best connected region using spatial and temporal consistencies in the video.
SegFlow: Joint Learning for Video Object Segmentation and Optical Flow
This paper proposes an end-to-end trainable network, SegFlow, for simultaneously predicting pixel-wise object segmentation and optical flow in videos.
Ranked #67 on
Semi-Supervised Video Object Segmentation
on DAVIS 2016
Referring Expression Generation and Comprehension via Attributes
In this paper, we explore the role of attributes by incorporating them into both referring expression generation and comprehension.
Blind Image Deblurring With Outlier Handling
We analyze the relationship between the proposed algorithm and other blind deblurring methods with outlier handling and show how to estimate intermediate latent images for blur kernel estimation principally.
Learning Discriminative Data Fitting Functions for Blind Image Deblurring
Solving blind image deblurring usually requires defining a data fitting function and image priors.
Learning to Super-Resolve Blurry Face and Text Images
We present an algorithm to directly restore a clear high-resolution image from a blurry low-resolution input.
Learning Affinity via Spatial Propagation Networks
Specifically, we develop a three-way connection for the linear propagation model, which (a) formulates a sparse transformation matrix, where all elements can be the output from a deep CNN, but (b) results in a dense affinity matrix that effectively models any task-specific pairwise similarity matrix.
Learning Affinity via Spatial Propagation Network
Specifically, we develop a three-way connection for the linear propagation model, which (a) formulates a sparse transformation matrix, where all elements can be the output from a deep CNN, but (b) results in a dense affinity matrix that effectively models any task-specific pairwise similarity matrix.
Visual Tracking via Dynamic Graph Learning
First, the graph is initialized by assigning binary weights of some image patches to indicate the object and background patches according to the predicted bounding box.
Fast and Accurate Image Super-Resolution with Deep Laplacian Pyramid Networks
However, existing methods often require a large number of network parameters and entail heavy computational loads at runtime for generating high-accuracy super-resolution results.
Tracking Persons-of-Interest via Unsupervised Representation Adaptation
Multi-face tracking in unconstrained videos is a challenging problem as faces of one person often appear drastically different in multiple shots due to significant variations in scale, pose, expression, illumination, and make-up.
Integrating Boundary and Center Correlation Filters for Visual Tracking with Aspect Ratio Variation
The aspect ratio variation frequently appears in visual tracking and has a severe influence on performance.
Joint Image Filtering with Deep Convolutional Networks
In contrast to existing methods that consider only the guidance image, the proposed algorithm can selectively transfer salient structures that are consistent with both guidance and target images.
Progressive Representation Adaptation for Weakly Supervised Object Localization
In classification adaptation, we transfer a pre-trained network to a multi-label classification task for recognizing the presence of a certain object in an image.
Scene Parsing with Global Context Embedding
We present a scene parsing method that utilizes global context information based on both the parametric and non- parametric models.
Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolation
Finally, the two input images are warped and linearly fused to form each intermediate frame.
Semi-Supervised Learning for Optical Flow with Generative Adversarial Networks
Convolutional neural networks (CNNs) have recently been applied to the optical flow estimation problem.
Learning Binary Residual Representations for Domain-specific Video Streaming
Specifically, we target a streaming setting where the videos to be streamed from a server to a client are all in the same domain and they have to be compressed to a small size for low-latency transmission.
Generative Single Image Reflection Separation
Single image reflection separation is an ill-posed problem since two scenes, a transmitted scene and a reflected scene, need to be inferred from a single observation.
Learning Video-Story Composition via Recurrent Neural Network
In this paper, we propose a learning-based method to compose a video-story from a group of video clips that describe an activity or experience.
A Closed-form Solution to Photorealistic Image Stylization
Photorealistic image stylization concerns transferring style of a reference photo to a content photo with the constraint that the stylized photo should remain photorealistic.
SPLATNet: Sparse Lattice Networks for Point Cloud Processing
We present a network architecture for processing point clouds that directly operates on a collection of points represented as a sparse set of samples in a high-dimensional lattice.
Ranked #30 on
Semantic Segmentation
on ScanNet
Adversarial Learning for Semi-Supervised Semantic Segmentation
We propose a method for semi-supervised semantic segmentation using an adversarial network.
Learning to Adapt Structured Output Space for Semantic Segmentation
In this paper, we propose an adversarial learning method for domain adaptation in the context of semantic segmentation.
Ranked #3 on
Domain Adaptation
on Synscapes-to-Cityscapes
Learning a Discriminative Prior for Blind Image Deblurring
We present an effective blind image deblurring method based on a data-driven discriminative prior. Our work is motivated by the fact that a good image prior should favor clear images over blurred images. In this work, we formulate the image prior as a binary classifier which can be achieved by a deep convolutional neural network (CNN). The learned prior is able to distinguish whether an input image is clear or not. Embedded into the maximum a posterior (MAP) framework, it helps blind deblurring in various scenarios, including natural, face, text, and low-illumination images. However, it is difficult to optimize the deblurring method with the learned image prior as it involves a non-linear CNN. Therefore, we develop an efficient numerical approach based on the half-quadratic splitting method and gradient decent algorithm to solve the proposed model. Furthermore, the proposed model can be easily extended to non-uniform deblurring. Both qualitative and quantitative experimental results show that our method performs favorably against state-of-the-art algorithms as well as domain-specific image deblurring approaches.
Deep Semantic Face Deblurring
In this paper, we present an effective and efficient face deblurring algorithm by exploiting semantic cues via deep convolutional neural networks (CNNs).
Learning to Localize Sound Source in Visual Scenes
We show that even with a few supervision, false conclusion is able to be corrected and the source of sound in a visual scene can be localized effectively.
Learning Spatial-Temporal Regularized Correlation Filters for Visual Tracking
Compared with SRDCF, STRCF with hand-crafted features provides a 5 times speedup and achieves a gain of 5. 4% and 3. 6% AUC score on OTB-2015 and Temple-Color, respectively.
Ranked #9 on
Visual Object Tracking
on VOT2017/18
Gated Fusion Network for Single Image Dehazing
The proposed algorithm hinges on an end-to-end trainable neural network that consists of an encoder and a decoder.
Ranked #23 on
Image Dehazing
on SOTS Outdoor
VITAL: VIsual Tracking via Adversarial Learning
To augment positive samples, we use a generative network to randomly generate masks, which are applied to adaptively dropout input features to capture a variety of appearance changes.
Simultaneous Fidelity and Regularization Learning for Image Restoration
For blind deconvolution, as estimation error of blur kernel is usually introduced, the subsequent non-blind deconvolution process does not restore the latent image well.
Switchable Temporal Propagation Network
Our approach is based on a temporal propagation network (TPN), which models the transition-related affinity between a pair of frames in a purely data-driven manner.
Correlation Tracking via Joint Discrimination and Reliability Learning
To address this issue, we propose a novel CF-based optimization problem to jointly model the discrimination and reliability information.
Learning Dual Convolutional Neural Networks for Low-Level Vision
These problems usually involve the estimation of two components of the target signals: structures and details.
Learning to Deblur Images with Exemplars
However, existing methods are less effective as only few edges can be restored from blurry face images for kernel estimation.
Weakly Supervised Coupled Networks for Visual Sentiment Analysis
The second branch utilizes both the holistic and localized information by coupling the sentiment map with deep features for robust classification.
Learning Superpixels With Segmentation-Aware Affinity Loss
Specifically, we propose a new loss function that takes the segmentation error into account for affinity learning.
Dynamic Scene Deblurring Using Spatially Variant Recurrent Neural Networks
The proposed network is composed of three deep convolutional neural networks (CNNs) and a recurrent neural network (RNN).
Ranked #10 on
Deblurring
on RealBlur-R (trained on GoPro)
(SSIM (sRGB) metric)
Flow-Grounded Spatial-Temporal Video Prediction from Still Images
Existing video prediction methods mainly rely on observing multiple historical frames or focus on predicting the next one-frame.
Superpixel Sampling Networks
Superpixels provide an efficient low/mid-level representation of image data, which greatly reduces the number of image primitives for subsequent vision tasks.
Gated Fusion Network for Joint Image Deblurring and Super-Resolution
Single-image super-resolution is a fundamental task for vision applications to enhance the image quality with respect to spatial resolution.
Learning Blind Video Temporal Consistency
Our method takes the original unprocessed and per-frame processed videos as inputs to produce a temporally consistent video.
Physics-Based Generative Adversarial Models for Image Restoration and Beyond
We present an algorithm to directly solve numerous image restoration problems (e. g., image deblurring, image dehazing, image deraining, etc.).
Diverse Image-to-Image Translation via Disentangled Representations
Our model takes the encoded content features extracted from a given input and the attribute vectors sampled from the attribute space to produce diverse outputs at test time.
Learning Linear Transformations for Fast Arbitrary Style Transfer
Recent arbitrary style transfer methods transfer second order statistics from reference image onto content image via a multiplication between content image features and a transformation matrix, which is computed from features with a pre-determined algorithm.
Deep Regression Tracking with Shrinkage Loss
Regression trackers directly learn a mapping from regularly dense samples of target objects to soft labels, which are usually generated by a Gaussian function, to estimate target positions.
Learning Data Terms for Non-blind Deblurring
We propose a simple and effective discriminative framework to learn data terms that can adaptively handle blurred images in the presence of severe noise and outliers.
Sub-GAN: An Unsupervised Generative Model via Subspaces
The recent years have witnessed significant growth in constructing robust generative models to capture informative distributions of natural data.
DFT-based Transformation Invariant Pooling Layer for Visual Classification
The proposed methods are extensively evaluated on various classification tasks using the ImageNet, CUB 2010-2011, MIT Indoors, Caltech 101, FMD and DTD datasets.
Rendering Portraitures from Monocular Camera and Beyond
Specifically, we first exploit Convolutional Neural Networks to estimate the relative depth and portrait segmentation maps from a single input image.
Learning to Blend Photos
Specifically, given a foreground image and a background image, our proposed method automatically generates a set of blending photos with scores that indicate the aesthetics quality with the proposed quality network and policy network.
Deep Attentive Tracking via Reciprocative Learning
Visual attention, derived from cognitive neuroscience, facilitates human perception on the most pertinent subset of the sensory data.
MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Interpolation and Enhancement
Recently, a number of data-driven frame interpolation methods based on convolutional neural networks have been proposed.
Ranked #22 on
Video Frame Interpolation
on Vimeo90K
MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Frame Interpolation and Enhancement
In this work, we propose a motion estimation and motion compensation driven neural network for video frame interpolation.
Ranked #6 on
Video Frame Interpolation
on Middlebury
A Fusion Approach for Multi-Frame Optical Flow Estimation
To date, top-performing optical flow estimation methods only take pairs of consecutive frames into account.
Joint Face Hallucination and Deblurring via Structure Generation and Detail Enhancement
We first propose a facial component guided deep Convolutional Neural Network (CNN) to restore a coarse face image, which is denoted as the base image where the facial component is automatically generated from the input face image.
Deep Non-Blind Deconvolution via Generalized Low-Rank Approximation
In this paper, we present a deep convolutional neural network to capture the inherent properties of image degradation, which can handle different kernels and saturated pixels in a unified framework.
Context-Aware Synthesis and Placement of Object Instances
Learning to insert an object instance into an image in a semantically coherent manner is a challenging and interesting problem.
PiCANet: Pixel-wise Contextual Attention Learning for Accurate Saliency Detection
We propose three specific formulations of the PiCANet via embedding the pixel-wise contextual attention mechanism into the pooling and convolution operations with attending to global or local contexts.
Unseen Object Segmentation in Videos via Transferable Representations
The entire process is decomposed into two tasks: 1) solving a submodular function for selecting object-like segments, and 2) learning a CNN model with a transferable module for adapting seen categories in the source domain to the unseen target video.
Online Multi-Object Tracking with Dual Matching Attention Networks
In this paper, we propose an online Multi-Object Tracking (MOT) approach which integrates the merits of single object tracking and data association methods in a unified framework to handle noisy detections and frequent interactions between targets.
Ranked #5 on
Online Multi-Object Tracking
on MOT16
Putting Humans in a Scene: Learning Affordance in 3D Indoor Environments
In order to predict valid affordances and learn possible 3D human poses in indoor scenes, we need to understand the semantic and geometric structure of a scene as well as its potential interactions with a human.
Mode Seeking Generative Adversarial Networks for Diverse Image Synthesis
In this work, we propose a simple yet effective regularization term to address the mode collapse issue for cGANs.
Multimodal Unsupervised Image-To-Image Translation
Translation
Inserting Videos into Videos
To synthesize a realistic video, the network renders each frame based on the current input and previous frames.
Im2Pencil: Controllable Pencil Illustration from Photographs
We propose a high-quality photo-to-pencil translation method with fine-grained control over the drawing style.
Depth-Aware Video Frame Interpolation
The proposed model then warps the input frames, depth maps, and contextual features based on the optical flow and local interpolation kernels for synthesizing the output frame.
Ranked #5 on
Video Frame Interpolation
on Middlebury
Res2Net: A New Multi-scale Backbone Architecture
We evaluate the Res2Net block on all these models and demonstrate consistent performance gains over baseline models on widely-used datasets, e. g., CIFAR-100 and ImageNet.
Ranked #2 on
Image Classification
on GasHisSDB
Target-Aware Deep Tracking
Despite demonstrated successes for numerous vision tasks, the contributions of using pre-trained deep features for visual tracking are not as significant as that for object recognition.
Eidetic 3D LSTM: A Model for Video Prediction and Beyond
We first evaluate the E3D-LSTM network on widely-used future video prediction datasets and achieve the state-of-the-art performance.
Ranked #1 on
Video Prediction
on KTH
(Cond metric)
DRIT++: Diverse Image-to-Image Translation via Disentangled Representations
In this work, we present an approach based on disentangled representation for generating diverse outputs without paired training images.
SCOPS: Self-Supervised Co-Part Segmentation
Parts provide a good intermediate representation of objects that is robust with respect to the camera, pose and appearance variations.
Ranked #4 on
Unsupervised Keypoint Estimation
on CUB
Few-Shot Viewpoint Estimation
Through extensive experimentation on the ObjectNet3D and Pascal3D+ benchmark datasets, we demonstrate that our framework, which we call MetaView, significantly outperforms fine-tuning the state-of-the-art models with few examples, and that the specific architectural innovations of our method are crucial to achieving good performance.
Weakly-supervised Caricature Face Parsing through Domain Adaptation
However, current state-of-the-art face parsing methods require large amounts of labeled data on the pixel-level and such process for caricature is tedious and labor-intensive.
Self-supervised Audio Spatialization with Correspondence Classifier
Spatial audio is an essential medium to audiences for 3D visual and auditory experience.
An Adaptive Random Path Selection Approach for Incremental Learning
In a conventional supervised learning setting, a machine learning model has access to examples of all object classes that are desired to be recognized during the inference stage.
Detection and Tracking of Multiple Mice Using Part Proposal Networks
The study of mouse social behaviours has been increasingly undertaken in neuroscience research.
Show, Match and Segment: Joint Weakly Supervised Learning of Semantic Matching and Object Co-segmentation
In contrast to existing algorithms that tackle the tasks of semantic matching and object co-segmentation in isolation, our method exploits the complementary nature of the two tasks.
Video Stitching for Linear Camera Arrays
Despite the long history of image and video stitching research, existing academic and commercial solutions still produce strong artifacts.
Joint-task Self-supervised Learning for Temporal Correspondence
Our learning process integrates two highly related tasks: tracking large image regions \emph{and} establishing fine-grained pixel-level associations between consecutive video frames.
Referring Expression Object Segmentation with Caption-Aware Consistency
To this end, we propose an end-to-end trainable comprehension network that consists of the language and visual encoders to extract feature representations from both domains.
Ranked #19 on
Referring Expression Segmentation
on RefCOCO testB
Progressive Domain Adaptation for Object Detection
This intermediate domain is constructed by translating the source images to mimic the ones in the target domain.
Quadratic video interpolation
Video interpolation is an important problem in computer vision, which helps overcome the temporal limitation of camera sensors.
Dancing to Music
In the analysis phase, we decompose a dance into a series of basic dance units, through which the model learns how to move.
Ranked #3 on
Motion Synthesis
on BRACE
Learning to Localize Sound Sources in Visual Scenes: Analysis and Applications
Visual events are usually accompanied by sounds in our daily lives.
Adversarial Learning of Privacy-Preserving and Task-Oriented Representations
For instance, there could be a potential privacy risk of machine learning systems via the model inversion attack, whose goal is to reconstruct the input data from the latent representation of deep networks.
Neural Design Network: Graphic Layout Generation with Constraints
The first module predicts a graph with complete relations from a graph with user-specified relations.
Controllable and Progressive Image Extrapolation
Image extrapolation aims at expanding the narrow field of view of a given image patch.
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.
CrDoCo: Pixel-level Domain Transfer with Cross-Domain Consistency
Unsupervised domain adaptation algorithms aim to transfer the knowledge learned from one domain to another (e. g., synthetic to real images).
Visual Question Answering on 360° Images
We then study two different VQA models on VQA 360, including one conventional model that takes an equirectangular image (with intrinsic distortion) as input and one dedicated model that first projects a 360 image onto cubemaps and subsequently aggregates the information from multiple spatial resolutions.
Exploiting Semantics for Face Image Deblurring
Specifically, we first use a coarse deblurring network to reduce the motion blur on the input face image.
Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformation
Few-shot classification aims to recognize novel categories with only few labeled images in each class.
Ranked #6 on
Cross-Domain Few-Shot
on CUB
Weakly-Supervised Semantic Segmentation by Iterative Affinity Learning
In this paper, we propose an iterative algorithm to learn such pairwise relations, which consists of two branches, a unary segmentation network which learns the label probabilities for each pixel, and a pairwise affinity network which learns affinity matrix and refines the probability map generated from the unary network.
Structured Sparsification with Joint Optimization of Group Convolution and Channel Shuffle
Recent advances in convolutional neural networks(CNNs) usually come with the expense of excessive computational overhead and memory footprint.
Gated Fusion Network for Degraded Image Super Resolution
To address this problem, we propose a dual-branch convolutional neural network to extract base features and recovered features separately.
Self-supervised Single-view 3D Reconstruction via Semantic Consistency
To the best of our knowledge, we are the first to try and solve the single-view reconstruction problem without a category-specific template mesh or semantic keypoints.
Learning Enriched Features for Real Image Restoration and Enhancement
With the goal of recovering high-quality image content from its degraded version, image restoration enjoys numerous applications, such as in surveillance, computational photography, medical imaging, and remote sensing.
Ranked #5 on
Spectral Reconstruction
on ARAD-1K
CycleISP: Real Image Restoration via Improved Data Synthesis
This is mainly because the AWGN is not adequate for modeling the real camera noise which is signal-dependent and heavily transformed by the camera imaging pipeline.
Ranked #10 on
Image Denoising
on DND
(using extra training data)
Collaborative Distillation for Ultra-Resolution Universal Style Transfer
In this work, we present a new knowledge distillation method (named Collaborative Distillation) for encoder-decoder based neural style transfer to reduce the convolutional filters.
Rethinking Class-Balanced Methods for Long-Tailed Visual Recognition from a Domain Adaptation Perspective
Object frequency in the real world often follows a power law, leading to a mismatch between datasets with long-tailed class distributions seen by a machine learning model and our expectation of the model to perform well on all classes.
Ranked #27 on
Long-tail Learning
on Places-LT
TapLab: A Fast Framework for Semantic Video Segmentation Tapping into Compressed-Domain Knowledge
Real-time semantic video segmentation is a challenging task due to the strict requirements of inference speed.
Deep Semantic Matching with Foreground Detection and Cycle-Consistency
Establishing dense semantic correspondences between object instances remains a challenging problem due to background clutter, significant scale and pose differences, and large intra-class variations.
Learning to See Through Obstructions
We present a learning-based approach for removing unwanted obstructions, such as window reflections, fence occlusions or raindrops, from a short sequence of images captured by a moving camera.
Single-Image HDR Reconstruction by Learning to Reverse the Camera Pipeline
We model the HDRto-LDR image formation pipeline as the (1) dynamic range clipping, (2) non-linear mapping from a camera response function, and (3) quantization.
Regularizing Meta-Learning via Gradient Dropout
With the growing attention on learning-to-learn new tasks using only a few examples, meta-learning has been widely used in numerous problems such as few-shot classification, reinforcement learning, and domain generalization.
Multi-Scale Boosted Dehazing Network with Dense Feature Fusion
To address the issue of preserving spatial information in the U-Net architecture, we design a dense feature fusion module using the back-projection feedback scheme.
Ranked #9 on
Image Dehazing
on Haze4k
Generalized Convolutional Forest Networks for Domain Generalization and Visual Recognition
When constructing random forests, it is of prime importance to ensure high accuracy and low correlation of individual tree classifiers for good performance.
WW-Nets: Dual Neural Networks for Object Detection
Inspired by this structure, we have proposed an object detection framework involving the integration of a "What Network" and a "Where Network".
Ventral-Dorsal Neural Networks: Object Detection via Selective Attention
The coarse functional distinction between these streams is between object recognition -- the "what" of the signal -- and extracting location related information -- the "where" of the signal.
Semi-Supervised Learning with Meta-Gradient
In this work, we propose a simple yet effective meta-learning algorithm in semi-supervised learning.
Modeling Artistic Workflows for Image Generation and Editing
People often create art by following an artistic workflow involving multiple stages that inform the overall design.
Controllable Image Synthesis via SegVAE
We also apply an off-the-shelf image-to-image translation model to generate realistic RGB images to better understand the quality of the synthesized semantic maps.
RetrieveGAN: Image Synthesis via Differentiable Patch Retrieval
Image generation from scene description is a cornerstone technique for the controlled generation, which is beneficial to applications such as content creation and image editing.
Learnable Cost Volume Using the Cayley Representation
Cost volume is an essential component of recent deep models for optical flow estimation and is usually constructed by calculating the inner product between two feature vectors.
Mixup-CAM: Weakly-supervised Semantic Segmentation via Uncertainty Regularization
Obtaining object response maps is one important step to achieve weakly-supervised semantic segmentation using image-level labels.
Weakly-Supervised Semantic Segmentation via Sub-category Exploration
Existing weakly-supervised semantic segmentation methods using image-level annotations typically rely on initial responses to locate object regions.
Spatiotemporal Contrastive Video Representation Learning
Our representations are learned using a contrastive loss, where two augmented clips from the same short video are pulled together in the embedding space, while clips from different videos are pushed away.
Ranked #1 on
Self-Supervised Action Recognition
on Kinetics-600
Learning to See Through Obstructions with Layered Decomposition
We present a learning-based approach for removing unwanted obstructions, such as window reflections, fence occlusions, or adherent raindrops, from a short sequence of images captured by a moving camera.
Learning to Caricature via Semantic Shape Transform
Caricature is an artistic drawing created to abstract or exaggerate facial features of a person.
SoDA: Multi-Object Tracking with Soft Data Association
Robust multi-object tracking (MOT) is a prerequisite fora safe deployment of self-driving cars.
Every Pixel Matters: Center-aware Feature Alignment for Domain Adaptive Object Detector
A domain adaptive object detector aims to adapt itself to unseen domains that may contain variations of object appearance, viewpoints or backgrounds.
Multi-path Neural Networks for On-device Multi-domain Visual Classification
This approach effectively reduces the total number of parameters and FLOPS, encouraging positive knowledge transfer while mitigating negative interference across domains.
Unsupervised Domain Adaptation for Spatio-Temporal Action Localization
Spatio-temporal action localization is an important problem in computer vision that involves detecting where and when activities occur, and therefore requires modeling of both spatial and temporal features.
Continuous and Diverse Image-to-Image Translation via Signed Attribute Vectors
Generating a smooth sequence of intermediate results bridges the gap of two different domains, facilitating the morphing effect across domains.
Shaping Deep Feature Space towards Gaussian Mixture for Visual Classification
In this work, we propose a Gaussian mixture (GM) loss function for deep neural networks for visual classification.
Unsupervised Discovery of Disentangled Manifolds in GANs
Interpretable generation process is beneficial to various image editing applications.
Online Adaptation for Consistent Mesh Reconstruction in the Wild
This paper presents an algorithm to reconstruct temporally consistent 3D meshes of deformable object instances from videos in the wild.
D2-Net: Weakly-Supervised Action Localization via Discriminative Embeddings and Denoised Activations
The proposed formulation comprises a discriminative and a denoising loss term for enhancing temporal action localization.
Ranked #3 on
Weakly Supervised Action Localization
on THUMOS’14
Video Matting via Consistency-Regularized Graph Neural Networks
In this paper, we propose to enhance the temporal coherence by Consistency-Regularized Graph Neural Networks (CRGNN) with the aid of a synthesized video matting dataset.
Low Light Image Enhancement via Global and Local Context Modeling
These contexts can be crucial towards inferring several image enhancement tasks, e. g., local and global contrast, brightness and color corrections; which requires cues from both local and global spatial extent.
GAN Inversion: A Survey
GAN inversion aims to invert a given image back into the latent space of a pretrained GAN model, for the image to be faithfully reconstructed from the inverted code by the generator.
Learning Spatial and Spatio-Temporal Pixel Aggregations for Image and Video Denoising
We present a spatial pixel aggregation network and learn the pixel sampling and averaging strategies for image denoising.
