Search Results for author:
Found 280 papers, 143 papers with code
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
This paper presents a new vision Transformer, called Swin Transformer, that capably serves as a general-purpose backbone for computer vision.
Ranked #2 on
Image Classification
on OmniBenchmark
CrossWOZ: A Large-Scale Chinese Cross-Domain Task-Oriented Dialogue Dataset
To advance multi-domain (cross-domain) dialogue modeling as well as alleviate the shortage of Chinese task-oriented datasets, we propose CrossWOZ, the first large-scale Chinese Cross-Domain Wizard-of-Oz task-oriented dataset.
Swin Transformer V2: Scaling Up Capacity and Resolution
Three main techniques are proposed: 1) a residual-post-norm method combined with cosine attention to improve training stability; 2) A log-spaced continuous position bias method to effectively transfer models pre-trained using low-resolution images to downstream tasks with high-resolution inputs; 3) A self-supervised pre-training method, SimMIM, to reduce the needs of vast labeled images.
Ranked #4 on
Image Classification
on ImageNet V2
(using extra training data)
An Empirical Study of Spatial Attention Mechanisms in Deep Networks
Attention mechanisms have become a popular component in deep neural networks, yet there has been little examination of how different influencing factors and methods for computing attention from these factors affect performance.
MMDetection: Open MMLab Detection Toolbox and Benchmark
In this paper, we introduce the various features of this toolbox.
MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems
This paper describes both the API design and the system implementation of MXNet, and explains how embedding of both symbolic expression and tensor operation is handled in a unified fashion.
Star-Transformer
Although Transformer has achieved great successes on many NLP tasks, its heavy structure with fully-connected attention connections leads to dependencies on large training data.
Ranked #13 on
Sentiment Analysis
on SST-5 Fine-grained classification
Named Entity Recognition (NER)
Natural Language Inference
+2
Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks
Advancing research in the emerging field of deep graph learning requires new tools to support tensor computation over graphs.
Ranked #35 on
Node Classification
on Cora
DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs
To minimize the overheads associated with distributed computations, DistDGL uses a high-quality and light-weight min-cut graph partitioning algorithm along with multiple balancing constraints.
Learning Hierarchical Graph Neural Networks for Image Clustering
Our hierarchical GNN uses a novel approach to merge connected components predicted at each level of the hierarchy to form a new graph at the next level.
BP-Transformer: Modelling Long-Range Context via Binary Partitioning
The Transformer model is widely successful on many natural language processing tasks.
Ranked #1 on
Machine Translation
on IWSLT2015 Chinese-English
Local Relation Networks for Image Recognition
The convolution layer has been the dominant feature extractor in computer vision for years.
Ranked #867 on
Image Classification
on ImageNet
Self-Supervised Learning with Swin Transformers
We are witnessing a modeling shift from CNN to Transformers in computer vision.
Ranked #75 on
Self-Supervised Image Classification
on ImageNet
On Data Scaling in Masked Image Modeling
Our study reveals that: (i) Masked image modeling is also demanding on larger data.
Disentangled Non-Local Neural Networks
This paper first studies the non-local block in depth, where we find that its attention computation can be split into two terms, a whitened pairwise term accounting for the relationship between two pixels and a unary term representing the saliency of every pixel.
Ranked #20 on
Semantic Segmentation
on Cityscapes test
(using extra training data)
Video Swin Transformer
The vision community is witnessing a modeling shift from CNNs to Transformers, where pure Transformer architectures have attained top accuracy on the major video recognition benchmarks.
Ranked #28 on
Action Classification
on Kinetics-600
(using extra training data)
DGL-KE: Training Knowledge Graph Embeddings at Scale
Experiments on knowledge graphs consisting of over 86M nodes and 338M edges show that DGL-KE can compute embeddings in 100 minutes on an EC2 instance with 8 GPUs and 30 minutes on an EC2 cluster with 4 machines with 48 cores/machine.
Distributed, Parallel, and Cluster Computing
Relation Networks for Object Detection
Although it is well believed for years that modeling relations between objects would help object recognition, there has not been evidence that the idea is working in the deep learning era.
End-to-End Semi-Supervised Object Detection with Soft Teacher
This paper presents an end-to-end semi-supervised object detection approach, in contrast to previous more complex multi-stage methods.
Ranked #6 on
Semi-Supervised Object Detection
on COCO 100% labeled data
(using extra training data)
SimMIM: A Simple Framework for Masked Image Modeling
We also leverage this approach to facilitate the training of a 3B model (SwinV2-G), that by $40\times$ less data than that in previous practice, we achieve the state-of-the-art on four representative vision benchmarks.
Representation Learning
Self-Supervised Image Classification
+1
ConvLab: Multi-Domain End-to-End Dialog System Platform
We present ConvLab, an open-source multi-domain end-to-end dialog system platform, that enables researchers to quickly set up experiments with reusable components and compare a large set of different approaches, ranging from conventional pipeline systems to end-to-end neural models, in common environments.
EVA: An Open-Domain Chinese Dialogue System with Large-Scale Generative Pre-Training
Although pre-trained language models have remarkably enhanced the generation ability of dialogue systems, open-domain Chinese dialogue systems are still limited by the dialogue data and the model size compared with English ones.
FP8-LM: Training FP8 Large Language Models
In this paper, we explore FP8 low-bit data formats for efficient training of large language models (LLMs).
ConvLab-2: An Open-Source Toolkit for Building, Evaluating, and Diagnosing Dialogue Systems
We present ConvLab-2, an open-source toolkit that enables researchers to build task-oriented dialogue systems with state-of-the-art models, perform an end-to-end evaluation, and diagnose the weakness of systems.
InstructDiffusion: A Generalist Modeling Interface for Vision Tasks
We present InstructDiffusion, a unifying and generic framework for aligning computer vision tasks with human instructions.
Propagate Yourself: Exploring Pixel-Level Consistency for Unsupervised Visual Representation Learning
We argue that the power of contrastive learning has yet to be fully unleashed, as current methods are trained only on instance-level pretext tasks, leading to representations that may be sub-optimal for downstream tasks requiring dense pixel predictions.
EVA2.0: Investigating Open-Domain Chinese Dialogue Systems with Large-Scale Pre-Training
Large-scale pre-training has shown remarkable performance in building open-domain dialogue systems.
Dense RepPoints: Representing Visual Objects with Dense Point Sets
We present a new object representation, called Dense RepPoints, that utilizes a large set of points to describe an object at multiple levels, including both box level and pixel level.
RepPoints V2: Verification Meets Regression for Object Detection
Though RepPoints provides high performance, we find that its heavy reliance on regression for object localization leaves room for improvement.
Ranked #27 on
Object Detection
on COCO-O
Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity
This survey addresses the crucial issue of factuality in Large Language Models (LLMs).
Side Adapter Network for Open-Vocabulary Semantic Segmentation
A side network is attached to a frozen CLIP model with two branches: one for predicting mask proposals, and the other for predicting attention bias which is applied in the CLIP model to recognize the class of masks.
TrojanZoo: Towards Unified, Holistic, and Practical Evaluation of Neural Backdoors
To bridge this gap, we design and implement TROJANZOO, the first open-source platform for evaluating neural backdoor attacks/defenses in a unified, holistic, and practical manner.
A Closer Look at Local Aggregation Operators in Point Cloud Analysis
Our investigation reveals that despite the different designs of these operators, all of these operators make surprisingly similar contributions to the network performance under the same network input and feature numbers and result in the state-of-the-art accuracy on standard benchmarks.
Ranked #4 on
3D Semantic Segmentation
on PartNet
Group-Free 3D Object Detection via Transformers
Instead of grouping local points to each object candidate, our method computes the feature of an object from all the points in the point cloud with the help of an attention mechanism in the Transformers \cite{vaswani2017attention}, where the contribution of each point is automatically learned in the network training.
Ranked #3 on
3D Object Detection
on SUN-RGBD
Label Sleuth: From Unlabeled Text to a Classifier in a Few Hours
Text classification can be useful in many real-world scenarios, saving a lot of time for end users.
Contrastive Learning Rivals Masked Image Modeling in Fine-tuning via Feature Distillation
These properties, which we aggregately refer to as optimization friendliness, are identified and analyzed by a set of attention- and optimization-related diagnosis tools.
Ranked #2 on
Instance Segmentation
on COCO test-dev
(using extra training data)
Improving CLIP Fine-tuning Performance
Experiments suggest that the feature map distillation approach significantly boosts the fine-tuning performance of CLIP models on several typical downstream vision tasks.
A Unified Generative Framework for Various NER Subtasks
To that end, we propose to formulate the NER subtasks as an entity span sequence generation task, which can be solved by a unified sequence-to-sequence (Seq2Seq) framework.
Ranked #10 on
Nested Named Entity Recognition
on GENIA
Efficiently Measuring the Cognitive Ability of LLMs: An Adaptive Testing Perspective
Different tests for different models using efficient adaptive testing -- we believe this has the potential to become a new norm in evaluating large language models.
DETR Doesn't Need Multi-Scale or Locality Design
This paper presents an improved DETR detector that maintains a "plain" nature: using a single-scale feature map and global cross-attention calculations without specific locality constraints, in contrast to previous leading DETR-based detectors that reintroduce architectural inductive biases of multi-scale and locality into the decoder.
DETR Does Not Need Multi-Scale or Locality Design
This paper presents an improved DETR detector that maintains a "plain" nature: using a single-scale feature map and global cross-attention calculations without specific locality constraints, in contrast to previous leading DETR-based detectors that reintroduce architectural inductive biases of multi-scale and locality into the decoder.
A Simple Baseline for Open-Vocabulary Semantic Segmentation with Pre-trained Vision-language Model
However, semantic segmentation and the CLIP model perform on different visual granularity, that semantic segmentation processes on pixels while CLIP performs on images.
Fusing Hierarchical Convolutional Features for Human Body Segmentation and Clothing Fashion Classification
The clothing fashion reflects the common aesthetics that people share with each other in dressing.
Revealing the Dark Secrets of Masked Image Modeling
In this paper, we compare MIM with the long-dominant supervised pre-trained models from two perspectives, the visualizations and the experiments, to uncover their key representational differences.
Ranked #3 on
Depth Estimation
on NYU-Depth V2
Segment and Caption Anything
We propose a method to efficiently equip the Segment Anything Model (SAM) with the ability to generate regional captions.
Hallucination of Multimodal Large Language Models: A Survey
By drawing the granular classification and landscapes of hallucination causes, evaluation benchmarks, and mitigation methods, this survey aims to deepen the understanding of hallucinations in MLLMs and inspire further advancements in the field.
TinyMIM: An Empirical Study of Distilling MIM Pre-trained Models
Our TinyMIM model of tiny size achieves 79. 6% top-1 accuracy on ImageNet-1K image classification, which sets a new record for small vision models of the same size and computation budget.
Negative Margin Matters: Understanding Margin in Few-shot Classification
This paper introduces a negative margin loss to metric learning based few-shot learning methods.
A Unified Generative Framework for Aspect-Based Sentiment Analysis
Aspect-based Sentiment Analysis (ABSA) aims to identify the aspect terms, their corresponding sentiment polarities, and the opinion terms.
Ranked #1 on
Aspect Sentiment Triplet Extraction
on SemEval
Aspect-Based Sentiment Analysis
Aspect-oriented Opinion Extraction
+2
PSALM: Pixelwise SegmentAtion with Large Multi-Modal Model
PSALM is a powerful extension of the Large Multi-modal Model (LMM) to address the segmentation task challenges.
CoLAKE: Contextualized Language and Knowledge Embedding
With the emerging branch of incorporating factual knowledge into pre-trained language models such as BERT, most existing models consider shallow, static, and separately pre-trained entity embeddings, which limits the performance gains of these models.
ConvLab-3: A Flexible Dialogue System Toolkit Based on a Unified Data Format
Task-oriented dialogue (TOD) systems function as digital assistants, guiding users through various tasks such as booking flights or finding restaurants.
All in Tokens: Unifying Output Space of Visual Tasks via Soft Token
With these new techniques and other designs, we show that the proposed general-purpose task-solver can perform both instance segmentation and depth estimation well.
Ranked #14 on
Monocular Depth Estimation
on NYU-Depth V2
CycleGT: Unsupervised Graph-to-Text and Text-to-Graph Generation via Cycle Training
Due to the difficulty and high cost of data collection, the supervised data available in the two fields are usually on the magnitude of tens of thousands, for example, 18K in the WebNLG~2017 dataset after preprocessing, which is far fewer than the millions of data for other tasks such as machine translation.
Fork or Fail: Cycle-Consistent Training with Many-to-One Mappings
Cycle-consistent training is widely used for jointly learning a forward and inverse mapping between two domains of interest without the cumbersome requirement of collecting matched pairs within each domain.
Exploring Discrete Diffusion Models for Image Captioning
The image captioning task is typically realized by an auto-regressive method that decodes the text tokens one by one.
An Efficient Neighborhood-based Interaction Model for Recommendation on Heterogeneous Graph
To the best of our knowledge, this is the first work providing an efficient neighborhood-based interaction model in the HIN-based recommendations.
GraphHINGE: Learning Interaction Models of Structured Neighborhood on Heterogeneous Information Network
Heterogeneous information network (HIN) has been widely used to characterize entities of various types and their complex relations.
Multi-Oriented Text Detection with Fully Convolutional Networks
In this paper, we propose a novel approach for text detec- tion in natural images.
Ranked #40 on
Scene Text Detection
on ICDAR 2015
DS-TransUNet:Dual Swin Transformer U-Net for Medical Image Segmentation
To alleviate these problems, we propose a novel deep medical image segmentation framework called Dual Swin Transformer U-Net (DS-TransUNet), which might be the first attempt to concurrently incorporate the advantages of hierarchical Swin Transformer into both encoder and decoder of the standard U-shaped architecture to enhance the semantic segmentation quality of varying medical images.
Spatially Adaptive Inference with Stochastic Feature Sampling and Interpolation
In the feature maps of CNNs, there commonly exists considerable spatial redundancy that leads to much repetitive processing.
Bridging the Gap to Real-World Object-Centric Learning
Humans naturally decompose their environment into entities at the appropriate level of abstraction to act in the world.
Bootstrap Your Object Detector via Mixed Training
We introduce MixTraining, a new training paradigm for object detection that can improve the performance of existing detectors for free.
Cross-Modal Retrieval: A Systematic Review of Methods and Future Directions
With the exponential surge in diverse multi-modal data, traditional uni-modal retrieval methods struggle to meet the needs of users demanding access to data from various modalities.
Parametric Instance Classification for Unsupervised Visual Feature Learning
This paper presents parametric instance classification (PIC) for unsupervised visual feature learning.
DeepMIM: Deep Supervision for Masked Image Modeling
Deep supervision, which involves extra supervisions to the intermediate features of a neural network, was widely used in image classification in the early deep learning era since it significantly reduces the training difficulty and eases the optimization like avoiding gradient vanish over the vanilla training.
Adapting Language Models for Zero-shot Learning by Meta-tuning on Dataset and Prompt Collections
However, the next word prediction training objective is still misaligned with the target zero-shot learning objective.
Graph Neural Networks Inspired by Classical Iterative Algorithms
Despite the recent success of graph neural networks (GNN), common architectures often exhibit significant limitations, including sensitivity to oversmoothing, long-range dependencies, and spurious edges, e. g., as can occur as a result of graph heterophily or adversarial attacks.
Bag of Tricks for Node Classification with Graph Neural Networks
Over the past few years, graph neural networks (GNN) and label propagation-based methods have made significant progress in addressing node classification tasks on graphs.
Ranked #1 on
Node Property Prediction
on ogbn-proteins
Vega-MT: The JD Explore Academy Translation System for WMT22
As for model sizes, we scale the Transformer-Big up to the extremely large model that owns nearly 4. 7 Billion parameters, to fully enhance the model capacity for our Vega-MT.
Ranked #1 on
Machine Translation
on WMT 2022 English-Russian
Semantics Disentangling for Generalized Zero-Shot Learning
Generalized zero-shot learning (GZSL) aims to classify samples under the assumption that some classes are not observable during training.
Prototype-supervised Adversarial Network for Targeted Attack of Deep Hashing
However, deep hashing networks are vulnerable to adversarial examples, which is a practical secure problem but seldom studied in hashing-based retrieval field.
A Survey on Incomplete Multi-view Clustering
However, in practical applications, such as disease diagnosis, multimedia analysis, and recommendation system, it is common to observe that not all views of samples are available in many cases, which leads to the failure of the conventional multi-view clustering methods.
SSEGCN: Syntactic and Semantic Enhanced Graph Convolutional Network for Aspect-based Sentiment Analysis
Furthermore, to combine syntactic structure and semantic information, we equip the attention score matrices by syntactic mask matrices.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+1
Coarse-to-Fine Amodal Segmentation with Shape Prior
To address this issue, we propose a convolution refine module to inject fine-grained information and provide a more precise amodal object segmentation based on visual features and coarse-predicted segmentation.
Transformer on a Diet
Transformer has been widely used thanks to its ability to capture sequence information in an efficient way.
Object-Centric Multiple Object Tracking
Unsupervised object-centric learning methods allow the partitioning of scenes into entities without additional localization information and are excellent candidates for reducing the annotation burden of multiple-object tracking (MOT) pipelines.
MConv: An Environment for Multimodal Conversational Search across Multiple Domains
Second, a set of benchmark results for dialogue state tracking, conversational recommendation, response generation as well as a unified model for multiple tasks are reported.
Ranked #2 on
Dialogue State Tracking
on MMConv
Zero-1-to-3: Domain-level Zero-shot Cognitive Diagnosis via One Batch of Early-bird Students towards Three Diagnostic Objectives
Consequently, we refine the cognitive states of cold-start students as diagnostic outcomes via virtual data, aligning with the diagnosis-oriented goal.
Relation-Aware Diffusion Model for Controllable Poster Layout Generation
To advance research in this field, we have constructed a poster layout dataset named CGL-Dataset V2.
Relation of the Relations: A New Paradigm of the Relation Extraction Problem
In natural language, often multiple entities appear in the same text.
It is AI's Turn to Ask Humans a Question: Question-Answer Pair Generation for Children's Story Books
Existing question answering (QA) techniques are created mainly to answer questions asked by humans.
DeepZero: Scaling up Zeroth-Order Optimization for Deep Model Training
Our extensive experiments show that DeepZero achieves state-of-the-art (SOTA) accuracy on ResNet-20 trained on CIFAR-10, approaching FO training performance for the first time.
Efficient Generation and Processing of Word Co-occurrence Networks Using corpus2graph
Corpus2graph is an open-source NLP-application-oriented tool that generates a word co-occurrence network from a large corpus.
StoryBuddy: A Human-AI Collaborative Chatbot for Parent-Child Interactive Storytelling with Flexible Parental Involvement
Despite its benefits for children's skill development and parent-child bonding, many parents do not often engage in interactive storytelling by having story-related dialogues with their child due to limited availability or challenges in coming up with appropriate questions.
Fantastic Questions and Where to Find Them: FairytaleQA -- An Authentic Dataset for Narrative Comprehension
Through benchmarking with QG models, we show that the QG model trained on FairytaleQA is capable of asking high-quality and more diverse questions.
Ranked #1 on
Question Generation
on FairytaleQA
Plan, Verify and Switch: Integrated Reasoning with Diverse X-of-Thoughts
As large language models (LLMs) have shown effectiveness with different prompting methods, such as Chain of Thought, Program of Thought, we find that these methods have formed a great complementarity to each other on math reasoning tasks.
GenWiki: A Dataset of 1.3 Million Content-Sharing Text and Graphs for Unsupervised Graph-to-Text Generation
With a human-annotated test set, we provide this new benchmark dataset for future research on unsupervised text generation from knowledge graphs.
Ranked #1 on
Unsupervised KG-to-Text Generation
on GenWiki (Fine)
Learning Enhanced Representations for Tabular Data via Neighborhood Propagation
Prediction over tabular data is an essential and fundamental problem in many important downstream tasks.
Masked Structural Growth for 2x Faster Language Model Pre-training
In terms of growth schedule, the impact of each single dimension on a schedule's efficiency is under-explored by existing work.
An AMR-based Link Prediction Approach for Document-level Event Argument Extraction
Motivated by the fact that all event structures can be inferred from AMR, this work reformulates EAE as a link prediction problem on AMR graphs.
TransAttUnet: Multi-level Attention-guided U-Net with Transformer for Medical Image Segmentation
Accurate segmentation of organs or lesions from medical images is crucial for reliable diagnosis of diseases and organ morphometry.
Evaluating Open-QA Evaluation
This study focuses on the evaluation of the Open Question Answering (Open-QA) task, which can directly estimate the factuality of large language models (LLMs).
Improved Deep Hashing with Soft Pairwise Similarity for Multi-label Image Retrieval
In this paper, a new deep hashing method is proposed for multi-label image retrieval by re-defining the pairwise similarity into an instance similarity, where the instance similarity is quantified into a percentage based on the normalized semantic labels.
Transductive Zero-Shot Hashing for Multilabel Image Retrieval
Given semantic annotations such as class labels and pairwise similarities of the training data, hashing methods can learn and generate effective and compact binary codes.
Representation Learning on Spatial Networks
Specifically, a provably information-lossless and roto-translation invariant representation of spatial information on networks is presented.
Trojaning Language Models for Fun and Profit
Recent years have witnessed the emergence of a new paradigm of building natural language processing (NLP) systems: general-purpose, pre-trained language models (LMs) are composed with simple downstream models and fine-tuned for a variety of NLP tasks.
Towards Robust Neural Networks via Close-loop Control
We connect the robustness of neural networks with optimal control using the geometrical information of underlying data to design the control objective.
AugESC: Dialogue Augmentation with Large Language Models for Emotional Support Conversation
Applying this approach, we construct AugESC, an augmented dataset for the ESC task, which largely extends the scale and topic coverage of the crowdsourced ESConv corpus.
CASE: Aligning Coarse-to-Fine Cognition and Affection for Empathetic Response Generation
Empathetic conversation is psychologically supposed to be the result of conscious alignment and interaction between the cognition and affection of empathy.
Click: Controllable Text Generation with Sequence Likelihood Contrastive Learning
It has always been an important yet challenging problem to control language models to avoid generating texts with undesirable attributes, such as toxic language and unnatural repetition.
Partial Vessels Annotation-based Coronary Artery Segmentation with Self-training and Prototype Learning
To this end, we propose partial vessels annotation (PVA) based on the challenges of coronary artery segmentation and clinical diagnostic characteristics.
4DBInfer: A 4D Benchmarking Toolbox for Graph-Centric Predictive Modeling on Relational DBs
Although RDBs store vast amounts of rich, informative data spread across interconnected tables, the progress of predictive machine learning models as applied to such tasks arguably falls well behind advances in other domains such as computer vision or natural language processing.
Breaking Shortcut: Exploring Fully Convolutional Cycle-Consistency for Video Correspondence Learning
Previous cycle-consistency correspondence learning methods usually leverage image patches for training.
Dual Learning with Dynamic Knowledge Distillation for Partially Relevant Video Retrieval
During the knowledge distillation, an inheritance student branch is devised to absorb the knowledge from the teacher model.
Enhancing Uncertainty-Based Hallucination Detection with Stronger Focus
Large Language Models (LLMs) have gained significant popularity for their impressive performance across diverse fields.
LoRETTA: Low-Rank Economic Tensor-Train Adaptation for Ultra-Low-Parameter Fine-Tuning of Large Language Models
Various parameter-efficient fine-tuning (PEFT) techniques have been proposed to enable computationally efficient fine-tuning while maintaining model performance.
Attentional Neural Network: Feature Selection Using Cognitive Feedback
Attentional Neural Network is a new framework that integrates top-down cognitive bias and bottom-up feature extraction in one coherent architecture.
Towards Compact Neural Networks via End-to-End Training: A Bayesian Tensor Approach with Automatic Rank Determination
This paper presents a novel end-to-end framework for low-rank tensorized training of neural networks.
DORE: Document Ordered Relation Extraction based on Generative Framework
In this paper, we investigate the root cause of the underwhelming performance of the existing generative DocRE models and discover that the culprit is the inadequacy of the training paradigm, instead of the capacities of the models.
RLET: A Reinforcement Learning Based Approach for Explainable QA with Entailment Trees
RLET iteratively performs single step reasoning with sentence selection and deduction generation modules, from which the training signal is accumulated across the tree with elaborately designed aligned reward function that is consistent with the evaluation.
Partition-A-Medical-Image: Extracting Multiple Representative Sub-regions for Few-shot Medical Image Segmentation
In this work, we present an approach to extract multiple representative sub-regions from a given support medical image, enabling fine-grained selection over the generated image regions.
RTQ: Rethinking Video-language Understanding Based on Image-text Model
Recent advancements in video-language understanding have been established on the foundation of image-text models, resulting in promising outcomes due to the shared knowledge between images and videos.
Ranked #9 on
Video Retrieval
on MSR-VTT-1kA
Adversarial Bipartite Graph Learning for Video Domain Adaptation
To further enhance the model capacity and testify the robustness of the proposed architecture on difficult transfer tasks, we extend our model to work in a semi-supervised setting using an additional video-level bipartite graph.
Ranked #2 on
Domain Adaptation
on HMDB --> UCF (full)
Expediting Large-Scale Vision Transformer for Dense Prediction without Fine-tuning
Vision transformers have recently achieved competitive results across various vision tasks but still suffer from heavy computation costs when processing a large number of tokens.
CBNet: A Plug-and-Play Network for Segmentation-Based Scene Text Detection
Recently, segmentation-based methods are quite popular in scene text detection, which mainly contain two steps: text kernel segmentation and expansion.
Interactive Text-to-SQL Generation via Editable Step-by-Step Explanations
Many techniques have been proposed to automatically generate SQL from natural language, but they suffer from two issues: (1) they still make many mistakes, particularly for complex queries, and (2) they do not provide a flexible way for non-expert users to validate and refine incorrect queries.
Active Subspace of Neural Networks: Structural Analysis and Universal Attacks
Secondly, we propose analyzing the vulnerability of a neural network using active subspace and finding an additive universal adversarial attack vector that can misclassify a dataset with a high probability.
Quantum-Inspired Hamiltonian Monte Carlo for Bayesian Sampling
Hamiltonian Monte Carlo (HMC) is an efficient Bayesian sampling method that can make distant proposals in the parameter space by simulating a Hamiltonian dynamical system.
Dialogue Meaning Representation for Task-Oriented Dialogue Systems
We propose Dialogue Meaning Representation (DMR), a pliable and easily extendable representation for task-oriented dialogue.
Rethinking Amodal Video Segmentation from Learning Supervised Signals with Object-centric Representation
Furthermore, we propose a multi-view fusion layer based temporal module which is equipped with a set of object slots and interacts with features from different views by attention mechanism to fulfill sufficient object representation completion.
Self-Healing Robust Neural Networks via Closed-Loop Control
While numerous attack and defense techniques have been developed, this work investigates the robustness issue from a new angle: can we design a self-healing neural network that can automatically detect and fix the vulnerability issue by itself?
Exploiting Abstract Meaning Representation for Open-Domain Question Answering
The Open-Domain Question Answering (ODQA) task involves retrieving and subsequently generating answers from fine-grained relevant passages within a database.
StoryAnalogy: Deriving Story-level Analogies from Large Language Models to Unlock Analogical Understanding
Analogy-making between narratives is crucial for human reasoning.
Semantic-Aware Adversarial Training for Reliable Deep Hashing Retrieval
Further, we, for the first time, formulate the formalized adversarial training of deep hashing into a unified minimax optimization under the guidance of the generated mainstay codes.
BiPFT: Binary Pre-trained Foundation Transformer with Low-rank Estimation of Binarization Residual Polynomials
Specifically, we first analyze the binarization error in self-attention operations and derive the polynomials of binarization error.
Bayesian Inference with Certifiable Adversarial Robustness
We consider adversarial training of deep neural networks through the lens of Bayesian learning, and present a principled framework for adversarial training of Bayesian Neural Networks (BNNs) with certifiable guarantees.
Tractable Clustering of Data on the Curve Manifold
However the data may actually be functional i. e.\ each data point is a function of some variable such as time and the function is discretely sampled.
Investigating and Mitigating the Side Effects of Noisy Views for Self-Supervised Clustering Algorithms in Practical Multi-View Scenarios
Multi-view clustering (MVC) aims at exploring category structures among multi-view data in self-supervised manners.
KLoB: a Benchmark for Assessing Knowledge Locating Methods in Language Models
KLoB can serve as a benchmark for evaluating existing locating methods in language models, and can contributes a method to reassessing the validity of locality hypothesis of factual knowledge.
Bayesian Tensorized Neural Networks with Automatic Rank Selection
Tensor decomposition is an effective approach to compress over-parameterized neural networks and to enable their deployment on resource-constrained hardware platforms.
Online, Informative MCMC Thinning with Kernelized Stein Discrepancy
A fundamental challenge in Bayesian inference is efficient representation of a target distribution.
Faint Features Tell: Automatic Vertebrae Fracture Screening Assisted by Contrastive Learning
Long-term vertebral fractures severely affect the life quality of patients, causing kyphotic, lumbar deformity and even paralysis.
Whole-Body Lesion Segmentation in 18F-FDG PET/CT
There has been growing research interest in using deep learning based method to achieve fully automated segmentation of lesion in Positron emission tomography computed tomography(PET CT) scans for the prognosis of various cancers.
EventGround: Narrative Reasoning by Grounding to Eventuality-centric Knowledge Graphs
In this work, we propose an initial comprehensive framework called EventGround, which aims to tackle the problem of grounding free-texts to eventuality-centric KGs for contextualized narrative reasoning.
Structure Regularized Attentive Network for Automatic Femoral Head Necrosis Diagnosis and Localization
However, due to the tissue overlap, X-ray images are difficult to provide fine-grained features for early diagnosis.
Self-supervised Amodal Video Object Segmentation
The key intuition is that the occluded part of an object can be explained away if that part is visible in other frames, possibly deformed as long as the deformation can be reasonably learned.
EASpace: Enhanced Action Space for Policy Transfer
In this paper, a novel algorithm named EASpace (Enhanced Action Space) is proposed, which formulates macro actions in an alternative form to accelerate the learning process using multiple available sub-optimal expert policies.
Mutual Query Network for Multi-Modal Product Image Segmentation
Product image segmentation is vital in e-commerce.
Non-Euclidean Spatial Graph Neural Network
Besides, existing spatial network representation learning methods can only consider networks embedded in Euclidean space, and can not well exploit the rich geometric information carried by irregular and non-uniform non-Euclidean space.
Memory-augmented Dialogue Management for Task-oriented Dialogue Systems
Dialogue management (DM) decides the next action of a dialogue system according to the current dialogue state, and thus plays a central role in task-oriented dialogue systems.
Loss Functions for Multiset Prediction
In this paper, we propose a novel multiset loss function by viewing this problem from the perspective of sequential decision making.
Saliency-based Sequential Image Attention with Multiset Prediction
Humans process visual scenes selectively and sequentially using attention.
Discriminative Block-Diagonal Representation Learning for Image Recognition
In particular, the elaborate BDLRR is formulated as a joint optimization problem of shrinking the unfavorable representation from off-block-diagonal elements and strengthening the compact block-diagonal representation under the semi-supervised framework of low-rank representation.
FERA 2017 - Addressing Head Pose in the Third Facial Expression Recognition and Analysis Challenge
The FG 2017 Facial Expression Recognition and Analysis challenge (FERA 2017) extends FERA 2015 to the estimation of Action Units occurrence and intensity under different camera views.
Who Leads the Clothing Fashion: Style, Color, or Texture? A Computational Study
Specifically, a classification-based model is proposed to quantify the influence of different visual stimuli, in which each visual stimulus's influence is quantified by its corresponding accuracy in fashion classification.
Learning Word Embeddings from Intrinsic and Extrinsic Views
To prove the effectiveness of our model, we evaluate it on four tasks, including word similarity, reverse dictionaries, Wiki link prediction, and document classification.
Local Multiple Directional Pattern of Palmprint Image
Conventional methods usually capture the only one of the most dominant direction of palmprint images.
Natural Scene Character Recognition Using Robust PCA and Sparse Representation
Natural scene character recognition is challenging due to the cluttered background, which is hard to separate from text.
A survey of sparse representation: algorithms and applications
The main purpose of this article is to provide a comprehensive study and an updated review on sparse representation and to supply a guidance for researchers.
Learning to Point and Count
This paper proposes the problem of point-and-count as a test case to break the what-and-where deadlock.
First Step toward Model-Free, Anonymous Object Tracking with Recurrent Neural Networks
In this paper, we propose and study a novel visual object tracking approach based on convolutional networks and recurrent networks.
The Application of Two-level Attention Models in Deep Convolutional Neural Network for Fine-grained Image Classification
Our pipeline integrates three types of attention: the bottom-up attention that propose candidate patches, the object-level top-down attention that selects relevant patches to a certain object, and the part-level top-down attention that localizes discriminative parts.
Scale-Invariant Convolutional Neural Networks
Even though convolutional neural networks (CNN) has achieved near-human performance in various computer vision tasks, its ability to tolerate scale variations is limited.
Effective Occlusion Handling for Fast Correlation Filter-based Trackers
Correlation filter-based trackers heavily suffer from the problem of multiple peaks in their response maps incurred by occlusions.
Top-Down Tree Structured Text Generation
Text generation is a fundamental building block in natural language processing tasks.
Variational Bayesian Inference for Robust Streaming Tensor Factorization and Completion
Streaming tensor factorization is a powerful tool for processing high-volume and multi-way temporal data in Internet networks, recommender systems and image/video data analysis.
Incomplete Multi-view Clustering via Graph Regularized Matrix Factorization
Clustering with incomplete views is a challenge in multi-view clustering.
Highly-Economized Multi-View Binary Compression for Scalable Image Clustering
How to economically cluster large-scale multi-view images is a long-standing problem in computer vision.
Melodic Phrase Segmentation By Deep Neural Networks
Automated melodic phrase detection and segmentation is a classical task in content-based music information retrieval and also the key towards automated music structure analysis.
Integrated Object Detection and Tracking with Tracklet-Conditioned Detection
Accurate detection and tracking of objects is vital for effective video understanding.
Ranked #19 on
Video Object Detection
on ImageNet VID
GNEG: Graph-Based Negative Sampling for word2vec
Negative sampling is an important component in word2vec for distributed word representation learning.
zNLP: Identifying Parallel Sentences in Chinese-English Comparable Corpora
This paper describes the zNLP system for the BUCC 2017 shared task.
Gradient-based learning for F-measure and other performance metrics
Many important classification performance metrics, e. g. $F$-measure, are non-differentiable and non-decomposable, and are thus unfriendly to gradient descent algorithm.
Prediction of multi-dimensional spatial variation data via Bayesian tensor completion
This paper presents a multi-dimensional computational method to predict the spatial variation data inside and across multiple dies of a wafer.
Adaptive Locality Preserving Regression
This paper proposes a novel discriminative regression method, called adaptive locality preserving regression (ALPR) for classification.
Bilinear Supervised Hashing Based on 2D Image Features
Hashing has been recognized as an efficient representation learning method to effectively handle big data due to its low computational complexity and memory cost.
Symmetry-Based Text Line Detection in Natural Scenes
Recently, a variety of real-world applications have triggered huge demand for techniques that can extract textual information from natural scenes.
Multimodal Spontaneous Emotion Corpus for Human Behavior Analysis
The corpus further includes derived features from 3D, 2D, and IR (infrared) sensors and baseline results for facial expression and action unit detection.
Multiple Granularity Descriptors for Fine-Grained Categorization
Fine-grained categorization, which aims to distinguish subordinate-level categories such as bird species or dog breeds, is an extremely challenging task.
Explainable Text-Driven Neural Network for Stock Prediction
It has been shown that financial news leads to the fluctuation of stock prices.
SADIH: Semantic-Aware DIscrete Hashing
Due to its low storage cost and fast query speed, hashing has been recognized to accomplish similarity search in large-scale multimedia retrieval applications.
Spatial-Temporal Relation Networks for Multi-Object Tracking
Recent progress in multiple object tracking (MOT) has shown that a robust similarity score is key to the success of trackers.
Scalable Block-Diagonal Locality-Constrained Projective Dictionary Learning
More importantly, LC-PDL avoids using the complementary data matrix to learn the sub-dictionary over each class.
Joint Subspace Recovery and Enhanced Locality Driven Robust Flexible Discriminative Dictionary Learning
We propose a joint subspace recovery and enhanced locality based robust flexible label consistent dictionary learning method called Robust Flexible Discriminative Dictionary Learning (RFDDL).
Curiosity-driven Reinforcement Learning for Diverse Visual Paragraph Generation
Visual paragraph generation aims to automatically describe a given image from different perspectives and organize sentences in a coherent way.
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.
Learning Structured Twin-Incoherent Twin-Projective Latent Dictionary Pairs for Classification
In this setting, our TP-DPL integrates the twin-incoherence based latent flexible DPL and the joint embedding of codes as well as salient features by twin-projection into a unified model in an adaptive neighborhood-preserving manner.
Equity2Vec: End-to-end Deep Learning Framework for Cross-sectional Asset Pricing
Pricing assets has attracted significant attention from the financial technology community.
Cross-Lingual Contextual Word Embeddings Mapping With Multi-Sense Words In Mind
Recent work in cross-lingual contextual word embedding learning cannot handle multi-sense words well.
Deep Collaborative Discrete Hashing with Semantic-Invariant Structure
Existing deep hashing approaches fail to fully explore semantic correlations and neglect the effect of linguistic context on visual attention learning, leading to inferior performance.
Learning from the Past: Continual Meta-Learning via Bayesian Graph Modeling
Meta-learning for few-shot learning allows a machine to leverage previously acquired knowledge as a prior, thus improving the performance on novel tasks with only small amounts of data.
Discriminative Local Sparse Representation by Robust Adaptive Dictionary Pair Learning
In this paper, we propose a structured Robust Adaptive Dic-tionary Pair Learning (RA-DPL) framework for the discrim-inative sparse representation learning.
Multi-Scale Self-Attention for Text Classification
In this paper, we introduce the prior knowledge, multi-scale structure, into self-attention modules.
Fully-Convolutional Intensive Feature Flow Neural Network for Text Recognition
But existing CNNs based frameworks still have several drawbacks: 1) the traditaional pooling operation may lose important feature information and is unlearnable; 2) the tradi-tional convolution operation optimizes slowly and the hierar-chical features from different layers are not fully utilized.
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.
Tucker Tensor Decomposition on FPGA
This paper presents an hardware accelerator for a classical tensor computation framework, Tucker decomposition.
Signal Processing Hardware Architecture
Dense Residual Network: Enhancing Global Dense Feature Flow for Character Recognition
To construct FDRN, we propose a new fast residual dense block (f-RDB) to retain the ability of local feature fusion and local residual learning of original RDB, which can reduce the computing efforts at the same time.
Recent Advances and Challenges in Task-oriented Dialog System
Due to the significance and value in human-computer interaction and natural language processing, task-oriented dialog systems are attracting more and more attention in both academic and industrial communities.
Task-adaptive Asymmetric Deep Cross-modal Hashing
Unlike previous cross-modal hashing approaches, our learning framework jointly optimizes semantic preserving that transforms deep features of multimedia data into binary hash codes, and the semantic regression which directly regresses query modality representation to explicit label.
Learning Goal-oriented Dialogue Policy with Opposite Agent Awareness
Most existing approaches for goal-oriented dialogue policy learning used reinforcement learning, which focuses on the target agent policy and simply treat the opposite agent policy as part of the environment.
Repurpose Open Data to Discover Therapeutics for COVID-19 using Deep Learning
While this study, by no means recommends specific drugs, it demonstrates a powerful deep learning methodology to prioritize existing drugs for further investigation, which holds the potential of accelerating therapeutic development for COVID-19.
Dual-level Semantic Transfer Deep Hashing for Efficient Social Image Retrieval
Specifically, we design a complementary dual-level semantic transfer mechanism to efficiently discover the potential semantics of tags and seamlessly transfer them into binary hash codes.
FeatGraph: A Flexible and Efficient Backend for Graph Neural Network Systems
FeatGraph provides a flexible programming interface to express diverse GNN models by composing coarse-grained sparse templates with fine-grained user-defined functions (UDFs) on each vertex/edge.
Dual-constrained Deep Semi-Supervised Coupled Factorization Network with Enriched Prior
Nonnegative matrix factorization is usually powerful for learning the "shallow" parts-based representation, but it clearly fails to discover deep hierarchical information within both the basis and representation spaces.
Region Graph Embedding Network for Zero-Shot Learning
To exploit the progressive interactions among these regions, we represent them as a region graph, on which the parts relation reasoning is performed with graph convolutions, thus leading to our PRR branch.
Causal Inference of General Treatment Effects using Neural Networks with A Diverging Number of Confounders
This paper considers a generalized optimization framework for efficient estimation of general treatment effects using artificial neural networks (ANNs) to approximate the unknown nuisance function of growing-dimensional confounders.
Composite Adversarial Training for Multiple Adversarial Perturbations and Beyond
One intriguing property of deep neural networks (DNNs) is their vulnerability to adversarial perturbations.
Explore with Dynamic Map: Graph Structured Reinforcement Learning
In reinforcement learning, a map with states and transitions built based on historical trajectories is often helpful in exploration and exploitation.
Overview of the Ninth Dialog System Technology Challenge: DSTC9
Interactive evaluation of dialog, and 4.
