Search Results for author:
Found 88 papers, 56 papers with code
Label-Efficient Learning in Agriculture: A Comprehensive Review
In addition, a systematic review of various agricultural applications exploiting these label-efficient algorithms, such as precision agriculture, plant phenotyping, and postharvest quality assessment, is presented.
Make-It-3D: High-Fidelity 3D Creation from A Single Image with Diffusion Prior
In this work, we investigate the problem of creating high-fidelity 3D content from only a single image.
CiCo: Domain-Aware Sign Language Retrieval via Cross-Lingual Contrastive Learning
Our framework, termed as domain-aware sign language retrieval via Cross-lingual Contrastive learning or CiCo for short, outperforms the pioneering method by large margins on various datasets, e. g., +22. 4 T2V and +28. 0 V2T R@1 improvements on How2Sign dataset, and +13. 7 T2V and +17. 1 V2T R@1 improvements on PHOENIX-2014T dataset.
Ranked #1 on
Sign Language Retrieval
on CSL-Daily
IRGen: Generative Modeling for Image Retrieval
While generative modeling has been ubiquitous in natural language processing and computer vision, its application to image retrieval remains unexplored.
Efficient Diffusion Training via Min-SNR Weighting Strategy
Denoising diffusion models have been a mainstream approach for image generation, however, training these models often suffers from slow convergence.
Ranked #1 on
Unconditional Image Generation
on ImageNet 256x256
O2RNet: Occluder-Occludee Relational Network for Robust Apple Detection in Clustered Orchard Environments
One key technology to fully enable efficient automated harvesting is accurate and robust apple detection, which is challenging due to complex orchard environments that involve varying lighting conditions and foliage/branch occlusions.
Hyneter: Hybrid Network Transformer for Object Detection
In this paper, we point out that the essential differences between CNN-based and Transformer-based detectors, which cause the worse performance of small objects in Transformer-based methods, are the gap between local information and global dependencies in feature extraction and propagation.
FreeEnricher: Enriching Face Landmarks without Additional Cost
To evaluate our method, we manually label the dense landmarks on 300W testset.
MetaPortrait: Identity-Preserving Talking Head Generation with Fast Personalized Adaptation
In this work, we propose an ID-preserving talking head generation framework, which advances previous methods in two aspects.
Rodin: A Generative Model for Sculpting 3D Digital Avatars Using Diffusion
This paper presents a 3D generative model that uses diffusion models to automatically generate 3D digital avatars represented as neural radiance fields.
CLIP Itself is a Strong Fine-tuner: Achieving 85.7% and 88.0% Top-1 Accuracy with ViT-B and ViT-L on ImageNet
Recent studies have shown that CLIP has achieved remarkable success in performing zero-shot inference while its fine-tuning performance is not satisfactory.
X-Paste: Revisiting Scalable Copy-Paste for Instance Segmentation using CLIP and StableDiffusion
We demonstrate for the first time that using a text2image model to generate images or zero-shot recognition model to filter noisily crawled images for different object categories is a feasible way to make Copy-Paste truly scalable.
Ranked #4 on
Instance Segmentation
on LVIS v1.0 val
Paint by Example: Exemplar-based Image Editing with Diffusion Models
Language-guided image editing has achieved great success recently.
SinDiffusion: Learning a Diffusion Model from a Single Natural Image
We present SinDiffusion, leveraging denoising diffusion models to capture internal distribution of patches from a single natural image.
Ranked #1 on
Image Generation
on Places50
A Structure-Guided Diffusion Model for Large-Hole Diverse Image Completion
Our proposed SGDM consists of a structure generator and a texture generator, which are both diffusion probabilistic models (DMs).
Deep Data Augmentation for Weed Recognition Enhancement: A Diffusion Probabilistic Model and Transfer Learning Based Approach
In this paper, we present the first work of applying diffusion probabilistic models (also known as diffusion models) to generate high-quality synthetic weed images based on transfer learning.
DigiFace-1M: 1 Million Digital Face Images for Face Recognition
Such models are trained on large-scale datasets that contain millions of real human face images collected from the internet.
Ranked #1 on
Face Recognition
on AgeDB
3DFaceShop: Explicitly Controllable 3D-Aware Portrait Generation
In contrast to the traditional avatar creation pipeline which is a costly process, contemporary generative approaches directly learn the data distribution from photographs.
MaskCLIP: Masked Self-Distillation Advances Contrastive Language-Image Pretraining
Second, masked self-distillation is also consistent with vision-language contrastive from the perspective of training objective as both utilize the visual encoder for feature aligning, and thus is able to learn local semantics getting indirect supervision from the language.
Bootstrapped Masked Autoencoders for Vision BERT Pretraining
The first design is motivated by the observation that using a pretrained MAE to extract the features as the BERT prediction target for masked tokens can achieve better pretraining performance.
Semantic Image Synthesis via Diffusion Models
Denoising Diffusion Probabilistic Models (DDPMs) have achieved remarkable success in various image generation tasks compared with Generative Adversarial Nets (GANs).
I^2R-Net: Intra- and Inter-Human Relation Network for Multi-Person Pose Estimation
In this paper, we present the Intra- and Inter-Human Relation Networks (I^2R-Net) for Multi-Person Pose Estimation.
Ranked #1 on
Pose Estimation
on COCO
Robust Meta-learning with Sampling Noise and Label Noise via Eigen-Reptile
Moreover, when handling the data with noisy labels, the meta-learner could be extremely sensitive to label noise on a corrupted dataset.
Improved Vector Quantized Diffusion Models
When trained on ImageNet, we dramatically improve the FID score from 11. 89 to 4. 83, demonstrating the superiority of our proposed techniques.
Contrastive Learning Rivals Masked Image Modeling in Fine-tuning via Feature Distillation
These properties, which we aggregately refer to as optimization friendliness, are identified and analyzed by a set of attention- and optimization-related diagnosis tools.
Ranked #3 on
Instance Segmentation
on COCO test-dev
(using extra training data)
Pretraining is All You Need for Image-to-Image Translation
We propose to use pretraining to boost general image-to-image translation.
Real-Time Neural Character Rendering with Pose-Guided Multiplane Images
We propose pose-guided multiplane image (MPI) synthesis which can render an animatable character in real scenes with photorealistic quality.
Generative Adversarial Networks for Image Augmentation in Agriculture: A Systematic Review
In agricultural image analysis, optimal model performance is keenly pursued for better fulfilling visual recognition tasks (e. g., image classification, segmentation, object detection and localization), in the presence of challenges with biological variability and unstructured environments.
Large-Scale Pre-training for Person Re-identification with Noisy Labels
Since theses ID labels automatically derived from tracklets inevitably contain noises, we develop a large-scale Pre-training framework utilizing Noisy Labels (PNL), which consists of three learning modules: supervised Re-ID learning, prototype-based contrastive learning, and label-guided contrastive learning.
Ranked #7 on
Person Re-Identification
on CUHK03
Semi-Supervised Image-to-Image Translation using Latent Space Mapping
Thanks to the low dimensional feature space, it is easier to find the desired mapping function, resulting in improved quality of translation results as well as the stability of the translation model.
Protecting Celebrities from DeepFake with Identity Consistency Transformer
In this work we propose Identity Consistency Transformer, a novel face forgery detection method that focuses on high-level semantics, specifically identity information, and detecting a suspect face by finding identity inconsistency in inner and outer face regions.
Machine learning analysis of cocaine addiction informed by DAT, SERT, and NET-based interactome networks
Cocaine dependence is neurological and involves many interacting proteins in the interactome.
StyleSwin: Transformer-based GAN for High-resolution Image Generation
To this end, we believe that local attention is crucial to strike the balance between computational efficiency and modeling capacity.
Ranked #1 on
Image Generation
on CelebA-HQ 256x256
General Facial Representation Learning in a Visual-Linguistic Manner
In this paper, we study the transfer performance of pre-trained models on face analysis tasks and introduce a framework, called FaRL, for general Facial Representation Learning in a visual-linguistic manner.
Ranked #1 on
Face Parsing
on CelebAMask-HQ
(using extra training data)
Vector Quantized Diffusion Model for Text-to-Image Synthesis
Our experiments indicate that the VQ-Diffusion model with the reparameterization is fifteen times faster than traditional AR methods while achieving a better image quality.
Ranked #1 on
Text-to-Image Generation
on Oxford 102 Flowers
(using extra training data)
PeCo: Perceptual Codebook for BERT Pre-training of Vision Transformers
This paper explores a better prediction target for BERT pre-training of vision transformers.
Multi-agent Reinforcement Learning for Cooperative Lane Changing of Connected and Autonomous Vehicles in Mixed Traffic
In this paper, we formulate the lane-changing decision making of multiple AVs in a mixed-traffic highway environment as a multi-agent reinforcement learning (MARL) problem, where each AV makes lane-changing decisions based on the motions of both neighboring AVs and HDVs.
Performance Evaluation of Deep Transfer Learning on Multiclass Identification of Common Weed Species in Cotton Production Systems
Precision weed management offers a promising solution for sustainable cropping systems through the use of chemical-reduced/non-chemical robotic weeding techniques, which apply suitable control tactics to individual weeds.
Proteome-informed machine learning studies of cocaine addiction
Cocaine addiction accounts for a large portion of substance use disorders and threatens millions of lives worldwide.
Exploring Temporal Coherence for More General Video Face Forgery Detection
The first stage is a fully temporal convolution network (FTCN).
Dual Path Learning for Domain Adaptation of Semantic Segmentation
In this paper, based on the observation that domain adaptation frameworks performed in the source and target domain are almost complementary in terms of image translation and SSL, we propose a novel dual path learning (DPL) framework to alleviate visual inconsistency.
Instance-wise Hard Negative Example Generation for Contrastive Learning in Unpaired Image-to-Image Translation
In this paper, we uncover that the negative examples play a critical role in the performance of contrastive learning for image translation.
CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped Windows
By further pretraining on the larger dataset ImageNet-21K, we achieve 87. 5% Top-1 accuracy on ImageNet-1K and high segmentation performance on ADE20K with 55. 7 mIoU.
Ranked #24 on
Semantic Segmentation
on ADE20K val
Robust Mutual Learning for Semi-supervised Semantic Segmentation
The proposed robust mutual learning demonstrates state-of-the-art performance on semantic segmentation in low-data regime.
Deep Multi-agent Reinforcement Learning for Highway On-Ramp Merging in Mixed Traffic
On-ramp merging is a challenging task for autonomous vehicles (AVs), especially in mixed traffic where AVs coexist with human-driven vehicles (HDVs).
High-Fidelity and Arbitrary Face Editing
However, we observe that the generator tends to find a tricky way to hide information from the original image to satisfy the constraint of cycle consistency, making it impossible to maintain the rich details (e. g., wrinkles and moles) of non-editing areas.
Control Distance IoU and Control Distance IoU Loss Function for Better Bounding Box Regression
In this paper, we first present an evaluation-feedback module, which is proposed to consist of evaluation system and feedback mechanism.
Style-based Point Generator with Adversarial Rendering for Point Cloud Completion
In this paper, we proposed a novel Style-based Point Generator with Adversarial Rendering (SpareNet) for point cloud completion.
Ranked #1 on
Point Cloud Completion
on ShapeNet
(Earth Mover's Distance metric)
Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation
In this paper, we rely on representative prototypes, the feature centroids of classes, to address the two issues for unsupervised domain adaptation.
Ranked #10 on
Semantic Segmentation
on GTAV-to-Cityscapes Labels
Robust Meta-learning with Noise via Eigen-Reptile
In particular, we first cast the meta-overfitting problem (overfitting on sampling and label noise) as a gradient noise problem since few available samples cause meta-learner to overfit on existing examples (clean or corrupted) of an individual task at every gradient step.
Identity-Driven DeepFake Detection
Our approach takes as input the suspect image/video as well as the target identity information (a reference image or video).
Evidence of topological nodal lines and surface states in the centrosymmetric superconductor SnTaS2
Utilizing angle-resolved photoemission spectroscopy and first-principles calculations, here, we demonstrate the existence of topological nodal-line states and drumheadlike surface states in centrosymmetric superconductor SnTaS2, which is a type-II superconductor with a critical transition temperature of about 3 K. The valence bands from Ta 5d orbitals and the conduction bands from Sn 5p orbitals cross each other, forming two nodal lines in the vicinity of the Fermi energy without the inclusion of spin-orbit coupling (SOC), protected by the spatial-inversion symmetry and time-reversal symmetry.
Superconductivity
Unsupervised Pre-training for Person Re-identification
In this paper, we present a large scale unlabeled person re-identification (Re-ID) dataset "LUPerson" and make the first attempt of performing unsupervised pre-training for improving the generalization ability of the learned person Re-ID feature representation.
Ranked #2 on
Person Re-Identification
on Market-1501
(using extra training data)
CoCosNet v2: Full-Resolution Correspondence Learning for Image Translation
We present the full-resolution correspondence learning for cross-domain images, which aids image translation.
PowerNet: Multi-agent Deep Reinforcement Learning for Scalable Powergrid Control
Specifically, we consider the decentralized inverter-based secondary voltage control problem in distributed generators (DGs), which is first formulated as a cooperative multi-agent reinforcement learning (MARL) problem.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Learnable Sampling 3D Convolution for Video Enhancement and Action Recognition
A key challenge in video enhancement and action recognition is to fuse useful information from neighboring frames.
GreedyFool: Distortion-Aware Sparse Adversarial Attack
Sparse adversarial samples are a special branch of adversarial samples that can fool the target model by only perturbing a few pixels.
SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving
We open-source the SMARTS platform and the associated benchmark tasks and evaluation metrics to encourage and empower research on multi-agent learning for autonomous driving.
Old Photo Restoration via Deep Latent Space Translation
Unlike conventional restoration tasks that can be solved through supervised learning, the degradation in real photos is complex and the domain gap between synthetic images and real old photos makes the network fail to generalize.
Unified Representation Learning for Cross Model Compatibility
We propose a unified representation learning framework to address the Cross Model Compatibility (CMC) problem in the context of visual search applications.
PriorGAN: Real Data Prior for Generative Adversarial Nets
To address these two issues, we propose a novel prior that captures the whole real data distribution for GANs, which are called PriorGANs.
Cross-Task Transfer for Geotagged Audiovisual Aerial Scene Recognition
With the help of this dataset, we evaluate three proposed approaches for transferring the sound event knowledge to the aerial scene recognition task in a multimodal learning framework, and show the benefit of exploiting the audio information for the aerial scene recognition.
Disentangled and Controllable Face Image Generation via 3D Imitative-Contrastive Learning
Our method can also be used to embed real images into the disentangled latent space.
Deep 3D Portrait from a Single Image
We evaluate the accuracy of our method both in 3D and with pose manipulation tasks on 2D images.
Uncertainty Quantification for Hyperspectral Image Denoising Frameworks based on Low-rank Matrix Approximation
Sliding-window based low-rank matrix approximation (LRMA) is a technique widely used in hyperspectral images (HSIs) denoising or completion.
Bringing Old Photos Back to Life
Unlike conventional restoration tasks that can be solved through supervised learning, the degradation in real photos is complex and the domain gap between synthetic images and real old photos makes the network fail to generalize.
Cross-domain Correspondence Learning for Exemplar-based Image Translation
The output has the style (e. g., color, texture) in consistency with the semantically corresponding objects in the exemplar.
Ranked #1 on
Image-to-Image Translation
on ADE20K-Outdoor Labels-to-Photos
(FID metric)
Curved Buildings Reconstruction from Airborne LiDAR Data by Matching and Deforming Geometric Primitives
To this end, we propose a new framework for curved building reconstruction via assembling and deforming geometric primitives.
GIQA: Generated Image Quality Assessment
Generative adversarial networks (GANs) have achieved impressive results today, but not all generated images are perfect.
Online Semantic Exploration of Indoor Maps
In this paper we propose a method to extract an abstracted floor plan from typical grid maps using Bayesian reasoning.
Table-Top Scene Analysis Using Knowledge-Supervised MCMC
Our approach to generate scene graphs is probabilistic: Uncertainty in the object poses is addressed by a probabilistic sensor model that is embedded in a data driven MCMC process.
TCM-ICP: Transformation Compatibility Measure for Registering Multiple LIDAR Scans
In this work, we present an algorithm for registering multiple, overlapping LiDAR scans.
Face X-ray for More General Face Forgery Detection
For this reason, face X-ray provides an effective way for detecting forgery generated by most existing face manipulation algorithms.
FaceShifter: Towards High Fidelity And Occlusion Aware Face Swapping
We propose a novel attributes encoder for extracting multi-level target face attributes, and a new generator with carefully designed Adaptive Attentional Denormalization (AAD) layers to adaptively integrate the identity and the attributes for face synthesis.
Autonomous Driving using Safe Reinforcement Learning by Incorporating a Regret-based Human Lane-Changing Decision Model
The predicted decisions are incorporated in the safety constraints for reinforcement learning in training and in implementation.
Deep Exemplar-based Video Colorization
This paper presents the first end-to-end network for exemplar-based video colorization.
Face Parsing with RoI Tanh-Warping
It uses hierarchical local based method for inner facial components and global methods for outer facial components.
Mask-Guided Portrait Editing with Conditional GANs
Portrait editing is a popular subject in photo manipulation.
Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set
Recently, deep learning based 3D face reconstruction methods have shown promising results in both quality and efficiency. However, training deep neural networks typically requires a large volume of data, whereas face images with ground-truth 3D face shapes are scarce.
Ranked #2 on
3D Face Reconstruction
on REALY (side-view)
WIDER Face and Pedestrian Challenge 2018: Methods and Results
This paper presents a review of the 2018 WIDER Challenge on Face and Pedestrian.
Exploring Hypergraph Representation on Face Anti-spoofing Beyond 2D Attacks
Face anti-spoofing plays a crucial role in protecting face recognition systems from various attacks.
A novel active learning framework for classification: using weighted rank aggregation to achieve multiple query criteria
The proposed method serves as a heuristic means to select high-value samples of high scalability and generality and is implemented through a three-step process: (1) the transformation of the sample selection to sample ranking and scoring, (2) the computation of the self-adaptive weights of each criterion, and (3) the weighted aggregation of each sample rank list.
Towards Open-Set Identity Preserving Face Synthesis
We then recombine the identity vector and the attribute vector to synthesize a new face of the subject with the extracted attribute.
CVAE-GAN: Fine-Grained Image Generation through Asymmetric Training
Our approach models an image as a composition of label and latent attributes in a probabilistic model.
Supervised Transformer Network for Efficient Face Detection
For real-time performance, we run the cascaded network only on regions of interests produced from a boosting cascade face detector.
Ranked #5 on
Face Detection
on PASCAL Face
Neural Aggregation Network for Video Face Recognition
The network takes a face video or face image set of a person with a variable number of face images as its input, and produces a compact, fixed-dimension feature representation for recognition.
Ranked #7 on
Face Verification
on IJB-A
Blessing of Dimensionality: High-Dimensional Feature and Its Efficient Compression for Face Verification
Making a high-dimensional (e. g., 100K-dim) feature for face recognition seems not a good idea because it will bring difficulties on consequent training, computation, and storage.
Ranked #9 on
Age-Invariant Face Recognition
on CACDVS
