Search Results for author:
Found 21 papers, 5 papers with code
Improving LiDAR 3D Object Detection via Range-based Point Cloud Density Optimization
In recent years, much progress has been made in LiDAR-based 3D object detection mainly due to advances in detector architecture designs and availability of large-scale LiDAR datasets.
Domain Adaptation in 3D Object Detection with Gradual Batch Alternation Training
The idea is to initiate the training with the batch of samples from the source and target domain data in an alternate fashion, but then gradually reduce the amount of the source domain data over time as the training progresses.
Domain Adaptation in LiDAR Semantic Segmentation via Alternating Skip Connections and Hybrid Learning
We consider the setting where we have a fully-labeled data set from source domain and a target domain with a few labeled and many unlabeled examples.
Contrastive Learning for Unsupervised Video Highlight Detection
Our framework encodes a video into a vector representation by learning to pick video clips that help to distinguish it from other videos via a contrastive objective using dropout noise.
Unsupervised Domain Adaptation in LiDAR Semantic Segmentation with Self-Supervision and Gated Adapters
In this paper, we focus on a less explored, but more realistic and complex problem of domain adaptation in LiDAR semantic segmentation.
Referring Segmentation in Images and Videos with Cross-Modal Self-Attention Network
In this paper, we propose a cross-modal self-attention (CMSA) module to utilize fine details of individual words and the input image or video, which effectively captures the long-range dependencies between linguistic and visual features.
Ranked #5 on
Referring Expression Segmentation
on J-HMDB
(Precision@0.9 metric)
Joint Visual and Audio Learning for Video Highlight Detection
In video highlight detection, the goal is to identify the interesting moments within an unedited video.
AdaCrowd: Unlabeled Scene Adaptation for Crowd Counting
In particular, we propose a new problem called unlabeled scene-adaptive crowd counting.
Sentence Guided Temporal Modulation for Dynamic Video Thumbnail Generation
We consider the problem of sentence specified dynamic video thumbnail generation.
Adaptive Video Highlight Detection by Learning from User History
In this paper, we propose a simple yet effective framework that learns to adapt highlight detection to a user by exploiting the user's history in the form of highlights that the user has previously created.
Few-Shot Scene Adaptive Crowd Counting Using Meta-Learning
We consider the problem of few-shot scene adaptive crowd counting.
Cross-Modal Self-Attention Network for Referring Image Segmentation
This module controls the information flow of features at different levels.
Ranked #14 on
Referring Video Object Segmentation
on Refer-YouTube-VOS
(using extra training data)
Convolutional Temporal Attention Model for Video-based Person Re-identification
A common approach for person re-identification is to first extract image features for all frames in the video, then aggregate all the features to form a video-level feature.
Video-based Person Re-identification Using Spatial-Temporal Attention Networks
The goal is to identify a person from videos captured under different cameras.
Future Semantic Segmentation with Convolutional LSTM
We propose a novel model that uses convolutional LSTM (ConvLSTM) to encode the spatiotemporal information of observed frames for future prediction.
Gated Feedback Refinement Network for Coarse-to-Fine Dense Semantic Image Labeling
In order to address this issue, we also propose Gated Feedback Refinement Network (G-FRNet) that addresses this limitation.
Video Summarization by Learning from Unpaired Data
Our model aims to learn a mapping function $F : V \rightarrow S$ such that the distribution of resultant summary videos from $F(V)$ is similar to the distribution of $S$ with the help of an adversarial objective.
Video Summarization Using Fully Convolutional Sequence Networks
This paper addresses the problem of video summarization.
Gated Feedback Refinement Network for Dense Image Labeling
Effective integration of local and global contextual information is crucial for dense labeling problems.
Label Refinement Network for Coarse-to-Fine Semantic Segmentation
We propose a novel network architecture called the label refinement network that predicts segmentation labels in a coarse-to-fine fashion at several resolutions.
Weakly Supervised Localization of Novel Objects Using Appearance Transfer
We propose a method for transferring the appearance models of the familiar objects to the unseen object.
