Search Results for author:
Found 102 papers, 31 papers with code
VideoFusion: Decomposed Diffusion Models for High-Quality Video Generation
A diffusion probabilistic model (DPM), which constructs a forward diffusion process by gradually adding noise to data points and learns the reverse denoising process to generate new samples, has been shown to handle complex data distribution.
Ranked #7 on
Video Generation
on UCF-101
Cyclic Differentiable Architecture Search
First, the search network generates an initial architecture for evaluation, and the weights of the evaluation network are optimized.
Ranked #17 on
Neural Architecture Search
on NAS-Bench-201, CIFAR-10
Unfolding the Alternating Optimization for Blind Super Resolution
More importantly, \textit{Restorer} is trained with the kernel estimated by \textit{Estimator}, instead of ground-truth kernel, thus \textit{Restorer} could be more tolerant to the estimation error of \textit{Estimator}.
Ranked #2 on
Blind Super-Resolution
on Set5 - 2x upscaling
End-to-end Alternating Optimization for Blind Super Resolution
More importantly, \textit{Restorer} is trained with the kernel estimated by \textit{Estimator}, instead of the ground-truth kernel, thus \textit{Restorer} could be more tolerant to the estimation error of \textit{Estimator}.
Ranked #2 on
Blind Super-Resolution
on DIV2KRK - 4x upscaling
End-to-end Alternating Optimization for Real-World Blind Super Resolution
To address this issue, instead of considering these two problems independently, we adopt an alternating optimization algorithm, which can estimate the degradation and restore the SR image in a single model.
BEVBert: Multimodal Map Pre-training for Language-guided Navigation
Concretely, we build a local metric map to explicitly aggregate incomplete observations and remove duplicates, while modeling navigation dependency in a global topological map.
Ranked #2 on
Visual Navigation
on R2R
1st Place Solutions for RxR-Habitat Vision-and-Language Navigation Competition (CVPR 2022)
Our model consists of three modules: the candidate waypoints predictor (CWP), the history enhanced planner and the tryout controller.
ETPNav: Evolving Topological Planning for Vision-Language Navigation in Continuous Environments
To develop a robust VLN-CE agent, we propose a new navigation framework, ETPNav, which focuses on two critical skills: 1) the capability to abstract environments and generate long-range navigation plans, and 2) the ability of obstacle-avoiding control in continuous environments.
Learning the Degradation Distribution for Blind Image Super-Resolution
Compared with previous deterministic degradation models, PDM could model more diverse degradations and generate HR-LR pairs that may better cover the various degradations of test images, and thus prevent the SR model from over-fitting to specific ones.
Rethinking the Heatmap Regression for Bottom-up Human Pose Estimation
However, for bottom-up methods, which need to handle a large variance of human scales and labeling ambiguities, the current practice seems unreasonable.
Mask-Guided Contrastive Attention Model for Person Re-Identification
We may be the first one to successfully introduce the binary mask into person ReID task and the first one to propose region-level contrastive learning.
Neighbor-view Enhanced Model for Vision and Language Navigation
Specifically, our NvEM utilizes a subject module and a reference module to collect contexts from neighbor views.
Ranked #82 on
Vision and Language Navigation
on VLN Challenge
Regularized Graph Structure Learning with Semantic Knowledge for Multi-variates Time-Series Forecasting
In this paper, we propose Regularized Graph Structure Learning (RGSL) model to incorporate both explicit prior structure and implicit structure together, and learn the forecasting deep networks along with the graph structure.
Efficient Multimodal Semantic Segmentation via Dual-Prompt Learning
Existing approaches often fully fine-tune a dual-branch encoder-decoder framework with a complicated feature fusion strategy for achieving multimodal semantic segmentation, which is training-costly due to the massive parameter updates in feature extraction and fusion.
Ranked #2 on
Semantic Segmentation
on SUN-RGBD
(using extra training data)
Bag of Tricks for Training Data Extraction from Language Models
With the advance of language models, privacy protection is receiving more attention.
Context-Guided Spatio-Temporal Video Grounding
The key of CG-STVG lies in two specially designed modules, including instance context generation (ICG), which focuses on discovering visual context information (in both appearance and motion) of the instance, and instance context refinement (ICR), which aims to improve the instance context from ICG by eliminating irrelevant or even harmful information from the context.
Ranked #1 on
Spatio-Temporal Video Grounding
on HC-STVG1
3D Shape Temporal Aggregation for Video-Based Clothing-Change Person Re-Identication
However, existing Re-ID methods usually generate 3D body shapes without considering identity modeling, which severely weakens the discriminability of 3D human shapes.
Efficient Token-Guided Image-Text Retrieval with Consistent Multimodal Contrastive Training
Most previous works either simply learn coarse-grained representations of the overall image and text, or elaborately establish the correspondence between image regions or pixels and text words.
DGSSC: A Deep Generative Spectral-Spatial Classifier for Imbalanced Hyperspectral Imagery
Specifically, the DGSSC comprises three components, a two-stage encoder, a decoder, and a classifier, which are trained in an end-to-end manner.
Landmark-RxR: Solving Vision-and-Language Navigation with Fine-Grained Alignment Supervision
In Vision-and-Language Navigation (VLN) task, an agent is asked to navigate inside 3D indoor environments following given instructions.
Target-Grounded Graph-Aware Transformer for Aerial Vision-and-Dialog Navigation
This report details the methods of the winning entry of the AVDN Challenge in ICCV CLVL 2023.
Box-driven Class-wise Region Masking and Filling Rate Guided Loss for Weakly Supervised Semantic Segmentation
To address this problem, it is a good choice to learn to segment with weak supervision from bounding boxes.
Weakly-supervised Learning
Weakly supervised Semantic Segmentation
+1
SIN:Superpixel Interpolation Network
The former is non-differentiable and the latter needs a non-differentiable post-processing step to enforce connectivity, which constraints the integration of superpixels and downstream tasks.
GLARE: A Dataset for Traffic Sign Detection in Sun Glare
It provides an essential enrichment to the widely used LISA Traffic Sign dataset.
A Hierarchical Contextual Attention-based GRU Network for Sequential Recommendation
We fuse the current hidden state and a contextual hidden state built by the attention mechanism, which leads to a more suitable user's overall interest.
CMF: Cascaded Multi-model Fusion for Referring Image Segmentation
To solve this problem, we propose a simple yet effective Cascaded Multi-modal Fusion (CMF) module, which stacks multiple atrous convolutional layers in parallel and further introduces a cascaded branch to fuse visual and linguistic features.
VI-Diff: Unpaired Visible-Infrared Translation Diffusion Model for Single Modality Labeled Visible-Infrared Person Re-identification
In this paper, we propose VI-Diff, a diffusion model that effectively addresses the task of Visible-Infrared person image translation.
Co-Driven Recognition of Semantic Consistency via the Fusion of Transformer and HowNet Sememes Knowledge
Multi-level encoding of internal sentence structures via data-driven is carried out firstly by Transformer, sememes knowledge base HowNet is introduced for knowledge-driven to model the semantic knowledge association among sentence pairs.
Illumination Distillation Framework for Nighttime Person Re-Identification and A New Benchmark
The illumination enhancement branch first estimates an enhanced image from the nighttime image using a nonlinear curve mapping method and then extracts the enhanced features.
Adversarial-Metric Learning for Audio-Visual Cross-Modal Matching
AML aims to generate a modality-independent representation for each person in each modality via adversarial learning, while simultaneously learns a robust similarity measure for cross-modality matching via metric learning.
Relational Network for Skeleton-Based Action Recognition
We introduce the Recurrent Relational Network to learn the spatial features in a single skeleton, followed by a multi-layer LSTM to learn the temporal features in the skeleton sequences.
Ranked #95 on
Skeleton Based Action Recognition
on NTU RGB+D
Multi-pseudo Regularized Label for Generated Data in Person Re-Identification
Unlike the traditional label which usually is a single integral number, the virtual label proposed in this work is a set of weight-based values each individual of which is a number in (0, 1] called multi-pseudo label and reflects the degree of relation between each generated data to every pre-defined class of real data.
Learning Semantic Concepts and Order for Image and Sentence Matching
This mainly arises from that the representation of pixel-level image usually lacks of high-level semantic information as in its matched sentence.
Ranked #11 on
Image Retrieval
on Flickr30K 1K test
Multimodal Memory Modelling for Video Captioning
In this paper, we propose a Multimodal Memory Model (M3) to describe videos, which builds a visual and textual shared memory to model the long-term visual-textual dependency and further guide global visual attention on described targets.
Instance-aware Image and Sentence Matching with Selective Multimodal LSTM
Based on the observation that such a global similarity arises from a complex aggregation of multiple local similarities between pairwise instances of image (objects) and sentence (words), we propose a selective multimodal Long Short-Term Memory network (sm-LSTM) for instance-aware image and sentence matching.
Ranked #14 on
Image Retrieval
on Flickr30K 1K test
Anchoring and Agreement in Syntactic Annotations
Our agreement results control for parser bias, and are consequential in that they are on par with state of the art parsing performance for English newswire.
DeepMove: Learning Place Representations through Large Scale Movement Data
DeepMove is spatial and temporal context aware.
RetGK: Graph Kernels based on Return Probabilities of Random Walks
Graph-structured data arise in wide applications, such as computer vision, bioinformatics, and social networks.
Bidirectional Recurrent Convolutional Networks for Multi-Frame Super-Resolution
Super resolving a low-resolution video is usually handled by either single-image super-resolution (SR) or multi-frame SR. Single-Image SR deals with each video frame independently, and ignores intrinsic temporal dependency of video frames which actually plays a very important role in video super-resolution.
Ranked #18 on
Video Super-Resolution
on Vid4 - 4x upscaling
Aligning Infinite-Dimensional Covariance Matrices in Reproducing Kernel Hilbert Spaces for Domain Adaptation
Domain shift, which occurs when there is a mismatch between the distributions of training (source) and testing (target) datasets, usually results in poor performance of the trained model on the target domain.
M3: Multimodal Memory Modelling for Video Captioning
Inspired by the facts that memory modelling poses potential advantages to long-term sequential problems [35] and working memory is the key factor of visual attention [33], we propose a Multimodal Memory Model (M3) to describe videos, which builds a visual and textual shared memory to model the long-term visual-textual dependency and further guide visual attention on described visual targets to solve visual-textual alignments.
Cross-Modal Ranking with Soft Consistency and Noisy Labels for Robust RGB-T Tracking
To address this problem, this paper presents a novel approach to suppress background effects for RGB-T tracking.
Sparse Coding for Classification via Discrimination Ensemble
Discriminative sparse coding has emerged as a promising technique in image analysis and recognition, which couples the process of classifier training and the process of dictionary learning for improving the discriminability of sparse codes.
See the Forest for the Trees: Joint Spatial and Temporal Recurrent Neural Networks for Video-Based Person Re-Identification
Accordingly, a demanding need is to recognize a person under different cameras, which is called person re-identification.
Dynamic Texture Recognition via Orthogonal Tensor Dictionary Learning
In addition, based on the proposed dictionary learning method, a DT descriptor is developed, which has better adaptivity, discriminability and scalability than the existing approaches.
Conditional High-Order Boltzmann Machine: A Supervised Learning Model for Relation Learning
Relation learning is a fundamental operation in many computer vision tasks.
Improving Description-based Person Re-identification by Multi-granularity Image-text Alignments
Firstly, the global-global alignment in the Global Contrast (GC) module is for matching the global contexts of images and descriptions.
Ranked #19 on
Text based Person Retrieval
on CUHK-PEDES
Learning Compact Target-Oriented Feature Representations for Visual Tracking
Many state-of-the-art trackers usually resort to the pretrained convolutional neural network (CNN) model for correlation filtering, in which deep features could usually be redundant, noisy and less discriminative for some certain instances, and the tracking performance might thus be affected.
SBSGAN: Suppression of Inter-Domain Background Shift for Person Re-Identification
We observe that if backgrounds in the training and testing datasets are very different, it dramatically introduces difficulties to extract robust pedestrian features, and thus compromises the cross-domain person re-ID performance.
ACMM: Aligned Cross-Modal Memory for Few-Shot Image and Sentence Matching
Image and sentence matching has drawn much attention recently, but due to the lack of sufficient pairwise data for training, most previous methods still cannot well associate those challenging pairs of images and sentences containing rarely appeared regions and words, i. e., few-shot content.
Advances in Online Audio-Visual Meeting Transcription
This increases marginally to 1. 6% when 50% of the attendees are unknown to the system.
T-Drive: Driving Directions Based on Taxi Trajectories
GPS-equipped taxis can be regarded as mobile sensors probing traffic flows on road surfaces, and taxi drivers are usually experienced in finding the fastest (quickest) route to a destination based on their knowledge.
Large-scale Real-time Personalized Similar Product Recommendations
Similar product recommendation is one of the most common scenes in e-commerce.
Learning Goal-oriented Dialogue Policy with Opposite Agent Awareness
Most existing approaches for goal-oriented dialogue policy learning used reinforcement learning, which focuses on the target agent policy and simply treat the opposite agent policy as part of the environment.
L-Vector: Neural Label Embedding for Domain Adaptation
We propose a novel neural label embedding (NLE) scheme for the domain adaptation of a deep neural network (DNN) acoustic model with unpaired data samples from source and target domains.
Crowd, Lending, Machine, and Bias
We then use the machine to make investment decisions, and find that the machine benefits not only the lenders but also the borrowers.
Recurrent Deconvolutional Generative Adversarial Networks with Application to Text Guided Video Generation
The RDN is a deconvolutional version of conventional recurrent neural network, which can well model the long-range temporal dependency of generated video frames and make good use of conditional information.
Algorithmic Transparency with Strategic Users
We show that, in some cases, even the predictive power of machine learning algorithms may increase if the firm makes them transparent.
Towards Part-aware Monocular 3D Human Pose Estimation: An Architecture Search Approach
With roughly the same computational complexity as previous models, our approach achieves state-of-the-art results on both the single-person and multi-person 3D pose estimation benchmarks.
Prediction and Recovery for Adaptive Low-Resolution Person Re-Identification
In this paper, we propose a novel Prediction, Recovery and Identification (PRI) model for LR re-id, which adaptively recovers missing details by predicting a preferable scale factor based on the image content.
Actor and Action Modular Network for Text-based Video Segmentation
Previous methods fail to explicitly align the video content with the textual query in a fine-grained manner according to the actor and its action, due to the problem of \emph{semantic asymmetry}.
Ranked #9 on
Referring Expression Segmentation
on J-HMDB
Collaborative City Digital Twin For Covid-19 Pandemic: A Federated Learning Solution
In this work, we propose a collaborative city digital twin based on FL, a novel paradigm that allowing multiple city DT to share the local strategy and status in a timely manner.
Observation of Magnetic Droplets in Magnetic Tunnel Junctions
Magnetic droplets, a class of highly non-linear magnetodynamical solitons, can be nucleated and stabilized in nanocontact spin-torque nano-oscillators where they greatly increase the microwave output power.
Applied Physics
Efficient Human Pose Estimation by Learning Deeply Aggregated Representations
Instead, we focus on exploiting multi-scale information from layers with different receptive-field sizes and then making full of use this information by improving the fusion method.
FWB-Net:Front White Balance Network for Color Shift Correction in Single Image Dehazing via Atmospheric Light Estimation
However, for images taken in real-world, the illumination is not uniformly distributed over whole image which brings model mismatch and possibly results in color shift of the deep models using ASM.
FDAN: Flow-guided Deformable Alignment Network for Video Super-Resolution
Recently, deformable alignment has drawn extensive attention in VSR community for its remarkable performance, which can adaptively align neighboring frames with the reference one.
Hate Speech Detection in Clubhouse
This makes the hate speech detection challenging in new social media like Clubhouse.
Statistical Analysis of Perspective Scores on Hate Speech Detection
It has proven that, state-of-the-art hate speech classifiers are efficient only when tested on the data with the same feature distribution as training data.
Adaptive Dilated Convolution For Human Pose Estimation
It can generate and fuse multi-scale features of the same spatial sizes by setting different dilation rates for different channels.
Fully Non-Homogeneous Atmospheric Scattering Modeling with Convolutional Neural Networks for Single Image Dehazing
However, it is noted that ASM-based SIDM degrades its performance in dehazing real world hazy images due to the limited modelling ability of ASM where the atmospheric light factor (ALF) and the angular scattering coefficient (ASC) are assumed as constants for one image.
Pointing to Select: A Fast Pointer-LSTM for Long Text Classification
Recurrent neural networks (RNNs) suffer from well-known limitations and complications which include slow inference and vanishing gradients when processing long sequences in text classification.
Clothing Status Awareness for Long-Term Person Re-Identification
This work argues that these approaches in fact are not aware of clothing status (i. e., change or no-change) of a pedestrian.
AI in Human-computer Gaming: Techniques, Challenges and Opportunities
Through this survey, we 1) compare the main difficulties among different kinds of games and the corresponding techniques utilized for achieving professional human level AIs; 2) summarize the mainstream frameworks and techniques that can be properly relied on for developing AIs for complex human-computer gaming; 3) raise the challenges or drawbacks of current techniques in the successful AIs; and 4) try to point out future trends in human-computer gaming AIs.
Uncovering the Source of Machine Bias
We find two types of biases in gender, preference-based bias and belief-based bias, are present in human evaluators' decisions.
Generalizable Person Re-Identification via Self-Supervised Batch Norm Test-Time Adaption
In this paper, we investigate the generalization problem of person re-identification (re-id), whose major challenge is the distribution shift on an unseen domain.
Generative Compression for Face Video: A Hybrid Scheme
As the latest video coding standard, versatile video coding (VVC) has shown its ability in retaining pixel quality.
A Closer Look at Personalization in Federated Image Classification
This paper shows that it is possible to achieve flexible personalization after the convergence of the global model by introducing representation learning.
Intra Encoding Complexity Control with a Time-Cost Model for Versatile Video Coding
Inspired by rate control algorithms, we propose a scheme to precisely control the intra encoding complexity of VVC.
Tackling Data Heterogeneity: A New Unified Framework for Decentralized SGD with Sample-induced Topology
We develop a general framework unifying several gradient-based stochastic optimization methods for empirical risk minimization problems both in centralized and distributed scenarios.
Neural-FacTOR: Neural Representation Learning for Website Fingerprinting Attack over TOR Anonymity
TOR (The Onion Router) network is a widely used open source anonymous communication tool, the abuse of TOR makes it difficult to monitor the proliferation of online crimes such as to access criminal websites.
CNTN: Cyclic Noise-tolerant Network for Gait Recognition
Gait recognition aims to identify individuals by recognizing their walking patterns.
Generalized Inter-class Loss for Gait Recognition
The proposed method can be generalized to different gait recognition networks and achieves significant improvements.
CFNet: Conditional Filter Learning with Dynamic Noise Estimation for Real Image Denoising
A mainstream type of the state of the arts (SOTAs) based on convolutional neural network (CNN) for real image denoising contains two sub-problems, i. e., noise estimation and non-blind denoising.
Few-shot Detection of Anomalies in Industrial Cyber-Physical System via Prototypical Network and Contrastive Learning
The rapid development of Industry 4. 0 has amplified the scope and destructiveness of industrial Cyber-Physical System (CPS) by network attacks.
PlanarTrack: A Large-scale Challenging Benchmark for Planar Object Tracking
In addition, we further derive a variant named PlanarTrack$_{\mathbf{BB}}$ for generic object tracking from PlanarTrack.
Inclusive FinTech Lending via Contrastive Learning and Domain Adaptation
We use contrastive learning to train our feature extractor on unapproved (unlabeled) loan applications and use domain adaptation to generalize the performance of our label predictor.
Clothing-Change Feature Augmentation for Person Re-Identification
Specifically, to formulate meaningful clothing variations in the feature space, our method first estimates a clothing-change normal distribution with intra-ID cross-clothing variances.
A Unified Framework to Super-Resolve Face Images of Varied Low Resolutions
By contrast, we explore in this work a unified framework that is trained once and then used to super-resolve input face images of varied low resolutions.
On the Computation-Communication Trade-Off with A Flexible Gradient Tracking Approach
We propose a flexible gradient tracking approach with adjustable computation and communication steps for solving distributed stochastic optimization problem over networks.
A 3D grain-based reconstruction method from a 2D surface image for the Distinct Lattice Spring Model
The 3D GBM reconstruction is generated by a simulated annealing algorithm, with the Monte Carlo algorithm extending the calculation of the two-point probability function as the target function to the random particle model.
Gender-tuning: Empowering Fine-tuning for Debiasing Pre-trained Language Models
Recent studies have revealed that the widely-used Pre-trained Language Models (PLMs) propagate societal biases from the large unmoderated pre-training corpora.
Improving the Reusability of Pre-trained Language Models in Real-world Applications
The reusability of state-of-the-art Pre-trained Language Models (PLMs) is often limited by their generalization problem, where their performance drastically decreases when evaluated on examples that differ from the training dataset, known as Out-of-Distribution (OOD)/unseen examples.
Robust Fully-Asynchronous Methods for Distributed Training over General Architecture
Perfect synchronization in distributed machine learning problems is inefficient and even impossible due to the existence of latency, package losses and stragglers.
Trajectory Generation and Tracking based on Energy Minimization for a Four-Link Brachiation Robot
Aiming to mimic the brachiation locomotion of primates, we establish a brachiation robot model capable of swinging between different bars.
Free Lunch for Gait Recognition: A Novel Relation Descriptor
Gait recognition is to seek correct matches for query individuals by their unique walking patterns.
Neural Network-Based Histologic Remission Prediction In Ulcerative Colitis
A total of 5105 images of 154 intestinal segments from 87 patients undergoing EC treatment at a center in China between March 2022 and March 2023 are scored according to the Geboes score.
MTS-DVGAN: Anomaly Detection in Cyber-Physical Systems using a Dual Variational Generative Adversarial Network
Then, by exploiting the distribution property and modeling the normal patterns of multivariate time series, a variational autoencoder is introduced to force the generative adversarial network (GAN) to generate diverse samples.
Joint Design of ISAC Waveform under PAPR Constraints
In this paper, we formulate the precoding problem of integrated sensing and communication (ISAC) waveform as a non-convex quadratically constrainted quadratic program (QCQP), in which the weighted sum of communication multi-user interference (MUI) and the gap between dual-use waveform and ideal radar waveform is minimized with peak-to-average power ratio (PAPR) constraints.
TDeLTA: A Light-weight and Robust Table Detection Method based on Learning Text Arrangement
The diversity of tables makes table detection a great challenge, leading to existing models becoming more tedious and complex.
Unsupervised Spatio-Temporal State Estimation for Fine-grained Adaptive Anomaly Diagnosis of Industrial Cyber-physical Systems
Subsequently, based on these two types of state matrices, a three-branch structure of series-temporal-spatial attention module is designed to simultaneously capture the series, temporal, and space dependencies among MTS.
HCL-MTSAD: Hierarchical Contrastive Consistency Learning for Accurate Detection of Industrial Multivariate Time Series Anomalies
To address this issue, we propose a novel self-supervised hierarchical contrastive consistency learning method for detecting anomalies in MTS, named HCL-MTSAD.
