Search Results for author:
Found 196 papers, 92 papers with code
Don’t Take It Literally: An Edit-Invariant Sequence Loss for Text Generation
Such training objective is sub-optimal when the target sequence is not perfect, e. g., when the target sequence is corrupted with noises, or when only weak sequence supervision is available.
Reciprocal Learning of Knowledge Retriever and Response Ranker for Knowledge-Grounded Conversations
In this paper, we propose a reciprocal learning approach to jointly optimize a knowledge retriever and a response ranker for knowledge-grounded response retrieval without ground-truth knowledge labels.
Towards Content-Independent Multi-Reference Super-Resolution: Adaptive Pattern Matching and Feature Aggregation
Recovering realistic textures from a largely down-sampled low resolution (LR) image with complicated patterns is a challenging problem in image super-resolution.
ETSM: Automating Dissection Trajectory Suggestion and Confidence Map-Based Safety Margin Prediction for Robot-assisted Endoscopic Submucosal Dissection
Robot-assisted Endoscopic Submucosal Dissection (ESD) improves the surgical procedure by providing a more comprehensive view through advanced robotic instruments and bimanual operation, thereby enhancing dissection efficiency and accuracy.
PDZSeg: Adapting the Foundation Model for Dissection Zone Segmentation with Visual Prompts in Robot-assisted Endoscopic Submucosal Dissection
Conclusion: The PDZSeg model effectively utilizes visual prompts to enhance segmentation performance and user experience, supported by the novel ESD-DZSeg dataset as a benchmark for dissection zone segmentation in ESD.
D$^2$-World: An Efficient World Model through Decoupled Dynamic Flow
This technical report summarizes the second-place solution for the Predictive World Model Challenge held at the CVPR-2024 Workshop on Foundation Models for Autonomous Systems.
Privacy-Preserving Federated Foundation Model for Generalist Ultrasound Artificial Intelligence
Notably, UltraFedFM surpasses the diagnostic accuracy of mid-level ultrasonographers and matches the performance of expert-level sonographers in the joint diagnosis of 8 common systemic diseases.
VisionPAD: A Vision-Centric Pre-training Paradigm for Autonomous Driving
This paper introduces VisionPAD, a novel self-supervised pre-training paradigm designed for vision-centric algorithms in autonomous driving.
CoPESD: A Multi-Level Surgical Motion Dataset for Training Large Vision-Language Models to Co-Pilot Endoscopic Submucosal Dissection
In this paper, we design a hierarchical decomposition of ESD motion granularity and introduce a multi-level surgical motion dataset (CoPESD) for training LVLMs as the robotic \textbf{Co}-\textbf{P}ilot of \textbf{E}ndoscopic \textbf{S}ubmucosal \textbf{D}issection.
Emu3: Next-Token Prediction is All You Need
While next-token prediction is considered a promising path towards artificial general intelligence, it has struggled to excel in multimodal tasks, which are still dominated by diffusion models (e. g., Stable Diffusion) and compositional approaches (e. g., CLIP combined with LLMs).
Ranked #104 on
Visual Question Answering
on MM-Vet
MixPolyp: Integrating Mask, Box and Scribble Supervision for Enhanced Polyp Segmentation
Limited by the expensive labeling, polyp segmentation models are plagued by data shortages.
A Scalable Matrix Visualization for Understanding Tree Ensemble Classifiers
To ensure the inclusion of anomalous rules, we develop an anomaly-biased model reduction method to prioritize these rules at each hierarchical level.
Let Video Teaches You More: Video-to-Image Knowledge Distillation using DEtection TRansformer for Medical Video Lesion Detection
The teacher network aims at extracting temporal contexts from multiple frames and transferring them to the student network, and the student network is an image-based model dedicated to fast prediction in inference.
Towards a Benchmark for Colorectal Cancer Segmentation in Endorectal Ultrasound Videos: Dataset and Model Development
Based on this dataset, we further introduce a benchmark model for colorectal cancer segmentation, named the Adaptive Sparse-context TRansformer (ASTR).
Exploring Query Understanding for Amazon Product Search
Next, we delve into how the query understanding system contributes to understanding the performance of a ranking model.
Improve ROI with Causal Learning and Conformal Prediction
However, the DRP model confronts issues like covariate shift and insufficient training data, hindering its real-world effectiveness.
DV-3DLane: End-to-end Multi-modal 3D Lane Detection with Dual-view Representation
3) A 3D dual-view deformable attention mechanism, which aggregates discriminative features from both PV and BEV spaces into queries for accurate 3D lane detection.
EndoUIC: Promptable Diffusion Transformer for Unified Illumination Correction in Capsule Endoscopy
While researchers have discussed the challenges of low-light enhancement in WCE, the issue of correcting for different exposure levels remains underexplored.
GUI-WORLD: A Dataset for GUI-oriented Multimodal LLM-based Agents
We evaluate the capabilities of current state-of-the-art MLLMs, including ImageLLMs and VideoLLMs, in understanding various types of GUI content, especially dynamic and sequential content.
Fully Test-Time Adaptation for Monocular 3D Object Detection
However, applying this paradigm in Mono 3Det poses significant challenges due to OOD test data causing a remarkable decline in object detection scores.
SA-GS: Semantic-Aware Gaussian Splatting for Large Scene Reconstruction with Geometry Constrain
We provide extensive experiments on publicly available large-scale scene reconstruction datasets with highly accurate point clouds as ground truth and our novel dataset.
SearchLVLMs: A Plug-and-Play Framework for Augmenting Large Vision-Language Models by Searching Up-to-Date Internet Knowledge
In this paper, we propose a plug-and-play framework, for augmenting existing LVLMs in handling visual question answering (VQA) about up-to-date knowledge, dubbed SearchLVLMs.
SEGAN: semi-supervised learning approach for missing data imputation
In addition, the SE-GAN model introduces a missing hint matrix to allow the discriminator to more effectively distinguish between known data and data filled by the generator.
Generalize Polyp Segmentation via Inpainting across Diverse Backgrounds and Pseudo-Mask Refinement
Inpainting lesions within different normal backgrounds is a potential method of addressing the generalization problem, which is crucial for polyp segmentation models.
Magnetic-Guided Flexible Origami Robot toward Long-Term Phototherapy of H. pylori in the Stomach
Helicobacter pylori, a pervasive bacterial infection associated with gastrointestinal disorders such as gastritis, peptic ulcer disease, and gastric cancer, impacts approximately 50% of the global population.
Time Evidence Fusion Network: Multi-source View in Long-Term Time Series Forecasting
In practical scenarios, time series forecasting necessitates not only accuracy but also efficiency.
Ranked #5 on
Time Series Forecasting
on ETTm2 (720) Multivariate
Instance-free Text to Point Cloud Localization with Relative Position Awareness
In this paper, we address two key limitations of existing approaches: 1) their reliance on ground-truth instances as input; and 2) their neglect of the relative positions among potential instances.
Ranked #3 on
Visual Place Recognition
on KITTI360pose
Extracting Clean and Balanced Subset for Noisy Long-tailed Classification
To solve the joint issue of long-tailed distribution and label noise, most previous works usually aim to design a noise detector to distinguish the noisy and clean samples.
VISION2UI: A Real-World Dataset with Layout for Code Generation from UI Designs
Automatically generating UI code from webpage design visions can significantly alleviate the burden of developers, enabling beginner developers or designers to directly generate Web pages from design diagrams.
GauU-Scene V2: Assessing the Reliability of Image-Based Metrics with Expansive Lidar Image Dataset Using 3DGS and NeRF
The experimental results on our multimodal dataset highlight the unreliability of current image-based metrics and reveal significant drawbacks in geometric reconstruction using the current Gaussian Splatting-based method, further illustrating the necessity of our dataset for assessing geometry reconstruction tasks.
Random Walk in Random Permutation Set Theory
Random walk is an explainable approach for modeling natural processes at the molecular level.
An Open-source End-to-End Logic Optimization Framework for Large-scale Boolean Network with Reinforcement Learning
We propose an open-source end-to-end logic optimization framework for large-scale boolean network with reinforcement learning.
Learnable WSN Deployment of Evidential Collaborative Sensing Model
A learnable sensor deployment network (LSDNet) considering both sensor contribution and detection capability, is proposed for achieving the optimal deployment of WSNs.
Bridging scales in multiscale bubble growth dynamics with correlated fluctuations using neural operator learning
In order to bridge the gap between microscale stochastic fluid models and continuum-based fluid models for bubble dynamics, we develop a composite neural operator model to unify the analysis of nonlinear bubble dynamics across microscale and macroscale regimes by integrating a many-body dissipative particle dynamics (mDPD) model with a continuum-based Rayleigh-Plesset (RP) model through a novel neural network architecture, which consists of a deep operator network for learning the mean behavior of bubble growth subject to pressure variations and a long short-term memory network for learning the statistical features of correlated fluctuations in microscale bubble dynamics.
Limit of the Maximum Random Permutation Set Entropy
In this paper, a new concept, the envelope of entropy function, is defined.
Hierarchical Query Classification in E-commerce Search
E-commerce platforms typically store and structure product information and search data in a hierarchy.
Unified Generation, Reconstruction, and Representation: Generalized Diffusion with Adaptive Latent Encoding-Decoding
The vast applications of deep generative models are anchored in three core capabilities -- generating new instances, reconstructing inputs, and learning compact representations -- across various data types, such as discrete text/protein sequences and continuous images.
Random Graph Set and Evidence Pattern Reasoning Model
Therefore, Random Graph Set (RGS) were proposed to model complex relationships and represent more event types.
GauU-Scene: A Scene Reconstruction Benchmark on Large Scale 3D Reconstruction Dataset Using Gaussian Splatting
We introduce a novel large-scale scene reconstruction benchmark using the newly developed 3D representation approach, Gaussian Splatting, on our expansive U-Scene dataset.
Forging Vision Foundation Models for Autonomous Driving: Challenges, Methodologies, and Opportunities
The rise of large foundation models, trained on extensive datasets, is revolutionizing the field of AI.
Leveraging Large Language Models for NLG Evaluation: Advances and Challenges
In the rapidly evolving domain of Natural Language Generation (NLG) evaluation, introducing Large Language Models (LLMs) has opened new avenues for assessing generated content quality, e. g., coherence, creativity, and context relevance.
ECC-PolypDet: Enhanced CenterNet with Contrastive Learning for Automatic Polyp Detection
Accurate polyp detection is critical for early colorectal cancer diagnosis.
VQA4CIR: Boosting Composed Image Retrieval with Visual Question Answering
By feeding the retrieved image and question to the VQA model, one can find the images inconsistent with relative caption when the answer by VQA is inconsistent with the answer in the QA pair.
CrossBind: Collaborative Cross-Modal Identification of Protein Nucleic-Acid-Binding Residues
Accurate identification of protein nucleic-acid-binding residues poses a significant challenge with important implications for various biological processes and drug design.
RadOcc: Learning Cross-Modality Occupancy Knowledge through Rendering Assisted Distillation
3D occupancy prediction is an emerging task that aims to estimate the occupancy states and semantics of 3D scenes using multi-view images.
X4D-SceneFormer: Enhanced Scene Understanding on 4D Point Cloud Videos through Cross-modal Knowledge Transfer
The field of 4D point cloud understanding is rapidly developing with the goal of analyzing dynamic 3D point cloud sequences.
GSmoothFace: Generalized Smooth Talking Face Generation via Fine Grained 3D Face Guidance
Our proposed GSmoothFace model mainly consists of the Audio to Expression Prediction (A2EP) module and the Target Adaptive Face Translation (TAFT) module.
PhotoMaker: Customizing Realistic Human Photos via Stacked ID Embedding
Recent advances in text-to-image generation have made remarkable progress in synthesizing realistic human photos conditioned on given text prompts.
Ranked #6 on
Diffusion Personalization Tuning Free
on AgeDB
Diffusion Personalization Tuning Free
Text-to-Image Generation
Visual Programming for Zero-shot Open-Vocabulary 3D Visual Grounding
Building on this, we design a visual program that consists of three types of modules, i. e., view-independent, view-dependent, and functional modules.
ScribblePolyp: Scribble-Supervised Polyp Segmentation through Dual Consistency Alignment
The second branch leverages affinity propagation to refine predictions into a soft version, extending additional supervision to unlabeled pixels.
Variational Quantum Linear Solver-based Combination Rules in Dempster–Shafer Theory
Dempster–Shafer Theory (DST), as a method of handling uncertain information, is widely used in decisionmaking and information fusion.
SupFusion: Supervised LiDAR-Camera Fusion for 3D Object Detection
In this paper, we propose a novel training strategy called SupFusion, which provides an auxiliary feature level supervision for effective LiDAR-Camera fusion and significantly boosts detection performance.
ArSDM: Colonoscopy Images Synthesis with Adaptive Refinement Semantic Diffusion Models
Colonoscopy analysis, particularly automatic polyp segmentation and detection, is essential for assisting clinical diagnosis and treatment.
LATR: 3D Lane Detection from Monocular Images with Transformer
On the one hand, each query is generated based on 2D lane-aware features and adopts a hybrid embedding to enhance lane information.
Ranked #3 on
3D Lane Detection
on OpenLane
WeakPolyp: You Only Look Bounding Box for Polyp Segmentation
In contrast, polyp bounding box annotations are much cheaper and more accessible.
Ranked #4 on
Video Polyp Segmentation
on SUN-SEG-Easy
Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generation
To test the potential of the dataset, we introduce three tasks in this work: (1) next-product recommendation, (2) next-product recommendation with domain shifts, and (3) next-product title generation.
Characterization of partial wetting by CMAS droplets using multiphase many-body dissipative particle dynamics and data-driven discovery based on PINNs
Subsequently, the closed form dependency of parameter values found by PINN on the initial radii and contact angles are given using symbolic regression.
SkeletonMAE: Graph-based Masked Autoencoder for Skeleton Sequence Pre-training
Moreover, these methods ignore how to utilize the fine-grained dependencies among different skeleton joints to pre-train an efficient skeleton sequence learning model that can generalize well across different datasets.
YONA: You Only Need One Adjacent Reference-frame for Accurate and Fast Video Polyp Detection
YONA fully exploits the information of one previous adjacent frame and conducts polyp detection on the current frame without multi-frame collaborations.
SR-OOD: Out-of-Distribution Detection via Sample Repairing
Specifically, our framework consists of two components: a sample repairing module and a detection module.
Generative Adversarial Network
Out-of-Distribution Detection
+1
Deep Learning based Forecasting: a case study from the online fashion industry
These include the volume of data, the irregularity, the high amount of turn-over in the catalog and the fixed inventory assumption.
Differential Convolutional Fuzzy Time Series Forecasting
Thanks to the learnable ability of the neural network, the length of fuzzy rules established in FTSF is expended to an arbitrary length that the expert is not able to handle by the expert system.
Hierarchical Weight Averaging for Deep Neural Networks
Despite the simplicity, stochastic gradient descent (SGD)-like algorithms are successful in training deep neural networks (DNNs).
Decouple Non-parametric Knowledge Distillation For End-to-end Speech Translation
Existing techniques often attempt to make knowledge transfer from a powerful machine translation (MT) to speech translation (ST) model with some elaborate techniques, which often requires transcription as extra input during training.
AMT: All-Pairs Multi-Field Transforms for Efficient Frame Interpolation
It is based on two essential designs.
Semantic Human Parsing via Scalable Semantic Transfer over Multiple Label Domains
This paper presents Scalable Semantic Transfer (SST), a novel training paradigm, to explore how to leverage the mutual benefits of the data from different label domains (i. e. various levels of label granularity) to train a powerful human parsing network.
Fair-CDA: Continuous and Directional Augmentation for Group Fairness
By adjusting the perturbation strength in the direction of the paths, our proposed augmentation is controllable and auditable.
Deep neural operator for learning transient response of interpenetrating phase composites subject to dynamic loading
After an offline training, the DNO model can act as surrogate of physics-based FEA to predict the transient mechanical response in terms of reaction force and stress distribution of the IPCs to various strain loads in one second at an accuracy of 98%.
An Effective Motion-Centric Paradigm for 3D Single Object Tracking in Point Clouds
Due to the motion-centric nature, our method shows its impressive generalizability with limited training labels and provides good differentiability for end-to-end cycle training.
SRFormerV2: Taking a Closer Look at Permuted Self-Attention for Image Super-Resolution
Without any bells and whistles, we show that our SRFormer achieves a 33. 86dB PSNR score on the Urban100 dataset, which is 0. 46dB higher than that of SwinIR but uses fewer parameters and computations.
Adaptive Context Selection for Polyp Segmentation
To tackle these issues, we propose an adaptive context selection based encoder-decoder framework which is composed of Local Context Attention (LCA) module, Global Context Module (GCM) and Adaptive Selection Module (ASM).
Benchmarking the Robustness of LiDAR Semantic Segmentation Models
In this paper, we aim to comprehensively analyze the robustness of LiDAR semantic segmentation models under various corruptions.
Learning Transformation-Predictive Representations for Detection and Description of Local Features
The task of key-points detection and description is to estimate the stable location and discriminative representation of local features, which is essential for image matching.
DNF: Decouple and Feedback Network for Seeing in the Dark
The exclusive properties of RAW data have shown great potential for low-light image enhancement.
RankMatch: Fostering Confidence and Consistency in Learning with Noisy Labels
Confidence-wise, we propose a novel sample selection strategy based on confidence representation voting instead of the widely-used small-loss criterion.
BEV@DC: Bird's-Eye View Assisted Training for Depth Completion
In practice, the proposed BEV@DC model comprehensively takes advantage of LiDARs with rich geometric details in training, employing an enhanced depth completion manner in inference, which takes only images (RGB and depth) as input.
Exploring the Effect of Primitives for Compositional Generalization in Vision-and-Language
Compositional generalization is critical to simulate the compositional capability of humans, and has received much attention in the vision-and-language (V&L) community.
DOSnet as a Non-Black-Box PDE Solver: When Deep Learning Meets Operator Splitting
Deep neural networks (DNNs) recently emerged as a promising tool for analyzing and solving complex differential equations arising in science and engineering applications.
BoxPolyp:Boost Generalized Polyp Segmentation Using Extra Coarse Bounding Box Annotations
In practice, box annotations are applied to alleviate the over-fitting issue of previous polyp segmentation models, which generate fine-grained polyp area through the iterative boosted segmentation model.
Geometry-Aware Network for Domain Adaptive Semantic Segmentation
Moreover, to improve the 2D classifier in the target domain, we perform domain-invariant geometric adaptation from source to target and unify the 2D semantic and 3D geometric segmentation results in two domains.
Multi-view Inverse Rendering for Large-scale Real-world Indoor Scenes
We present a efficient multi-view inverse rendering method for large-scale real-world indoor scenes that reconstructs global illumination and physically-reasonable SVBRDFs.
Divide and Contrast: Source-free Domain Adaptation via Adaptive Contrastive Learning
We investigate a practical domain adaptation task, called source-free domain adaptation (SFUDA), where the source-pretrained model is adapted to the target domain without access to the source data.
Ranked #5 on
Source-Free Domain Adaptation
on VisDA-2017
Let Images Give You More:Point Cloud Cross-Modal Training for Shape Analysis
Specifically, this paper introduces a simple but effective point cloud cross-modality training (PointCMT) strategy, which utilizes view-images, i. e., rendered or projected 2D images of the 3D object, to boost point cloud analysis.
Ranked #13 on
3D Point Cloud Classification
on ModelNet40
Low Error-Rate Approximate Multiplier Design for DNNs with Hardware-Driven Co-Optimization
In this paper, two approximate 3*3 multipliers are proposed and the synthesis results of the ASAP-7nm process library justify that they can reduce the area by 31. 38% and 36. 17%, and the power consumption by 36. 73% and 35. 66% compared with the exact multiplier, respectively.
Robust Unsupervised Cross-Lingual Word Embedding using Domain Flow Interpolation
Further experiments on the downstream task of Cross-Lingual Natural Language Inference show that the proposed model achieves significant performance improvement for distant language pairs in downstream tasks compared to state-of-the-art adversarial and non-adversarial models.
APAUNet: Axis Projection Attention UNet for Small Target in 3D Medical Segmentation
In 3D medical image segmentation, small targets segmentation is crucial for diagnosis but still faces challenges.
SAVAnet: Surgical Action-Driven Visual Attention Network for Autonomous Endoscope Control
Finally, the simulation results show that the SAVAnet can endow endoscope with surgeons’ visual attention to perform self-control in real time.
M$^2$-3DLaneNet: Exploring Multi-Modal 3D Lane Detection
Estimating accurate lane lines in 3D space remains challenging due to their sparse and slim nature.
Ranked #10 on
3D Lane Detection
on OpenLane
Composable Text Controls in Latent Space with ODEs
This paper proposes a new efficient approach for composable text operations in the compact latent space of text.
Ranked #2 on
Unsupervised Text Style Transfer
on Yelp
Designing An Illumination-Aware Network for Deep Image Relighting
Lighting is a determining factor in photography that affects the style, expression of emotion, and even quality of images.
2DPASS: 2D Priors Assisted Semantic Segmentation on LiDAR Point Clouds
As camera and LiDAR sensors capture complementary information used in autonomous driving, great efforts have been made to develop semantic segmentation algorithms through multi-modality data fusion.
Ranked #4 on
Robust 3D Semantic Segmentation
on nuScenes-C
Toward Explainable and Fine-Grained 3D Grounding through Referring Textual Phrases
Recent progress in 3D scene understanding has explored visual grounding (3DVG) to localize a target object through a language description.
Toward Clinically Assisted Colorectal Polyp Recognition via Structured Cross-modal Representation Consistency
By aligning the class tokens and spatial attention maps of paired NBI and WL images at different levels, the Transformer achieves the ability to keep both global and local representation consistency for the above two modalities.
Toward Unpaired Multi-modal Medical Image Segmentation via Learning Structured Semantic Consistency
Integrating multi-modal data to promote medical image analysis has recently gained great attention.
A Unified Understanding of Deep NLP Models for Text Classification
The rapid development of deep natural language processing (NLP) models for text classification has led to an urgent need for a unified understanding of these models proposed individually.
AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation
Constraint by the high cost of collecting and labeling 3D medical data, most of the deep learning models to date are driven by datasets with a limited number of organs of interest or samples, which still limits the power of modern deep models and makes it difficult to provide a fully comprehensive and fair estimate of various methods.
Universality and approximation bounds for echo state networks with random weights
We study the uniform approximation of echo state networks with randomly generated internal weights.
Contextual Bandits with Knapsacks for a Conversion Model
At each round, given the stochastic i. i. d.\ context $\mathbf{x}_t$ and the arm picked $a_t$ (corresponding, e. g., to a discount level), a customer conversion may be obtained, in which case a reward $r(a,\mathbf{x}_t)$ is gained and vector costs $c(a_t,\mathbf{x}_t)$ are suffered (corresponding, e. g., to losses of earnings).
6GAN: IPv6 Multi-Pattern Target Generation via Generative Adversarial Nets with Reinforcement Learning
6GAN forces multiple generators to train with a multi-class discriminator and an alias detector to generate non-aliased active targets with different addressing pattern types.
SiamHAN: IPv6 Address Correlation Attacks on TLS Encrypted Traffic via Siamese Heterogeneous Graph Attention Network
To do this, we propose an IPv6 address correlation model - SiamHAN.
CowClip: Reducing CTR Prediction Model Training Time from 12 hours to 10 minutes on 1 GPU
Our experiments show that previous scaling rules fail in the training of CTR prediction neural networks.
CLMLF:A Contrastive Learning and Multi-Layer Fusion Method for Multimodal Sentiment Detection
Compared with unimodal data, multimodal data can provide more features to help the model analyze the sentiment of data.
CholecTriplet2021: A benchmark challenge for surgical action triplet recognition
In this paper, we present the challenge setup and assessment of the state-of-the-art deep learning methods proposed by the participants during the challenge.
Ranked #1 on
Action Triplet Recognition
on CholecT50 (Challenge)
(using extra training data)
Towards An End-to-End Framework for Flow-Guided Video Inpainting
Optical flow, which captures motion information across frames, is exploited in recent video inpainting methods through propagating pixels along its trajectories.
Ranked #2 on
Seeing Beyond the Visible
on KITTI360-EX
Graph Enhanced Contrastive Learning for Radiology Findings Summarization
To address the limitation, we propose a unified framework for exploiting both extra knowledge and the original findings in an integrated way so that the critical information (i. e., key words and their relations) can be extracted in an appropriate way to facilitate impression generation.
Beyond 3D Siamese Tracking: A Motion-Centric Paradigm for 3D Single Object Tracking in Point Clouds
3D single object tracking (3D SOT) in LiDAR point clouds plays a crucial role in autonomous driving.
Ranked #1 on
Object Tracking
on KITTI
X-Trans2Cap: Cross-Modal Knowledge Transfer using Transformer for 3D Dense Captioning
Thus, a more faithful caption can be generated only using point clouds during the inference.
ET-BERT: A Contextualized Datagram Representation with Pre-training Transformers for Encrypted Traffic Classification
In this paper, we propose a new traffic representation model called Encrypted Traffic Bidirectional Encoder Representations from Transformer (ET-BERT), which pre-trains deep contextualized datagram-level representation from large-scale unlabeled data.
RoPGen: Towards Robust Code Authorship Attribution via Automatic Coding Style Transformation
Recent studies show that current source code authorship attribution methods can be compromised by attackers exploiting adversarial examples and coding style manipulation.
HEAM: High-Efficiency Approximate Multiplier Optimization for Deep Neural Networks
We propose an optimization method for the automatic design of approximate multipliers, which minimizes the average error according to the operand distributions.
Heuristic Search for Rank Aggregation with Application to Label Ranking
Rank aggregation aims to combine the preference rankings of a number of alternatives from different voters into a single consensus ranking.
PhyIR: Physics-Based Inverse Rendering for Panoramic Indoor Images
In this paper, we present PhyIR, a neural inverse rendering method with a more completed SVBRDF representation and a physics-based in-network rendering layer, which can handle complex material and incorporate physical constraints by re-rendering realistic and detailed specular reflectance.
Comprehensive Visual Question Answering on Point Clouds through Compositional Scene Manipulation
To tackle this problem, we propose the CLEVR3D, a large-scale VQA-3D dataset consisting of 171K questions from 8, 771 3D scenes.
CO2Sum:Contrastive Learning for Factual-Consistent Abstractive Summarization
We propose CO2Sum (Contrastive for Consistency), a contrastive learning scheme that can be easily applied on sequence-to-sequence models for factual-consistent abstractive summarization, proving that the model can be fact-aware without modifying the architecture.
Active Learning for Event Extraction with Memory-based Loss Prediction Model
During the selection process, we use an internal-external sample loss ranking method to evaluate the sample importance by using local information.
Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial Intelligence
Artificial intelligence (AI) provides a promising substitution for streamlining COVID-19 diagnoses.
Rapid Assessments of Light-Duty Gasoline Vehicle Emissions Using On-Road Remote Sensing and Machine Learning
Our results show that the ORRS measurements, assisted by the machine-learning-based ensemble model developed here, can realize day-to-day supervision of on-road vehicle-specific emissions.
Medical-VLBERT: Medical Visual Language BERT for COVID-19 CT Report Generation With Alternate Learning
Medical imaging technologies, including computed tomography (CT) or chest X-Ray (CXR), are largely employed to facilitate the diagnosis of the COVID-19.
Box-Aware Feature Enhancement for Single Object Tracking on Point Clouds
Current 3D single object tracking approaches track the target based on a feature comparison between the target template and the search area.
Ranked #2 on
Object Tracking
on KITTI
Colorectal Polyp Classification from White-light Colonoscopy Images via Domain Alignment
Thus, we propose a novel framework based on a teacher-student architecture for the accurate colorectal polyp classification (CPC) through directly using white-light (WL) colonoscopy images in the examination.
Towards Making Deep Learning-based Vulnerability Detectors Robust
Automatically detecting software vulnerabilities in source code is an important problem that has attracted much attention.
Shallow Attention Network for Polyp Segmentation
To address the above issues, we propose the Shallow Attention Network (SANet) for polyp segmentation.
Ranked #11 on
Video Polyp Segmentation
on SUN-SEG-Easy (Unseen)
Shallow Feature Matters for Weakly Supervised Object Localization
In practice, our SPOL model first generates the CAMs through a novel element-wise multiplication of shallow and deep feature maps, which filters the background noise and generates sharper boundaries robustly.
Inverse Problem of Nonlinear Schrödinger Equation as Learning of Convolutional Neural Network
In this work, we use an explainable convolutional neural network (NLS-Net) to solve an inverse problem of the nonlinear Schr\"odinger equation, which is widely used in fiber-optic communications.
Don't Take It Literally: An Edit-Invariant Sequence Loss for Text Generation
Such training objective is sub-optimal when the target sequence is not perfect, e. g., when the target sequence is corrupted with noises, or when only weak sequence supervision is available.
Multi-Compound Transformer for Accurate Biomedical Image Segmentation
The recent vision transformer(i. e. for image classification) learns non-local attentive interaction of different patch tokens.
An error analysis of generative adversarial networks for learning distributions
This paper studies how well generative adversarial networks (GANs) learn probability distributions from finite samples.
CARLS: Cross-platform Asynchronous Representation Learning System
In this work, we propose CARLS, a novel framework for augmenting the capacity of existing deep learning frameworks by enabling multiple components -- model trainers, knowledge makers and knowledge banks -- to concertedly work together in an asynchronous fashion across hardware platforms.
Combining Supervised and Un-supervised Learning for Automatic Citrus Segmentation
Compared with most of the existing citrus segmentation methods, our method uses a small amount of supervised data and a large number of unsupervised data, while learning the pixel level location information and the temporal information of citrus changes to enhance the segmentation effect.
PointLIE: Locally Invertible Embedding for Point Cloud Sampling and Recovery
In this paper, we address a fundamental problem in PCSR: How to downsample the dense point cloud with arbitrary scales while preserving the local topology of discarding points in a case-agnostic manner (i. e. without additional storage for point relationship)?
Temporal Modulation Network for Controllable Space-Time Video Super-Resolution
To well exploit the temporal information, we propose a Locally-temporal Feature Comparison (LFC) module, along with the Bi-directional Deformable ConvLSTM, to extract short-term and long-term motion cues in videos.
Free-form Description Guided 3D Visual Graph Network for Object Grounding in Point Cloud
There are three main challenges in 3D object grounding: to find the main focus in the complex and diverse description; to understand the point cloud scene; and to locate the target object.
InstanceRefer: Cooperative Holistic Understanding for Visual Grounding on Point Clouds through Instance Multi-level Contextual Referring
Compared with the visual grounding on 2D images, the natural-language-guided 3D object localization on point clouds is more challenging.
Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision
In this paper, we leverage a noisy dataset of over one billion image alt-text pairs, obtained without expensive filtering or post-processing steps in the Conceptual Captions dataset.
Ranked #1 on
Image Classification
on VTAB-1k
(using extra training data)
On the capacity of deep generative networks for approximating distributions
Furthermore, it is shown that the approximation error in Wasserstein distance grows at most linearly on the ambient dimension and that the approximation order only depends on the intrinsic dimension of the target distribution.
GAKP: GRU Association and Kalman Prediction for Multiple Object Tracking
Multiple Object Tracking (MOT) has been a useful yet challenging task in many real-world applications such as video surveillance, intelligent retail, and smart city.
Operator learning for predicting multiscale bubble growth dynamics
Simulating and predicting multiscale problems that couple multiple physics and dynamics across many orders of spatiotemporal scales is a great challenge that has not been investigated systematically by deep neural networks (DNNs).
Computational Physics
Formal modeling and performance evaluation for hybrid systems:a probabilistic hybrid process algebra-based approach
After that, we present a performance evaluation language, CTRML, to reason over probabilistic systems, which extend the results to real number.
Formal Languages and Automata Theory F.4.3