Search Results for author:
Found 108 papers, 57 papers with code
Interpretable Visual Reasoning via Probabilistic Formulation under Natural Supervision
In this paper, we rethink implicit reasoning process in VQA, and propose a new formulation which maximizes the log-likelihood of joint distribution for the observed question and predicted answer.
Weakly-Supervised Crowd Counting Learns from Sorting rather than Locations
We propose a soft-label sorting network along with the counting network, which sorts the given images by their crowd numbers.
Neighborhood Contrastive Transformer for Change Captioning
Change captioning is to describe the semantic change between a pair of similar images in natural language.
Stable Attribute Group Editing for Reliable Few-shot Image Generation
SAGE takes use of all given few-shot images and estimates a class center embedding based on the category-relevant attribute dictionary.
Multi-Projection Fusion and Refinement Network for Salient Object Detection in 360° Omnidirectional Image
Salient object detection (SOD) aims to determine the most visually attractive objects in an image.
Consistency-Aware Anchor Pyramid Network for Crowd Localization
Crowd localization aims to predict the spatial position of humans in a crowd scenario.
Exploiting Completeness and Uncertainty of Pseudo Labels for Weakly Supervised Video Anomaly Detection
Weakly supervised video anomaly detection aims to identify abnormal events in videos using only video-level labels.
Progressive Multi-resolution Loss for Crowd Counting
Crowd counting is usually handled in a density map regression fashion, which is supervised via a L2 loss between the predicted density map and ground truth.
Learning to Dub Movies via Hierarchical Prosody Models
Given a piece of text, a video clip and a reference audio, the movie dubbing (also known as visual voice clone V2C) task aims to generate speeches that match the speaker's emotion presented in the video using the desired speaker voice as reference.
Dist-PU: Positive-Unlabeled Learning from a Label Distribution Perspective
Positive-Unlabeled (PU) learning tries to learn binary classifiers from a few labeled positive examples with many unlabeled ones.
OpenAUC: Towards AUC-Oriented Open-Set Recognition
In this paper, a systematic analysis reveals that most existing metrics are essentially inconsistent with the aforementioned goal of OSR: (1) For metrics extended from close-set classification, such as Open-set F-score, Youden's index, and Normalized Accuracy, a poor open-set prediction can escape from a low performance score with a superior close-set prediction.
Towards Understanding and Boosting Adversarial Transferability from a Distribution Perspective
We conduct comprehensive transferable attacks against multiple DNNs to demonstrate the effectiveness of the proposed method.
Does Thermal Really Always Matter for RGB-T Salient Object Detection?
In addition, considering the role of thermal modality, we set up different cross-modality interaction mechanisms in the encoding phase and the decoding phase.
Asymptotically Unbiased Instance-wise Regularized Partial AUC Optimization: Theory and Algorithm
sample size and a slow convergence rate, especially for TPAUC.
CIR-Net: Cross-modality Interaction and Refinement for RGB-D Salient Object Detection
Focusing on the issue of how to effectively capture and utilize cross-modality information in RGB-D salient object detection (SOD) task, we present a convolutional neural network (CNN) model, named CIR-Net, based on the novel cross-modality interaction and refinement.
A Unified Framework against Topology and Class Imbalance
We develop a multi-class AUC optimization work to deal with the class imbalance problem.
The Minority Matters: A Diversity-Promoting Collaborative Metric Learning Algorithm
Collaborative Metric Learning (CML) has recently emerged as a popular method in recommendation systems (RS), closing the gap between metric learning and Collaborative Filtering.
Exploring the Algorithm-Dependent Generalization of AUPRC Optimization with List Stability
Stochastic optimization of the Area Under the Precision-Recall Curve (AUPRC) is a crucial problem for machine learning.
MaxMatch: Semi-Supervised Learning with Worst-Case Consistency
In recent years, great progress has been made to incorporate unlabeled data to overcome the inefficiently supervised problem via semi-supervised learning (SSL).
A Tale of HodgeRank and Spectral Method: Target Attack Against Rank Aggregation Is the Fixed Point of Adversarial Game
From the perspective of the dynamical system, the attack behavior with a target ranking list is a fixed point belonging to the composition of the adversary and the victim.
Optimizing Partial Area Under the Top-k Curve: Theory and Practice
Finally, the experimental results on four benchmark datasets validate the effectiveness of our proposed framework.
Multi-Attention Network for Compressed Video Referring Object Segmentation
To address this problem, we propose a multi-attention network which consists of dual-path dual-attention module and a query-based cross-modal Transformer module.
Ranked #2 on
Referring Expression Segmentation
on A2D Sentences
Referring Expression Segmentation
Referring Video Object Segmentation
+2
Entity-enhanced Adaptive Reconstruction Network for Weakly Supervised Referring Expression Grounding
Second, most previous weakly supervised REG methods ignore the discriminative location and context of the referent, causing difficulties in distinguishing the target from other same-category objects.
Meta-Wrapper: Differentiable Wrapping Operator for User Interest Selection in CTR Prediction
In this paper, we regard user interest modeling as a feature selection problem, which we call user interest selection.
AdAUC: End-to-end Adversarial AUC Optimization Against Long-tail Problems
Our analysis differs from the existing studies since the algorithm is asked to generate adversarial examples by calculating the gradient of a min-max problem.
Geometry Interaction Knowledge Graph Embeddings
Knowledge graph (KG) embeddings have shown great power in learning representations of entities and relations for link prediction tasks.
ER: Equivariance Regularizer for Knowledge Graph Completion
To address this issue, we propose a new regularizer, namely, Equivariance Regularizer (ER), which can suppress overfitting by leveraging the implicit semantic information.
Optimizing Two-way Partial AUC with an End-to-end Framework
The critical challenge along this course lies in the difficulty of performing gradient-based optimization with end-to-end stochastic training, even with a proper choice of surrogate loss.
Rethinking Collaborative Metric Learning: Toward an Efficient Alternative without Negative Sampling
However, in this work, by taking a theoretical analysis, we find that negative sampling would lead to a biased estimation of the generalization error.
Automatic Relation-aware Graph Network Proliferation
However, the currently used graph search space overemphasizes learning node features and neglects mining hierarchical relational information.
Ranked #1 on
Link Prediction
on TSP/HCP Benchmark set
Global-and-Local Collaborative Learning for Co-Salient Object Detection
In this paper, we propose a global-and-local collaborative learning architecture, which includes a global correspondence modeling (GCM) and a local correspondence modeling (LCM) to capture comprehensive inter-image corresponding relationship among different images from the global and local perspectives.
CenterNet++ for Object Detection
Our approach, named CenterNet, detects each object as a triplet keypoints (top-left and bottom-right corners and the center keypoint).
Ranked #31 on
Object Detection
on COCO test-dev
IR-GAN: Image Manipulation with Linguistic Instruction by Increment Reasoning
Conditional image generation is an active research topic including text2image and image translation.
Attribute Group Editing for Reliable Few-shot Image Generation
Few-shot image generation is a challenging task even using the state-of-the-art Generative Adversarial Networks (GANs).
Few Shot Generative Model Adaption via Relaxed Spatial Structural Alignment
A feasible solution is to start with a GAN well-trained on a large scale source domain and adapt it to the target domain with a few samples, termed as few shot generative model adaption.
General Greedy De-bias Learning
Existing de-bias learning frameworks try to capture specific dataset bias by annotations but they fail to handle complicated OOD scenarios.
When False Positive is Intolerant: End-to-End Optimization with Low FPR for Multipartite Ranking
To leverage high performance under low FPRs, we consider an alternative metric for multipartite ranking evaluating the True Positive Rate (TPR) at a given FPR, denoted as TPR@FPR.
Hierarchical Modular Network for Video Captioning
(II) Predicate level, which learns the actions conditioned on highlighted objects and is supervised by the predicate in captions.
Self-Regulated Learning for Egocentric Video Activity Anticipation
Future activity anticipation is a challenging problem in egocentric vision.
Modeling Temporal Concept Receptive Field Dynamically for Untrimmed Video Analysis
Based on TDC, we propose the temporal dynamic concept modeling network (TDCMN) to learn an accurate and complete concept representation for efficient untrimmed video analysis.
DVCFlow: Modeling Information Flow Towards Human-like Video Captioning
Dense video captioning (DVC) aims to generate multi-sentence descriptions to elucidate the multiple events in the video, which is challenging and demands visual consistency, discoursal coherence, and linguistic diversity.
Implicit Feedbacks are Not Always Favorable: Iterative Relabeled One-Class Collaborative Filtering against Noisy Interactions
As the core of the framework, the iterative relabeling module exploits the self-training principle to dynamically generate pseudo labels for user preferences.
Learning Meta-path-aware Embeddings for Recommender Systems
Heterogeneous information networks (HINs) have become a popular tool to capture complicated user-item relationships in recommendation problems in recent years.
Semi-Autoregressive Image Captioning
Non-autoregressive image captioning with continuous iterative refinement, which eliminates the sequential dependence in a sentence generation, can achieve comparable performance to the autoregressive counterparts with a considerable acceleration.
Pareto Optimality for Fairness-constrained Collaborative Filtering
To balance overall recommendation performance and fairness, prevalent solutions apply fairness constraints or regularizations to enforce equality of certain performance across different subgroups.
Edge-featured Graph Neural Architecture Search
Recently, researchers study neural architecture search (NAS) to reduce the dependence of human expertise and explore better GNN architectures, but they over-emphasize entity features and ignore latent relation information concealed in the edges.
Learning with Multiclass AUC: Theory and Algorithms
Our foundation is based on the M metric, which is a well-known multiclass extension of AUC.
Greedy Gradient Ensemble for Robust Visual Question Answering
Language bias is a critical issue in Visual Question Answering (VQA), where models often exploit dataset biases for the final decision without considering the image information.
Ranked #2 on
Visual Question Answering (VQA)
on VQA-CP
Fast Batch Nuclear-norm Maximization and Minimization for Robust Domain Adaptation
Due to the domain discrepancy in visual domain adaptation, the performance of source model degrades when bumping into the high data density near decision boundary in target domain.
Poisoning Attack against Estimating from Pairwise Comparisons
In this paper, to the best of our knowledge, we initiate the first systematic investigation of data poisoning attacks on pairwise ranking algorithms, which can be formalized as the dynamic and static games between the ranker and the attacker and can be modeled as certain kinds of integer programming problems.
When False Positive is Intolerant: End-to-End Optimization with Low FPR for Multipartite Ranking
To leverage high performance under low FPRs, we consider an alternative metric for multipartite ranking evaluating the True Positive Rate (TPR) at a given FPR, denoted as TPR@FPR.
Location-Sensitive Visual Recognition with Cross-IOU Loss
Object detection, instance segmentation, and pose estimation are popular visual recognition tasks which require localizing the object by internal or boundary landmarks.
Ranked #54 on
Object Detection
on COCO test-dev
Rethinking Graph Neural Architecture Search from Message-passing
Inspired by the strong searching capability of neural architecture search (NAS) in CNN, this paper proposes Graph Neural Architecture Search (GNAS) with novel-designed search space.
Seeking the Shape of Sound: An Adaptive Framework for Learning Voice-Face Association
Targeting at (a), we propose a two-level modality alignment loss where both global and local information are considered.
Viewpoint and Scale Consistency Reinforcement for UAV Vehicle Re-Identification
Moreover, our method also achieves competitive performance compared with recent works on existing vehicle ReID datasets including VehicleID, VeRi-776 and VERI-Wild.
Exploiting Sample Correlation for Crowd Counting With Multi-Expert Network
Specifically, we propose a task-driven similarity metric based on sample's mutual enhancement, referred as co-fine-tune similarity, which can find a more efficient subset of data for training the expert network.
Heuristic Domain Adaptation
In visual domain adaptation (DA), separating the domain-specific characteristics from the domain-invariant representations is an ill-posed problem.
Semantic Editing On Segmentation Map Via Multi-Expansion Loss
Each MEx area has the mask area of the generation as the majority and the boundary of original context as the minority.
Label Decoupling Framework for Salient Object Detection
Though remarkable progress has been achieved, we observe that the closer the pixel is to the edge, the more difficult it is to be predicted, because edge pixels have a very imbalance distribution.
Ranked #1 on
Saliency Detection
on HKU-IS
Corner Proposal Network for Anchor-free, Two-stage Object Detection
On the MS-COCO dataset, CPN achieves an AP of 49. 2% which is competitive among state-of-the-art object detection methods.
Ranked #94 on
Object Detection
on COCO test-dev
Task-Feature Collaborative Learning with Application to Personalized Attribute Prediction
To this end, we propose a novel multi-task learning method called Task-Feature Collaborative Learning (TFCL).
Parsing-based View-aware Embedding Network for Vehicle Re-Identification
Vehicle Re-Identification is to find images of the same vehicle from various views in the cross-camera scenario.
State-Relabeling Adversarial Active Learning
In this paper, we propose a state relabeling adversarial active learning model (SRAAL), that leverages both the annotation and the labeled/unlabeled state information for deriving the most informative unlabeled samples.
Gradually Vanishing Bridge for Adversarial Domain Adaptation
On the discriminator, GVB contributes to enhance the discriminating ability, and balance the adversarial training process.
Towards Discriminability and Diversity: Batch Nuclear-norm Maximization under Label Insufficient Situations
We find by theoretical analysis that the prediction discriminability and diversity could be separately measured by the Frobenius-norm and rank of the batch output matrix.
DPANet: Depth Potentiality-Aware Gated Attention Network for RGB-D Salient Object Detection
There are two main issues in RGB-D salient object detection: (1) how to effectively integrate the complementarity from the cross-modal RGB-D data; (2) how to prevent the contamination effect from the unreliable depth map.
Ranked #22 on
Thermal Image Segmentation
on RGB-T-Glass-Segmentation
Global Context-Aware Progressive Aggregation Network for Salient Object Detection
Deep convolutional neural networks have achieved competitive performance in salient object detection, in which how to learn effective and comprehensive features plays a critical role.
Ranked #17 on
Dichotomous Image Segmentation
on DIS-TE1
DM2C: Deep Mixed-Modal Clustering
Instead of transforming all the samples into a joint modality-independent space, our framework learns the mappings across individual modal spaces by virtue of cycle-consistency.
Generalized Block-Diagonal Structure Pursuit: Learning Soft Latent Task Assignment against Negative Transfer
Different from most of the previous work, pursuing the Block-Diagonal structure of LTAM (assigning latent tasks to output tasks) alleviates negative transfer via collaboratively grouping latent tasks and output tasks such that inter-group knowledge transfer and sharing is suppressed.
F3Net: Fusion, Feedback and Focus for Salient Object Detection
Furthermore, different from binary cross entropy, the proposed PPA loss doesn't treat pixels equally, which can synthesize the local structure information of a pixel to guide the network to focus more on local details.
Ranked #3 on
Salient Object Detection
on DUT-OMRON
iSplit LBI: Individualized Partial Ranking with Ties via Split LBI
In this paper, instead of learning a global ranking which is agreed with the consensus, we pursue the tie-aware partial ranking from an individualized perspective.
Knowledge-guided Pairwise Reconstruction Network for Weakly Supervised Referring Expression Grounding
Weakly supervised referring expression grounding (REG) aims at localizing the referential entity in an image according to linguistic query, where the mapping between the image region (proposal) and the query is unknown in the training stage.
Adaptive Reconstruction Network for Weakly Supervised Referring Expression Grounding
It builds the correspondence between image region proposal and query in an adaptive manner: adaptive grounding and collaborative reconstruction.
Harmonized Multimodal Learning with Gaussian Process Latent Variable Models
Multimodal learning aims to discover the relationship between multiple modalities.
Learning Personalized Attribute Preference via Multi-task AUC Optimization
Traditionally, most of the existing attribute learning methods are trained based on the consensus of annotations aggregated from a limited number of annotators.
Multimodal Transformer with Multi-View Visual Representation for Image Captioning
Despite the success of existing studies, current methods only model the co-attention that characterizes the inter-modal interactions while neglecting the self-attention that characterizes the intra-modal interactions.
Cascaded Partial Decoder for Fast and Accurate Salient Object Detection
In this paper, we propose a novel Cascaded Partial Decoder (CPD) framework for fast and accurate salient object detection.
Ranked #1 on
RGB Salient Object Detection
on ISTD
Unsupervised Open Domain Recognition by Semantic Discrepancy Minimization
We address the unsupervised open domain recognition (UODR) problem, where categories in labeled source domain S is only a subset of those in unlabeled target domain T. The task is to correctly classify all samples in T including known and unknown categories.
CenterNet: Keypoint Triplets for Object Detection
In object detection, keypoint-based approaches often suffer a large number of incorrect object bounding boxes, arguably due to the lack of an additional look into the cropped regions.
Ranked #116 on
Object Detection
on COCO test-dev
Spatiotemporal CNN for Video Object Segmentation
Specifically, the temporal coherence branch pretrained in an adversarial fashion from unlabeled video data, is designed to capture the dynamic appearance and motion cues of video sequences to guide object segmentation.
Ranked #2 on
Semi-Supervised Video Object Segmentation
on YouTube
Semantic Segmentation
Semi-Supervised Video Object Segmentation
+3
Deep Robust Subjective Visual Property Prediction in Crowdsourcing
The problem of estimating subjective visual properties (SVP) of images (e. g., Shoes A is more comfortable than B) is gaining rising attention.
See Better Before Looking Closer: Weakly Supervised Data Augmentation Network for Fine-Grained Visual Classification
Specifically, for each training image, we first generate attention maps to represent the object's discriminative parts by weakly supervised learning.
Ranked #12 on
Fine-Grained Image Classification
on CUB-200-2011
HSCS: Hierarchical Sparsity Based Co-saliency Detection for RGBD Images
In this paper, we propose a novel co-saliency detection method for RGBD images based on hierarchical sparsity reconstruction and energy function refinement.
Person Re-Identification by Semantic Region Representation and Topology Constraint
In this paper, we propose a novel person re-identification method, which consists of a reliable representation called Semantic Region Representation (SRR), and an effective metric learning with Mapping Space Topology Constraint (MSTC).
Weakly Supervised Bilinear Attention Network for Fine-Grained Visual Classification
Besides, we propose attention regularization and attention dropout to weakly supervise the generating process of attention maps.
A Margin-based MLE for Crowdsourced Partial Ranking
A preference order or ranking aggregated from pairwise comparison data is commonly understood as a strict total order.
RAM: A Region-Aware Deep Model for Vehicle Re-Identification
Specifically, in addition to extracting global features, RAM also extracts features from a series of local regions.
The Unmanned Aerial Vehicle Benchmark: Object Detection and Tracking
Selected from 10 hours raw videos, about 80, 000 representative frames are fully annotated with bounding boxes as well as up to 14 kinds of attributes (e. g., weather condition, flying altitude, camera view, vehicle category, and occlusion) for three fundamental computer vision tasks: object detection, single object tracking, and multiple object tracking.
Ranked #5 on
Object Detection
on UAVDT
Facial Landmarks Detection by Self-Iterative Regression based Landmarks-Attention Network
Only one self-iterative regressor is trained to learn the descent directions for samples from coarse stages to fine stages, and parameters are iteratively updated by the same regressor.
Ranked #14 on
Face Alignment
on 300W
(NME_inter-pupil (%, Common) metric)
Review of Visual Saliency Detection with Comprehensive Information
With the acquisition technology development, more comprehensive information, such as depth cue, inter-image correspondence, or temporal relationship, is available to extend image saliency detection to RGBD saliency detection, co-saliency detection, or video saliency detection.
From Social to Individuals: a Parsimonious Path of Multi-level Models for Crowdsourced Preference Aggregation
In crowdsourced preference aggregation, it is often assumed that all the annotators are subject to a common preference or social utility function which generates their comparison behaviors in experiments.
Less Is More: Picking Informative Frames for Video Captioning
We propose a plug-and-play PickNet to perform informative frame picking in video captioning.
Towards Realistic Face Photo-Sketch Synthesis via Composition-Aided GANs
Experimental results show that our method is capable of generating both visually comfortable and identity-preserving face sketches/photos over a wide range of challenging data.
Ranked #1 on
Face Sketch Synthesis
on CUFS
(FID metric)
From Common to Special: When Multi-Attribute Learning Meets Personalized Opinions
However, both categories ignore the joint effect of the two mentioned factors: the personal diversity with respect to the global consensus; and the intrinsic correlation among multiple attributes.
HodgeRank with Information Maximization for Crowdsourced Pairwise Ranking Aggregation
Recently, crowdsourcing has emerged as an effective paradigm for human-powered large scale problem solving in various domains.
An Iterative Co-Saliency Framework for RGBD Images
In this paper, we propose an iterative RGBD co-saliency framework, which utilizes the existing single saliency maps as the initialization, and generates the final RGBD cosaliency map by using a refinement-cycle model.
Saliency Detection for Stereoscopic Images Based on Depth Confidence Analysis and Multiple Cues Fusion
Stereoscopic perception is an important part of human visual system that allows the brain to perceive depth.
Co-saliency Detection for RGBD Images Based on Multi-constraint Feature Matching and Cross Label Propagation
Different from the most existing co-saliency methods focusing on RGB images, this paper proposes a novel co-saliency detection model for RGBD images, which utilizes the depth information to enhance identification of co-saliency.
Multimodal Gaussian Process Latent Variable Models With Harmonization
We incorporate the harmonization mechanism into the learning process of multimodal GPLVMs.
Exploring Outliers in Crowdsourced Ranking for QoE
Outlier detection is a crucial part of robust evaluation for crowdsourceable assessment of Quality of Experience (QoE) and has attracted much attention in recent years.
Online Asymmetric Similarity Learning for Cross-Modal Retrieval
In this paper, we propose an online learning method to learn the similarity function between heterogeneous modalities by preserving the relative similarity in the training data, which is modeled as a set of bi-directional hinge loss constraints on the cross-modal training triplets.
A Graph Regularized Deep Neural Network for Unsupervised Image Representation Learning
Deep Auto-Encoder (DAE) has shown its promising power in high-level representation learning.
Hedged Deep Tracking
In recent years, several methods have been developed to utilize hierarchical features learned from a deep convolutional neural network (CNN) for visual tracking.
Geometric Hypergraph Learning for Visual Tracking
Graph based representation is widely used in visual tracking field by finding correct correspondences between target parts in consecutive frames.
Relay Backpropagation for Effective Learning of Deep Convolutional Neural Networks
Learning deeper convolutional neural networks becomes a tendency in recent years.
Ranked #5 on
Long-tail Learning
on VOC-MLT
Similarity Gaussian Process Latent Variable Model for Multi-Modal Data Analysis
Data from real applications involve multiple modalities representing content with the same semantics and deliver rich information from complementary aspects.
Evaluating Visual Properties via Robust HodgeRank
In this paper we study the problem of how to estimate such visual properties from a ranking perspective with the help of the annotators from online crowdsourcing platforms.
Multi-level Discriminative Dictionary Learning towards Hierarchical Visual Categorization
For each internode of the hierarchical category structure, a discriminative dictionary and a set of classification models are learnt for visual categorization, and the dictionaries in different layers are learnt to exploit the discriminative visual properties of different granularity.
