Search Results for author:
Found 32 papers, 8 papers with code
CLR-GAM: Contrastive Point Cloud Learning with Guided Augmentation and Feature Mapping
Point cloud data plays an essential role in robotics and self-driving applications.
Shape-aware Text-driven Layered Video Editing
Temporal consistency is essential for video editing applications.
Learning Road Scene-level Representations via Semantic Region Prediction
In this work, we tackle two vital tasks in automated driving systems, i. e., driver intent prediction and risk object identification from egocentric images.
Audio-Visual Speech Enhancement and Separation by Utilizing Multi-Modal Self-Supervised Embeddings
In summary, our results confirm the effectiveness of our proposed model for the AVSS task with proper fine-tuning strategies, demonstrating that multi-modal self-supervised embeddings obtained from AV-HuBERT can be generalized to audio-visual regression tasks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+6
Orbeez-SLAM: A Real-time Monocular Visual SLAM with ORB Features and NeRF-realized Mapping
A spatial AI that can perform complex tasks through visual signals and cooperate with humans is highly anticipated.
MetaDIP: Accelerating Deep Image Prior with Meta Learning
Deep image prior (DIP) is a recently proposed technique for solving imaging inverse problems by fitting the reconstructed images to the output of an untrained convolutional neural network.
ADAM Challenge: Detecting Age-related Macular Degeneration from Fundus Images
The ADAM challenge consisted of four tasks which cover the main aspects of detecting and characterizing AMD from fundus images, including detection of AMD, detection and segmentation of optic disc, localization of fovea, and detection and segmentation of lesions.
Semi-supervised 3D Object Detection via Temporal Graph Neural Networks
However, these detectors usually require training on large amounts of annotated data that is expensive and time-consuming to collect.
Stage Conscious Attention Network (SCAN) : A Demonstration-Conditioned Policy for Few-Shot Imitation
(3) Learning from a different expert.
Combined Scaling for Zero-shot Transfer Learning
Second, while increasing the dataset size and the model size has been the defacto method to improve the performance of deep learning models like BASIC, the effect of a large contrastive batch size on such contrastive-trained image-text models is not well-understood.
DROID: Driver-centric Risk Object Identification
Identification of high-risk driving situations is generally approached through collision risk estimation or accident pattern recognition.
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.
Multimodal Object Detection via Probabilistic Ensembling
Object detection with multimodal inputs can improve many safety-critical systems such as autonomous vehicles (AVs).
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)
Unsupervised Domain Adaptation for Spatio-Temporal Action Localization
Spatio-temporal action localization is an important problem in computer vision that involves detecting where and when activities occur, and therefore requires modeling of both spatial and temporal features.
Uncertainty-aware Self-supervised 3D Data Association
Instead, we propose leveraging vast unlabeled datasets by self-supervised metric learning of 3D object trackers, with a focus on data association.
MPDD: A Multi-Party Dialogue Dataset for Analysis of Emotions and Interpersonal Relationships
In this paper, we collect the conversions from TV series scripts, and annotate emotion and interpersonal relationship labels on each utterance.
Deep Quaternion Features for Privacy Protection
We propose a method to revise the neural network to construct the quaternion-valued neural network (QNN), in order to prevent intermediate-layer features from leaking input information.
Who Make Drivers Stop? Towards Driver-centric Risk Assessment: Risk Object Identification via Causal Inference
We formulate the task as the cause-effect problem and present a novel two-stage risk object identification framework based on causal inference with the proposed object-level manipulable driving model.
Grounding Human-to-Vehicle Advice for Self-driving Vehicles
We show that taking advice improves the performance of the end-to-end network, while the network cues on a variety of visual features that are provided by advice.
Learning 3D-aware Egocentric Spatial-Temporal Interaction via Graph Convolutional Networks
First, we decompose egocentric interactions into ego-thing and ego-stuff interaction, modeled by two GCNs.
The H3D Dataset for Full-Surround 3D Multi-Object Detection and Tracking in Crowded Urban Scenes
Finally, sources of errors are discussed for the development of future algorithms.
Graph-RISE: Graph-Regularized Image Semantic Embedding
Learning image representations to capture fine-grained semantics has been a challenging and important task enabling many applications such as image search and clustering.
Ranked #10 on
Image Classification
on iNaturalist
Unsupervised Data Uncertainty Learning in Visual Retrieval Systems
We introduce an unsupervised formulation to estimate heteroscedastic uncertainty in retrieval systems.
Boosting Standard Classification Architectures Through a Ranking Regularizer
We employ triplet loss as a feature embedding regularizer to boost classification performance.
Exploring Uncertainty in Conditional Multi-Modal Retrieval Systems
We cast visual retrieval as a regression problem by posing triplet loss as a regression loss.
Temporal Recurrent Networks for Online Action Detection
Most work on temporal action detection is formulated as an offline problem, in which the start and end times of actions are determined after the entire video is fully observed.
Ranked #6 on
Online Action Detection
on TVSeries
Toward Driving Scene Understanding: A Dataset for Learning Driver Behavior and Causal Reasoning
We present the Honda Research Institute Driving Dataset (HDD), a challenging dataset to enable research on learning driver behavior in real-life environments.
Semi-supervised Learning: Fusion of Self-supervised, Supervised Learning, and Multimodal Cues for Tactical Driver Behavior Detection
First, predefined driving behaviors are sparse in a naturalistic driving setting.
Two Stream Self-Supervised Learning for Action Recognition
The self-supervised pre-trained weights effectiveness is validated on the action recognition task.
Multi-Instance Object Segmentation With Occlusion Handling
We present a multi-instance object segmentation algorithm to tackle occlusions.
