Search Results for author:
Found 35 papers, 21 papers with code
Objects as Points
We model an object as a single point --- the center point of its bounding box.
Deep Layer Aggregation
We augment standard architectures with deeper aggregation to better fuse information across layers.
Joint Monocular 3D Vehicle Detection and Tracking
The framework can not only associate detections of vehicles in motion over time, but also estimate their complete 3D bounding box information from a sequence of 2D images captured on a moving platform.
Ranked #12 on
Multiple Object Tracking
on KITTI Tracking test
Tent: Fully Test-time Adaptation by Entropy Minimization
A model must adapt itself to generalize to new and different data during testing.
Fighting Gradients with Gradients: Dynamic Defenses against Adversarial Attacks
Adversarial attacks optimize against models to defeat defenses.
MedFMC: A Real-world Dataset and Benchmark For Foundation Model Adaptation in Medical Image Classification
Foundation models, often pre-trained with large-scale data, have achieved paramount success in jump-starting various vision and language applications.
On-target Adaptation
Domain adaptation seeks to mitigate the shift between training on the \emph{source} domain and testing on the \emph{target} domain.
ActNN: Reducing Training Memory Footprint via 2-Bit Activation Compressed Training
On all these tasks, ActNN compresses the activation to 2 bits on average, with negligible accuracy loss.
VisDA: The Visual Domain Adaptation Challenge
We present the 2017 Visual Domain Adaptation (VisDA) dataset and challenge, a large-scale testbed for unsupervised domain adaptation across visual domains.
An Extensible Framework for Open Heterogeneous Collaborative Perception
In this paper, we introduce a new open heterogeneous problem: how to accommodate continually emerging new heterogeneous agent types into collaborative perception, while ensuring high perception performance and low integration cost?
Contrastive Test-Time Adaptation
We propose a novel way to leverage self-supervised contrastive learning to facilitate target feature learning, along with an online pseudo labeling scheme with refinement that significantly denoises pseudo labels.
FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation
In this paper, we introduce the first domain adaptive semantic segmentation method, proposing an unsupervised adversarial approach to pixel prediction problems.
Ranked #2 on
Image-to-Image Translation
on SYNTHIA Fall-to-Winter
Back to the Source: Diffusion-Driven Test-Time Adaptation
We instead update the target data, by projecting all test inputs toward the source domain with a generative diffusion model.
Convolutional Neural Networks on non-uniform geometrical signals using Euclidean spectral transformation
It has been challenging to analyze signals with mixed topologies (for example, point cloud with surface mesh).
Text-guided Foundation Model Adaptation for Pathological Image Classification
In this paper, we propose to Connect Image and Text Embeddings (CITE) to enhance pathological image classification.
Data-Centric Foundation Models in Computational Healthcare: A Survey
The advent of foundation models (FMs) as an emerging suite of AI techniques has struck a wave of opportunities in computational healthcare.
GACT: Activation Compressed Training for Generic Network Architectures
Training large neural network (NN) models requires extensive memory resources, and Activation Compressed Training (ACT) is a promising approach to reduce training memory footprint.
Algorithm-hardware Co-design for Deformable Convolution
In this work, we first investigate the overhead of the deformable convolution on embedded FPGA SoCs, and then show the accuracy-latency tradeoffs for a set of algorithm modifications including full versus depthwise, fixed-shape, and limited-range.
CoDeNet: Efficient Deployment of Input-Adaptive Object Detection on Embedded FPGAs
Deploying deep learning models on embedded systems has been challenging due to limited computing resources.
Decentralized Vehicle Coordination: The Berkeley DeepDrive Drone Dataset
Decentralized multiagent planning has been an important field of research in robotics.
BayesDiff: Estimating Pixel-wise Uncertainty in Diffusion via Bayesian Inference
In particular, we derive a novel uncertainty iteration principle to characterize the uncertainty dynamics in diffusion, and leverage the last-layer Laplace approximation for efficient Bayesian inference.
Iterative Object and Part Transfer for Fine-Grained Recognition
On both datasets, we achieve better results than many state-of-the-art approaches, including a few using oracle (manually annotated) bounding boxes in the test images.
Learning to Point and Count
This paper proposes the problem of point-and-count as a test case to break the what-and-where deadlock.
Deep Object-Centric Policies for Autonomous Driving
While learning visuomotor skills in an end-to-end manner is appealing, deep neural networks are often uninterpretable and fail in surprising ways.
Weakly Supervised Semantic Segmentation for Social Images
Image semantic segmentation is the task of partitioning image into several regions based on semantic concepts.
Multiple Granularity Descriptors for Fine-Grained Categorization
Fine-grained categorization, which aims to distinguish subordinate-level categories such as bird species or dog breeds, is an extremely challenging task.
Blurring the Line Between Structure and Learning to Optimize and Adapt Receptive Fields
Adapting receptive fields by dynamic Gaussian structure further improves results, equaling the accuracy of free-form deformation while improving efficiency.
Monocular Plan View Networks for Autonomous Driving
Convolutions on monocular dash cam videos capture spatial invariances in the image plane but do not explicitly reason about distances and depth.
Dynamic Scale Inference by Entropy Minimization
Given the variety of the visual world there is not one true scale for recognition: objects may appear at drastically different sizes across the visual field.
BEV-Seg: Bird's Eye View Semantic Segmentation Using Geometry and Semantic Point Cloud
Bird's-eye-view (BEV) is a powerful and widely adopted representation for road scenes that captures surrounding objects and their spatial locations, along with overall context in the scene.
Efficient Receptive Field Learning by Dynamic Gaussian Structure
The visual world is vast and varied, but its variations divide into structured and unstructured factors.
Blurring Structure and Learning to Optimize and Adapt Receptive Fields
Adapting receptive fields by dynamic Gaussian structure further improves results, equaling the accuracy of free-form deformation while improving efficiency.
Towards General Purpose Medical AI: Continual Learning Medical Foundation Model
Therefore, in this work, we further explore the possibility of leveraging pre-trained VLMs as medical foundation models for building general-purpose medical AI, where we thoroughly investigate three machine-learning paradigms, i. e., domain/task-specialized learning, joint learning, and continual learning, for training the VLMs and evaluate their generalization performance on cross-domain and cross-task test sets.
Back to the Source: Diffusion-Driven Adaptation To Test-Time Corruption
We update the target data instead, and project all test inputs toward the source domain with a generative diffusion model.
OpenMEDLab: An Open-source Platform for Multi-modality Foundation Models in Medicine
The emerging trend of advancing generalist artificial intelligence, such as GPTv4 and Gemini, has reshaped the landscape of research (academia and industry) in machine learning and many other research areas.
