Search Results for author:
Found 85 papers, 36 papers with code
SEED: Semantics Enhanced Encoder-Decoder Framework for Scene Text Recognition
Scene text recognition is a hot research topic in computer vision.
An Empirical Study of Instruction-tuning Large Language Models in Chinese
This paper will release a powerful Chinese LLMs that is comparable to ChatGLM.
Language Prior Is Not the Only Shortcut: A Benchmark for Shortcut Learning in VQA
To overcome this limitation, we propose a new dataset that considers varying types of shortcuts by constructing different distribution shifts in multiple OOD test sets.
Check It Again: Progressive Visual Question Answering via Visual Entailment
Besides, they only explore the interaction between image and question, ignoring the semantics of candidate answers.
Check It Again:Progressive Visual Question Answering via Visual Entailment
Besides, they only explore the interaction between image and question, ignoring the semantics of candidate answers.
Empathetic Dialogue Generation via Sensitive Emotion Recognition and Sensible Knowledge Selection
We use a fine-grained encoding strategy which is more sensitive to the emotion dynamics (emotion flow) in the conversations to predict the emotion-intent characteristic of response.
Imagine by Reasoning: A Reasoning-Based Implicit Semantic Data Augmentation for Long-Tailed Classification
Since the covariance matrix of each category represents the feature transformation directions, we can sample new directions from similar categories to generate definitely different instances.
Ranked #50 on
Long-tail Learning
on CIFAR-10-LT (ρ=10)
Gaussian Constrained Attention Network for Scene Text Recognition
In this paper, we propose Gaussian Constrained Attention Network to deal with this problem.
Neutral Utterances are Also Causes: Enhancing Conversational Causal Emotion Entailment with Social Commonsense Knowledge
Conversational Causal Emotion Entailment aims to detect causal utterances for a non-neutral targeted utterance from a conversation.
Ranked #1 on
Causal Emotion Entailment
on RECCON
TPSNet: Reverse Thinking of Thin Plate Splines for Arbitrary Shape Scene Text Representation
The research focus of scene text detection and recognition has shifted to arbitrary shape text in recent years, where the text shape representation is a fundamental problem.
Ranked #5 on
Text Spotting
on SCUT-CTW1500
A Hierarchical Transformer with Speaker Modeling for Emotion Recognition in Conversation
It can be regarded as a personalized and interactive emotion recognition task, which is supposed to consider not only the semantic information of text but also the influences from speakers.
Ranked #33 on
Emotion Recognition in Conversation
on IEMOCAP
Past, Present, and Future: Conversational Emotion Recognition through Structural Modeling of Psychological Knowledge
Furthermore, we utilize CSK to enrich edges with knowledge representations and process the SKAIG with a graph transformer.
Ranked #9 on
Emotion Recognition in Conversation
on DailyDialog
A Win-win Deal: Towards Sparse and Robust Pre-trained Language Models
In response to the efficiency problem, recent studies show that dense PLMs can be replaced with sparse subnetworks without hurting the performance.
PIMNet: A Parallel, Iterative and Mimicking Network for Scene Text Recognition
In this paper, we propose a Parallel, Iterative and Mimicking Network (PIMNet) to balance accuracy and efficiency.
On the Exploration of Incremental Learning for Fine-grained Image Retrieval
On the other hand, fine-tuning the learned representation only with the new classes leads to catastrophic forgetting.
Learning Class-Transductive Intent Representations for Zero-shot Intent Detection
A critical problem behind these limitations is that the representations of unseen intents cannot be learned in the training stage.
Learning to Win Lottery Tickets in BERT Transfer via Task-agnostic Mask Training
Firstly, we discover that the success of magnitude pruning can be attributed to the preserved pre-training performance, which correlates with the downstream transferability.
Towards Robust Visual Question Answering: Making the Most of Biased Samples via Contrastive Learning
However, these models reveal a trade-off that the improvements on OOD data severely sacrifice the performance on the in-distribution (ID) data (which is dominated by the biased samples).
Combo of Thinking and Observing for Outside-Knowledge VQA
Some existing solutions draw external knowledge into the cross-modality space which overlooks the much vaster textual knowledge in natural-language space, while others transform the image into a text that further fuses with the textual knowledge into the natural-language space and completely abandons the use of visual features.
Video Cloze Procedure for Self-Supervised Spatio-Temporal Learning
As a proxy task, it converts rich self-supervised representations into video clip operations (options), which enhances the flexibility and reduces the complexity of representation learning.
Ranked #11 on
Self-supervised Video Retrieval
on HMDB51
UATVR: Uncertainty-Adaptive Text-Video Retrieval
In the refined embedding space, we represent text-video pairs as probabilistic distributions where prototypes are sampled for matching evaluation.
AREA: Adaptive Reweighting via Effective Area for Long-Tailed Classification
Consequently, we estimate the size of the spanned space for each category, namely effective area, by detailedly analyzing its samples' distribution.
TAKE: Topic-shift Aware Knowledge sElection for Dialogue Generation
The knowledge selector generally constructs a query based on the dialogue context and selects the most appropriate knowledge to help response generation.
COST-EFF: Collaborative Optimization of Spatial and Temporal Efficiency with Slenderized Multi-exit Language Models
Motivated by such considerations, we propose a collaborative optimization for PLMs that integrates static model compression and dynamic inference acceleration.
Applying Self-supervised Learning to Network Intrusion Detection for Network Flows with Graph Neural Network
To the best of our knowledge, it is the first GNN-based self-supervised method for the multiclass classification of network flows in NIDS.
Automated Spectral Kernel Learning
The generalization performance of kernel methods is largely determined by the kernel, but common kernels are stationary thus input-independent and output-independent, that limits their applications on complicated tasks.
Compressing And Debiasing Vision-Language Pre-Trained Models for Visual Question Answering
To this end, we systematically study the design of a training and compression pipeline to search the subnetworks, as well as the assignment of sparsity to different modality-specific modules.
Exploring Instance Relations for Unsupervised Feature Embedding
Despite the great progress achieved in unsupervised feature embedding, existing contrastive learning methods typically pursue view-invariant representations through attracting positive sample pairs and repelling negative sample pairs in the embedding space, while neglecting to systematically explore instance relations.
Towards Sharp Analysis for Distributed Learning with Random Features
In this paper, using refined proof techniques, we first extend the optimal rates for distributed learning with random features to the non-attainable case.
Marginal Utility Diminishes: Exploring the Minimum Knowledge for BERT Knowledge Distillation
In this paper, however, we observe that although distilling the teacher's hidden state knowledge (HSK) is helpful, the performance gain (marginal utility) diminishes quickly as more HSK is distilled.
Progressive Cluster Purification for Unsupervised Feature Learning
In this work, we propose a novel clustering based method, which, by iteratively excluding class inconsistent samples during progressive cluster formation, alleviates the impact of noise samples in a simple-yet-effective manner.
Target Really Matters: Target-aware Contrastive Learning and Consistency Regularization for Few-shot Stance Detection
Despite the significant progress on this task, it is extremely time-consuming and budget-unfriendly to collect sufficient high-quality labeled data for every new target under fully-supervised learning, whereas unlabeled data can be collected easier.
Max-Diversity Distributed Learning: Theory and Algorithms
We study the risk performance of distributed learning for the regularization empirical risk minimization with fast convergence rate, substantially improving the error analysis of the existing divide-and-conquer based distributed learning.
Multi-Class Learning: From Theory to Algorithm
In this paper, we study the generalization performance of multi-class classification and obtain a shaper data-dependent generalization error bound with fast convergence rate, substantially improving the state-of-art bounds in the existing data-dependent generalization analysis.
Efficient Cross-Validation for Semi-Supervised Learning
In this paper, we provide a method to approximate the CV for manifold regularization based on a notion of robust statistics, called Bouligand influence function (BIF).
Curved Text Detection in Natural Scene Images with Semi- and Weakly-Supervised Learning
The performance of the proposed method is comparable with the state-of-the-art methods with only 10% pixel-level annotated data and 90% rectangle-level weakly annotated data.
Semi-supervised Vector-valued Learning: Improved Bounds and Algorithms
Vector-valued learning, where the output space admits a vector-valued structure, is an important problem that covers a broad family of important domains, e. g. multi-task learning and transfer learning.
Weighted Distributed Differential Privacy ERM: Convex and Non-convex
By detailed theoretical analysis, we show that in distributed setting, the noise bound and the excess empirical risk bound can be improved by considering different weights held by multiple parties.
Ranking and Sampling in Open-Domain Question Answering
Open-domain question answering (OpenQA) aims to answer questions based on a number of unlabeled paragraphs.
Data Heterogeneity Differential Privacy: From Theory to Algorithm
Traditionally, the random noise is equally injected when training with different data instances in the field of differential privacy (DP).
Input Perturbation: A New Paradigm between Central and Local Differential Privacy
By adding noise to the original training data and training with the `perturbed data', we achieve ($\epsilon$,$\delta$)-differential privacy on the final model, along with some kind of privacy on the original data.
Convolutional Spectral Kernel Learning
Recently, non-stationary spectral kernels have drawn much attention, owing to its powerful feature representation ability in revealing long-range correlations and input-dependent characteristics.
Theoretical Analysis of Divide-and-Conquer ERM: Beyond Square Loss and RKHS
However, the studies on learning theory for general loss functions and hypothesis spaces remain limited.
Nearly Optimal Clustering Risk Bounds for Kernel K-Means
In this paper, we study the statistical properties of kernel $k$-means and obtain a nearly optimal excess clustering risk bound, substantially improving the state-of-art bounds in the existing clustering risk analyses.
Keyphrase Prediction With Pre-trained Language Model
Considering the different characteristics of extractive and generative methods, we propose to divide the keyphrase prediction into two subtasks, i. e., present keyphrase extraction (PKE) and absent keyphrase generation (AKG), to fully exploit their respective advantages.
Self-Training for Domain Adaptive Scene Text Detection
To address this problem, we propose a self-training framework to automatically mine hard examples with pseudo-labels from unannotated videos or images.
Neural Architecture Optimization with Graph VAE
The encoder and the decoder belong to a graph VAE, mapping architectures between continuous representations and network architectures.
FC2RN: A Fully Convolutional Corner Refinement Network for Accurate Multi-Oriented Scene Text Detection
Accurate detection of multi-oriented text with large variations of scales, orientations, and aspect ratios is of great significance.
Expert Training: Task Hardness Aware Meta-Learning for Few-Shot Classification
Some works in the optimization of deep neural networks have shown that a better arrangement of training data can make the classifier converge faster and perform better.
Two-Level Residual Distillation based Triple Network for Incremental Object Detection
Modern object detection methods based on convolutional neural network suffer from severe catastrophic forgetting in learning new classes without original data.
Exploring Relations in Untrimmed Videos for Self-Supervised Learning
Then a designed sampling strategy is used to model relations for video clips.
New Ideas and Trends in Deep Multimodal Content Understanding: A Review
The focus of this survey is on the analysis of two modalities of multimodal deep learning: image and text.
Dirichlet type extensions of Euler sums
In this paper, we study the alternating Euler $T$-sums and $\S$-sums, which are infinite series involving (alternating) odd harmonic numbers, and have similar forms and close relations to the Dirichlet beta functions.
Number Theory
Deep Learning for Instance Retrieval: A Survey
In recent years a vast amount of visual content has been generated and shared from many fields, such as social media platforms, medical imaging, and robotics.
Rescuing Deep Hashing from Dead Bits Problem
The proposed gradient amplifier and error-aware quantization loss are compatible with a variety of deep hashing methods.
Towards Sharper Utility Bounds for Differentially Private Pairwise Learning
Pairwise learning focuses on learning tasks with pairwise loss functions, depends on pairs of training instances, and naturally fits for modeling relationships between pairs of samples.
Modeling Intra and Inter-modality Incongruity for Multi-Modal Sarcasm Detection
Inspired by this, we propose a BERT architecture-based model, which concentrates on both intra and inter-modality incongruity for multi-modal sarcasm detection.
MMF: Multi-Task Multi-Structure Fusion for Hierarchical Image Classification
Hierarchical classification is significant for complex tasks by providing multi-granular predictions and encouraging better mistakes.
Multi-View Correlation Distillation for Incremental Object Detection
Due to the storage burden and the privacy of old data, sometimes it is impractical to train the model from scratch with both old and new data.
Video 3D Sampling for Self-supervised Representation Learning
As a result, we can leverage the spatial information (the size of objects), temporal information (the direction and magnitude of motions) as our learning target.
Which and Where to Focus: A Simple yet Accurate Framework for Arbitrary-Shaped Nearby Text Detection in Scene Images
In this paper, we propose a simple yet effective method for accurate arbitrary-shaped nearby scene text detection.
Mask is All You Need: Rethinking Mask R-CNN for Dense and Arbitrary-Shaped Scene Text Detection
We propose to use an MLP decoder instead of the "deconv-conv" decoder in the mask head, which alleviates the issue and promotes robustness significantly.
Dense Semantic Contrast for Self-Supervised Visual Representation Learning
Concretely, these downstream tasks require more accurate representation, in other words, the pixels from the same object must belong to a shared semantic category, which is lacking in the previous methods.
Towards Escaping from Language Bias and OCR Error: Semantics-Centered Text Visual Question Answering
Texts in scene images convey critical information for scene understanding and reasoning.
Optical Character Recognition
Optical Character Recognition (OCR)
+3
Stability and Generalization of Differentially Private Minimax Problems
To the best of our knowledge, this is the first time to analyze the generalization performance of general minimax paradigm, taking differential privacy into account.
Sharper Utility Bounds for Differentially Private Models
In this paper, by introducing Generalized Bernstein condition, we propose the first $\mathcal{O}\big(\frac{\sqrt{p}}{n\epsilon}\big)$ high probability excess population risk bound for differentially private algorithms under the assumptions $G$-Lipschitz, $L$-smooth, and Polyak-{\L}ojasiewicz condition, based on gradient perturbation method.
UNITS: Unsupervised Intermediate Training Stage for Scene Text Detection
Recent scene text detection methods are almost based on deep learning and data-driven.
Towards Practical Differential Privacy in Data Analysis: Understanding the Effect of Epsilon on Utility in Private ERM
In this paper, we focus our attention on private Empirical Risk Minimization (ERM), which is one of the most commonly used data analysis method.
Handling Label Uncertainty for Camera Incremental Person Re-Identification
Specifically, new data collected from new cameras may probably contain an unknown proportion of identities seen before.
Question-Interlocutor Scope Realized Graph Modeling over Key Utterances for Dialogue Reading Comprehension
Although these tasks are effective, there are still urging problems: (1) randomly masking speakers regardless of the question cannot map the speaker mentioned in the question to the corresponding speaker in the dialogue, and ignores the speaker-centric nature of utterances.
Beyond Instance Discrimination: Relation-aware Contrastive Self-supervised Learning
In this paper, we present relation-aware contrastive self-supervised learning (ReCo) to integrate instance relations, i. e., global distribution relation and local interpolation relation, into the CSL framework in a plug-and-play fashion.
Operation-level Progressive Differentiable Architecture Search
It can effectively alleviate the unfair competition between operations during the search phase of DARTS by offsetting the inherent unfair advantage of the skip connection over other operations.
Improving Differentiable Architecture Search via Self-Distillation
Differentiable Architecture Search (DARTS) is a simple yet efficient Neural Architecture Search (NAS) method.
Robust Neural Architecture Search
Neural Architectures Search (NAS) becomes more and more popular over these years.
Towards Robust Real-Time Scene Text Detection: From Semantic to Instance Representation Learning
Different from existing methods which integrate multiple-granularity features or multiple outputs, we resort to the perspective of representation learning in which auxiliary tasks are utilized to enable the encoder to jointly learn robust features with the main task of per-pixel classification during optimization.
A Survey on Model Compression for Large Language Models
As these challenges become increasingly pertinent, the field of model compression has emerged as a pivotal research area to alleviate these limitations.
Multi-level Adaptive Contrastive Learning for Knowledge Internalization in Dialogue Generation
Knowledge-grounded dialogue generation aims to mitigate the issue of text degeneration by incorporating external knowledge to supplement the context.
Enhancing Empathetic and Emotion Support Dialogue Generation with Prophetic Commonsense Inference
The interest in Empathetic and Emotional Support conversations among the public has significantly increased.
ASWT-SGNN: Adaptive Spectral Wavelet Transform-based Self-Supervised Graph Neural Network
Graph Comparative Learning (GCL) is a self-supervised method that combines the advantages of Graph Convolutional Networks (GCNs) and comparative learning, making it promising for learning node representations.
IPAD: Iterative, Parallel, and Diffusion-based Network for Scene Text Recognition
Nowadays, scene text recognition has attracted more and more attention due to its diverse applications.
FedNS: A Fast Sketching Newton-Type Algorithm for Federated Learning
We provide convergence analysis based on statistical learning for the federated Newton sketch approaches.
Distilling Mathematical Reasoning Capabilities into Small Language Models
This work addresses the challenge of democratizing advanced Large Language Models (LLMs) by compressing their mathematical reasoning capabilities into sub-billion parameter Small Language Models (SLMs) without compromising performance.
Are Large Language Models Table-based Fact-Checkers?
Finally, we analyze some possible directions to promote the accuracy of TFV via LLMs, which is beneficial to further research of table reasoning.
Previously on the Stories: Recap Snippet Identification for Story Reading
Similar to the "previously-on" scenes in TV shows, recaps can help book reading by recalling the readers' memory about the important elements in previous texts to better understand the ongoing plot.
