Search Results for author:
Found 43 papers, 15 papers with code
LMFNet: An Efficient Multimodal Fusion Approach for Semantic Segmentation in High-Resolution Remote Sensing
Addressing this gap, we propose a novel \textbf{L}ightweight \textbf{M}ultimodal data \textbf{F}usion \textbf{Net}work (LMFNet) to accomplish the tasks of fusion and semantic segmentation of multimodal remote sensing images.
Few-Shot Class Incremental Learning with Attention-Aware Self-Adaptive Prompt
Few-Shot Class-Incremental Learning (FSCIL) models aim to incrementally learn new classes with scarce samples while preserving knowledge of old ones.
Your Vision-Language Model Itself Is a Strong Filter: Towards High-Quality Instruction Tuning with Data Selection
In the first stage, we devise a scoring network to evaluate the difficulty of training instructions, which is co-trained with the VLM.
Discovery of the Hidden World with Large Language Models
The rise of large language models (LLMs) that are trained to learn rich knowledge from the massive observations of the world, provides a new opportunity to assist with discovering high-level hidden variables from the raw observational data.
Spatial-Temporal Large Language Model for Traffic Prediction
In this paper, we propose a Spatial-Temporal Large Language Model (ST-LLM) for traffic prediction.
Federated Continual Novel Class Learning
In a privacy-focused era, Federated Learning (FL) has emerged as a promising machine learning technique.
Gemini: A Family of Highly Capable Multimodal Models
This report introduces a new family of multimodal models, Gemini, that exhibit remarkable capabilities across image, audio, video, and text understanding.
Ranked #1 on
Multi-task Language Understanding
on MMLU
(using extra training data)
De-Diffusion Makes Text a Strong Cross-Modal Interface
We demonstrate text as a strong cross-modal interface.
GPT-4 Vision on Medical Image Classification -- A Case Study on COVID-19 Dataset
This technical report delves into the application of GPT-4 Vision (GPT-4V) in the nuanced realm of COVID-19 image classification, leveraging the transformative potential of in-context learning to enhance diagnostic processes.
LEF: Late-to-Early Temporal Fusion for LiDAR 3D Object Detection
We propose a late-to-early recurrent feature fusion scheme for 3D object detection using temporal LiDAR point clouds.
I3DOD: Towards Incremental 3D Object Detection via Prompting
Meanwhile, the current class-incremental 3D object detection methods neglect the relationships between the object localization information and category semantic information and assume all the knowledge of old model is reliable.
MoDAR: Using Motion Forecasting for 3D Object Detection in Point Cloud Sequences
The MoDAR modality propagates object information from temporal contexts to a target frame, represented as a set of virtual points, one for each object from a waypoint on a forecasted trajectory.
DEJA VU: Continual Model Generalization For Unseen Domains
To overcome these limitations of DA and DG in handling the Unfamiliar Period during continual domain shift, we propose RaTP, a framework that focuses on improving models' target domain generalization (TDG) capability, while also achieving effective target domain adaptation (TDA) capability right after training on certain domains and forgetting alleviation (FA) capability on past domains.
LidarAugment: Searching for Scalable 3D LiDAR Data Augmentations
Data augmentations are important in training high-performance 3D object detectors for point clouds.
SWFormer: Sparse Window Transformer for 3D Object Detection in Point Clouds
3D object detection in point clouds is a core component for modern robotics and autonomous driving systems.
LidarNAS: Unifying and Searching Neural Architectures for 3D Point Clouds
Developing neural models that accurately understand objects in 3D point clouds is essential for the success of robotics and autonomous driving.
How Many Grid-Forming Converters Do We Need? A Perspective From Small Signal Stability and Power Grid Strength
Based on our analysis, we further study the problem of how to configure GFM converters in the grid and how many GFM converters we will need.
Multi-Class 3D Object Detection with Single-Class Supervision
While multi-class 3D detectors are needed in many robotics applications, training them with fully labeled datasets can be expensive in labeling cost.
PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions
Cross-entropy loss and focal loss are the most common choices when training deep neural networks for classification problems.
Ranked #99 on
Image Classification
on ImageNet
(using extra training data)
Scene Transformer: A unified architecture for predicting future trajectories of multiple agents
In this work, we formulate a model for predicting the behavior of all agents jointly, producing consistent futures that account for interactions between agents.
Scene Transformer: A unified architecture for predicting multiple agent trajectories
In this work, we formulate a model for predicting the behavior of all agents jointly, producing consistent futures that account for interactions between agents.
Progressive Stage-wise Learning for Unsupervised Feature Representation Enhancement
For a given unsupervised task, we design multilevel tasks and define different learning stages for the deep network.
Large Scale Interactive Motion Forecasting for Autonomous Driving : The Waymo Open Motion Dataset
Furthermore, we introduce a new set of metrics that provides a comprehensive evaluation of both single agent and joint agent interaction motion forecasting models.
Fresh, Fair and Energy-Efficient Content Provision in a Private and Cache-Enabled UAV Network
Particularly, we first propose to decompose the sequential decision problem into multiple repeated optimization subproblems via a Lyapunov technique.
Networking and Internet Architecture Signal Processing
Large Scale Interactive Motion Forecasting for Autonomous Driving: The Waymo Open Motion Dataset
Furthermore, we introduce a new set of metrics that provides a comprehensive evaluation of both single agent and joint agent interaction motion forecasting models.
Are Labels Necessary for Neural Architecture Search?
Existing neural network architectures in computer vision -- whether designed by humans or by machines -- were typically found using both images and their associated labels.
Identifying Model Weakness with Adversarial Examiner
Different from the existing strategy to always give the same (distribution of) test data, the adversarial examiner will dynamically select the next test data to hand out based on the testing history so far, with the goal being to undermine the model's performance.
Rethinking Normalization and Elimination Singularity in Neural Networks
To address this issue, we propose BatchChannel Normalization (BCN), which uses batch knowledge to avoid the elimination singularities in the training of channel-normalized models.
V-NAS: Neural Architecture Search for Volumetric Medical Image Segmentation
Deep learning algorithms, in particular 2D and 3D fully convolutional neural networks (FCNs), have rapidly become the mainstream methodology for volumetric medical image segmentation.
Micro-Batch Training with Batch-Channel Normalization and Weight Standardization
Batch Normalization (BN) has become an out-of-box technique to improve deep network training.
Ranked #76 on
Instance Segmentation
on COCO minival
Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation
Therefore, we propose to search the network level structure in addition to the cell level structure, which forms a hierarchical architecture search space.
Ranked #7 on
Semantic Segmentation
on PASCAL VOC 2012 val
CLEVR-Ref+: Diagnosing Visual Reasoning with Referring Expressions
Yet there has been evidence that current benchmark datasets suffer from bias, and current state-of-the-art models cannot be easily evaluated on their intermediate reasoning process.
Ranked #1 on
Referring Expression Segmentation
on CLEVR-Ref+
Transmit Antenna Selection in MIMO Wiretap Channels: A Machine Learning Approach
In this letter, we exploit the potential benefits of machine learning in enhancing physical layer security in multi-input multi-output multi-antenna-eavesdropper wiretap channels.
Deep Nets: What have they ever done for Vision?
We argue that Deep Nets in their current form are unlikely to be able to overcome the fundamental problem of computer vision, namely how to deal with the combinatorial explosion, caused by the enormous complexity of natural images, and obtain the rich understanding of visual scenes that the human visual achieves.
Scene Graph Parsing as Dependency Parsing
The scene graphs generated by our learned neural dependency parser achieve an F-score similarity of 49. 67% to ground truth graphs on our evaluation set, surpassing best previous approaches by 5%.
Progressive Neural Architecture Search
We propose a new method for learning the structure of convolutional neural networks (CNNs) that is more efficient than recent state-of-the-art methods based on reinforcement learning and evolutionary algorithms.
Ranked #15 on
Neural Architecture Search
on NAS-Bench-201, ImageNet-16-120
(Accuracy (Val) metric)
Adversarial Attacks Beyond the Image Space
Though image-space adversaries can be interpreted as per-pixel albedo change, we verify that they cannot be well explained along these physically meaningful dimensions, which often have a non-local effect.
Few-Shot Image Recognition by Predicting Parameters from Activations
In this paper, we are interested in the few-shot learning problem.
ScaleNet: Guiding Object Proposal Generation in Supermarkets and Beyond
We argue that estimation of object scales in images is helpful for generating object proposals, especially for supermarket images where object scales are usually within a small range.
Recurrent Multimodal Interaction for Referring Image Segmentation
In this paper we are interested in the problem of image segmentation given natural language descriptions, i. e. referring expressions.
SORT: Second-Order Response Transform for Visual Recognition
In this paper, we reveal the importance and benefits of introducing second-order operations into deep neural networks.
Attention Correctness in Neural Image Captioning
Attention mechanisms have recently been introduced in deep learning for various tasks in natural language processing and computer vision.
Rent3D: Floor-Plan Priors for Monocular Layout Estimation
What sets us apart from past work in layout estimation is the use of floor plans as a source of prior knowledge, as well as localization of each image within a bigger space (apartment).
