Search Results for author:
Found 84 papers, 29 papers with code
Memory Regulation and Alignment toward Generalizer RGB-Infrared Person
The domain shift, coming from unneglectable modality gap and non-overlapped identity classes between training and test sets, is a major issue of RGB-Infrared person re-identification.
Data-driven advice for interpreting local and global model predictions in bioinformatics problems
For random forests, we find extremely high similarities and correlations of both local and global SHAP values and CFC scores, leading to very similar rankings and interpretations.
Communicative Learning with Natural Gestures for Embodied Navigation Agents with Human-in-the-Scene
In a series of experiments, we demonstrate that human gesture cues, even without predefined semantics, improve the object-goal navigation for an embodied agent, outperforming various state-of-the-art methods.
Data Hiding with Deep Learning: A Survey Unifying Digital Watermarking and Steganography
Data hiding is the process of embedding information into a noise-tolerant signal such as a piece of audio, video, or image.
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.
Sketch, Ground, and Refine: Top-Down Dense Video Captioning
The dense video captioning task aims to detect and describe a sequence of events in a video for detailed and coherent storytelling.
VLN BERT: A Recurrent Vision-and-Language BERT for Navigation
In this paper we propose a recurrent BERT model that is time-aware for use in VLN.
Room-and-Object Aware Knowledge Reasoning for Remote Embodied Referring Expression
The Remote Embodied Referring Expression (REVERIE) is a recently raised task that requires an agent to navigate to and localise a referred remote object according to a high-level language instruction.
Proposal-free One-stage Referring Expression via Grid-Word Cross-Attention
Referring Expression Comprehension (REC) has become one of the most important tasks in visual reasoning, since it is an essential step for many vision-and-language tasks such as visual question answering.
Chop Chop BERT: Visual Question Answering by Chopping VisualBERT's Heads
Based on this observation, we design a dynamic chopping module that can automatically remove heads and layers of the VisualBERT at an instance level when dealing with different questions.
Towards Accurate Text-based Image Captioning with Content Diversity Exploration
This task, however, is very challenging because an image often contains complex texts and visual information that is hard to be described comprehensively.
The Road to Know-Where: An Object-and-Room Informed Sequential BERT for Indoor Vision-Language Navigation
Vision-and-Language Navigation (VLN) requires an agent to find a path to a remote location on the basis of natural-language instructions and a set of photo-realistic panoramas.
Diagnosing Vision-and-Language Navigation: What Really Matters
Results show that indoor navigation agents refer to both object tokens and direction tokens in the instruction when making decisions.
Non-Salient Region Object Mining for Weakly Supervised Semantic Segmentation
To further mine the non-salient region objects, we propose to exert the segmentation network's self-correction ability.
Jo-SRC: A Contrastive Approach for Combating Noisy Labels
Due to the memorization effect in Deep Neural Networks (DNNs), training with noisy labels usually results in inferior model performance.
Higher-Order Orthogonal Causal Learning for Treatment Effect
Most existing studies on the double/debiased machine learning method concentrate on the causal parameter estimation recovering from the first-order orthogonal score function.
Learning for Visual Navigation by Imagining the Success
ForeSIT is trained to imagine the recurrent latent representation of a future state that leads to success, e. g. either a sub-goal state that is important to reach before the target, or the goal state itself.
Multi-intersection Traffic Optimisation: A Benchmark Dataset and a Strong Baseline
To address these issues, in this work we carefully design our settings and propose a new dataset including both synthetic and real traffic data in more complex scenarios.
How to Train Your Agent to Read and Write
Typically, this requires an agent to fully understand the knowledge from the given text materials and generate correct and fluent novel paragraphs, which is very challenging in practice.
Ranked #3 on
KG-to-Text Generation
on AGENDA
Semantics for Robotic Mapping, Perception and Interaction: A Survey
In robotics and related research fields, the study of understanding is often referred to as semantics, which dictates what does the world "mean" to a robot, and is strongly tied to the question of how to represent that meaning.
Memory-Gated Recurrent Networks
The essence of multivariate sequential learning is all about how to extract dependencies in data.
The Causal Learning of Retail Delinquency
Classical estimators overlook the confounding effects and hence the estimation error can be magnificent.
MeisterMorxrc at SemEval-2020 Task 9: Fine-Tune Bert and Multitask Learning for Sentiment Analysis of Code-Mixed Tweets
Natural language processing (NLP) has been applied to various fields including text classification and sentiment analysis.
Simple is not Easy: A Simple Strong Baseline for TextVQA and TextCaps
Texts appearing in daily scenes that can be recognized by OCR (Optical Character Recognition) tools contain significant information, such as street name, product brand and prices.
Confidence-aware Non-repetitive Multimodal Transformers for TextCaps
Our CNMT consists of a reading, a reasoning and a generation modules, in which Reading Module employs better OCR systems to enhance text reading ability and a confidence embedding to select the most noteworthy tokens.
Generative Learning of Heterogeneous Tail Dependence
We propose a multivariate generative model to capture the complex dependence structure often encountered in business and financial data.
A Recurrent Vision-and-Language BERT for Navigation
In this paper we propose a recurrent BERT model that is time-aware for use in VLN.
Modular Graph Attention Network for Complex Visual Relational Reasoning
Moreover, to capture the complex logic in a query, we construct a relational graph to represent the visual objects and their relationships, and propose a multi-step reasoning method to progressively understand the complex logic.
Ranked #2 on
Referring Expression Comprehension
on CLEVR-Ref+
Language-guided Navigation via Cross-Modal Grounding and Alternate Adversarial Learning
We then propose to recursively alternate the learning schemes of imitation and exploration to narrow the discrepancy between training and inference.
Language and Visual Entity Relationship Graph for Agent Navigation
From both the textual and visual perspectives, we find that the relationships among the scene, its objects, and directional clues are essential for the agent to interpret complex instructions and correctly perceive the environment.
Parsimonious Quantile Regression of Financial Asset Tail Dynamics via Sequential Learning
We propose a parsimonious quantile regression framework to learn the dynamic tail behaviors of financial asset returns.
MARS: Mixed Virtual and Real Wearable Sensors for Human Activity Recognition with Multi-Domain Deep Learning Model
Together with the rapid development of the Internet of Things (IoT), human activity recognition (HAR) using wearable Inertial Measurement Units (IMUs) becomes a promising technology for many research areas.
CogTree: Cognition Tree Loss for Unbiased Scene Graph Generation
We first build a cognitive structure CogTree to organize the relationships based on the prediction of a biased SGG model.
Ranked #1 on
Scene Graph Generation
on Visual Genome
(mean Recall @20 metric)
Attention-SLAM: A Visual Monocular SLAM Learning from Human Gaze
This paper proposes a novel simultaneous localization and mapping (SLAM) approach, namely Attention-SLAM, which simulates human navigation mode by combining a visual saliency model (SalNavNet) with traditional monocular visual SLAM.
Data-driven Meta-set Based Fine-Grained Visual Classification
To this end, we propose a data-driven meta-set based approach to deal with noisy web images for fine-grained recognition.
Object-and-Action Aware Model for Visual Language Navigation
The first is object description (e. g., 'table', 'door'), each presenting as a tip for the agent to determine the next action by finding the item visible in the environment, and the second is action specification (e. g., 'go straight', 'turn left') which allows the robot to directly predict the next movements without relying on visual perceptions.
Soft Expert Reward Learning for Vision-and-Language Navigation
In this paper, we introduce a Soft Expert Reward Learning (SERL) model to overcome the reward engineering designing and generalisation problems of the VLN task.
Semantic Equivalent Adversarial Data Augmentation for Visual Question Answering
However, there are few works studying the data augmentation problem for VQA and none of the existing image based augmentation schemes (such as rotation and flipping) can be directly applied to VQA due to its semantic structure -- an $\langle image, question, answer\rangle$ triplet needs to be maintained correctly.
Referring Expression Comprehension: A Survey of Methods and Datasets
In this survey, we first examine the state of the art by comparing modern approaches to the problem.
Length-Controllable Image Captioning
We verify the merit of the proposed length level embedding on three models: two state-of-the-art (SOTA) autoregressive models with different types of decoder, as well as our proposed non-autoregressive model, to show its generalization ability.
DAM: Deliberation, Abandon and Memory Networks for Generating Detailed and Non-repetitive Responses in Visual Dialogue
The ability of generating detailed and non-repetitive responses is crucial for the agent to achieve human-like conversation.
Foreground-Background Imbalance Problem in Deep Object Detectors: A Review
Recent years have witnessed the remarkable developments made by deep learning techniques for object detection, a fundamentally challenging problem of computer vision.
Mucko: Multi-Layer Cross-Modal Knowledge Reasoning for Fact-based Visual Question Answering
In this paper, we depict an image by a multi-modal heterogeneous graph, which contains multiple layers of information corresponding to the visual, semantic and factual features.
Give Me Something to Eat: Referring Expression Comprehension with Commonsense Knowledge
In this case, we need to use commonsense knowledge to identify the objects in the image.
Structured Multimodal Attentions for TextVQA
Most of the state-of-the-art (SoTA) VQA methods fail to answer these questions because of i) poor text reading ability; ii) lacking of text-visual reasoning capacity; and iii) adopting a discriminative answering mechanism instead of a generative one which is hard to cover both OCR tokens and general text tokens in the final answer.
Optimistic Agent: Accurate Graph-Based Value Estimation for More Successful Visual Navigation
Through empirical studies, we show that our agent, dubbed as the optimistic agent, has a more realistic estimate of the state value during a navigation episode which leads to a higher success rate.
Sub-Instruction Aware Vision-and-Language Navigation
Vision-and-language navigation requires an agent to navigate through a real 3D environment following natural language instructions.
Say As You Wish: Fine-grained Control of Image Caption Generation with Abstract Scene Graphs
From the ASG, we propose a novel ASG2Caption model, which is able to recognise user intentions and semantics in the graph, and therefore generate desired captions according to the graph structure.
Cops-Ref: A new Dataset and Task on Compositional Referring Expression Comprehension
To bridge the gap, we propose a new dataset for visual reasoning in context of referring expression comprehension with two main features.
Intelligent Home 3D: Automatic 3D-House Design from Linguistic Descriptions Only
To this end, we propose a House Plan Generative Model (HPGM) that first translates the language input to a structural graph representation and then predicts the layout of rooms with a Graph Conditioned Layout Prediction Network (GC LPN) and generates the interior texture with a Language Conditioned Texture GAN (LCT-GAN).
Fine-grained Video-Text Retrieval with Hierarchical Graph Reasoning
To improve fine-grained video-text retrieval, we propose a Hierarchical Graph Reasoning (HGR) model, which decomposes video-text matching into global-to-local levels.
DualVD: An Adaptive Dual Encoding Model for Deep Visual Understanding in Visual Dialogue
More importantly, we can tell which modality (visual or semantic) has more contribution in answering the current question by visualizing the gate values.
Ranked #6 on
Visual Dialog
on VisDial v0.9 val
Integrating Temporal and Spatial Attentions for VATEX Video Captioning Challenge 2019
This notebook paper presents our model in the VATEX video captioning challenge.
Neural Learning of Online Consumer Credit Risk
This paper takes a deep learning approach to understand consumer credit risk when e-commerce platforms issue unsecured credit to finance customers' purchase.
Understanding Distributional Ambiguity via Non-robust Chance Constraint
Without the boundedness constraint, the CCO problem is shown to perform uniformly better than the DRO problem, irrespective of the radius of the ambiguity set, the choice of the divergence measure, or the tail heaviness of the center distribution.
Show, Price and Negotiate: A Negotiator with Online Value Look-Ahead
Negotiation, as an essential and complicated aspect of online shopping, is still challenging for an intelligent agent.
REVERIE: Remote Embodied Visual Referring Expression in Real Indoor Environments
One of the long-term challenges of robotics is to enable robots to interact with humans in the visual world via natural language, as humans are visual animals that communicate through language.
You Only Look & Listen Once: Towards Fast and Accurate Visual Grounding
Most state-of-the-art methods in VG operate in a two-stage manner, wherein the first stage an object detector is adopted to generate a set of object proposals from the input image and the second stage is simply formulated as a cross-modal matching problem that finds the best match between the language query and all region proposals.
What's to know? Uncertainty as a Guide to Asking Goal-oriented Questions
We propose a solution to this problem based on a Bayesian model of the uncertainty in the implicit model maintained by the visual dialogue agent, and in the function used to select an appropriate output.
Gold Seeker: Information Gain from Policy Distributions for Goal-oriented Vision-and-Langauge Reasoning
For each potential action a distribution of the expected outcomes is calculated, and the value of the potential information gain assessed.
Neighbourhood Watch: Referring Expression Comprehension via Language-guided Graph Attention Networks
Being composed of node attention component and edge attention component, the proposed graph attention mechanism explicitly represents inter-object relationships, and properties with a flexibility and power impossible with competing approaches.
Deep Template Matching for Offline Handwritten Chinese Character Recognition
Just like its remarkable achievements in many computer vision tasks, the convolutional neural networks (CNN) provide an end-to-end solution in handwritten Chinese character recognition (HCCR) with great success.
Offline Handwritten Chinese Character Recognition
Template Matching
Goal-Oriented Visual Question Generation via Intermediate Rewards
Despite significant progress in a variety of vision-and-language problems, developing a method capable of asking intelligent, goal-oriented questions about images is proven to be an inscrutable challenge.
Connecting Language and Vision to Actions
A long-term goal of AI research is to build intelligent agents that can see the rich visual environment around us, communicate this understanding in natural language to humans and other agents, and act in a physical or embodied environment.
Topological Data Analysis Made Easy with the Topology ToolKit
This tutorial presents topological methods for the analysis and visualization of scientific data from a user's perspective, with the Topology ToolKit (TTK), a recently released open-source library for topological data analysis.
Visual Grounding via Accumulated Attention
There are three main challenges in VG: 1) what is the main focus in a query; 2) how to understand an image; 3) how to locate an object.
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 #8 on
Cross-Modal Retrieval
on Flickr30k
Are You Talking to Me? Reasoned Visual Dialog Generation through Adversarial Learning
The Visual Dialogue task requires an agent to engage in a conversation about an image with a human.
Ranked #4 on
Visual Dialog
on VisDial v0.9 val
Asking the Difficult Questions: Goal-Oriented Visual Question Generation via Intermediate Rewards
Despite significant progress in a variety of vision-and-language problems, developing a method capable of asking intelligent, goal-oriented questions about images is proven to be an inscrutable challenge.
Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments
This is significant because a robot interpreting a natural-language navigation instruction on the basis of what it sees is carrying out a vision and language process that is similar to Visual Question Answering.
Ranked #3 on
Visual Navigation
on R2R
Kill Two Birds with One Stone: Weakly-Supervised Neural Network for Image Annotation and Tag Refinement
These comments can be a description of the image, or some objects, attributes, scenes in it, which are normally used as the user-provided tags.
Parallel Attention: A Unified Framework for Visual Object Discovery through Dialogs and Queries
To this end we propose a unified framework, the ParalleL AttentioN (PLAN) network, to discover the object in an image that is being referred to in variable length natural expression descriptions, from short phrases query to long multi-round dialogs.
Visual Question Answering with Memory-Augmented Networks
In this paper, we exploit a memory-augmented neural network to predict accurate answers to visual questions, even when those answers occur rarely in the training set.
Classification of Medical Images and Illustrations in the Biomedical Literature Using Synergic Deep Learning
The Classification of medical images and illustrations in the literature aims to label a medical image according to the modality it was produced or label an illustration according to its production attributes.
Care about you: towards large-scale human-centric visual relationship detection
In addressing this problem we first construct a large-scale human-centric visual relationship detection dataset (HCVRD), which provides many more types of relationship annotation (nearly 10K categories) than the previous released datasets.
Human-Object Interaction Detection
Visual Relationship Detection
The VQA-Machine: Learning How to Use Existing Vision Algorithms to Answer New Questions
To train a method to perform even one of these operations accurately from {image, question, answer} tuples would be challenging, but to aim to achieve them all with a limited set of such training data seems ambitious at best.
Multi-Label Image Classification with Regional Latent Semantic Dependencies
Recent state-of-the-art approaches to multi-label image classification exploit the label dependencies in an image, at global level, largely improving the labeling capacity.
Visual Question Answering: A Survey of Methods and Datasets
Visual Question Answering (VQA) is a challenging task that has received increasing attention from both the computer vision and the natural language processing communities.
FVQA: Fact-based Visual Question Answering
We evaluate several baseline models on the FVQA dataset, and describe a novel model which is capable of reasoning about an image on the basis of supporting facts.
Image Captioning and Visual Question Answering Based on Attributes and External Knowledge
Much recent progress in Vision-to-Language problems has been achieved through a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).
Ask Me Anything: Free-form Visual Question Answering Based on Knowledge from External Sources
Priming a recurrent neural network with this combined information, and the submitted question, leads to a very flexible visual question answering approach.
Explicit Knowledge-based Reasoning for Visual Question Answering
We describe a method for visual question answering which is capable of reasoning about contents of an image on the basis of information extracted from a large-scale knowledge base.
What value do explicit high level concepts have in vision to language problems?
Much of the recent progress in Vision-to-Language (V2L) problems has been achieved through a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).
The Cross-Depiction Problem: Computer Vision Algorithms for Recognising Objects in Artwork and in Photographs
The cross-depiction problem is that of recognising visual objects regardless of whether they are photographed, painted, drawn, etc.
