Search Results for author:
Found 298 papers, 137 papers with code
S^2SQL: Injecting Syntax to Question-Schema Interaction Graph Encoder for Text-to-SQL Parsers
The task of converting a natural language question into an executable SQL query, known as text-to-SQL, is an important branch of semantic parsing.
LeAD: The LLM Enhanced Planning System Converged with End-to-end Autonomous Driving
A principal barrier to large-scale deployment of urban autonomous driving systems lies in the prevalence of complex scenarios and edge cases.
MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention
We release two versions of MiniMax-M1 models with 40K and 80K thinking budgets respectively, where the 40K model represents an intermediate phase of the 80K training.
3D Hand Mesh-Guided AI-Generated Malformed Hand Refinement with Hand Pose Transformation via Diffusion Model
Beyond malformed hand refinement, we propose a novel hand pose transformation method.
BaryIR: Learning Multi-Source Unified Representation in Continuous Barycenter Space for Generalizable All-in-One Image Restoration
Despite remarkable advances made in all-in-one image restoration (AIR) for handling different types of degradations simultaneously, existing methods remain vulnerable to out-of-distribution degradations and images, limiting their real-world applicability.
Towards Prospective Medical Image Reconstruction via Knowledge-Informed Dynamic Optimal Transport
To address this challenge, this paper introduces imaging Knowledge-Informed Dynamic Optimal Transport (KIDOT), a novel dynamic optimal transport framework with optimality in the sense of preserving consistency with imaging physics in transport, that conceptualizes reconstruction as finding a dynamic transport path.
Joint Velocity-Growth Flow Matching for Single-Cell Dynamics Modeling
The destructive measurement technique and cell proliferation/death result in unpaired and unbalanced data between snapshots, making the learning of the underlying dynamics challenging.
Data-driven Internal Model Control for Output Regulation
Extensions to both linear and nonlinear MASs are discussed.
Multi-agent Embodied AI: Advances and Future Directions
Embodied artificial intelligence (Embodied AI) plays a pivotal role in the application of advanced technologies in the intelligent era, where AI systems are integrated with physical bodies that enable them to perceive, reason, and interact with their environments.
Seeking to Collide: Online Safety-Critical Scenario Generation for Autonomous Driving with Retrieval Augmented Large Language Models
Simulation-based testing is crucial for validating autonomous vehicles (AVs), yet existing scenario generation methods either overfit to common driving patterns or operate in an offline, non-interactive manner that fails to expose rare, safety-critical corner cases.
OnSiteVRU: A High-Resolution Trajectory Dataset for High-Density Vulnerable Road Users
With the acceleration of urbanization and the growth of transportation demands, the safety of vulnerable road users (VRUs, such as pedestrians and cyclists) in mixed traffic flows has become increasingly prominent, necessitating high-precision and diverse trajectory data to support the development and optimization of autonomous driving systems.
GMapLatent: Geometric Mapping in Latent Space
In this work, we innovate a cross-domain alignment and generation model that introduces a canonical latent space representation based on geometric mapping to align the cross-domain latent spaces in a rigorous and precise manner, thus avoiding mode collapse and mixture in the encoder-decoder generation architectures.
Robust Offline Imitation Learning Through State-level Trajectory Stitching
Imitation learning (IL) has proven effective for enabling robots to acquire visuomotor skills through expert demonstrations.
Exploring the Roles of Large Language Models in Reshaping Transportation Systems: A Survey, Framework, and Roadmap
For each role, our review spans diverse applications, from traffic prediction and autonomous driving to safety analytics and urban mobility optimization, highlighting how emergent capabilities of LLMs such as in-context learning and step-by-step reasoning can enhance the operation and management of transportation systems.
TeLL-Drive: Enhancing Autonomous Driving with Teacher LLM-Guided Deep Reinforcement Learning
To address these limitations, we propose TeLL-Drive, a hybrid framework that integrates a Teacher LLM to guide an attention-based Student DRL policy.
LLM-attacker: Enhancing Closed-loop Adversarial Scenario Generation for Autonomous Driving with Large Language Models
However, the complexity of real-world scenarios, with numerous participants and diverse behaviors, makes identification challenging.
Collaborative Imputation of Urban Time Series through Cross-city Meta-learning
We then introduce a cross-city collaborative learning scheme through model-agnostic meta learning, incorporating hierarchical modulation and normalization techniques to accommodate multiscale representations and reduce variance in response to heterogeneity.
Re-Visible Dual-Domain Self-Supervised Deep Unfolding Network for MRI Reconstruction
Additionally, we design a deep unfolding network based on Chambolle and Pock Proximal Point Algorithm (DUN-CP-PPA) to achieve end-to-end reconstruction, incorporating imaging physics and image priors to guide the reconstruction process.
PersonaAI: Leveraging Retrieval-Augmented Generation and Personalized Context for AI-Driven Digital Avatars
This paper introduces PersonaAI, a cutting-edge application that leverages Retrieval-Augmented Generation (RAG) and the LLAMA model to create highly personalized digital avatars capable of accurately mimicking individual personalities.
Lightweight Design and Optimization methods for DCNNs: Progress and Futures
Lightweight design, as a key approach to mitigate disparity between computational requirements of deep learning models and hardware performance, plays a pivotal role in advancing application of deep learning technologies on mobile and embedded devices, alongside rapid development of smart home, telemedicine, and autonomous driving.
LAMBDA: Covering the Multimodal Critical Scenarios for Automated Driving Systems by Search Space Quantization
In fact, all the subspaces representing danger in the logical scenario space, rather than only the most critical concrete scenario, play a more significant role for the safety evaluation.
Degradation-Aware Residual-Conditioned Optimal Transport for Unified Image Restoration
More crucially, we design the transport map for restoration as a two-pass DA-RCOT map, in which the transport residual is computed in the first pass and then encoded as multi-scale residual embeddings to condition the second-pass restoration.
Ranked #1 on
Unified Image Restoration
on Rain100L
(Average PSNR (dB) metric, using extra
training data)
5-Degradation Blind All-in-One Image Restoration
Blind All-in-One Image Restoration
A Review of Deep Learning Approaches for Non-Invasive Cognitive Impairment Detection
This review paper explores recent advances in deep learning approaches for non-invasive cognitive impairment detection.
Towards Interactive and Learnable Cooperative Driving Automation: a Large Language Model-Driven Decision-Making Framework
At present, Connected Autonomous Vehicles (CAVs) have begun to open road testing around the world, but their safety and efficiency performance in complex scenarios is still not satisfactory.
SA-MLP: A Low-Power Multiplication-Free Deep Network for 3D Point Cloud Classification in Resource-Constrained Environments
Point cloud classification plays a crucial role in the processing and analysis of data from 3D sensors such as LiDAR, which are commonly used in applications like autonomous vehicles, robotics, and environmental monitoring.
PMT-MAE: Dual-Branch Self-Supervised Learning with Distillation for Efficient Point Cloud Classification
This paper introduces PMT-MAE (Point MLP-Transformer Masked Autoencoder), a novel self-supervised learning framework for point cloud classification.
Efficient Point Cloud Classification via Offline Distillation Framework and Negative-Weight Self-Distillation Technique
To address these challenges, we introduce an innovative offline recording strategy that avoids the simultaneous loading of both teacher and student models, thereby reducing hardware demands.
Joint Estimation and Prediction of City-wide Delivery Demand: A Large Language Model Empowered Graph-based Learning Approach
The proliferation of e-commerce and urbanization has significantly intensified delivery operations in urban areas, boosting the volume and complexity of delivery demand.
SAM-UNet:Enhancing Zero-Shot Segmentation of SAM for Universal Medical Images
We train SAM-UNet on SA-Med2D-16M, the largest 2-dimensional medical image segmentation dataset to date, yielding a universal pretrained model for medical images.
PointMT: Efficient Point Cloud Analysis with Hybrid MLP-Transformer Architecture
In recent years, point cloud analysis methods based on the Transformer architecture have made significant progress, particularly in the context of multimedia applications such as 3D modeling, virtual reality, and autonomous systems.
Prototypical Partial Optimal Transport for Universal Domain Adaptation
The existence of domain and category shift makes the task challenging and requires us to distinguish "known" samples (i. e., samples whose labels exist in both domains) and "unknown" samples (i. e., samples whose labels exist in only one domain) in both domains before reducing the domain gap.
High-Dimensional Fault Tolerance Testing of Highly Automated Vehicles Based on Low-Rank Models
Hence, Fault Injection (FI) testing is conducted by practitioners to evaluate the safety level of HAVs.
Piecewise constant tuning gain based singularity-free MRAC with application to aircraft control systems
This paper introduces an innovative singularity-free output feedback model reference adaptive control (MRAC) method applicable to a wide range of continuous-time linear time-invariant (LTI) systems with general relative degrees.
Channel-Aware Low-Rank Adaptation in Time Series Forecasting
The former adopts individual channel treatment and has been shown to be more robust to distribution shifts, but lacks sufficient capacity to model meaningful channel interactions.
OE-BevSeg: An Object Informed and Environment Aware Multimodal Framework for Bird's-eye-view Vehicle Semantic Segmentation
Based on prior knowledge about the main composition of the BEV surrounding environment varying with the increase of distance intervals, long-sequence global modeling is utilized to improve the model's understanding and perception of the environment.
Hierarchical and Decoupled BEV Perception Learning Framework for Autonomous Driving
Perception is essential for autonomous driving system.
Flow to Rare Events: An Application of Normalizing Flow in Temporal Importance Sampling for Automated Vehicle Validation
To solve it, we devise a method to represent, generate, and reweight the distribution of risky rare events.
PointViG: A Lightweight GNN-based Model for Efficient Point Cloud Analysis
These results underscore the potential and efficiency of PointViG in point cloud analysis.
Generalizable Implicit Neural Representation As a Universal Spatiotemporal Traffic Data Learner
$\textbf{This is the conference version of our paper: Spatiotemporal Implicit Neural Representation as a Generalized Traffic Data Learner}$.
Spatiotemporal Implicit Neural Representation as a Generalized Traffic Data Learner
Spatiotemporal Traffic Data (STTD) measures the complex dynamical behaviors of the multiscale transportation system.
Residual-Conditioned Optimal Transport: Towards Structure-Preserving Unpaired and Paired Image Restoration
In this work, we propose a novel Residual-Conditioned Optimal Transport (RCOT) approach, which models image restoration as an optimal transport (OT) problem for both unpaired and paired settings, introducing the transport residual as a unique degradation-specific cue for both the transport cost and the transport map.
Ranked #4 on
Image Super-Resolution
on DIV2K val - 4x upscaling
Adversarial Reweighting with $α$-Power Maximization for Domain Adaptation
To theoretically analyze our method, we deduce an upper bound of target domain expected error for PDA, which is approximately minimized in our approach.
Data Quality Matters: Suicide Intention Detection on Social Media Posts Using RoBERTa-CNN
Additionally, we found that data quality significantly impacts the training of a robust model.
Floorplanning of VLSI by Mixed-Variable Optimization
FA-GSS uses the Golden Section strategy to optimize both wirelength and area targets.
Distributed Data-driven Unknown-input Observers for State Estimation
A sufficient condition for the existence of such a DUIO is recalled, and a new one is proposed, that is prone to a data-driven adaption.
Pose-Transformed Equivariant Network for 3D Point Trajectory Prediction
In this paper we propose a novel pose-transformed equivariant network in which the points are firstly uniquely normalized and then transformed by the learned pose transformations upon which the points after motion are predicted and aggregated.
ImputeFormer: Low Rankness-Induced Transformers for Generalizable Spatiotemporal Imputation
The exploitation of the inherent structures of spatiotemporal data enables our model to learn balanced signal-noise representations, making it generalizable for a variety of imputation problems.
A WINNER+ Based 3-D Non-Stationary Wideband MIMO Channel Model
In this paper, a three-dimensional (3-D) non-stationary wideband multiple-input multiple-output (MIMO) channel model based on the WINNER+ channel model is proposed.
Robust Control of Unknown Switched Linear Systems from Noisy Data
This paper investigates the problem of data-driven stabilization for linear discrete-time switched systems with unknown switching dynamics.
Data-driven Control Against False Data Injection Attacks
To this end, a general FDI attack model is presented, which imposes minimally constraints on the switching frequency of attack channels and the magnitude of attack matrices.
Optimal Transport-Guided Conditional Score-Based Diffusion Models
Conditional score-based diffusion model (SBDM) is for conditional generation of target data with paired data as condition, and has achieved great success in image translation.
Self-triggered Consensus Control of Multi-agent Systems from Data
In the presence of external disturbances, a model-based STC scheme is put forth for $\mathcal{H}_{\infty}$-consensus of MASs, serving as a baseline for the data-driven STC.
STORM: Efficient Stochastic Transformer based World Models for Reinforcement Learning
The performance of these algorithms heavily relies on the sequence modeling and generation capabilities of the world model.
Ranked #5 on
Atari Games 100k
on Atari 100k
Identifying factors associated with fast visual field progression in patients with ocular hypertension based on unsupervised machine learning
We characterized the subtypes based on demographic, clinical, ocular, and VF factors at the baseline.
Towards High-Fidelity Text-Guided 3D Face Generation and Manipulation Using only Images
In this paper, we propose a text-guided 3D faces generation method, refer as TG-3DFace, for generating realistic 3D faces using text guidance.
Soft Decomposed Policy-Critic: Bridging the Gap for Effective Continuous Control with Discrete RL
Discrete reinforcement learning (RL) algorithms have demonstrated exceptional performance in solving sequential decision tasks with discrete action spaces, such as Atari games.
Data-driven Polytopic Output Synchronization of Heterogeneous Multi-agent Systems from Noisy Data
This method utilizes measured data and a noise-matrix polytope to ensure near-optimal output synchronization.
Contextualizing MLP-Mixers Spatiotemporally for Urban Data Forecast at Scale
Our findings contribute to the exploration of simple-yet-effective models for real-world STTD forecasting.
Flexible Job Shop Scheduling via Dual Attention Network Based Reinforcement Learning
The complex relationships between operations and machines are represented precisely and concisely, for which a dual-attention network (DAN) comprising several interconnected operation message attention blocks and machine message attention blocks is proposed.
A Survey on Out-of-Distribution Detection in NLP
Out-of-distribution (OOD) detection is essential for the reliable and safe deployment of machine learning systems in the real world.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
+1
Learning Robust Data-based LQG Controllers from Noisy Data
In this work, a data-based formulation for computing the steady-state Kalman gain is proposed based on semi-definite programming (SDP) using some noise-free input-state-output data.
MC-ViViT: Multi-branch Classifier-ViViT to detect Mild Cognitive Impairment in older adults using facial videos
Deep machine learning models including Convolutional Neural Networks (CNN) have been successful in the detection of Mild Cognitive Impairment (MCI) using medical images, questionnaires, and videos.
Keypoint-Guided Optimal Transport
In this paper, we propose a novel KeyPoint-Guided model by ReLation preservation (KPG-RL) that searches for the optimal matching (i. e., transport plan) guided by the keypoints in OT.
Towards better traffic volume estimation: Jointly addressing the underdetermination and nonequilibrium problems with correlation-adaptive GNNs
Traffic volume is an indispensable ingredient to provide fine-grained information for traffic management and control.
Generalized Semantic Segmentation by Self-Supervised Source Domain Projection and Multi-Level Contrastive Learning
SSDP aims to reduce domain gap by projecting data to the source domain, while MLCL is a learning scheme to learn discriminative and generalizable features on the projected data.
EdgeYOLO: An Edge-Real-Time Object Detector
This paper proposes an efficient, low-complexity and anchor-free object detector based on the state-of-the-art YOLO framework, which can be implemented in real time on edge computing platforms.
Self-triggered Resilient Stabilization of Linear Systems with Quantized Output
If, in addition, the transmission protocols at the controller-to-actuator (C-A) and sensor-to-controller (S-C) channels can be adapted, the self-triggered control architecture can be considerably simplified, leveraging a delicate observer-based deadbeat controller to eliminate the need for running the controller in parallel at the encoder side.
Dynamic Grained Encoder for Vision Transformers
Specifically, we propose a Dynamic Grained Encoder for vision transformers, which can adaptively assign a suitable number of queries to each spatial region.
Towards Generalized Open Information Extraction
Open Information Extraction (OpenIE) facilitates the open-domain discovery of textual facts.
Semi-Supervised Lifelong Language Learning
In this paper, we explore a novel setting, semi-supervised lifelong language learning (SSLL), where a model learns sequentially arriving language tasks with both labeled and unlabeled data.
CGoDial: A Large-Scale Benchmark for Chinese Goal-oriented Dialog Evaluation
Practical dialog systems need to deal with various knowledge sources, noisy user expressions, and the shortage of annotated data.
Estimating Soft Labels for Out-of-Domain Intent Detection
Out-of-Domain (OOD) intent detection is important for practical dialog systems.
Correlating sparse sensing for large-scale traffic speed estimation: A Laplacian-enhanced low-rank tensor kriging approach
In addition, sensors are prone to error or missing data due to various kinds of reasons, speeds from these sensors can become highly noisy.
Doc2Bot: Accessing Heterogeneous Documents via Conversational Bots
This paper introduces Doc2Bot, a novel dataset for building machines that help users seek information via conversations.
Prompt Conditioned VAE: Enhancing Generative Replay for Lifelong Learning in Task-Oriented Dialogue
Lifelong learning (LL) is vital for advanced task-oriented dialogue (ToD) systems.
Stacking Ensemble Learning in Deep Domain Adaptation for Ophthalmic Image Classification
We extend Maximum Mean Discrepancy (MMD), Low-rank coding, and Correlation Alignment (CORAL) to compute the adaptation loss in three base models.
SPACE-3: Unified Dialog Model Pre-training for Task-Oriented Dialog Understanding and Generation
To capture the structured dialog semantics, we pre-train the dialog understanding module via a novel tree-induced semi-supervised contrastive learning objective with the help of extra dialog annotations.
Implicit Full Waveform Inversion with Deep Neural Representation
Compared to FWI, which is sensitive to the initial model, IFWI benefits from the increased degrees of freedom with deep learning optimization, thus allowing to start from a random initialization, which greatly reduces the risk of non-uniqueness and being trapped in local minima.
A Survey on Text-to-SQL Parsing: Concepts, Methods, and Future Directions
In recent years, deep neural networks have significantly advanced this task by neural generation models, which automatically learn a mapping function from an input NL question to an output SQL query.
Data-Driven Control of Distributed Event-Triggered Network Systems
The present paper deals with data-driven event-triggered control of a class of unknown discrete-time interconnected systems (a. k. a.
Differentiable Architecture Search with Random Features
Differentiable architecture search (DARTS) has significantly promoted the development of NAS techniques because of its high search efficiency and effectiveness but suffers from performance collapse.
Ranked #13 on
Neural Architecture Search
on NAS-Bench-201, CIFAR-10
Event-triggered Consensus Control of Heterogeneous Multi-agent Systems: Model- and Data-based Analysis
This article deals with model- and data-based consensus control of heterogenous leader-following multi-agent systems (MASs) under an event-triggering transmission scheme.
DBQ-SSD: Dynamic Ball Query for Efficient 3D Object Detection
Many point-based 3D detectors adopt point-feature sampling strategies to drop some points for efficient inference.
StreamYOLO: Real-time Object Detection for Streaming Perception
In this paper, we explore the performance of real time models on this metric and endow the models with the capacity of predicting the future, significantly improving the results for streaming perception.
Data-driven Self-triggered Control via Trajectory Prediction
Self-triggered control, a well-documented technique for reducing the communication overhead while ensuring desired system performance, is gaining increasing popularity.
Layout-Aware Information Extraction for Document-Grounded Dialogue: Dataset, Method and Demonstration
In this paper, we propose a Layout-aware document-level Information Extraction dataset, LIE, to facilitate the study of extracting both structural and semantic knowledge from visually rich documents (VRDs), so as to generate accurate responses in dialogue systems.
Dense Teacher: Dense Pseudo-Labels for Semi-supervised Object Detection
To date, the most powerful semi-supervised object detectors (SS-OD) are based on pseudo-boxes, which need a sequence of post-processing with fine-tuned hyper-parameters.
PETRv2: A Unified Framework for 3D Perception from Multi-Camera Images
More specifically, we extend the 3D position embedding (3D PE) in PETR for temporal modeling.
Ranked #2 on
Bird's-Eye View Semantic Segmentation
on nuScenes
(IoU lane - 224x480 - 100x100 at 0.5 metric)
Unifying Voxel-based Representation with Transformer for 3D Object Detection
To this end, the modality-specific space is first designed to represent different inputs in the voxel feature space.
Voxel Field Fusion for 3D Object Detection
In this work, we present a conceptually simple yet effective framework for cross-modality 3D object detection, named voxel field fusion.
VoGE: A Differentiable Volume Renderer using Gaussian Ellipsoids for Analysis-by-Synthesis
The Gaussian reconstruction kernels have been proposed by Westover (1990) and studied by the computer graphics community back in the 90s, which gives an alternative representation of object 3D geometry from meshes and point clouds.
Duplex Conversation: Towards Human-like Interaction in Spoken Dialogue Systems
In this paper, we present Duplex Conversation, a multi-turn, multimodal spoken dialogue system that enables telephone-based agents to interact with customers like a human.
NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods and Results
The challenge is composed of two tracks with an emphasis on fidelity and complexity constraints: In Track 1, participants are asked to optimize objective fidelity scores while imposing a low-complexity constraint (i. e. solutions can not exceed a given number of operations).
A Survey on Neural Open Information Extraction: Current Status and Future Directions
Open Information Extraction (OpenIE) facilitates domain-independent discovery of relational facts from large corpora.
Ranked #1 on
Open Information Extraction
on CaRB
Knowledge Base Construction
Natural Language Understanding
+2
Truncated tensor Schatten p-norm based approach for spatiotemporal traffic data imputation with complicated missing patterns
Rapid advances in sensor, wireless communication, cloud computing and data science have brought unprecedented amount of data to assist transportation engineers and researchers in making better decisions.
NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results
The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29. 00dB on DIV2K validation set.
Focal Sparse Convolutional Networks for 3D Object Detection
In this paper, we introduce two new modules to enhance the capability of Sparse CNNs, both are based on making feature sparsity learnable with position-wise importance prediction.
BSRT: Improving Burst Super-Resolution with Swin Transformer and Flow-Guided Deformable Alignment
To overcome the challenges in BurstSR, we propose a Burst Super-Resolution Transformer (BSRT), which can significantly improve the capability of extracting inter-frame information and reconstruction.
Ranked #1 on
Burst Image Super-Resolution
on SyntheticBurst
When NAS Meets Trees: An Efficient Algorithm for Neural Architecture Search
TNAS performs a modified bi-level Breadth-First Search in the proposed trees to discover a high-performance architecture.
Simple Baselines for Image Restoration
Although there have been significant advances in the field of image restoration recently, the system complexity of the state-of-the-art (SOTA) methods is increasing as well, which may hinder the convenient analysis and comparison of methods.
Ranked #1 on
Deblurring
on MSU BASED
FS6D: Few-Shot 6D Pose Estimation of Novel Objects
6D object pose estimation networks are limited in their capability to scale to large numbers of object instances due to the close-set assumption and their reliance on high-fidelity object CAD models.
Boosting Black-Box Adversarial Attacks with Meta Learning
Extensive experiments demonstrate that our method can not only improve the attack success rates, but also reduces the number of queries compared to other methods.
Real-time Object Detection for Streaming Perception
In this paper, instead of searching trade-offs between accuracy and speed like previous works, we point out that endowing real-time models with the ability to predict the future is the key to dealing with this problem.
Ranked #1 on
Real-Time Object Detection
on Argoverse-HD (Full-Stack, Val)
(sAP metric, using extra
training data)
Improving Meta-learning for Low-resource Text Classification and Generation via Memory Imitation
Building models of natural language processing (NLP) is challenging in low-resource scenarios where only limited data are available.
Rebalanced Siamese Contrastive Mining for Long-Tailed Recognition
Given the promising performance of contrastive learning, we propose Rebalanced Siamese Contrastive Mining (ResCom) to tackle imbalanced recognition.
Ranked #5 on
Long-tail Learning
on CIFAR-10-LT (ρ=10)
A Slot Is Not Built in One Utterance: Spoken Language Dialogs with Sub-Slots
We find some new linguistic phenomena and interactive manners in SSTOD which raise critical challenges of building dialog agents for the task.
Ranked #1 on
SSTOD
on SSD_NAME
Tree Energy Loss: Towards Sparsely Annotated Semantic Segmentation
The tree energy loss is effective and easy to be incorporated into existing frameworks by combining it with a traditional segmentation loss.
Progressive End-to-End Object Detection in Crowded Scenes
Experiments show that our method can significantly boost the performance of query-based detectors in crowded scenes.
Ranked #1 on
Object Detection
on CrowdHuman
S$^2$SQL: Injecting Syntax to Question-Schema Interaction Graph Encoder for Text-to-SQL Parsers
The task of converting a natural language question into an executable SQL query, known as text-to-SQL, is an important branch of semantic parsing.
Scaling Up Your Kernels to 31x31: Revisiting Large Kernel Design in CNNs
We revisit large kernel design in modern convolutional neural networks (CNNs).
Ranked #68 on
Image Classification
on ImageNet
PETR: Position Embedding Transformation for Multi-View 3D Object Detection
Object query can perceive the 3D position-aware features and perform end-to-end object detection.
Ranked #3 on
3D Object Detection
on TruckScenes
Towards Self-Supervised Category-Level Object Pose and Size Estimation
Instead, we propose a label-free method that learns to enforce the geometric consistency between category template mesh and observed object point cloud under a self-supervision manner.
Model-Based and Data-Driven Control of Event- and Self-Triggered Discrete-Time LTI Systems
To this end, we begin by presenting a dynamic event-triggering scheme (ETS) based on periodic sampling, and a discrete-time looped-functional approach, through which a model-based stability condition is derived.
Relieving Long-tailed Instance Segmentation via Pairwise Class Balance
In this paper, we explore to excavate the confusion matrix, which carries the fine-grained misclassification details, to relieve the pairwise biases, generalizing the coarse one.
Learning to Select the Next Reasonable Mention for Entity Linking
To address the problem, we propose a novel model, called DyMen, to dynamically adjust the subsequent linking target based on the previously linked entities via reinforcement learning, enabling the model to select a link target that can fully use previously linked information.
Adversarial Reweighting for Partial Domain Adaptation
To tackle the challenge of negative domain transfer, we propose a novel Adversarial Reweighting (AR) approach that adversarially learns the weights of source domain data to align the source and target domain distributions, and the transferable deep recognition network is learned on the reweighted source domain data.
Ranked #1 on
Partial Domain Adaptation
on DomainNet
Spherical Motion Dynamics: Learning Dynamics of Normalized Neural Network using SGD and Weight Decay
Specifically, 1) we introduce the assumptions that can lead to equilibrium state in SMD, and prove equilibrium can be reached in a linear rate regime under given assumptions; 2) we propose ``angular update" as a substitute for effective learning rate to depict the state of SMD, and derive the theoretical value of angular update in equilibrium state; 3) we verify our assumptions and theoretical results on various large-scale computer vision tasks including ImageNet and MSCOCO with standard settings.
GALAXY: A Generative Pre-trained Model for Task-Oriented Dialog with Semi-Supervised Learning and Explicit Policy Injection
Pre-trained models have proved to be powerful in enhancing task-oriented dialog systems.
Ranked #1 on
End-To-End Dialogue Modelling
on MULTIWOZ 2.0
XnODR and XnIDR: Two Accurate and Fast Fully Connected Layers For Convolutional Neural Networks
To address the computational burdens of the Dynamic Routing mechanism, this paper proposes new Fully Connected (FC) layers by xnorizing the linear projection outside or inside the Dynamic Routing within the CapsFC layer.
Ranked #12 on
Image Classification
on MNIST
(Accuracy metric)
Linking-Enhanced Pre-Training for Table Semantic Parsing
Recently pre-training models have significantly improved the performance of various NLP tasks by leveraging large-scale text corpora to improve the contextual representation ability of the neural network.
Distributed stochastic proximal algorithm with random reshuffling for non-smooth finite-sum optimization
We prove that local variable estimates generated by the proposed algorithm achieve consensus and are attracted to a neighborhood of the optimal solution in expectation with an $\mathcal{O}(\frac{1}{T}+\frac{1}{\sqrt{T}})$ convergence rate, where $T$ is the total number of iterations.
Path-Enhanced Multi-Relational Question Answering with Knowledge Graph Embeddings
The multi-relational Knowledge Base Question Answering (KBQA) system performs multi-hop reasoning over the knowledge graph (KG) to achieve the answer.
Knowledge Base Question Answering
Knowledge Graph Embedding
+1
Instance-Conditional Knowledge Distillation for Object Detection
Knowledge distillation has shown great success in classification, however, it is still challenging for detection.
Data-driven Control of Dynamic Event-triggered Systems with Delays
Data-based representations for time-invariant linear systems with known or unknown system input matrices are first developed, along with a novel class of dynamic triggering schemes for sampled-data systems with time delays.
Data-Driven Resilient Predictive Control under Denial-of-Service
Finally, a numerical example is given to validate the effectiveness of the proposed control method.
Partial to Whole Knowledge Distillation: Progressive Distilling Decomposed Knowledge Boosts Student Better
Knowledge distillation field delicately designs various types of knowledge to shrink the performance gap between compact student and large-scale teacher.
DialogueCSE: Dialogue-based Contrastive Learning of Sentence Embeddings
Learning sentence embeddings from dialogues has drawn increasing attention due to its low annotation cost and high domain adaptability.
LGD: Label-guided Self-distillation for Object Detection
Instead, we generate an instructive knowledge based only on student representations and regular labels.
Anchor DETR: Query Design for Transformer-Based Object Detection
Thanks to the query design and the attention variant, the proposed detector that we called Anchor DETR, can achieve better performance and run faster than the DETR with 10$\times$ fewer training epochs.
Ranked #115 on
Object Detection
on COCO minival
Fully Convolutional Networks for Panoptic Segmentation with Point-based Supervision
In particular, Panoptic FCN encodes each object instance or stuff category with the proposed kernel generator and produces the prediction by convolving the high-resolution feature directly.
MMChat: Multi-Modal Chat Dataset on Social Media
To better investigate this issue, we manually annotate 100K dialogues from MMChat and further filter the corpus accordingly, which yields MMChat-hf.
Learning Canonical View Representation for 3D Shape Recognition with Arbitrary Views
In this way, each 3D shape with arbitrary views is represented by a fixed number of canonical view features, which are further aggregated to generate a rich and robust 3D shape representation for shape recognition.
Preview, Attend and Review: Schema-Aware Curriculum Learning for Multi-Domain Dialogue State Tracking
Existing dialog state tracking (DST) models are trained with dialog data in a random order, neglecting rich structural information in a dataset.
Workshop on Autonomous Driving at CVPR 2021: Technical Report for Streaming Perception Challenge
In this report, we introduce our real-time 2D object detection system for the realistic autonomous driving scenario.
A Unified Hyper-GAN Model for Unpaired Multi-contrast MR Image Translation
Hyper-GAN consists of a pair of hyper-encoder and hyper-decoder to first map from the source contrast to a common feature space, and then further map to the target contrast image.
YOLOX: Exceeding YOLO Series in 2021
In this report, we present some experienced improvements to YOLO series, forming a new high-performance detector -- YOLOX.
Ranked #1 on
Real-Time Object Detection
on Argoverse-HD (Detection-Only, Val)
(using extra training data)
Ill-posed Surface Emissivity Retrieval from Multi-Geometry Hyperspectral Images using a Hybrid Deep Neural Network
Atmospheric correction errors can significantly alter the spectral signature of the observations, and lead to invalid classifications or target detection.
EBSR: Feature Enhanced Burst Super-Resolution With Deformable Alignment
We propose a novel architecture to handle the problem of multi-frame super-resolution (MFSR).
Ranked #2 on
Burst Image Super-Resolution
on SyntheticBurst
NTIRE 2021 Challenge on Burst Super-Resolution: Methods and Results
This paper reviews the NTIRE2021 challenge on burst super-resolution.
Preview, Attend and Review: Schema-Aware Curriculum Learning for Multi-Domain Dialog State Tracking
Existing dialog state tracking (DST) models are trained with dialog data in a random order, neglecting rich structural information in a dataset.
Ranked #1 on
Multi-domain Dialogue State Tracking
on MULTIWOZ 2.1
(using extra training data)
ADNet: Attention-guided Deformable Convolutional Network for High Dynamic Range Imaging
In this paper, we present an attention-guided deformable convolutional network for hand-held multi-frame high dynamic range (HDR) imaging, namely ADNet.
Ranked #5 on
Face Alignment
on WFW (Extra Data)
Generalized Few-Shot Object Detection without Forgetting
Recently few-shot object detection is widely adopted to deal with data-limited situations.
Fast Camera Image Denoising on Mobile GPUs with Deep Learning, Mobile AI 2021 Challenge: Report
A detailed description of all models developed in the challenge is provided in this paper.
Relational Learning with Gated and Attentive Neighbor Aggregator for Few-Shot Knowledge Graph Completion
Moreover, modeling and inferring complex relations of one-to-many (1-N), many-to-one (N-1), and many-to-many (N-N) by previous knowledge graph completion approaches requires high model complexity and a large amount of training instances.
Points as Queries: Weakly Semi-supervised Object Detection by Points
We propose a novel point annotated setting for the weakly semi-supervised object detection task, in which the dataset comprises small fully annotated images and large weakly annotated images by points.
IQDet: Instance-wise Quality Distribution Sampling for Object Detection
We propose a dense object detector with an instance-wise sampling strategy, named IQDet.
Learning to Jointly Deblur, Demosaick and Denoise Raw Images
We address the problem of non-blind deblurring and demosaicking of noisy raw images.
Distribution Alignment: A Unified Framework for Long-tail Visual Recognition
Motivated by our discovery, we propose a unified distribution alignment strategy for long-tail visual recognition.
Ranked #19 on
Long-tail Learning
on Places-LT
OTA: Optimal Transport Assignment for Object Detection
Recent advances in label assignment in object detection mainly seek to independently define positive/negative training samples for each ground-truth (gt) object.
Ranked #77 on
Object Detection
on COCO test-dev
Resilient Control under Quantization and Denial-of-Service: Co-designing a Deadbeat Controller and Transmission Protocol
When both input and output channels are subject to DoS attacks and quantization, the proposed structure is shown able to decouple the encoding schemes for input, output, and estimated output signals.
You Only Look One-level Feature
From the perspective of optimization, we introduce an alternative way to address the problem instead of adopting the complex feature pyramids - {\em utilizing only one-level feature for detection}.
Ranked #146 on
Object Detection
on COCO test-dev
Training Networks in Null Space of Feature Covariance for Continual Learning
To balance plasticity and stability of network in continual learning, in this paper, we propose a novel network training algorithm called Adam-NSCL, which sequentially optimizes network parameters in the null space of previous tasks.
End-to-End Human Object Interaction Detection with HOI Transformer
We propose HOI Transformer to tackle human object interaction (HOI) detection in an end-to-end manner.
Ranked #32 on
Human-Object Interaction Detection
on HICO-DET
(using extra training data)
Improving Text-to-SQL with Schema Dependency Learning
In this paper, we present the Schema Dependency guided multi-task Text-to-SQL model (SDSQL) to guide the network to effectively capture the interactions between questions and schemas.
FFB6D: A Full Flow Bidirectional Fusion Network for 6D Pose Estimation
Moreover, at the output representation stage, we designed a simple but effective 3D keypoints selection algorithm considering the texture and geometry information of objects, which simplifies keypoint localization for precise pose estimation.
Ranked #1 on
6D Pose Estimation
on LineMOD
Using Long Short-Term Memory (LSTM) and Internet of Things (IoT) for localized surface temperature forecasting in an urban environment
Meanwhile, by using IoT observations, the spatial resolution of air temperature predictions is significantly improved.
Neural Architecture Search with Random Labels
In this paper, we investigate a new variant of neural architecture search (NAS) paradigm -- searching with random labels (RLNAS).
Momentum^2 Teacher: Momentum Teacher with Momentum Statistics for Self-Supervised Learning
The teacher's weight is a momentum update of the student, and the teacher's BN statistics is a momentum update of those in history.
RepVGG: Making VGG-style ConvNets Great Again
We present a simple but powerful architecture of convolutional neural network, which has a VGG-like inference-time body composed of nothing but a stack of 3x3 convolution and ReLU, while the training-time model has a multi-branch topology.
Ranked #49 on
Semantic Segmentation
on Cityscapes val
Dynamic Hybrid Relation Network for Cross-Domain Context-Dependent Semantic Parsing
Semantic parsing has long been a fundamental problem in natural language processing.
Ranked #5 on
Dialogue State Tracking
on CoSQL
Domain-Free Adversarial Splitting for Domain Generalization
In this framework, we model the domain generalization as a learning problem that enforces the learner to be able to generalize well for any train/val subsets splitting of the training dataset.
Implicit Feature Pyramid Network for Object Detection
In this paper, we present an implicit feature pyramid network (i-FPN) for object detection.
Efficient Human Pose Estimation by Learning Deeply Aggregated Representations
Instead, we focus on exploiting multi-scale information from layers with different receptive-field sizes and then making full of use this information by improving the fusion method.
Fine-Grained Dynamic Head for Object Detection
To this end, we propose a fine-grained dynamic head to conditionally select a pixel-level combination of FPN features from different scales for each instance, which further releases the ability of multi-scale feature representation.
Rethinking Learnable Tree Filter for Generic Feature Transform
The Learnable Tree Filter presents a remarkable approach to model structure-preserving relations for semantic segmentation.
End-to-End Object Detection with Fully Convolutional Network
Mainstream object detectors based on the fully convolutional network has achieved impressive performance.
Decentralized TD Tracking with Linear Function Approximation and its Finite-Time Analysis
The present contribution deals with decentralized policy evaluation in multi-agent Markov decision processes using temporal-difference (TD) methods with linear function approximation for scalability.
Dual Attention Network for Cross-lingual Entity Alignment
The hierarchical attention adaptively aggregates the low-hierarchy and the high-hierarchy information, which is beneficial to balance the neighborhood information of counterpart entities and distinguish non-counterpart entities with similar structures.
UPFlow: Upsampling Pyramid for Unsupervised Optical Flow Learning
By integrating these two components together, our method achieves the best performance for unsupervised optical flow learning on multiple leading benchmarks, including MPI-SIntel, KITTI 2012 and KITTI 2015.
Ranked #1 on
Optical Flow Estimation
on Sintel Final unsupervised
Fully Convolutional Networks for Panoptic Segmentation
In this paper, we present a conceptually simple, strong, and efficient framework for panoptic segmentation, called Panoptic FCN.
Ranked #1 on
Panoptic Segmentation
on COCO minival
(SQ metric)
Self-EMD: Self-Supervised Object Detection without ImageNet
Our Faster R-CNN (ResNet50-FPN) baseline achieves 39. 8% mAP on COCO, which is on par with the state of the art self-supervised methods pre-trained on ImageNet.
Deep Positional and Relational Feature Learning for Rotation-Invariant Point Cloud Analysis
In this work, we propose a novel deep network for point clouds by incorporating positional information of points as inputs while yielding rotation-invariance.
Joint COCO and Mapillary Workshop at ICCV 2019: COCO Instance Segmentation Challenge Track
In this report, we present our object detection/instance segmentation system, MegDetV2, which works in a two-pass fashion, first to detect instances then to obtain segmentation.
EqCo: Equivalent Rules for Self-supervised Contrastive Learning
In this paper, we propose EqCo (Equivalent Rules for Contrastive Learning) to make self-supervised learning irrelevant to the number of negative samples in the contrastive learning framework.
Activate or Not: Learning Customized Activation
We present a simple, effective, and general activation function we term ACON which learns to activate the neurons or not.
Deep Reinforcement Learning for Dynamic Spectrum Sensing and Aggregation in Multi-Channel Wireless Networks
Then, the vacant channels in the selected segment will be aggregated for satisfying the user requirement.
A Non-Stationary VVLC MIMO Channel Model for Street Corner Scenarios
The study of the underlying VLC channel is the basis for designing the VLC communication system.
Multi-Frequency Multi-Scenario Millimeter Wave MIMO Channel Measurements and Modeling for B5G Wireless Communication Systems
Millimeter wave (mmWave) bands have been utilized for the fifth generation (5G) communication systems and will no doubt continue to be deployed for beyond 5G (B5G).
A Survey on Complex Question Answering over Knowledge Base: Recent Advances and Challenges
Question Answering (QA) over Knowledge Base (KB) aims to automatically answer natural language questions via well-structured relation information between entities stored in knowledge bases.
WeightNet: Revisiting the Design Space of Weight Networks
WeightNet is easy and memory-conserving to train, on the kernel space instead of the feature space.
Funnel Activation for Visual Recognition
We present a conceptually simple but effective funnel activation for image recognition tasks, called Funnel activation (FReLU), that extends ReLU and PReLU to a 2D activation by adding a negligible overhead of spatial condition.
BorderDet: Border Feature for Dense Object Detection
In this paper, We propose a simple and efficient operator called Border-Align to extract "border features" from the extreme point of the border to enhance the point feature.
Learning from Manipulable Signals
The principal learns about the agent's type from a noisy performance measure, which can be manipulated by the agent via a costly and hidden action.
AutoAssign: Differentiable Label Assignment for Dense Object Detection
During training, to both satisfy the prior distribution of data and adapt to category characteristics, we present Center Weighting to adjust the category-specific prior distributions.
LabelEnc: A New Intermediate Supervision Method for Object Detection
In this paper we propose a new intermediate supervision method, named LabelEnc, to boost the training of object detection systems.
End-to-end Interpretable Learning of Non-blind Image Deblurring
Non-blind image deblurring is typically formulated as a linear least-squares problem regularized by natural priors on the corresponding sharp picture's gradients, which can be solved, for example, using a half-quadratic splitting method with Richardson fixed-point iterations for its least-squares updates and a proximal operator for the auxiliary variable updates.
Learning Low-Resource End-To-End Goal-Oriented Dialog for Fast and Reliable System Deployment
Existing end-to-end dialog systems perform less effectively when data is scarce.
Structured and Localized Image Restoration
We present a novel approach to image restoration that leverages ideas from localized structured prediction and non-linear multi-task learning.
Online Reinforcement Learning Control by Direct Heuristic Dynamic Programming: from Time-Driven to Event-Driven
In this paper time-driven learning refers to the machine learning method that updates parameters in a prediction model continuously as new data arrives.
Spherical Motion Dynamics: Learning Dynamics of Neural Network with Normalization, Weight Decay, and SGD
In this work, we comprehensively reveal the learning dynamics of neural network with normalization, weight decay (WD), and SGD (with momentum), named as Spherical Motion Dynamics (SMD).
Joint Multi-Dimension Pruning via Numerical Gradient Update
To tackle these three naturally different dimensions, we proposed a general framework by defining pruning as seeking the best pruning vector (i. e., the numerical value of layer-wise channel number, spacial size, depth) and construct a unique mapping from the pruning vector to the pruned network structures.
Dynamic Memory Induction Networks for Few-Shot Text Classification
This paper proposes Dynamic Memory Induction Networks (DMIN) for few-shot text classification.
A Survey on Dialog Management: Recent Advances and Challenges
Dialog management (DM) is a crucial component in a task-oriented dialog system.
Angle-based Search Space Shrinking for Neural Architecture Search
Comprehensive experiments show that ABS can dramatically enhance existing NAS approaches by providing a promising shrunk search space.
Dynamic Scale Training for Object Detection
We propose a Dynamic Scale Training paradigm (abbreviated as DST) to mitigate scale variation challenge in object detection.
Attentive Normalization for Conditional Image Generation
Traditional convolution-based generative adversarial networks synthesize images based on hierarchical local operations, where long-range dependency relation is implicitly modeled with a Markov chain.
Learning Human-Object Interaction Detection using Interaction Points
Human-object interaction (HOI) detection strives to localize both the human and an object as well as the identification of complex interactions between them.
Dynamic Region-Aware Convolution
More gracefully, our DRConv transfers the increasing channel-wise filters to spatial dimension with learnable instructor, which not only improve representation ability of convolution, but also maintains computational cost and the translation-invariance as standard convolution dose.
Ranked #22 on
Semantic Segmentation
on MCubeS
Learning Dynamic Routing for Semantic Segmentation
To demonstrate the superiority of the dynamic property, we compare with several static architectures, which can be modeled as special cases in the routing space.
Detection in Crowded Scenes: One Proposal, Multiple Predictions
We propose a simple yet effective proposal-based object detector, aiming at detecting highly-overlapped instances in crowded scenes.
Ranked #2 on
Pedestrian Detection
on TJU-Ped-campus
