Search Results for author:
Found 75 papers, 29 papers with code
Multi-Turn Target-Guided Topic Prediction with Monte Carlo Tree Search
In this paper, we formulate the target-guided conversation as a problem of multi-turn topic prediction and model it under the framework of Markov decision process (MDP).
Give the Truth: Incorporate Semantic Slot into Abstractive Dialogue Summarization
Abstractive dialogue summarization suffers from a lots of factual errors, which are due to scattered salient elements in the multi-speaker information interaction process.
Semantic Gaussians: Open-Vocabulary Scene Understanding with 3D Gaussian Splatting
Open-vocabulary 3D scene understanding presents a significant challenge in computer vision, withwide-ranging applications in embodied agents and augmented reality systems.
Benchmarking Segmentation Models with Mask-Preserved Attribute Editing
We evaluate a broad variety of semantic segmentation models, spanning from conventional close-set models to recent open-vocabulary large models on their robustness to different types of variations.
Vision-language Assisted Attribute Learning
Attribute labeling at large scale is typically incomplete and partial, posing significant challenges to model optimization.
HumanRecon: Neural Reconstruction of Dynamic Human Using Geometric Cues and Physical Priors
These priors ensure the color rendered along rays to be robust to view direction and reduce the inherent ambiguities of density estimated along rays.
MIR2: Towards Provably Robust Multi-Agent Reinforcement Learning by Mutual Information Regularization
Existing max-min optimization techniques in robust MARL seek to enhance resilience by training agents against worst-case adversaries, but this becomes intractable as the number of agents grows, leading to exponentially increasing worst-case scenarios.
Enhancing Cell Proliferation and Migration by MIR-Carbonyl Vibrational Coupling: Insights from Transcriptome Profiling
Cell proliferation and migration highly relate to normal tissue self-healing, therefore it is highly significant for artificial controlling.
Isolation and Induction: Training Robust Deep Neural Networks against Model Stealing Attacks
However, these defenses are now suffering problems of high inference computational overheads and unfavorable trade-offs between benign accuracy and stealing robustness, which challenges the feasibility of deployed models in practice.
Regulating the Hydrophobic Domain in Peptide-Catecholamine Coassembled Nanostructures for Fluorescence Enhancement
Hydrophobic domains provide specific microenvironment for essential functional activities in life.
SiCL: Silhouette-Driven Contrastive Learning for Unsupervised Person Re-Identification with Clothes Change
In this paper, we address a highly challenging yet critical task: unsupervised long-term person re-identification with clothes change.
Ranked #1 on
Unsupervised Person Re-Identification
on PRCC
Clothes Changing Person Re-Identification
Contrastive Learning
+3
X-Adv: Physical Adversarial Object Attacks against X-ray Prohibited Item Detection
In this paper, we take the first step toward the study of adversarial attacks targeted at X-ray prohibited item detection, and reveal the serious threats posed by such attacks in this safety-critical scenario.
Attacking Cooperative Multi-Agent Reinforcement Learning by Adversarial Minority Influence
To achieve maximum deviation in victim policies under complex agent-wise interactions, our unilateral attack aims to characterize and maximize the impact of the adversary on the victims.
Bi-directional Feature Reconstruction Network for Fine-Grained Few-Shot Image Classification
Conventional few-shot learning methods however cannot be naively adopted for this fine-grained setting -- a quick pilot study reveals that they in fact push for the opposite (i. e., lower inter-class variations and higher intra-class variations).
Learning Invariant Visual Representations for Compositional Zero-Shot Learning
Compositional Zero-Shot Learning (CZSL) aims to recognize novel compositions using knowledge learned from seen attribute-object compositions in the training set.
A Survey on Long-Tailed Visual Recognition
Data quality directly dominates the effect of deep learning models, and the long-tailed distribution is one of the factors affecting data quality.
Towards Comprehensive Testing on the Robustness of Cooperative Multi-agent Reinforcement Learning
To overcome the challenge, we propose MARLSafe, the first robustness testing framework for c-MARL algorithms.
Unsupervised Long-Term Person Re-Identification with Clothes Change
We investigate unsupervised person re-identification (Re-ID) with clothes change, a new challenging problem with more practical usability and scalability to real-world deployment.
Clustering
Unsupervised Long Term Person Re-Identification
+2
Clue Me In: Semi-Supervised FGVC with Out-of-Distribution Data
Despite great strides made on fine-grained visual classification (FGVC), current methods are still heavily reliant on fully-supervised paradigms where ample expert labels are called for.
TODSum: Task-Oriented Dialogue Summarization with State Tracking
Previous dialogue summarization datasets mainly focus on open-domain chitchat dialogues, while summarization datasets for the broadly used task-oriented dialogue haven't been explored yet.
Cluster-guided Asymmetric Contrastive Learning for Unsupervised Person Re-Identification
To be specific, we propose a novel cluster-level contrastive loss to help the siamese network effectively mine the invariance in feature learning with respect to the cluster structure within and between different data augmentation views, respectively.
A Joint Model for Dropped Pronoun Recovery and Conversational Discourse Parsing in Chinese Conversational Speech
A second (multi-relational) GCN is then applied to the utterance states to produce a discourse relation-augmented representation for the utterances, which are then fused together with token states in each utterance as input to a dropped pronoun recovery layer.
Ranked #5 on
Discourse Parsing
on STAC
DF^2AM: Dual-level Feature Fusion and Affinity Modeling for RGB-Infrared Cross-modality Person Re-identification
Meanwhile, to further mining the relationships between global features from person images, we propose an Affinities Modeling (AM) module to obtain the optimal intra- and inter-modality image matching.
Cross-Modality Person Re-identification
Person Re-Identification
Unsupervised Person Re-identification via Simultaneous Clustering and Consistency Learning
Unsupervised person re-identification (re-ID) has become an important topic due to its potential to resolve the scalability problem of supervised re-ID models.
A Comprehensive Evaluation Framework for Deep Model Robustness
To mitigate this problem, we establish a model robustness evaluation framework containing 23 comprehensive and rigorous metrics, which consider two key perspectives of adversarial learning (i. e., data and model).
Progressive Co-Attention Network for Fine-grained Visual Classification
Fine-grained visual classification aims to recognize images belonging to multiple sub-categories within a same category.
Ranked #32 on
Fine-Grained Image Classification
on FGVC Aircraft
Improving Abstractive Dialogue Summarization with Conversational Structure and Factual Knowledge
In this paper, we present a Knowledge Graph Enhanced Dual-Copy network (KGEDC), a novel framework for abstractive dialogue summarization with conversational structure and factual knowledge.
Joint Topology-Preserving and Feature-Refinement Network for Curvilinear Structure Segmentation
To the best of our knowledge, this is the first work that jointly addresses topology preserving and feature refinement for CSS.
Knowledge Transfer Based Fine-grained Visual Classification
Convolution neural networks (CNNs), which employ the cross entropy loss (CE-loss) as the loss function, show poor performance since the model can only learn the most discriminative part and ignore other meaningful regions.
Ranked #36 on
Fine-Grained Image Classification
on CUB-200-2011
Improving Abstractive Dialogue Summarization with Graph Structures and Topic Words
A masked graph self-attention mechanism is used to integrate cross-sentence information flows and focus more on the related utterances, which makes it better to understand the dialogue.
Your "Flamingo" is My "Bird": Fine-Grained, or Not
For that, we re-envisage the traditional setting of FGVC, from single-label classification, to that of top-down traversal of a pre-defined coarse-to-fine label hierarchy -- so that our answer becomes "bird"-->"Phoenicopteriformes"-->"Phoenicopteridae"-->"flamingo".
Ranked #16 on
Fine-Grained Image Classification
on FGVC Aircraft
DS-UI: Dual-Supervised Mixture of Gaussian Mixture Models for Uncertainty Inference
In the DS-UI, we combine the classifier of a DNN, i. e., the last fully-connected (FC) layer, with a mixture of Gaussian mixture models (MoGMM) to obtain an MoGMM-FC layer.
Advanced Dropout: A Model-free Methodology for Bayesian Dropout Optimization
Due to lack of data, overfitting ubiquitously exists in real-world applications of deep neural networks (DNNs).
Transformer-GCRF: Recovering Chinese Dropped Pronouns with General Conditional Random Fields
Exploratory analysis also demonstrates that the GCRF did help to capture the dependencies between pronouns in neighboring utterances, thus contributes to the performance improvements.
SSKD: Self-Supervised Knowledge Distillation for Cross Domain Adaptive Person Re-Identification
To this end, we propose a Self-Supervised Knowledge Distillation (SSKD) technique containing two modules, the identity learning and the soft label learning.
ReMarNet: Conjoint Relation and Margin Learning for Small-Sample Image Classification
Despite achieving state-of-the-art performance, deep learning methods generally require a large amount of labeled data during training and may suffer from overfitting when the sample size is small.
Attention-guided Context Feature Pyramid Network for Object Detection
For object detection, how to address the contradictory requirement between feature map resolution and receptive field on high-resolution inputs still remains an open question.
Ranked #61 on
Object Detection
on COCO test-dev
OSLNet: Deep Small-Sample Classification with an Orthogonal Softmax Layer
A deep neural network of multiple nonlinear layers forms a large function space, which can easily lead to overfitting when it encounters small-sample data.
GPCA: A Probabilistic Framework for Gaussian Process Embedded Channel Attention
In this paper, we propose Gaussian process embedded channel attention (GPCA) module and further interpret the channel attention schemes in a probabilistic way.
Dual-attention Guided Dropblock Module for Weakly Supervised Object Localization
This module contains two key components, the channel attention guided dropout (CAGD) and the spatial attention guided dropblock (SAGD).
Fine-Grained Visual Classification via Progressive Multi-Granularity Training of Jigsaw Patches
In this work, we propose a novel framework for fine-grained visual classification to tackle these problems.
Ranked #17 on
Fine-Grained Image Classification
on Stanford Cars
Mind the Gap: Enlarging the Domain Gap in Open Set Domain Adaptation
The key insight lies with how we exploit the mutually beneficial information between two networks; (a) to separate samples of known and unknown classes, (b) to maximize the domain confusion between source and target domain without the influence of unknown samples.
A Video Analysis Method on Wanfang Dataset via Deep Neural Network
To solve this problem, in this paper, we describe the new function for real-time multi-object detection in sports competition and pedestrians flow detection in public based on deep learning.
Fine-Grained Instance-Level Sketch-Based Video Retrieval
Existing sketch-analysis work studies sketches depicting static objects or scenes.
The Devil is in the Channels: Mutual-Channel Loss for Fine-Grained Image Classification
The proposed loss function, termed as mutual-channel loss (MC-Loss), consists of two channel-specific components: a discriminality component and a diversity component.
Ranked #29 on
Fine-Grained Image Classification
on FGVC Aircraft
Convolutional Subspace Clustering Network with Block Diagonal Prior
Standard methods of subspace clustering are based on self-expressiveness in the original data space, which states that a data point in a subspace can be expressed as a linear combination of other points.
Scalable NAS with Factorizable Architectural Parameters
Neural Architecture Search (NAS) is an emerging topic in machine learning and computer vision.
Wasserstein Collaborative Filtering for Item Cold-start Recommendation
We further adopt the idea of CF and propose Wasserstein CF (WCF) to improve the recommendation performance on cold-start items.
PMD: An Optimal Transportation-based User Distance for Recommender Systems
Collaborative filtering, a widely-used recommendation technique, predicts a user's preference by aggregating the ratings from similar users.
Finding Salient Context based on Semantic Matching for Relevance Ranking
In this paper, we propose a salient-context based semantic matching method to improve relevance ranking in information retrieval.
Recovering Dropped Pronouns in Chinese Conversations via Modeling Their Referents
Pronouns are often dropped in Chinese sentences, and this happens more frequently in conversational genres as their referents can be easily understood from context.
Self-Supervised Convolutional Subspace Clustering Network
However, the applicability of subspace clustering has been limited because practical visual data in raw form do not necessarily lie in such linear subspaces.
Ranked #2 on
Image Clustering
on Extended Yale-B
Pre-training of Context-aware Item Representation for Next Basket Recommendation
In the offline phase, IERT pre-trains deep item representations conditioning on their transaction contexts.
On the Convergence of Extended Variational Inference for Non-Gaussian Statistical Models
Recently, an improved framework, namely the extended variational inference (EVI), has been introduced and applied to derive analytically tractable solution by employing lower-bound approximation to the variational objective function.
Using Quantization to Deploy Heterogeneous Nodes in Two-Tier Wireless Sensor Networks
We study a heterogeneous two-tier wireless sensor network in which N heterogeneous access points (APs) collect sensing data from densely distributed sensors and then forward the data to M heterogeneous fusion centers (FCs).
Information Theory Information Theory
Impacts of Weather Conditions on District Heat System
Using artificial neural network for the prediction of heat demand has attracted more and more attention.
SEA: A Combined Model for Heat Demand Prediction
ENN and ARIMA are used to predict seasonal and trend components, respectively.
BALSON: Bayesian Least Squares Optimization with Nonnegative L1-Norm Constraint
In order to explicitly characterize the nonnegative L1-norm constraint of the parameters, we further approximate the true posterior distribution by a Dirichlet distribution.
Constrained Sparse Subspace Clustering with Side-Information
Subspace clustering refers to the problem of segmenting high dimensional data drawn from a union of subspaces into the respective subspaces.
Density-Adaptive Kernel based Efficient Reranking Approaches for Person Reidentification
In this paper, we propose to exploit a density-adaptive smooth kernel technique to achieve efficient and effective reranking.
SketchMate: Deep Hashing for Million-Scale Human Sketch Retrieval
Key to our network design is the embedding of unique characteristics of human sketch, where (i) a two-branch CNN-RNN architecture is adapted to explore the temporal ordering of strokes, and (ii) a novel hashing loss is specifically designed to accommodate both the temporal and abstract traits of sketches.
Robust Actor-Critic Contextual Bandit for Mobile Health (mHealth) Interventions
Extensive experiment results on two datasets demonstrate that our method can achieve almost identical results compared with state-of-the-art contextual bandit methods on the dataset without outliers, and significantly outperform those state-of-the-art methods on the badly noised dataset with outliers in a variety of parameter settings.
Group-driven Reinforcement Learning for Personalized mHealth Intervention
Due to the popularity of smartphones and wearable devices nowadays, mobile health (mHealth) technologies are promising to bring positive and wide impacts on people's health.
Decorrelation of Neutral Vector Variables: Theory and Applications
In this paper, we propose novel strategies for neutral vector variable decorrelation.
Cross-modal Subspace Learning for Fine-grained Sketch-based Image Retrieval
Sketch-based image retrieval (SBIR) is challenging due to the inherent domain-gap between sketch and photo.
Ranked #5 on
Sketch-Based Image Retrieval
on Chairs
DNN Filter Bank Cepstral Coefficients for Spoofing Detection
In order to improve the reliability of speaker verification systems, we develop a new filter bank based cepstral feature, deep neural network filter bank cepstral coefficients (DNN-FBCC), to distinguish between natural and spoofed speech.
One-to-Many Network for Visually Pleasing Compression Artifacts Reduction
We consider the compression artifacts reduction problem, where a compressed image is transformed into an artifact-free image.
Learning Semi-Supervised Representation Towards a Unified Optimization Framework for Semi-Supervised Learning
State of the art approaches for Semi-Supervised Learning (SSL) usually follow a two-stage framework -- constructing an affinity matrix from the data and then propagating the partial labels on this affinity matrix to infer those unknown labels.
Making Better Use of Edges via Perceptual Grouping
We propose a perceptual grouping framework that organizes image edges into meaningful structures and demonstrate its usefulness on various computer vision tasks.
HEp-2 Cell Classification via Fusing Texture and Shape Information
Indirect Immunofluorescence (IIF) HEp-2 cell image is an effective evidence for diagnosis of autoimmune diseases.
Linear Ranking Analysis
We extend the classical linear discriminant analysis (LDA) technique to linear ranking analysis (LRA), by considering the ranking order of classes centroids on the projected subspace.
In Defense of Sparsity Based Face Recognition
The success of sparse representation based classification (SRC) has largely boosted the research of sparsity based face recognition in recent years.
