Search Results for author:
Found 43 papers, 13 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.
Towards Graph Prompt Learning: A Survey and Beyond
Large-scale "pre-train and prompt learning" paradigms have demonstrated remarkable adaptability, enabling broad applications across diverse domains such as question answering, image recognition, and multimodal retrieval.
Ada-adapter:Fast Few-shot Style Personlization of Diffusion Model with Pre-trained Image Encoder
Fine-tuning advanced diffusion models for high-quality image stylization usually requires large training datasets and substantial computational resources, hindering their practical applicability.
Unveiling Delay Effects in Traffic Forecasting: A Perspective from Spatial-Temporal Delay Differential Equations
To tackle the above issues, we propose a neural Spatial-Temporal Delay Differential Equation model, namely STDDE.
ACR: Attention Collaboration-based Regressor for Arbitrary Two-Hand Reconstruction
Our method significantly outperforms the best interacting-hand approaches on the InterHand2. 6M dataset while yielding comparable performance with the state-of-the-art single-hand methods on the FreiHand dataset.
Ranked #2 on
3D Interacting Hand Pose Estimation
on InterHand2.6M
Instances as Queries
The key insight of QueryInst is to leverage the intrinsic one-to-one correspondence in object queries across different stages, as well as one-to-one correspondence between mask RoI features and object queries in the same stage.
Ranked #13 on
Object Detection
on COCO-O
Crossover Learning for Fast Online Video Instance Segmentation
For temporal information modeling in VIS, we present a novel crossover learning scheme that uses the instance feature in the current frame to pixel-wisely localize the same instance in other frames.
Ranked #36 on
Video Instance Segmentation
on OVIS validation
Unsupervised Low-light Image Enhancement with Decoupled Networks
In this paper, we tackle the problem of enhancing real-world low-light images with significant noise in an unsupervised fashion.
Human Motion Transfer from Poses in the Wild
In this paper, we tackle the problem of human motion transfer, where we synthesize novel motion video for a target person that imitates the movement from a reference video.
Anatomy-aware 3D Human Pose Estimation with Bone-based Pose Decomposition
In this work, we propose a new solution to 3D human pose estimation in videos.
Ranked #12 on
Monocular 3D Human Pose Estimation
on Human3.6M
EnlightenGAN: Deep Light Enhancement without Paired Supervision
Deep learning-based methods have achieved remarkable success in image restoration and enhancement, but are they still competitive when there is a lack of paired training data?
Creative Procedural-Knowledge Extraction From Web Design Tutorials
Complex design tasks often require performing diverse actions in a specific order.
Multimodal Style Transfer via Graph Cuts
An assumption widely used in recent neural style transfer methods is that image styles can be described by global statics of deep features like Gram or covariance matrices.
PaintBot: A Reinforcement Learning Approach for Natural Media Painting
Action selection is guided by a given reference image, which the agent attempts to replicate subject to the limitations of the action space and the agent's learned policy.
Dance Dance Generation: Motion Transfer for Internet Videos
This work presents computational methods for transferring body movements from one person to another with videos collected in the wild.
Im2Pencil: Controllable Pencil Illustration from Photographs
We propose a high-quality photo-to-pencil translation method with fine-grained control over the drawing style.
A Multi-Agent-Based Rolling Optimization Method for Restoration Scheduling of Electrical Distribution Systems with Distributed Generation
A multi-agent-based rolling optimization method for EDS restoration scheduling is proposed in this paper.
Signal Processing
Learning to Sketch with Deep Q Networks and Demonstrated Strokes
Doodling is a useful and common intelligent skill that people can learn and master.
Product Quantization Network for Fast Image Retrieval
Product quantization has been widely used in fast image retrieval due to its effectiveness of coding high-dimensional visual features.
``Factual'' or ``Emotional'': Stylized Image Captioning with Adaptive Learning and Attention
It uses two groups of matrices to capture the factual and stylized knowledge, respectively, and automatically learns the word-level weights of the two groups based on previous context.
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.
"Factual" or "Emotional": Stylized Image Captioning with Adaptive Learning and Attention
It uses two groups of matrices to capture the factual and stylized knowledge, respectively, and automatically learns the word-level weights of the two groups based on previous context.
Learning from Multi-domain Artistic Images for Arbitrary Style Transfer
We propose a fast feed-forward network for arbitrary style transfer, which can generate stylized image for previously unseen content and style image pairs.
Exploring Asymmetric Encoder-Decoder Structure for Context-based Sentence Representation Learning
Context information plays an important role in human language understanding, and it is also useful for machines to learn vector representations of language.
Visual to Sound: Generating Natural Sound for Videos in the Wild
As two of the five traditional human senses (sight, hearing, taste, smell, and touch), vision and sound are basic sources through which humans understand the world.
A global feature extraction model for the effective computer aided diagnosis of mild cognitive impairment using structural MRI images
Multiple modalities of biomarkers have been proved to be very sensitive in assessing the progression of Alzheimer's disease (AD), and using these modalities and machine learning algorithms, several approaches have been proposed to assist in the early diagnosis of AD.
The Cultural Evolution of National Constitutions
We explore how ideas from infectious disease and genetics can be used to uncover patterns of cultural inheritance and innovation in a corpus of 591 national constitutions spanning 1789 - 2008.
Visually-Aware Fashion Recommendation and Design with Generative Image Models
Here, we seek to extend this contribution by showing that recommendation performance can be significantly improved by learning `fashion aware' image representations directly, i. e., by training the image representation (from the pixel level) and the recommender system jointly; this contribution is related to recent work using Siamese CNNs, though we are able to show improvements over state-of-the-art recommendation techniques such as BPR and variants that make use of pre-trained visual features.
Speeding up Context-based Sentence Representation Learning with Non-autoregressive Convolutional Decoding
We carefully designed experiments to show that neither an autoregressive decoder nor an RNN decoder is required.
Sketching With Style: Visual Search With Sketches and Aesthetic Context
We propose a novel measure of visual similarity for image retrieval that incorporates both structural and aesthetic (style) constraints.
Spatial-Semantic Image Search by Visual Feature Synthesis
We train a convolutional neural network to synthesize appropriate visual features that captures the spatial-semantic constraints from the user canvas query.
TextureGAN: Controlling Deep Image Synthesis with Texture Patches
In this paper, we investigate deep image synthesis guided by sketch, color, and texture.
Ranked #2 on
Image Reconstruction
on Edge-to-Shoes
Rethinking Skip-thought: A Neighborhood based Approach
We train our skip-thought neighbor model on a large corpus with continuous sentences, and then evaluate the trained model on 7 tasks, which include semantic relatedness, paraphrase detection, and classification benchmarks.
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.
BAM! The Behance Artistic Media Dataset for Recognition Beyond Photography
Furthermore, we carry out baseline experiments to show the value of this dataset for artistic style prediction, for improving the generality of existing object classifiers, and for the study of visual domain adaptation.
Diversified Texture Synthesis with Feed-forward Networks
Recent progresses on deep discriminative and generative modeling have shown promising results on texture synthesis.
Scribbler: Controlling Deep Image Synthesis with Sketch and Color
In this paper, we propose a deep adversarial image synthesis architecture that is conditioned on sketched boundaries and sparse color strokes to generate realistic cars, bedrooms, or faces.
Vista: A Visually, Socially, and Temporally-aware Model for Artistic Recommendation
Understanding users' interactions with highly subjective content---like artistic images---is challenging due to the complex semantics that guide our preferences.
Image Captioning with Semantic Attention
Automatically generating a natural language description of an image has attracted interests recently both because of its importance in practical applications and because it connects two major artificial intelligence fields: computer vision and natural language processing.
Multi-Instance Visual-Semantic Embedding
Visual-semantic embedding models have been recently proposed and shown to be effective for image classification and zero-shot learning, by mapping images into a continuous semantic label space.
Collaborative Feature Learning from Social Media
We validate our feature learning paradigm on this dataset and find that the learned feature significantly outperforms the state-of-the-art image features in learning better image similarities.
Multi-Task Metric Learning on Network Data
In particular SPML learns a Mahalanobis distance metric for node attributes using network structure as supervision, so that the learned distance function encodes the structure and can be used to predict link patterns from attributes.
