Search Results for author:
Found 68 papers, 19 papers with code
De-Biased Court's View Generation with Causality
Court{'}s view generation is a novel but essential task for legal AI, aiming at improving the interpretability of judgment prediction results and enabling automatic legal document generation.
Compositional Temporal Grounding with Structured Variational Cross-Graph Correspondence Learning
To systematically measure the compositional generalizability of temporal grounding models, we introduce a new Compositional Temporal Grounding task and construct two new dataset splits, i. e., Charades-CG and ActivityNet-CG.
Parallel Instance Query Network for Named Entity Recognition
Each instance query predicts one entity, and by feeding all instance queries simultaneously, we can query all entities in parallel.
Ranked #1 on
Nested Named Entity Recognition
on GENIA
DAMO-NLP at SemEval-2022 Task 11: A Knowledge-based System for Multilingual Named Entity Recognition
Our system wins 10 out of 13 tracks in the MultiCoNER shared task.
Boosting RGB-D Saliency Detection by Leveraging Unlabeled RGB Images
The depth estimation branch is trained with RGB-D images and then used to estimate the pseudo depth maps for all unlabeled RGB images to form the paired data.
MAGIC: Multimodal relAtional Graph adversarIal inferenCe for Diverse and Unpaired Text-based Image Captioning
We propose the Multimodal relAtional Graph adversarIal inferenCe (MAGIC) framework for diverse and unpaired TextCap.
Relational Graph Learning for Grounded Video Description Generation
Such a setting can help explain the decisions of captioning models and prevents the model from hallucinating object words in its description.
Consensus Graph Representation Learning for Better Grounded Image Captioning
The contemporary visual captioning models frequently hallucinate objects that are not actually in a scene, due to the visual misclassification or over-reliance on priors that resulting in the semantic inconsistency between the visual information and the target lexical words.
Learning to Generate Visual Questions with Noisy Supervision
The task of visual question generation (VQG) aims to generate human-like neural questions from an image and potentially other side information (e. g., answer type or the answer itself).
Self-Supervised Class Incremental Learning
Existing Class Incremental Learning (CIL) methods are based on a supervised classification framework sensitive to data labels.
Towards Communication-Efficient and Privacy-Preserving Federated Representation Learning
This paper investigates the feasibility of federated representation learning under the constraints of communication cost and privacy protection.
Robust Meta-learning with Sampling Noise and Label Noise via Eigen-Reptile
Furthermore, to obtain a more accurate main direction for Eigen-Reptile in the presence of label noise, we further propose Introspective Self-paced Learning (ISPL).
Natural Language Video Localization with Learnable Moment Proposals
Given an untrimmed video and a natural language query, Natural Language Video Localization (NLVL) aims to identify the video moment described by the query.
On the Efficacy of Small Self-Supervised Contrastive Models without Distillation Signals
It is a consensus that small models perform quite poorly under the paradigm of self-supervised contrastive learning.
Adaptive Hierarchical Graph Reasoning with Semantic Coherence for Video-and-Language Inference
Secondly, we introduce semantic coherence learning to explicitly encourage the semantic coherence of the adaptive hierarchical graph network from three hierarchies.
Revisiting Catastrophic Forgetting in Class Incremental Learning
After investigating existing strategies, we observe that there is a lack of study on how to prevent the inter-phase confusion.
CIL: Contrastive Instance Learning Framework for Distantly Supervised Relation Extraction
The journey of reducing noise from distant supervision (DS) generated training data has been started since the DS was first introduced into the relation extraction (RE) task.
Empower Distantly Supervised Relation Extraction with Collaborative Adversarial Training
In this paper, we propose collaborative adversarial training to improve the data utilization, which coordinates virtual adversarial training (VAT) and adversarial training (AT) at different levels.
Deep Learning for Weakly-Supervised Object Detection and Object Localization: A Survey
With the success of deep neural networks in object detection, both WSOD and WSOL have received unprecedented attention.
A Sequence-to-Set Network for Nested Named Entity Recognition
We utilize a non-autoregressive decoder to predict the final set of entities in one pass, in which we are able to capture dependencies between entities.
Ranked #3 on
Nested Named Entity Recognition
on ACE 2005
VL-NMS: Breaking Proposal Bottlenecks in Two-Stage Visual-Language Matching
In this paper, we argue that these methods overlook an obvious \emph{mismatch} between the roles of proposals in the two stages: they generate proposals solely based on the detection confidence (i. e., query-agnostic), hoping that the proposals contain all instances mentioned in the text query (i. e., query-aware).
Disentangled Motif-aware Graph Learning for Phrase Grounding
In this paper, we propose a novel graph learning framework for phrase grounding in the image.
Ranked #3 on
Phrase Grounding
on Flickr30k Entities Test
Self-Supervised Noisy Label Learning for Source-Free Unsupervised Domain Adaptation
Usually, the given source domain pre-trained model is expected to optimize with only unlabeled target data, which is termed as source-free unsupervised domain adaptation.
Box Re-Ranking: Unsupervised False Positive Suppression for Domain Adaptive Pedestrian Detection
Therefore, it yields our attention to suppress false positive in each target domain in an unsupervised way.
Semi-Supervised Active Learning for Semi-Supervised Models: Exploit Adversarial Examples With Graph-Based Virtual Labels
Although current mainstream methods begin to combine SSL and AL (SSL-AL) to excavate the diverse expressions of unlabeled samples, these methods' fully supervised task models are still trained only with labeled data.
Robust Meta-learning with Noise via Eigen-Reptile
In particular, we first cast the meta-overfitting problem (overfitting on sampling and label noise) as a gradient noise problem since few available samples cause meta-learner to overfit on existing examples (clean or corrupted) of an individual task at every gradient step.
Differentiable Graph Optimization for Neural Architecture Search
To this end, we learn a differentiable graph neural network as a surrogate model to rank candidate architectures, which enable us to obtain gradient w. r. t the input architectures.
Ask Question with Double Hints: Visual Question Generation with Answer-awareness and Region-reference
The task of visual question generation~(VQG) aims to generate human-like questions from an image and potentially other side information (e. g. answer type or the answer itself).
Run Away From your Teacher: a New Self-Supervised Approach Solving the Puzzle of BYOL
Recently, a newly proposed self-supervised framework Bootstrap Your Own Latent (BYOL) seriously challenges the necessity of negative samples in contrastive-based learning frameworks.
Connection-Adaptive Meta-Learning
In this paper, we aim to obtain better meta-learners by co-optimizing the architecture and meta-weights simultaneously.
A Free Lunch for Unsupervised Domain Adaptive Object Detection without Source Data
However, it is difficult to evaluate the quality of pseudo labels since no labels are available in target domain.
Run Away From your Teacher: Understanding BYOL by a Novel Self-Supervised Approach
Recently, a newly proposed self-supervised framework Bootstrap Your Own Latent (BYOL) seriously challenges the necessity of negative samples in contrastive learning frameworks.
Pixel-Level Cycle Association: A New Perspective for Domain Adaptive Semantic Segmentation
The conventional solution to this task is to minimize the discrepancy between source and target to enable effective knowledge transfer.
Ranked #18 on
Synthetic-to-Real Translation
on SYNTHIA-to-Cityscapes
Federated Unsupervised Representation Learning
FURL poses two new challenges: (1) data distribution shift (Non-IID distribution) among clients would make local models focus on different categories, leading to the inconsistency of representation spaces.
Two Step Joint Model for Drug Drug Interaction Extraction
When patients need to take medicine, particularly taking more than one kind of drug simultaneously, they should be alarmed that there possibly exists drug-drug interaction.
Drug–drug Interaction Extraction
Named Entity Recognition
+2
Topic Adaptation and Prototype Encoding for Few-Shot Visual Storytelling
In this paper, we focus on enhancing the generalization ability of the VIST model by considering the few-shot setting.
Neural-DINF: A Neural Network based Framework for Measuring Document Influence
Measuring the scholarly impact of a document without citations is an important and challenging problem.
Learning Decomposed Representation for Counterfactual Inference
The fundamental problem in treatment effect estimation from observational data is confounder identification and balancing.
Stable Prediction via Leveraging Seed Variable
By assuming the relationships between causal variables and response variable are invariant across data, to address this problem, we propose a conditional independence test based algorithm to separate those causal variables with a seed variable as priori, and adopt them for stable prediction.
Balance-Subsampled Stable Prediction
However, this assumption is often violated in practice because the sample selection bias may induce the distribution shift from training data to test data.
Counterfactual Samples Synthesizing for Robust Visual Question Answering
To reduce the language biases, several recent works introduce an auxiliary question-only model to regularize the training of targeted VQA model, and achieve dominating performance on VQA-CP.
Ranked #1 on
Visual Question Answering
on VQA-CP
(using extra training data)
Bi-Decoder Augmented Network for Neural Machine Translation
Neural Machine Translation (NMT) has become a popular technology in recent years, and the encoder-decoder framework is the mainstream among all the methods.
Deep Neural Network for Fast and Accurate Single Image Super-Resolution via Channel-Attention-based Fusion of Orientation-aware Features
Firstly, we proposed a novel Orientation-Aware feature extraction and fusion Module (OAM), which contains a mixture of 1D and 2D convolutional kernels (i. e., 5 x 1, 1 x 5, and 3 x 3) for extracting orientation-aware features.
Unsupervised Reinforcement Learning of Transferable Meta-Skills for Embodied Navigation
Visual navigation is a task of training an embodied agent by intelligently navigating to a target object (e. g., television) using only visual observations.
reinforcement-learning
Unsupervised Reinforcement Learning
+1
Time2Graph: Revisiting Time Series Modeling with Dynamic Shapelets
Time series modeling has attracted extensive research efforts; however, achieving both reliable efficiency and interpretability from a unified model still remains a challenging problem.
Video Dialog via Progressive Inference and Cross-Transformer
Video dialog is a new and challenging task, which requires the agent to answer questions combining video information with dialog history.
Learning Dynamic Context Augmentation for Global Entity Linking
Despite of the recent success of collective entity linking (EL) methods, these "global" inference methods may yield sub-optimal results when the "all-mention coherence" assumption breaks, and often suffer from high computational cost at the inference stage, due to the complex search space.
Walking with MIND: Mental Imagery eNhanceD Embodied QA
The experimental results and further analysis prove that the agent with the MIND module is superior to its counterparts not only in EQA performance but in many other aspects such as route planning, behavioral interpretation, and the ability to generalize from a few examples.
MacNet: Transferring Knowledge from Machine Comprehension to Sequence-to-Sequence Models
In this paper, we transfer knowledge learned from machine comprehension to the sequence-to-sequence tasks to deepen the understanding of the text.
Discourse Marker Augmented Network with Reinforcement Learning for Natural Language Inference
We observe that people usually use some discourse markers such as "so" or "but" to represent the logical relationship between two sentences.
Ranked #12 on
Natural Language Inference
on SNLI
Informative Visual Storytelling with Cross-modal Rules
To solve this problem, we propose a method to mine the cross-modal rules to help the model infer these informative concepts given certain visual input.
Weak Supervision Enhanced Generative Network for Question Generation
More specifically, we devise a discriminator, Relation Guider, to capture the relations between the whole passage and the associated answer and then the Multi-Interaction mechanism is deployed to transfer the knowledge dynamically for our question generation system.
KCAT: A Knowledge-Constraint Typing Annotation Tool
Fine-grained Entity Typing is a tough task which suffers from noise samples extracted from distant supervision.
ActivityNet-QA: A Dataset for Understanding Complex Web Videos via Question Answering
It is both crucial and natural to extend this research direction to the video domain for video question answering (VideoQA).
Ranked #2 on
Video Question Answering
on ActivityNet-QA
Posterior-regularized REINFORCE for Instance Selection in Distant Supervision
This paper provides a new way to improve the efficiency of the REINFORCE training process.
Improving Distantly-supervised Entity Typing with Compact Latent Space Clustering
Recently, distant supervision has gained great success on Fine-grained Entity Typing (FET).
Cross-relation Cross-bag Attention for Distantly-supervised Relation Extraction
Distant supervision leverages knowledge bases to automatically label instances, thus allowing us to train relation extractor without human annotations.
Representation Learning for Scale-free Networks
Existing network embedding works primarily focus on preserving the microscopic structure, such as the first- and second-order proximity of vertexes, while the macroscopic scale-free property is largely ignored.
NITE: A Neural Inductive Teaching Framework for Domain Specific NER
In domain-specific NER, due to insufficient labeled training data, deep models usually fail to behave normally.
Deeply-Learned Part-Aligned Representations for Person Re-Identification
In this paper, we address the problem of person re-identification, which refers to associating the persons captured from different cameras.
Ranked #86 on
Person Re-Identification
on Market-1501
Video Question Answering via Attribute-Augmented Attention Network Learning
Video Question Answering is a challenging problem in visual information retrieval, which provides the answer to the referenced video content according to the question.
Zero-Shot Recognition using Dual Visual-Semantic Mapping Paths
This mapping is learned on training data of seen classes and is expected to have transfer ability to unseen classes.
Task-driven Visual Saliency and Attention-based Visual Question Answering
Visual question answering (VQA) has witnessed great progress since May, 2015 as a classic problem unifying visual and textual data into a system.
Deep Learning Driven Visual Path Prediction from a Single Image
The highly effective visual representation and deep context models ensure that our framework makes a deep semantic understanding of the scene and motion pattern, consequently improving the performance of the visual path prediction task.
Hierarchical Recurrent Neural Encoder for Video Representation with Application to Captioning
In this paper, we propose a new approach, namely Hierarchical Recurrent Neural Encoder (HRNE), to exploit temporal information of videos.
DeepSaliency: Multi-Task Deep Neural Network Model for Salient Object Detection
A key problem in salient object detection is how to effectively model the semantic properties of salient objects in a data-driven manner.
Online Metric-Weighted Linear Representations for Robust Visual Tracking
Object identification results for an entire video sequence are achieved by systematically combining the tracking information and visual recognition at each frame.
Metric Learning Driven Multi-Task Structured Output Optimization for Robust Keypoint Tracking
As an important and challenging problem in computer vision and graphics, keypoint-based object tracking is typically formulated in a spatio-temporal statistical learning framework.
