Search Results for author:
Found 47 papers, 13 papers with code
Target-Free Text-guided Image Manipulation
We tackle the problem of target-free text-guided image manipulation, which requires one to modify the input reference image based on the given text instruction, while no ground truth target image is observed during training.
Paraphrasing Is All You Need for Novel Object Captioning
Since no ground truth captions are available for novel object images during training, our P2C leverages cross-modality (image-text) association modules to ensure the above caption characteristics can be properly preserved.
Self-Supervised Pyramid Representation Learning for Multi-Label Visual Analysis and Beyond
In particular, we present a cross-scale patch-level correlation learning in SS-PRL, which allows the model to aggregate and associate information learned across patch scales.
Frido: Feature Pyramid Diffusion for Complex Scene Image Synthesis
Diffusion models (DMs) have shown great potential for high-quality image synthesis.
Learning Facial Liveness Representation for Domain Generalized Face Anti-spoofing
Face anti-spoofing (FAS) aims at distinguishing face spoof attacks from the authentic ones, which is typically approached by learning proper models for performing the associated classification task.
Scene Graph Expansion for Semantics-Guided Image Outpainting
In particular, we propose a novel network of Scene Graph Transformer (SGT), which is designed to take node and edge features as inputs for modeling the associated structural information.
NeurMiPs: Neural Mixture of Planar Experts for View Synthesis
We present Neural Mixtures of Planar Experts (NeurMiPs), a novel planar-based scene representation for modeling geometry and appearance.
Domain-Generalized Textured Surface Anomaly Detection
By observing normal and abnormal surface data across multiple source domains, our model is expected to be generalized to an unseen textured surface of interest, in which only a small number of normal data can be observed during testing.
Few-Shot Classification in Unseen Domains by Episodic Meta-Learning Across Visual Domains
Few-shot classification aims to carry out classification given only few labeled examples for the categories of interest.
Meta-Learned Feature Critics for Domain Generalized Semantic Segmentation
How to handle domain shifts when recognizing or segmenting visual data across domains has been studied by learning and vision communities.
Adversarial Teacher-Student Representation Learning for Domain Generalization
Domain generalization (DG) aims to transfer the learning task from a single or multiple source domains to unseen target domains.
A Pixel-Level Meta-Learner for Weakly Supervised Few-Shot Semantic Segmentation
Few-shot semantic segmentation addresses the learning task in which only few images with ground truth pixel-level labels are available for the novel classes of interest.
Learning Visual-Linguistic Adequacy, Fidelity, and Fluency for Novel Object Captioning
Novel object captioning (NOC) learns image captioning models for describing objects or visual concepts which are unseen (i. e., novel) in the training captions.
LayoutTransformer: Scene Layout Generation With Conceptual and Spatial Diversity
To better exploit the text input, so that implicit objects or relationships can be properly inferred during layout generation, we propose a LayoutTransformer Network (LT-Net) in this paper.
Exploiting Audio-Visual Consistency with Partial Supervision for Spatial Audio Generation
Human perceives rich auditory experience with distinct sound heard by ears.
Dual-MTGAN: Stochastic and Deterministic Motion Transfer for Image-to-Video Synthesis
Generating videos with content and motion variations is a challenging task in computer vision.
LayoutTransformer: Relation-Aware Scene Layout Generation
In the areas of machine learning and computer vision, text-to-image synthesis aims at producing image outputs given the input text.
Representation Decomposition for Image Manipulation and Beyond
Representation disentanglement aims at learning interpretable features, so that the output can be recovered or manipulated accordingly.
Semantics-Guided Representation Learning with Applications to Visual Synthesis
Learning interpretable and interpolatable latent representations has been an emerging research direction, allowing researchers to understand and utilize the derived latent space for further applications such as visual synthesis or recognition.
Domain Generalized Person Re-Identification via Cross-Domain Episodic Learning
That is, while a number of labeled source-domain datasets are available, we do not have access to any target-domain training data.
Domain Generalization
Generalizable Person Re-identification
+1
Semantics-Guided Clustering with Deep Progressive Learning for Semi-Supervised Person Re-identification
As a result, our approach is able to augment the labeled training data in the semi-supervised setting.
Learning to Learn in a Semi-Supervised Fashion
To address semi-supervised learning from both labeled and unlabeled data, we present a novel meta-learning scheme.
Wavelet Channel Attention Module with a Fusion Network for Single Image Deraining
Wavelet transform and the inverse wavelet transform are substituted for down-sampling and up-sampling so feature maps from the wavelet transform and convolutions contain different frequencies and scales.
Transforming Multi-Concept Attention into Video Summarization
Video summarization is among challenging tasks in computer vision, which aims at identifying highlight frames or shots over a lengthy video input.
Cross-Resolution Adversarial Dual Network for Person Re-Identification and Beyond
Person re-identification (re-ID) aims at matching images of the same person across camera views.
Cross-Dataset Person Re-Identification via Unsupervised Pose Disentanglement and Adaptation
Person re-identification (re-ID) aims at recognizing the same person from images taken across different cameras.
Ranked #15 on
Unsupervised Domain Adaptation
on Market to Duke
Recover and Identify: A Generative Dual Model for Cross-Resolution Person Re-Identification
Person re-identification (re-ID) aims at matching images of the same identity across camera views.
Spatially and Temporally Efficient Non-local Attention Network for Video-based Person Re-Identification
Video-based person re-identification (Re-ID) aims at matching video sequences of pedestrians across non-overlapping cameras.
Ranked #11 on
Person Re-Identification
on MARS
Learning Resolution-Invariant Deep Representations for Person Re-Identification
Moreover, the extension of our model for semi-supervised re-ID further confirms the scalability of our proposed method for real-world scenarios and applications.
A Closer Look at Few-shot Classification
Few-shot classification aims to learn a classifier to recognize unseen classes during training with limited labeled examples.
Dual-modality seq2seq network for audio-visual event localization
Audio-visual event localization requires one to identify theevent which is both visible and audible in a video (eitherat a frame or video level).
3D Shape Reconstruction from a Single 2D Image via 2D-3D Self-Consistency
Aiming at inferring 3D shapes from 2D images, 3D shape reconstruction has drawn huge attention from researchers in computer vision and deep learning communities.
3D Reconstruction
3D Shape Reconstruction From A Single 2D Image
A Unified Feature Disentangler for Multi-Domain Image Translation and Manipulation
We present a novel and unified deep learning framework which is capable of learning domain-invariant representation from data across multiple domains.
Summarizing First-Person Videos from Third Persons' Points of View
Video highlight or summarization is among interesting topics in computer vision, which benefits a variety of applications like viewing, searching, or storage.
Deep Generative Models for Weakly-Supervised Multi-Label Classification
In order to train learning models for multi-label classification (MLC), it is typically desirable to have a large amount of fully annotated multi-label data.
Deep Reinforcement Learning for Playing 2.5D Fighting Games
Deep reinforcement learning has shown its success in game playing.
Adaptation and Re-Identification Network: An Unsupervised Deep Transfer Learning Approach to Person Re-Identification
Person re-identification (Re-ID) aims at recognizing the same person from images taken across different cameras.
Ranked #18 on
Unsupervised Domain Adaptation
on Duke to Market
Summarizing First-Person Videos from Third Persons' Points of Views
Video highlight or summarization is among interesting topics in computer vision, which benefits a variety of applications like viewing, searching, or storage.
Multi-Label Zero-Shot Learning with Structured Knowledge Graphs
In this paper, we propose a novel deep learning architecture for multi-label zero-shot learning (ML-ZSL), which is able to predict multiple unseen class labels for each input instance.
Order-Free RNN with Visual Attention for Multi-Label Classification
In this paper, we propose the joint learning attention and recurrent neural network (RNN) models for multi-label classification.
Learning Deep Latent Spaces for Multi-Label Classification
Multi-label classification is a practical yet challenging task in machine learning related fields, since it requires the prediction of more than one label category for each input instance.
Generative-Discriminative Variational Model for Visual Recognition
In other words, our GDVM casts the supervised learning task as a generative learning process, with data discrimination to be jointly exploited for improved classification.
Detach and Adapt: Learning Cross-Domain Disentangled Deep Representation
While representation learning aims to derive interpretable features for describing visual data, representation disentanglement further results in such features so that particular image attributes can be identified and manipulated.
No More Discrimination: Cross City Adaptation of Road Scene Segmenters
Despite the recent success of deep-learning based semantic segmentation, deploying a pre-trained road scene segmenter to a city whose images are not presented in the training set would not achieve satisfactory performance due to dataset biases.
Learning Cross-Domain Landmarks for Heterogeneous Domain Adaptation
With the goal of deriving a domain-invariant feature subspace for HDA, our CDLS is able to identify representative cross-domain data, including the unlabeled ones in the target domain, for performing adaptation.
Unsupervised Domain Adaptation With Imbalanced Cross-Domain Data
For standard unsupervised domain adaptation, one typically obtains labeled data in the source domain and only observes unlabeled data in the target domain.
Propagated Image Filtering
In this paper, we propose the propagation filter as a novel image filtering operator, with the goal of smoothing over neighboring image pixels while preserving image context like edges or textural regions.
