Search Results for author:
Found 102 papers, 41 papers with code
Grounding 3D Object Affordance from 2D Interactions in Images
Comprehensive experiments on PIAD demonstrate the reliability of the proposed task and the superiority of our method.
Spatial-Aware Token for Weakly Supervised Object Localization
Specifically, a spatial token is first introduced in the input space to aggregate representations for localization task.
Neural Dependencies Emerging from Learning Massive Categories
Through investigating the properties of the problem solution, we confirm that neural dependency is guaranteed by a redundant logit covariance matrix, which condition is easily met given massive categories, and that neural dependency is highly sparse, implying that one category correlates to only a few others.
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.
Enhancement by Your Aesthetic: An Intelligible Unsupervised Personalized Enhancer for Low-Light Images
Low-light image enhancement is an inherently subjective process whose targets vary with the user's aesthetic.
Rank Diminishing in Deep Neural Networks
By virtue of our numerical tools, we provide the first empirical analysis of the per-layer behavior of network rank in practical settings, i. e., ResNets, deep MLPs, and Transformers on ImageNet.
Label Noise-Resistant Mean Teaching for Weakly Supervised Fake News Detection
LNMT leverages unlabeled news and feedback comments of users to enlarge the amount of training data and facilitates model training by generating refined labels as weak supervision.
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
Principled Knowledge Extrapolation with GANs
Human can extrapolate well, generalize daily knowledge into unseen scenarios, raise and answer counterfactual questions.
Degradation-agnostic Correspondence from Resolution-asymmetric Stereo
Interestingly, we find that, although a stereo matching network trained with the photometric loss is not optimal, its feature extractor can produce degradation-agnostic and matching-specific features.
Unsupervised Coherent Video Cartoonization with Perceptual Motion Consistency
Furthermore, we devise the spatio-temporal correlative map as a style-independent, global-aware regularization on the perceptual motion consistency.
FAMLP: A Frequency-Aware MLP-Like Architecture For Domain Generalization
However, its generalization performance to heterogeneous tasks is inferior to other architectures (e. g., CNNs and transformers) due to the extensive retention of domain information.
ProgressiveMotionSeg: Mutually Reinforced Framework for Event-Based Motion Segmentation
Dynamic Vision Sensor (DVS) can asynchronously output the events reflecting apparent motion of objects with microsecond resolution, and shows great application potential in monitoring and other fields.
Location-Free Camouflage Generation Network
However, these methods struggle in 1) efficiently generating camouflage images using foreground and background with arbitrary structure; 2) camouflaging foreground objects to regions with multiple appearances (e. g. the junction of the vegetation and the mountains), which limit their practical application.
Self-Sustaining Representation Expansion for Non-Exemplar Class-Incremental Learning
Non-exemplar class-incremental learning is to recognize both the old and new classes when old class samples cannot be saved.
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.
Modality-Adaptive Mixup and Invariant Decomposition for RGB-Infrared Person Re-Identification
RGB-infrared person re-identification is an emerging cross-modality re-identification task, which is very challenging due to significant modality discrepancy between RGB and infrared images.
Debiased Batch Normalization via Gaussian Process for Generalizable Person Re-Identification
In this paper, we propose a novel Debiased Batch Normalization via Gaussian Process approach (GDNorm) for generalizable person re-identification, which models the feature statistic estimation from BN layers as a dynamically self-refining Gaussian process to alleviate the bias to unseen domain for improving the generalization.
Generalizable Person Re-identification
Representation Learning
Efficient Model-Driven Network for Shadow Removal
To address these issues, we first propose a new shadow illumination model for the shadow removal task.
Bijective Mapping Network for Shadow Removal
Shadow removal, which aims to restore the background in the shadow regions, is challenging due to the highly ill-posed nature.
Multi-Grained Spatio-Temporal Features Perceived Network for Event-Based Lip-Reading
To recognize words from the event data, we propose a novel Multi-grained Spatio-Temporal Features Perceived Network (MSTP) to perceive fine-grained spatio-temporal features from microsecond time-resolved event data.
Temporal Complementarity-Guided Reinforcement Learning for Image-to-Video Person Re-Identification
Image-to-video person re-identification aims to retrieve the same pedestrian as the image-based query from a video-based gallery set.
Image-To-Video Person Re-Identification
reinforcement-learning
+4
Lifelong Unsupervised Domain Adaptive Person Re-identification with Coordinated Anti-forgetting and Adaptation
In this paper, to address more practical scenarios, we propose a new task, Lifelong Unsupervised Domain Adaptive (LUDA) person ReID.
Domain Adaptive Person Re-Identification
Knowledge Distillation
+4
Calibrated Feature Decomposition for Generalizable Person Re-Identification
The calibrated person representation is subtly decomposed into the identity-relevant feature, domain feature, and the remaining entangled one.
Domain Generalization
Generalizable Person Re-identification
EMScore: Evaluating Video Captioning via Coarse-Grained and Fine-Grained Embedding Matching
The datasets will be released to facilitate the development of video captioning metrics.
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.
A Battle of Network Structures: An Empirical Study of CNN, Transformer, and MLP
Convolutional neural networks (CNN) are the dominant deep neural network (DNN) architecture for computer vision.
Multi-Modulation Network for Audio-Visual Event Localization
We propose a novel MultiModulation Network (M2N) to learn the above correlation and leverage it as semantic guidance to modulate the related auditory, visual, and fused features.
Self-Supervised Visual Representations Learning by Contrastive Mask Prediction
In this paper, we propose a novel contrastive mask prediction (CMP) task for visual representation learning and design a mask contrast (MaskCo) framework to implement the idea.
Learning Conditional Knowledge Distillation for Degraded-Reference Image Quality Assessment
And the reference space is optimized to capture deep image priors that are useful for quality assessment.
Pose-Guided Feature Learning with Knowledge Distillation for Occluded Person Re-Identification
Occluded person re-identification (ReID) aims to match person images with occlusion.
Exploring Sequence Feature Alignment for Domain Adaptive Detection Transformers
In DQFA, a novel domain query is used to aggregate and align global context from the token sequence of both domains.
Self-Promoted Prototype Refinement for Few-Shot Class-Incremental Learning
Few-shot class-incremental learning is to recognize the new classes given few samples and not forget the old classes.
Disentangle Your Dense Object Detector
Extensive experiments on MS COCO benchmark show that our approach can lead to 2. 0 mAP, 2. 4 mAP and 2. 2 mAP absolute improvements on RetinaNet, FCOS, and ATSS baselines with negligible extra overhead.
Structured Multi-Level Interaction Network for Video Moment Localization via Language Query
In particular, for cross-modal interaction, we interact the sentence-level query with the whole moment while interact the word-level query with content and boundary, as in a coarse-to-fine manner.
Image De-Raining via Continual Learning
The proposed model is capable of achieving superior performance on both inhomogeneous and incremental datasets, and is promising for highly compact systems to gradually learn myriad regularities of the different types of rain streaks.
Light Field Super-Resolution With Zero-Shot Learning
To fill this gap, we propose a zero-shot learning framework for light field SR, which learns a mapping to super-resolve the reference view with examples extracted solely from the input low-resolution light field itself.
Adaptive Domain-Specific Normalization for Generalizable Person Re-Identification
It describes unseen target domain as a combination of the known source ones, and explicitly learns domain-specific representation with target distribution to improve the model's generalization by a meta-learning pipeline.
Spatial-Temporal Correlation and Topology Learning for Person Re-Identification in Videos
The key factor for video person re-identification is to effectively exploit both spatial and temporal clues from video sequences.
Memory Enhanced Embedding Learning for Cross-Modal Video-Text Retrieval
The cross-modal memory module is employed to record the instance embeddings of all the datasets for global negative mining.
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.
Group-aware Label Transfer for Domain Adaptive Person Re-identification
In this paper, we propose a Group-aware Label Transfer (GLT) algorithm, which enables the online interaction and mutual promotion of pseudo-label prediction and representation learning.
Domain Adaptive Person Re-Identification
Online Clustering
+3
Synergy Between Semantic Segmentation and Image Denoising via Alternate Boosting
We observe that not only denoising helps combat the drop of segmentation accuracy due to noise, but also pixel-wise semantic information boosts the capability of denoising.
General-Purpose Speech Representation Learning through a Self-Supervised Multi-Granularity Framework
This paper presents a self-supervised learning framework, named MGF, for general-purpose speech representation learning.
VAE^2: Preventing Posterior Collapse of Variational Video Predictions in the Wild
We believe that VAE$^2$ is also applicable to other stochastic sequence prediction problems where training data are lack of stochasticity.
Attack-Guided Perceptual Data Generation for Real-World Re-Identification
In unconstrained real-world surveillance scenarios, person re-identification (Re-ID) models usually suffer from different low-level perceptual variations, e. g., cross-resolution and insufficient lighting.
Improving De-Raining Generalization via Neural Reorganization
Equipped with our NR algorithm, the deep model can be trained on a list of synthetic rainy datasets by overcoming catastrophic forgetting, making it a general-version de-raining network.
Learning Dual Priors for JPEG Compression Artifacts Removal
Specifically, we design a variational model to formulate the image de-blocking problem and propose two prior terms for the image content and gradient, respectively.
Cross-Patch Graph Convolutional Network for Image Denoising
However, the real noisy images in practical are mostly of high resolution rather than the cropped small patches and the vanilla training strategies ignore the cross-patch contextual dependency in the whole image.
Exploiting Sample Uncertainty for Domain Adaptive Person Re-Identification
Based on this finding, we propose to exploit the uncertainty (measured by consistency levels) to evaluate the reliability of the pseudo-label of a sample and incorporate the uncertainty to re-weight its contribution within various ReID losses, including the identity (ID) classification loss per sample, the triplet loss, and the contrastive loss.
Domain Adaptive Person Re-Identification
Person Re-Identification
+2
Learning Semantic-aware Normalization for Generative Adversarial Networks
Such a model disentangles latent factors according to the semantic of feature channels by channel-/group- wise fusion of latent codes and feature channels.
Hierarchical Granularity Transfer Learning
In this paper, we introduce a new task, named Hierarchical Granularity Transfer Learning (HGTL), to recognize sub-level categories with basic-level annotations and semantic descriptions for hierarchical categories.
Hierarchical Gumbel Attention Network for Text-based Person Search
This hard selection strategy is able to fuse the strong-relevant multi-modality features for alleviating the problem of matching redundancy.
Ranked #9 on
Text based Person Retrieval
on CUHK-PEDES
Temporal Attribute-Appearance Learning Network for Video-based Person Re-Identification
TALNet simultaneously exploits human attributes and appearance to learn comprehensive and effective pedestrian representations from videos.
DeepFacePencil: Creating Face Images from Freehand Sketches
In this paper, we explore the task of generating photo-realistic face images from hand-drawn sketches.
Nighttime Dehazing with a Synthetic Benchmark
To address this issue, we propose a novel synthetic method called 3R to simulate nighttime hazy images from daytime clear images, which first reconstructs the scene geometry, then simulates the light rays and object reflectance, and finally renders the haze effects.
Learning to Discretely Compose Reasoning Module Networks for Video Captioning
However, existing visual reasoning methods designed for visual question answering are not appropriate to video captioning, for it requires more complex visual reasoning on videos over both space and time, and dynamic module composition along the generation process.
Memory-Augmented Relation Network for Few-Shot Learning
Metric-based few-shot learning methods concentrate on learning transferable feature embedding that generalizes well from seen categories to unseen categories under the supervision of limited number of labelled instances.
Self-Supervised Tuning for Few-Shot Segmentation
Few-shot segmentation aims at assigning a category label to each image pixel with few annotated samples.
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.
Stacked Convolutional Deep Encoding Network for Video-Text Retrieval
Existing dominant approaches for cross-modal video-text retrieval task are to learn a joint embedding space to measure the cross-modal similarity.
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.
ContourNet: Taking a Further Step toward Accurate Arbitrary-shaped Scene Text Detection
Then a novel Local Orthogonal Texture-aware Module (LOTM) models the local texture information of proposal features in two orthogonal directions and represents text region with a set of contour points.
Spatiotemporal Fusion in 3D CNNs: A Probabilistic View
Based on the probability space, we further generate new fusion strategies which achieve the state-of-the-art performance on four well-known action recognition datasets.
Real-world Person Re-Identification via Degradation Invariance Learning
Person re-identification (Re-ID) in real-world scenarios usually suffers from various degradation factors, e. g., low-resolution, weak illumination, blurring and adverse weather.
Co-Saliency Spatio-Temporal Interaction Network for Person Re-Identification in Videos
Person re-identification aims at identifying a certain pedestrian across non-overlapping camera networks.
Iterative Context-Aware Graph Inference for Visual Dialog
Visual dialog is a challenging task that requires the comprehension of the semantic dependencies among implicit visual and textual contexts.
Ranked #12 on
Visual Dialog
on VisDial v0.9 val
Multi-Objective Matrix Normalization for Fine-grained Visual Recognition
In this paper, we propose an efficient Multi-Objective Matrix Normalization (MOMN) method that can simultaneously normalize a bilinear representation in terms of square-root, low-rank, and sparsity.
Domain-aware Visual Bias Eliminating for Generalized Zero-Shot Learning
Recent methods focus on learning a unified semantic-aligned visual representation to transfer knowledge between two domains, while ignoring the effect of semantic-free visual representation in alleviating the biased recognition problem.
Object Relational Graph with Teacher-Recommended Learning for Video Captioning
In this paper, we propose a complete video captioning system including both a novel model and an effective training strategy.
Ranked #2 on
Video Captioning
on VATEX
(using extra training data)
Convolutional Dictionary Pair Learning Network for Image Representation Learning
To address this issue, we propose a novel generalized end-to-end representation learning architecture, dubbed Convolutional Dictionary Pair Learning Network (CDPL-Net) in this paper, which integrates the learning schemes of the CNN and dictionary pair learning into a unified framework.
Deep Self-representative Concept Factorization Network for Representation Learning
In this paper, we investigate the unsupervised deep representation learning issue and technically propose a novel framework called Deep Self-representative Concept Factorization Network (DSCF-Net), for clustering deep features.
Identity Preserve Transform: Understand What Activity Classification Models Have Learnt
This can explain why an activity classification model usually fails to generalize to datasets it is not trained on.
Abstract Reasoning with Distracting Features
Abstraction reasoning is a long-standing challenge in artificial intelligence.
Progressive Retinex: Mutually Reinforced Illumination-Noise Perception Network for Low Light Image Enhancement
Contrast enhancement and noise removal are coupled problems for low-light image enhancement.
Learning Deep Bilinear Transformation for Fine-grained Image Representation
However, the computational cost to learn pairwise interactions between deep feature channels is prohibitively expensive, which restricts this powerful transformation to be used in deep neural networks.
LinesToFacePhoto: Face Photo Generation from Lines with Conditional Self-Attention Generative Adversarial Network
The large-scale discriminator enforces the completeness of global structures and the small-scale discriminator encourages fine details, thereby enhancing the realism of generated face images.
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.
Adaptive Structure-constrained Robust Latent Low-Rank Coding for Image Recovery
Specifically, AS-LRC performs the latent decomposition of given data into a low-rank reconstruction by a block-diagonal codes matrix, a group sparse locality-adaptive salient feature part and a sparse error part.
Domain-Specific Embedding Network for Zero-Shot Recognition
In contrast to previous methods, the DSEN decomposes the domain-shared projection function into one domain-invariant and two domain-specific sub-functions to explore the similarities and differences between two domains.
Robust Subspace Discovery by Block-diagonal Adaptive Locality-constrained Representation
Leveraging on the Frobenius-norm based latent low-rank representation model, rBDLR jointly learns the coding coefficients and salient features, and improves the results by enhancing the robustness to outliers and errors in given data, preserving local information of salient features adaptively and ensuring the block-diagonal structures of the coefficients.
Structure-Aware Residual Pyramid Network for Monocular Depth Estimation
We propose a Residual Pyramid Decoder (RPD) which expresses global scene structure in upper levels to represent layouts, and local structure in lower levels to present shape details.
Ranked #34 on
Monocular Depth Estimation
on NYU-Depth V2
Inferential Machine Comprehension: Answering Questions by Recursively Deducing the Evidence Chain from Text
This paper focuses on the topic of inferential machine comprehension, which aims to fully understand the meanings of given text to answer generic questions, especially the ones needed reasoning skills.
Posterior-Guided Neural Architecture Search
Accordingly, a hybrid network representation is presented which enables us to leverage the Variational Dropout so that the approximation of the posterior distribution becomes fully gradient-based and highly efficient.
Joint Visual Grounding with Language Scene Graphs
In this paper, we alleviate the missing-annotation problem and enable the joint reasoning by leveraging the language scene graph which covers both labeled referent and unlabeled contexts (other objects, attributes, and relationships).
Context-Aware Visual Policy Network for Fine-Grained Image Captioning
With the maturity of visual detection techniques, we are more ambitious in describing visual content with open-vocabulary, fine-grained and free-form language, i. e., the task of image captioning.
One-Shot Texture Retrieval with Global Context Metric
In this paper, we tackle one-shot texture retrieval: given an example of a new reference texture, detect and segment all the pixels of the same texture category within an arbitrary image.
Multimodal Semantic Attention Network for Video Captioning
Inspired by the fact that different modalities in videos carry complementary information, we propose a Multimodal Semantic Attention Network(MSAN), which is a new encoder-decoder framework incorporating multimodal semantic attributes for video captioning.
Camera Lens Super-Resolution
Existing methods for single image super-resolution (SR) are typically evaluated with synthetic degradation models such as bicubic or Gaussian downsampling.
Looking for the Devil in the Details: Learning Trilinear Attention Sampling Network for Fine-grained Image Recognition
Learning subtle yet discriminative features (e. g., beak and eyes for a bird) plays a significant role in fine-grained image recognition.
Ranked #1 on
Fine-Grained Image Classification
on iNaturalist
Fine-Grained Image Classification
Fine-Grained Image Recognition
Making History Matter: History-Advantage Sequence Training for Visual Dialog
We study the multi-round response generation in visual dialog, where a response is generated according to a visually grounded conversational history.
Ranked #10 on
Visual Dialog
on VisDial v0.9 val
Learning to Assemble Neural Module Tree Networks for Visual Grounding
In particular, we develop a novel modular network called Neural Module Tree network (NMTree) that regularizes the visual grounding along the dependency parsing tree of the sentence, where each node is a neural module that calculates visual attention according to its linguistic feature, and the grounding score is accumulated in a bottom-up direction where as needed.
CA3Net: Contextual-Attentional Attribute-Appearance Network for Person Re-Identification
An appearance network is developed to learn appearance features from the full body, horizontal and vertical body parts of pedestrians with spatial dependencies among body parts.
Towards Human-Level License Plate Recognition
License plate recognition (LPR) is a fundamental component of various intelligent transport systems, which is always expected to be accurate and efficient enough.
Context-Aware Visual Policy Network for Sequence-Level Image Captioning
To fill the gap, we propose a Context-Aware Visual Policy network (CAVP) for sequence-level image captioning.
A Two-Stream Mutual Attention Network for Semi-supervised Biomedical Segmentation with Noisy Labels
\begin{abstract} Learning-based methods suffer from a deficiency of clean annotations, especially in biomedical segmentation.
MiCT: Mixed 3D/2D Convolutional Tube for Human Action Recognition
Recent attempts use 3D convolutional neural networks (CNNs) to explore spatio-temporal information for human action recognition.
Frank-Wolfe Network: An Interpretable Deep Structure for Non-Sparse Coding
We propose an interpretable deep structure namely Frank-Wolfe Network (F-W Net), whose architecture is inspired by unrolling and truncating the Frank-Wolfe algorithm for solving an $L_p$-norm constrained problem with $p\geq 1$.
Learning Compact Appearance Representation for Video-based Person Re-Identification
This paper presents a novel approach for video-based person re-identification using multiple Convolutional Neural Networks (CNNs).
Comparative Deep Learning of Hybrid Representations for Image Recommendations
In many image-related tasks, learning expressive and discriminative representations of images is essential, and deep learning has been studied for automating the learning of such representations.
