Search Results for author:
Found 250 papers, 108 papers with code
Video Object Detection via Object-level Temporal Aggregation
While single-image object detectors can be naively applied to videos in a frame-by-frame fashion, the prediction is often temporally inconsistent.
Adversarial Training with Bi-directional Likelihood Regularization for Visual Classification
We call such a new robust training strategy the adversarial training with bi-directional likelihood regularization (ATBLR) method.
Learning Enriched Features for Fast Image Restoration and Enhancement
In the former case, spatial details are preserved but the contextual information cannot be precisely encoded.
An Extendable, Efficient and Effective Transformer-based Object Detector
Transformers have been widely used in numerous vision problems especially for visual recognition and detection.
Autoregressive 3D Shape Generation via Canonical Mapping
With the capacity of modeling long-range dependencies in sequential data, transformers have shown remarkable performances in a variety of generative tasks such as image, audio, and text generation.
Animatable Neural Radiance Fields from Monocular RGB-D
To tackle this problem, we introduce a novel method to integrate observations across frames and encode the appearance at each individual frame by utilizing the human pose that models the body shape and point clouds which cover partial part of the human as the input.
Adaptive Transformers for Robust Few-shot Cross-domain Face Anti-spoofing
While recent face anti-spoofing methods perform well under the intra-domain setups, an effective approach needs to account for much larger appearance variations of images acquired in complex scenes with different sensors for robust performance.
V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer
In this paper, we investigate the application of Vehicle-to-Everything (V2X) communication to improve the perception performance of autonomous vehicles.
Deep Image Deblurring: A Survey
Image deblurring is a classic problem in low-level computer vision, which aims to recover a sharp image from a blurred input image.
Towards a Unified Foundation Model: Jointly Pre-Training Transformers on Unpaired Images and Text
The experiments show that the resultant unified foundation transformer works surprisingly well on both the vision-only and text-only tasks, and the proposed knowledge distillation and gradient masking strategy can effectively lift the performance to approach the level of separately-trained models.
An Informative Tracking Benchmark
Along with the rapid progress of visual tracking, existing benchmarks become less informative due to redundancy of samples and weak discrimination between current trackers, making evaluations on all datasets extremely time-consuming.
Contextualized Spatio-Temporal Contrastive Learning with Self-Supervision
Modern self-supervised learning algorithms typically enforce persistency of instance representations across views.
Exploring Temporal Granularity in Self-Supervised Video Representation Learning
The training objective consists of two parts: a fine-grained temporal learning objective to maximize the similarity between corresponding temporal embeddings in the short clip and the long clip, and a persistent temporal learning objective to pull together global embeddings of the two clips.
Benchmarking Deep Deblurring Algorithms: A Large-Scale Multi-Cause Dataset and A New Baseline Model
Further, we propose a new baseline model, level-attention deblurring network, to adapt to multiple causes of blurs.
Exploring Cross-Video and Cross-Modality Signals for Weakly-Supervised Audio-Visual Video Parsing
The audio-visual video parsing task aims to temporally parse a video into audio or visual event categories.
Learning Discriminative Shrinkage Deep Networks for Image Deconvolution
Most existing methods usually formulate this problem into a maximum-a-posteriori framework and address it by designing kinds of regularization terms and data terms of the latent clear images.
Learning Continuous Environment Fields via Implicit Functions
We propose a novel scene representation that encodes reaching distance -- the distance between any position in the scene to a goal along a feasible trajectory.
Video Frame Interpolation Transformer
Existing methods for video interpolation heavily rely on deep convolution neural networks, and thus suffer from their intrinsic limitations, such as content-agnostic kernel weights and restricted receptive field.
Hierarchical Modular Network for Video Captioning
(II) Predicate level, which learns the actions conditioned on highlighted objects and is supervised by the predicate in captions.
Class-agnostic Object Detection with Multi-modal Transformer
This has been a long-standing question in computer vision.
Ranked #1 on
Class-agnostic Object Detection
on COCO
Class-agnostic Object Detection
Object Proposal Generation
+1
Correcting Face Distortion in Wide-Angle Videos
Video blogs and selfies are popular social media formats, which are often captured by wide-angle cameras to show human subjects and expanded background.
Restormer: Efficient Transformer for High-Resolution Image Restoration
Since convolutional neural networks (CNNs) perform well at learning generalizable image priors from large-scale data, these models have been extensively applied to image restoration and related tasks.
Ranked #1 on
Single Image Deraining
on Test1200
Video Salient Object Detection via Contrastive Features and Attention Modules
Video salient object detection aims to find the most visually distinctive objects in a video.
Semi-supervised Multi-task Learning for Semantics and Depth
Multi-Task Learning (MTL) aims to enhance the model generalization by sharing representations between related tasks for better performance.
ViDT: An Efficient and Effective Fully Transformer-based Object Detector
Transformers are transforming the landscape of computer vision, especially for recognition tasks.
Ranked #1 on
Object Detection
on COCO 2017 val
Learning Contrastive Representation for Semantic Correspondence
Dense correspondence across semantically related images has been extensively studied, but still faces two challenges: 1) large variations in appearance, scale and pose exist even for objects from the same category, and 2) labeling pixel-level dense correspondences is labor intensive and infeasible to scale.
Federated Multi-Target Domain Adaptation
We further take the server-client and inter-client domain shifts into account and pose a domain adaptation problem with one source (centralized server data) and multiple targets (distributed client data).
Discovering 3D Parts from Image Collections
Reasoning 3D shapes from 2D images is an essential yet challenging task, especially when only single-view images are at our disposal.
End-to-end Multi-modal Video Temporal Grounding
Specifically, we adopt RGB images for appearance, optical flow for motion, and depth maps for image structure.
Learning 3D Dense Correspondence via Canonical Point Autoencoder
We propose a canonical point autoencoder (CPAE) that predicts dense correspondences between 3D shapes of the same category.
Self-Supervised Tracking via Target-Aware Data Synthesis
While deep-learning based tracking methods have achieved substantial progress, they entail large-scale and high-quality annotated data for sufficient training.
Incremental False Negative Detection for Contrastive Learning
Self-supervised learning has recently shown great potential in vision tasks through contrastive learning, which aims to discriminate each image, or instance, in the dataset.
Large-scale Unsupervised Semantic Segmentation
Powered by the ImageNet dataset, unsupervised learning on large-scale data has made significant advances for classification tasks.
Learning to Stylize Novel Views
Second, we develop point cloud aggregation modules to gather the style information of the 3D scene, and then modulate the features in the point cloud with a linear transformation matrix.
Intriguing Properties of Vision Transformers
We show and analyze the following intriguing properties of ViT: (a) Transformers are highly robust to severe occlusions, perturbations and domain shifts, e. g., retain as high as 60% top-1 accuracy on ImageNet even after randomly occluding 80% of the image content.
COMISR: Compression-Informed Video Super-Resolution
Most video super-resolution methods focus on restoring high-resolution video frames from low-resolution videos without taking into account compression.
Decoupled Dynamic Filter Networks
Inspired by recent advances in attention, DDF decouples a depth-wise dynamic filter into spatial and channel dynamic filters.
2.5D Visual Relationship Detection
To enable progress on this task, we create a new dataset consisting of 220k human-annotated 2. 5D relationships among 512K objects from 11K images.
Understanding Synonymous Referring Expressions via Contrastive Features
While prior work usually treats each sentence and attends it to an object separately, we focus on learning a referring expression comprehension model that considers the property in synonymous sentences.
Weakly Supervised Object Localization and Detection: A Survey
As an emerging and challenging problem in the computer vision community, weakly supervised object localization and detection plays an important role for developing new generation computer vision systems and has received significant attention in the past decade.
The Road to Know-Where: An Object-and-Room Informed Sequential BERT for Indoor Vision-Language Navigation
Vision-and-Language Navigation (VLN) requires an agent to find a path to a remote location on the basis of natural-language instructions and a set of photo-realistic panoramas.
InfinityGAN: Towards Infinite-Pixel Image Synthesis
We present a novel framework, InfinityGAN, for arbitrary-sized image generation.
Regularizing Generative Adversarial Networks under Limited Data
Recent years have witnessed the rapid progress of generative adversarial networks (GANs).
Ranked #1 on
Image Generation
on 25% ImageNet 128x128
Unsupervised Sound Localization via Iterative Contrastive Learning
Sound localization aims to find the source of the audio signal in the visual scene.
In&Out : Diverse Image Outpainting via GAN Inversion
Existing image outpainting methods pose the problem as a conditional image-to-image translation task, often generating repetitive structures and textures by replicating the content available in the input image.
ReMix: Towards Image-to-Image Translation with Limited Data
We interpolate training samples at the feature level and propose a novel content loss based on the perceptual relations among samples.
Self-Attentive 3D Human Pose and Shape Estimation from Videos
The key insights of our method are two-fold.
Hybrid Neural Fusion for Full-frame Video Stabilization
Existing video stabilization methods often generate visible distortion or require aggressive cropping of frame boundaries, resulting in smaller field of views.
Multi-Stage Progressive Image Restoration
At each stage, we introduce a novel per-pixel adaptive design that leverages in-situ supervised attention to reweight the local features.
Ranked #3 on
Single Image Deraining
on Rain100H
Exploiting Raw Images for Real-Scene Super-Resolution
In this paper, we study the problem of real-scene single image super-resolution to bridge the gap between synthetic data and real captured images.
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.
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.
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.
Benchmarking Ultra-High-Definition Image Super-Resolution
Increasingly, modern mobile devices allow capturing images at Ultra-High-Definition (UHD) resolution, which includes 4K and 8K images.
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.
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 #2 on
Weakly Supervised Action Localization
on THUMOS’14
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.
Unsupervised Discovery of Disentangled Manifolds in GANs
Interpretable generation process is beneficial to various image editing applications.
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.
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.
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.
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.
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.
SoDA: Multi-Object Tracking with Soft Data Association
Robust multi-object tracking (MOT) is a prerequisite fora safe deployment of self-driving cars.
Learning to Caricature via Semantic Shape Transform
Caricature is an artistic drawing created to abstract or exaggerate facial features of a person.
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.
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
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.
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.
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.
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.
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.
Semi-Supervised Learning with Meta-Gradient
In this work, we propose a simple yet effective meta-learning algorithm in semi-supervised learning.
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.
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.
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.
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.
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.
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.
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.
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.
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 #22 on
Long-tail Learning
on Places-LT
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.
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 #9 on
Image Denoising
on DND
(using extra training data)
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 #4 on
Image Denoising
on DND
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.
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.
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.
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.
Exploiting Semantics for Face Image Deblurring
Specifically, we first use a coarse deblurring network to reduce the motion blur on the input face image.
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.
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).
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.
Controllable and Progressive Image Extrapolation
Image extrapolation aims at expanding the narrow field of view of a given image patch.
Neural Design Network: Graphic Layout Generation with Constraints
The first module predicts a graph with complete relations from a graph with user-specified relations.
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.
Learning to Localize Sound Sources in Visual Scenes: Analysis and Applications
Visual events are usually accompanied by sounds in our daily lives.
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.
Quadratic video interpolation
Video interpolation is an important problem in computer vision, which helps overcome the temporal limitation of camera sensors.
Progressive Domain Adaptation for Object Detection
This intermediate domain is constructed by translating the source images to mimic the ones in the target domain.
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 #8 on
Referring Expression Segmentation
on RefCoCo val
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.
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.
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.
Detection and Tracking of Multiple Mice Using Part Proposal Networks
The study of mouse social behaviours has been increasingly undertaken in neuroscience research.
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.
Ranked #7 on
Incremental Learning
on ImageNet100 - 10 steps
(Average Incremental Accuracy Top-5 metric)
Self-supervised Audio Spatialization with Correspondence Classifier
Spatial audio is an essential medium to audiences for 3D visual and auditory experience.
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.
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.
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.
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.
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)
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.
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 #7 on
RGB Salient Object Detection
on PASCAL-S
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 #4 on
Video Frame Interpolation
on Middlebury
Im2Pencil: Controllable Pencil Illustration from Photographs
We propose a high-quality photo-to-pencil translation method with fine-grained control over the drawing style.
Inserting Videos into Videos
To synthesize a realistic video, the network renders each frame based on the current input and previous frames.
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
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.
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 #4 on
Online Multi-Object Tracking
on MOT16
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.
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.
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.
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.
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.
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.
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 #13 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 #5 on
Video Frame Interpolation
on Middlebury
Deep Attentive Tracking via Reciprocative Learning
Visual attention, derived from cognitive neuroscience, facilitates human perception on the most pertinent subset of the sensory data.
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.
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.
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.
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.
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.
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.
Domain Adaptation
Multimodal Unsupervised Image-To-Image Translation
+3
Learning Blind Video Temporal Consistency
Our method takes the original unprocessed and per-frame processed videos as inputs to produce a temporally consistent video.
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.
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.
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.
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 #6 on
Deblurring
on RealBlur-R (trained on GoPro)
(SSIM (sRGB) metric)
Learning Superpixels With Segmentation-Aware Affinity Loss
Specifically, we propose a new loss function that takes the segmentation error into account for affinity learning.
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 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.
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.
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.
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.
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.
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.
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 #10 on
Image Dehazing
on SOTS Indoor
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 #8 on
Visual Object Tracking
on VOT2017/18
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.
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).