Search Results for author:
Found 76 papers, 20 papers with code
Mining Inter-Video Proposal Relations for Video Object Detection
Recent studies have shown that, context aggregating information from proposals in different frames can clearly enhance the performance of video object detection.
Ranked #1 on
Video Object Detection
on ImageNet VID
Knowledge Distillation via the Target-aware Transformer
To this end, we propose a novel one-to-all spatial matching knowledge distillation approach.
Towards Explanation for Unsupervised Graph-Level Representation Learning
Due to the superior performance of Graph Neural Networks (GNNs) in various domains, there is an increasing interest in the GNN explanation problem "\emph{which fraction of the input graph is the most crucial to decide the model's decision?}"
PRE-NAS: Predictor-assisted Evolutionary Neural Architecture Search
This often requires a high computational overhead to evaluate a number of candidate networks from the set of all possible networks in the search space during the search.
Dual-AI: Dual-path Actor Interaction Learning for Group Activity Recognition
Learning spatial-temporal relation among multiple actors is crucial for group activity recognition.
Automated Progressive Learning for Efficient Training of Vision Transformers
First, we develop a strong manual baseline for progressive learning of ViTs, by introducing momentum growth (MoGrow) to bridge the gap brought by model growth.
CGUA: Context-Guided and Unpaired-Assisted Weakly Supervised Person Search
On the other hand, there are numerous unpaired persons in real-world scene images.
Beyond Fixation: Dynamic Window Visual Transformer
Recently, a surge of interest in visual transformers is to reduce the computational cost by limiting the calculation of self-attention to a local window.
Exploring Inter-Channel Correlation for Diversity-preserved KnowledgeDistillation
Extensive experiments on two vision tasks, includ-ing ImageNet classification and Pascal VOC segmentation, demonstrate the superiority of our ICKD, which consis-tently outperforms many existing methods, advancing thestate-of-the-art in the fields of Knowledge Distillation.
Balancing Generalization and Specialization in Zero-shot Learning
In this paper, we propose an end-to-end network with balanced generalization and specialization abilities, termed as BGSNet, to take advantage of both abilities, and balance them at instance- and dataset-level.
BaLeNAS: Differentiable Architecture Search via the Bayesian Learning Rule
Differentiable Architecture Search (DARTS) has received massive attention in recent years, mainly because it significantly reduces the computational cost through weight sharing and continuous relaxation.
An Entropy-guided Reinforced Partial Convolutional Network for Zero-Shot Learning
Zero-Shot Learning (ZSL) aims to transfer learned knowledge from observed classes to unseen classes via semantic correlations.
Signature-Graph Networks
SGN constructs unique undirected graphs for each image based on the CNN feature maps.
Dynamic Slimmable Denoising Network
Here, we present dynamic slimmable denoising network (DDS-Net), a general method to achieve good denoising quality with less computational complexity, via dynamically adjusting the channel configurations of networks at test time with respect to different noisy images.
Active Learning for Deep Visual Tracking
In this paper, we propose an active learning method for deep visual tracking, which selects and annotates the unlabeled samples to train the deep CNNs model.
Reliable Shot Identification for Complex Event Detection via Visual-Semantic Embedding
In this paper, we decompose the video into several segments and intuitively model the task of complex event detection as a multiple instance learning problem by representing each video as a "bag" of segments in which each segment is referred to as an instance.
DS-Net++: Dynamic Weight Slicing for Efficient Inference in CNNs and Transformers
Dynamic networks have shown their promising capability in reducing theoretical computation complexity by adapting their architectures to the input during inference.
Semantics-Guided Contrastive Network for Zero-Shot Object detection
To address these issues, we develop a novel Semantics-Guided Contrastive Network for ZSD, named ContrastZSD, a detection framework that first brings contrastive learning mechanism into the realm of zero-shot detection.
Unsupervised Person Re-Identification: A Systematic Survey of Challenges and Solutions
Therefore, comprehensive surveys on this topic are essential to summarise challenges and solutions to foster future research.
FFA-IR: Towards an Explainable and Reliable Medical Report Generation Benchmark
Researchers have explored advanced methods from computer vision and natural language processing to incorporate medical domain knowledge for the generation of readable medical reports.
KGAP: Knowledge Graph Augmented Political Perspective Detection in News Media
Specifically, we construct a political knowledge graph to serve as domain-specific external knowledge.
Legislator Representation Learning with Social Context and Expert Knowledge
Modeling the ideological perspectives of political actors is an essential task in computational political science with applications in many downstream tasks.
Deep Learning for Embodied Vision Navigation: A Survey
A navigation agent is supposed to have various intelligent skills, such as visual perceiving, mapping, planning, exploring and reasoning, etc.
Differentiable Architecture Search Meets Network Pruning at Initialization: A More Reliable, Efficient, and Flexible Framework
Although Differentiable ARchiTecture Search (DARTS) has become the mainstream paradigm in Neural Architecture Search (NAS) due to its simplicity and efficiency, more recent works found that the performance of the searched architecture barely increases with the optimization proceeding in DARTS, and the final magnitudes obtained by DARTS could hardly indicate the importance of operations.
iDARTS: Differentiable Architecture Search with Stochastic Implicit Gradients
A key challenge to the scalability and quality of the learned architectures is the need for differentiating through the inner-loop optimisation.
Vision-Language Navigation with Random Environmental Mixup
Then, we cross-connect the key views of different scenes to construct augmented scenes.
Ranked #26 on
Vision and Language Navigation
on VLN Challenge
Person Search Challenges and Solutions: A Survey
This paper surveyed the recent works on image-based and text-based person search from the perspective of challenges and solutions.
Attribute-Modulated Generative Meta Learning for Zero-Shot Classification
The promising strategies for ZSL are to synthesize visual features of unseen classes conditioned on semantic side information and to incorporate meta-learning to eliminate the model's inherent bias towards seen classes.
SOON: Scenario Oriented Object Navigation with Graph-based Exploration
In this task, an agent is required to navigate from an arbitrary position in a 3D embodied environment to localize a target following a scene description.
Dynamic Slimmable Network
Here, we explore a dynamic network slimming regime, named Dynamic Slimmable Network (DS-Net), which aims to achieve good hardware-efficiency via dynamically adjusting filter numbers of networks at test time with respect to different inputs, while keeping filters stored statically and contiguously in hardware to prevent the extra burden.
BossNAS: Exploring Hybrid CNN-transformers with Block-wisely Self-supervised Neural Architecture Search
In this work, we present Block-wisely Self-supervised Neural Architecture Search (BossNAS), an unsupervised NAS method that addresses the problem of inaccurate architecture rating caused by large weight-sharing space and biased supervision in previous methods.
A Comprehensive Survey of Scene Graphs: Generation and Application
For example, given an image, we want to not only detect and recognize objects in the image, but also know the relationship between objects (visual relationship detection), and generate a text description (image captioning) based on the image content.
NAS-TC: Neural Architecture Search on Temporal Convolutions for Complex Action Recognition
Accordingly, because of the automated design of its network structure, Neural architecture search (NAS) has achieved great success in the image processing field and attracted substantial research attention in recent years.
UPDeT: Universal Multi-agent Reinforcement Learning via Policy Decoupling with Transformers
Recent advances in multi-agent reinforcement learning have been largely limited in training one model from scratch for every new task.
EXPLORING VULNERABILITIES OF BERT-BASED APIS
We then demonstrate how the extracted model can be exploited to develop effective attribute inference attack to expose sensitive information of the training data.
UPDeT: Universal Multi-agent RL via Policy Decoupling with Transformers
Recent advances in multi-agent reinforcement learning have been largely limited in training one model from scratch for every new task.
Exploring Inter-Channel Correlation for Diversity-Preserved Knowledge Distillation
Extensive experiments on two vision tasks, including ImageNet classification and Pascal VOC segmentation, demonstrate the superiority of our ICKD, which consistently outperforms many existing methods, advancing the state-of-the-art in the fields of Knowledge Distillation.
Ranked #6 on
Knowledge Distillation
on ImageNet
Differentiable Neural Architecture Search in Equivalent Space with Exploration Enhancement
A probabilistic exploration enhancement method is accordingly devised to encourage intelligent exploration during the architecture search in the latent space, to avoid local optimal in architecture search.
Hierarchical Neural Architecture Search for Deep Stereo Matching
To reduce the human efforts in neural network design, Neural Architecture Search (NAS) has been applied with remarkable success to various high-level vision tasks such as classification and semantic segmentation.
Ranked #2 on
Stereo Disparity Estimation
on Scene Flow
Self-Weighted Robust LDA for Multiclass Classification with Edge Classes
In this regard, a novel self-weighted robust LDA with l21-norm based pairwise between-class distance criterion, called SWRLDA, is proposed for multi-class classification especially with edge classes.
Accurate Bounding-box Regression with Distance-IoU Loss for Visual Tracking
Moreover, we introduce a classification part that is trained online and optimized with a Conjugate-Gradient-based strategy to guarantee real-time tracking speed.
Multi-view Drone-based Geo-localization via Style and Spatial Alignment
In this paper, we focus on the task of multi-view multi-source geo-localization, which serves as an important auxiliary method of GPS positioning by matching drone-view image and satellite-view image with pre-annotated GPS tag.
Melanoma Diagnosis with Spatio-Temporal Feature Learning on Sequential Dermoscopic Images
Existing studies for automated melanoma diagnosis are based on single-time point images of lesions.
Auxiliary Signal-Guided Knowledge Encoder-Decoder for Medical Report Generation
Firstly, the regions of primary interest to radiologists are usually located in a small area of the global image, meaning that the remainder parts of the image could be considered as irrelevant noise in the training procedure.
A Comprehensive Survey of Neural Architecture Search: Challenges and Solutions
Neural Architecture Search (NAS) is just such a revolutionary algorithm, and the related research work is complicated and rich.
Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks
Modeling multivariate time series has long been a subject that has attracted researchers from a diverse range of fields including economics, finance, and traffic.
Ranked #2 on
Univariate Time Series Forecasting
on Electricity
Unsupervised Multimodal Neural Machine Translation with Pseudo Visual Pivoting
In this paper, we investigate how to utilize visual content for disambiguation and promoting latent space alignment in unsupervised MMT.
Vision-Dialog Navigation by Exploring Cross-modal Memory
Benefiting from the collaborative learning of the L-mem and the V-mem, our CMN is able to explore the memory about the decision making of historical navigation actions which is for the current step.
ZSTAD: Zero-Shot Temporal Activity Detection
An integral part of video analysis and surveillance is temporal activity detection, which means to simultaneously recognize and localize activities in long untrimmed videos.
Unity Style Transfer for Person Re-Identification
To solve this problem, we propose a UnityStyle adaption method, which can smooth the style disparities within the same camera and across different cameras.
Argus: Efficient Activity Detection System for Extended Video Analysis
We propose an Efficient Activity Detection System, Argus, for Extended Video Analysis in the surveillance scenario.
Blockwisely Supervised Neural Architecture Search with Knowledge Distillation
Moreover, we find that the knowledge of a network model lies not only in the network parameters but also in the network architecture.
Ranked #1 on
Neural Architecture Search
on CIFAR-100
(Top-1 Error Rate metric)
Vision-Language Navigation with Self-Supervised Auxiliary Reasoning Tasks
In this paper, we introduce Auxiliary Reasoning Navigation (AuxRN), a framework with four self-supervised auxiliary reasoning tasks to take advantage of the additional training signals derived from the semantic information.
Ranked #7 on
Vision and Language Navigation
on VLN Challenge
Multi-Head Attention with Diversity for Learning Grounded Multilingual Multimodal Representations
With the aim of promoting and understanding the multilingual version of image search, we leverage visual object detection and propose a model with diverse multi-head attention to learn grounded multilingual multimodal representations.
Continual Reinforcement Learning with Diversity Exploration and Adversarial Self-Correction
We first develop an unsupervised diversity exploration method to learn task-specific skills using an unsupervised objective.
Ensemble Teaching for Hybrid Label Propagation
More importantly, HyDEnT conducts propagation under the guidance of an ensemble of teachers.
MMALFM: Explainable Recommendation by Leveraging Reviews and Images
Then the aspect importance is integrated into a novel aspect-aware latent factor model (ALFM), which learns user's and item's latent factors based on ratings.
Distributionally Robust Semi-Supervised Learning for People-Centric Sensing
Semi-supervised learning is crucial for alleviating labelling burdens in people-centric sensing.
RCAA: Relational Context-Aware Agents for Person Search
In this paper, we address this problem by training relational context-aware agents which learn the actions to localize the target person from the gallery of whole scene images.
Multi-shot Person Re-identification through Set Distance with Visual Distributional Representation
In this work, we propose the idea of visual distributional representation, which interprets an image set as samples drawn from an unknown distribution in appearance feature space.
Reinforcement Cutting-Agent Learning for Video Object Segmentation
In this paper, we formulate this problem as a Markov Decision Process, where agents are learned to segment object regions under a deep reinforcement learning framework.
Complex Event Detection by Identifying Reliable Shots From Untrimmed Videos
relevant) to the given event class, we formulate this task as a multi-instance learning (MIL) problem by taking each video as a bag and the video shots in each video as instances.
Simple to Complex Cross-modal Learning to Rank
The heterogeneity-gap between different modalities brings a significant challenge to multimedia information retrieval.
Uncovering Locally Discriminative Structure for Feature Analysis
We propose a method that utilizes both the manifold structure of data and local discriminant information.
Strategies for Searching Video Content with Text Queries or Video Examples
The large number of user-generated videos uploaded on to the Internet everyday has led to many commercial video search engines, which mainly rely on text metadata for search.
They Are Not Equally Reliable: Semantic Event Search Using Differentiated Concept Classifiers
Complex event detection on unconstrained Internet videos has seen much progress in recent years.
Dynamic Concept Composition for Zero-Example Event Detection
In this paper, we focus on automatically detecting events in unconstrained videos without the use of any visual training exemplars.
Unsupervised Feature Analysis with Class Margin Optimization
In this paper, we propose an unsupervised feature selection method seeking a feature coefficient matrix to select the most distinctive features.
Compound Rank-k Projections for Bilinear Analysis
In many real-world applications, data are represented by matrices or high-order tensors.
Improved Spectral Clustering via Embedded Label Propagation
Our algorithm is built upon two advancements of the state of the art:1) label propagation, which propagates a node\'s labels to neighboring nodes according to their proximity; and 2) manifold learning, which has been widely used in its capacity to leverage the manifold structure of data points.
A Convex Formulation for Spectral Shrunk Clustering
Thus, applying manifold information obtained from the original space to the clustering process in a low-dimensional subspace is prone to inferior performance.
Balanced k-Means and Min-Cut Clustering
Clustering is an effective technique in data mining to generate groups that are the matter of interest.
A Convex Sparse PCA for Feature Analysis
In addition, based on the sparse model used in CSPCA, an optimal weight is assigned to each of the original feature, which in turn provides the output with good interpretability.
Semi-supervised Feature Analysis by Mining Correlations among Multiple Tasks
In this paper, we propose a novel semi-supervised feature selection framework by mining correlations among multiple tasks and apply it to different multimedia applications.
