Search Results for author:
Found 48 papers, 32 papers with code
Continual Offline Reinforcement Learning via Diffusion-based Dual Generative Replay
Finally, by interleaving pseudo samples with real ones of the new task, we continually update the state and behavior generators to model progressively diverse behaviors, and regularize the multi-head critic via behavior cloning to mitigate forgetting.
ChemLLM: A Chemical Large Language Model
ChemLLM beats GPT-3. 5 on all three principal tasks in chemistry, i. e., name conversion, molecular caption, and reaction prediction, and surpasses GPT-4 on two of them.
InteractiveVideo: User-Centric Controllable Video Generation with Synergistic Multimodal Instructions
We introduce $\textit{InteractiveVideo}$, a user-centric framework for video generation.
Multimodal Pathway: Improve Transformers with Irrelevant Data from Other Modalities
We propose to improve transformers of a specific modality with irrelevant data from other modalities, e. g., improve an ImageNet model with audio or point cloud datasets.
Online Vectorized HD Map Construction using Geometry
In our work, we propose GeMap ($\textbf{Ge}$ometry $\textbf{Map}$), which end-to-end learns Euclidean shapes and relations of map instances beyond basic perception.
OneLLM: One Framework to Align All Modalities with Language
In detail, we first train an image projection module to connect a vision encoder with LLM.
Ranked #73 on
Visual Question Answering
on MM-Vet
UniRepLKNet: A Universal Perception Large-Kernel ConvNet for Audio, Video, Point Cloud, Time-Series and Image Recognition
1) We propose four architectural guidelines for designing large-kernel ConvNets, the core of which is to exploit the essential characteristics of large kernels that distinguish them from small kernels - they can see wide without going deep.
Ranked #1 on
Object Detection
on COCO 2017
(mAP metric)
Towards Unified and Effective Domain Generalization
We propose $\textbf{UniDG}$, a novel and $\textbf{Uni}$fied framework for $\textbf{D}$omain $\textbf{G}$eneralization that is capable of significantly enhancing the out-of-distribution generalization performance of foundation models regardless of their architectures.
Ranked #1 on
Domain Generalization
on TerraIncognita
Beyond One-Preference-Fits-All Alignment: Multi-Objective Direct Preference Optimization
A single language model (LM), despite aligning well with an average labeler through reinforcement learning from human feedback (RLHF), may not universally suit diverse human preferences.
ImageBind-LLM: Multi-modality Instruction Tuning
During training, we adopt a learnable bind network to align the embedding space between LLaMA and ImageBind's image encoder.
Revisiting DETR Pre-training for Object Detection
Motivated by the remarkable achievements of DETR-based approaches on COCO object detection and segmentation benchmarks, recent endeavors have been directed towards elevating their performance through self-supervised pre-training of Transformers while preserving a frozen backbone.
Beating Backdoor Attack at Its Own Game
Deep neural networks (DNNs) are vulnerable to backdoor attack, which does not affect the network's performance on clean data but would manipulate the network behavior once a trigger pattern is added.
Meta-Transformer: A Unified Framework for Multimodal Learning
Multimodal learning aims to build models that can process and relate information from multiple modalities.
Space Engage: Collaborative Space Supervision for Contrastive-based Semi-Supervised Semantic Segmentation
To improve the robustness of representations, powerful methods introduce a pixel-wise contrastive learning approach in latent space (i. e., representation space) that aggregates the representations to their prototypes in a fully supervised manner.
LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model
This strategy effectively alleviates the interference between the two tasks of image-text alignment and instruction following and achieves strong multi-modal reasoning with only a small-scale image-text and instruction dataset.
Ranked #6 on
Visual Question Answering (VQA)
on InfiMM-Eval
Instruction Following
Optical Character Recognition (OCR)
+7
Preventing Zero-Shot Transfer Degradation in Continual Learning of Vision-Language Models
To address this challenge, we propose a novel method ZSCL to prevent zero-shot transfer degradation in the continual learning of vision-language models in both feature and parameter space.
Prompt Vision Transformer for Domain Generalization
In this paper, we propose a novel approach DoPrompt based on prompt learning to embed the knowledge of source domains in domain prompts for target domain prediction.
RankSeg: Adaptive Pixel Classification with Image Category Ranking for Segmentation
Given an input image or video, our framework first conducts multi-label classification over the complete label, then sorts the complete label and selects a small subset according to their class confidence scores.
2nd Place Solution for VisDA 2021 Challenge -- Universally Domain Adaptive Image Recognition
The Visual Domain Adaptation (VisDA) 2021 Challenge calls for unsupervised domain adaptation (UDA) methods that can deal with both input distribution shift and label set variance between the source and target domains.
Multi-source Few-shot Domain Adaptation
Multi-source Domain Adaptation (MDA) aims to transfer predictive models from multiple, fully-labeled source domains to an unlabeled target domain.
Conditional Synthetic Data Generation for Robust Machine Learning Applications with Limited Pandemic Data
To tackle the challenges of limited data, and label scarcity in the available data, we propose generating conditional synthetic data, to be used alongside real data for developing robust ML models.
Scene-aware Learning Network for Radar Object Detection
In this paper, we propose a scene-aware radar learning framework for accurate and robust object detection.
Image2Point: 3D Point-Cloud Understanding with 2D Image Pretrained Models
We discover that we can indeed use the same architecture and pretrained weights of a neural net model to understand both images and point-clouds.
Prototypical Cross-domain Self-supervised Learning for Few-shot Unsupervised Domain Adaptation
In this paper, we propose an end-to-end Prototypical Cross-domain Self-Supervised Learning (PCS) framework for Few-shot Unsupervised Domain Adaptation (FUDA).
Ranked #6 on
Semantic Segmentation
on DensePASS
Self-Supervised Pretraining Improves Self-Supervised Pretraining
Through experimentation on 16 diverse vision datasets, we show HPT converges up to 80x faster, improves accuracy across tasks, and improves the robustness of the self-supervised pretraining process to changes in the image augmentation policy or amount of pretraining data.
A Customizable Dynamic Scenario Modeling and Data Generation Platform for Autonomous Driving
In this paper, we present a platform to model dynamic and interactive scenarios, generate the scenarios in simulation with different modalities of labeled sensor data, and collect this information for data augmentation.
Emotional Semantics-Preserved and Feature-Aligned CycleGAN for Visual Emotion Adaptation
First, we generate an adapted domain to align the source and target domains on the pixel-level by improving CycleGAN with a multi-scale structured cycle-consistency loss.
Curriculum CycleGAN for Textual Sentiment Domain Adaptation with Multiple Sources
C-CycleGAN transfers source samples at instance-level to an intermediate domain that is closer to the target domain with sentiment semantics preserved and without losing discriminative features.
Scenic: A Language for Scenario Specification and Data Generation
We design a domain-specific language, Scenic, for describing scenarios that are distributions over scenes and the behaviors of their agents over time.
Unsupervised Point Cloud Pre-Training via Occlusion Completion
We find that even when we construct a single pre-training dataset (from ModelNet40), this pre-training method improves accuracy across different datasets and encoders, on a wide range of downstream tasks.
3D Point Cloud Linear Classification
Few-Shot 3D Point Cloud Classification
+5
Pre-Training by Completing Point Clouds
There has recently been a flurry of exciting advances in deep learning models on point clouds.
A Review of Single-Source Deep Unsupervised Visual Domain Adaptation
To cope with limited labeled training data, many have attempted to directly apply models trained on a large-scale labeled source domain to another sparsely labeled or unlabeled target domain.
Generalizing Fault Detection Against Domain Shifts Using Stratification-Aware Cross-Validation
Incipient anomalies present milder symptoms compared to severe ones, and are more difficult to detect and diagnose due to their close resemblance to normal operating conditions.
Using Ensemble Classifiers to Detect Incipient Anomalies
Incipient anomalies present milder symptoms compared to severe ones, and are more difficult to detect and diagnose due to their close resemblance to normal operating conditions.
Exploiting Uncertainties from Ensemble Learners to Improve Decision-Making in Healthcare AI
Ensemble learning is widely applied in Machine Learning (ML) to improve model performance and to mitigate decision risks.
PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation
The need for fine-grained perception in autonomous driving systems has resulted in recently increased research on online semantic segmentation of single-scan LiDAR.
Ranked #11 on
Robust 3D Semantic Segmentation
on nuScenes-C
MADAN: Multi-source Adversarial Domain Aggregation Network for Domain Adaptation
Finally, feature-level alignment is performed between the aggregated domain and the target domain while training the task network.
Multi-source Domain Adaptation for Semantic Segmentation
In this paper, we propose to investigate multi-source domain adaptation for semantic segmentation.
Ranked #2 on
Domain Adaptation
on GTA5+Synscapes to Cityscapes
Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data
To this end, we propose a new approach of domain randomization and pyramid consistency to learn a model with high generalizability.
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.
A Novel Domain Adaptation Framework for Medical Image Segmentation
Our biophysics based domain adaptation achieves better results, as compared to the existing state-of-the-art GAN model used to create synthetic data for training.
Scenic: A Language for Scenario Specification and Scene Generation
We propose a new probabilistic programming language for the design and analysis of perception systems, especially those based on machine learning.
SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloud
When training our new model on synthetic data using the proposed domain adaptation pipeline, we nearly double test accuracy on real-world data, from 29. 0% to 57. 4%.
Ranked #21 on
Robust 3D Semantic Segmentation
on SemanticKITTI-C
Counterexample-Guided Data Augmentation
We present a novel framework for augmenting data sets for machine learning based on counterexamples.
A LiDAR Point Cloud Generator: from a Virtual World to Autonomous Driving
The framework supports data collection from both auto-driving scenes and user-configured scenes.
SqueezeNext: Hardware-Aware Neural Network Design
One of the main barriers for deploying neural networks on embedded systems has been large memory and power consumption of existing neural networks.
Shift: A Zero FLOP, Zero Parameter Alternative to Spatial Convolutions
Neural networks rely on convolutions to aggregate spatial information.
SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud
In this paper, we address semantic segmentation of road-objects from 3D LiDAR point clouds.
Ranked #22 on
Robust 3D Semantic Segmentation
on SemanticKITTI-C
