Search Results for author:
Found 53 papers, 20 papers with code
Normal-GS: 3D Gaussian Splatting with Normal-Involved Rendering
Previous attempts to regularize 3D Gaussian normals often degrade rendering quality due to the fundamental disconnect between normal vectors and the rendering pipeline in 3DGS-based methods.
TFS-NeRF: Template-Free NeRF for Semantic 3D Reconstruction of Dynamic Scene
Despite advancements in Neural Implicit models for 3D surface reconstruction, handling dynamic environments with interactions between arbitrary rigid, non-rigid, or deformable entities remains challenging.
Hi-SLAM: Scaling-up Semantics in SLAM with a Hierarchically Categorical Gaussian Splatting
To address this problem, we introduce a novel hierarchical representation that encodes semantic information in a compact form into 3D Gaussian Splatting, leveraging the capabilities of large language models (LLMs).
NEUSIS: A Compositional Neuro-Symbolic Framework for Autonomous Perception, Reasoning, and Planning in Complex UAV Search Missions
This paper addresses the problem of autonomous UAV search missions, where a UAV must locate specific Entities of Interest (EOIs) within a time limit, based on brief descriptions in large, hazard-prone environments with keep-out zones.
How Well Can Vision Language Models See Image Details?
In our study, we introduce a pixel value prediction task (PVP) to explore "How Well Can Vision Language Models See Image Details?"
DrVideo: Document Retrieval Based Long Video Understanding
Specifically, DrVideo first transforms a long video into a coarse text-based long document to initially retrieve key frames and then updates the documents with the augmented key frame information.
Social-MAE: Social Masked Autoencoder for Multi-person Motion Representation Learning
The framework uses masked modeling to pre-train the encoder to reconstruct masked human joint trajectories, enabling it to learn generalizable and data efficient representations of motion in human crowded scenes.
JRDB-Social: A Multifaceted Robotic Dataset for Understanding of Context and Dynamics of Human Interactions Within Social Groups
Understanding human social behaviour is crucial in computer vision and robotics.
DifFUSER: Diffusion Model for Robust Multi-Sensor Fusion in 3D Object Detection and BEV Segmentation
Diffusion models have recently gained prominence as powerful deep generative models, demonstrating unmatched performance across various domains.
JRDB-PanoTrack: An Open-world Panoptic Segmentation and Tracking Robotic Dataset in Crowded Human Environments
JRDB-PanoTrack includes (1) various data involving indoor and outdoor crowded scenes, as well as comprehensive 2D and 3D synchronized data modalities; (2) high-quality 2D spatial panoptic segmentation and temporal tracking annotations, with additional 3D label projections for further spatial understanding; (3) diverse object classes for closed- and open-world recognition benchmarks, with OSPA-based metrics for evaluation.
HYDRA: A Hyper Agent for Dynamic Compositional Visual Reasoning
Recent advances in visual reasoning (VR), particularly with the aid of Large Vision-Language Models (VLMs), show promise but require access to large-scale datasets and face challenges such as high computational costs and limited generalization capabilities.
Ranked #1 on
Visual Grounding
on RefCOCO+ testA
(IoU metric)
Improving Visual Perception of a Social Robot for Controlled and In-the-wild Human-robot Interaction
Social robots often rely on visual perception to understand their users and the environment.
Series2Vec: Similarity-based Self-supervised Representation Learning for Time Series Classification
Our evaluation of Series2Vec on nine large real-world datasets, along with the UCR/UEA archive, shows enhanced performance compared to current state-of-the-art self-supervised techniques for time series.
JRDB-Traj: A Dataset and Benchmark for Trajectory Forecasting in Crowds
To address this, we introduce a novel dataset for end-to-end trajectory forecasting, facilitating the evaluation of models in scenarios involving less-than-ideal preceding modules such as tracking.
Physically Plausible 3D Human-Scene Reconstruction from Monocular RGB Image using an Adversarial Learning Approach
This paper proposes using an implicit feature representation of the scene elements to distinguish a physically plausible alignment of humans and objects from an implausible one.
Real-time Trajectory-based Social Group Detection
These results demonstrate that our proposed method is suitable for real-time robotic applications.
Knowledge Combination to Learn Rotated Detection Without Rotated Annotation
Annotating rotated bounding boxes is such a laborious process that they are not provided in many detection datasets where axis-aligned annotations are used instead.
ProtoCon: Pseudo-label Refinement via Online Clustering and Prototypical Consistency for Efficient Semi-supervised Learning
Finally, ProtoCon addresses the poor training signal in the initial phase of training (due to fewer confident predictions) by introducing an auxiliary self-supervised loss.
Tracking Different Ant Species: An Unsupervised Domain Adaptation Framework and a Dataset for Multi-object Tracking
This approach is built upon a joint-detection-and-tracking framework that is extended by a set of domain discriminator modules integrating an adversarial training strategy in addition to the tracking loss.
Energy-based Self-Training and Normalization for Unsupervised Domain Adaptation
EBL can be used to improve the instance selection for a self-training task on the unlabelled target domain, and 2. alignment and normalizing energy scores can learn domain-invariant representations.
ActiveRMAP: Radiance Field for Active Mapping And Planning
We, for the first time, present an RGB-only active vision framework using radiance field representation for active 3D reconstruction and planning in an online manner.
Predicting Topological Maps for Visual Navigation in Unexplored Environments
We propose a robotic learning system for autonomous exploration and navigation in unexplored environments.
MARLIN: Masked Autoencoder for facial video Representation LearnINg
This paper proposes a self-supervised approach to learn universal facial representations from videos, that can transfer across a variety of facial analysis tasks such as Facial Attribute Recognition (FAR), Facial Expression Recognition (FER), DeepFake Detection (DFD), and Lip Synchronization (LS).
Ranked #1 on
Emotion Classification
on CMU-MOSEI
Unifying Flow, Stereo and Depth Estimation
We present a unified formulation and model for three motion and 3D perception tasks: optical flow, rectified stereo matching and unrectified stereo depth estimation from posed images.
Ranked #1 on
Optical Flow Estimation
on Sintel-clean
JRDB-Pose: A Large-scale Dataset for Multi-Person Pose Estimation and Tracking
In crowded human scenes with close-up human-robot interaction and robot navigation, a deep understanding requires reasoning about human motion and body dynamics over time with human body pose estimation and tracking.
LAVA: Label-efficient Visual Learning and Adaptation
We present LAVA, a simple yet effective method for multi-domain visual transfer learning with limited data.
SoMoFormer: Multi-Person Pose Forecasting with Transformers
Although there are several previous works targeting the problem of multi-person dynamic pose forecasting, they often model the entire pose sequence as time series (ignoring the underlying relationship between joints) or only output the future pose sequence of one person at a time.
Learning of Global Objective for Network Flow in Multi-Object Tracking
This paper concerns the problem of multi-object tracking based on the min-cost flow (MCF) formulation, which is conventionally studied as an instance of linear program.
Accurate and Real-time 3D Pedestrian Detection Using an Efficient Attentive Pillar Network
Efficiently and accurately detecting people from 3D point cloud data is of great importance in many robotic and autonomous driving applications.
GMFlow: Learning Optical Flow via Global Matching
Learning-based optical flow estimation has been dominated with the pipeline of cost volume with convolutions for flow regression, which is inherently limited to local correlations and thus is hard to address the long-standing challenge of large displacements.
Ranked #8 on
Optical Flow Estimation
on Spring
Guided-GAN: Adversarial Representation Learning for Activity Recognition with Wearables
Human activity recognition (HAR) is an important research field in ubiquitous computing where the acquisition of large-scale labeled sensor data is tedious, labor-intensive and time consuming.
Generative Adversarial Network
Human Activity Recognition
+1
ODAM: Object Detection, Association, and Mapping using Posed RGB Video
Localizing objects and estimating their extent in 3D is an important step towards high-level 3D scene understanding, which has many applications in Augmented Reality and Robotics.
Unsupervised Image Segmentation by Mutual Information Maximization and Adversarial Regularization
In this paper, we propose a novel fully unsupervised semantic segmentation method, the so-called Information Maximization and Adversarial Regularization Segmentation (InMARS).
Ranked #2 on
Unsupervised Semantic Segmentation
on COCO-Stuff-15
JRDB-Act: A Large-scale Dataset for Spatio-temporal Action, Social Group and Activity Detection
However, learning to recognise human actions and their social interactions in an unconstrained real-world environment comprising numerous people, with potentially highly unbalanced and long-tailed distributed action labels from a stream of sensory data captured from a mobile robot platform remains a significant challenge, not least owing to the lack of a reflective large-scale dataset.
TRiPOD: Human Trajectory and Pose Dynamics Forecasting in the Wild
Joint forecasting of human trajectory and pose dynamics is a fundamental building block of various applications ranging from robotics and autonomous driving to surveillance systems.
Looking Beyond Two Frames: End-to-End Multi-Object Tracking Using Spatial and Temporal Transformers
Tracking a time-varying indefinite number of objects in a video sequence over time remains a challenge despite recent advances in the field.
MOLTR: Multiple Object Localisation, Tracking, and Reconstruction from Monocular RGB Videos
Given a new RGB frame, MOLTR firstly applies a monocular 3D detector to localise objects of interest and extract their shape codes that represent the object shapes in a learned embedding space.
Probabilistic Tracklet Scoring and Inpainting for Multiple Object Tracking
Despite the recent advances in multiple object tracking (MOT), achieved by joint detection and tracking, dealing with long occlusions remains a challenge.
How Trustworthy are Performance Evaluations for Basic Vision Tasks?
This paper examines performance evaluation criteria for basic vision tasks involving sets of objects namely, object detection, instance-level segmentation and multi-object tracking.
Socially and Contextually Aware Human Motion and Pose Forecasting
In this paper, we propose a novel framework to tackle both tasks of human motion (or trajectory) and body skeleton pose forecasting in a unified end-to-end pipeline.
Attend And Discriminate: Beyond the State-of-the-Art for Human Activity Recognition using Wearable Sensors
Wearables are fundamental to improving our understanding of human activities, especially for an increasing number of healthcare applications from rehabilitation to fine-grained gait analysis.
Joint Learning of Social Groups, Individuals Action and Sub-group Activities in Videos
In this paper, we solve the problem of simultaneously grouping people by their social interactions, predicting their individual actions and the social activity of each social group, which we call the social task.
MOT20: A benchmark for multi object tracking in crowded scenes
The benchmark for Multiple Object Tracking, MOTChallenge, was launched with the goal to establish a standardized evaluation of multiple object tracking methods.
Multi-Object Tracking
Multiple Object Tracking with Transformer
+2
JRMOT: A Real-Time 3D Multi-Object Tracker and a New Large-Scale Dataset
In this work we present JRMOT, a novel 3D MOT system that integrates information from RGB images and 3D point clouds to achieve real-time, state-of-the-art tracking performance.
Ranked #17 on
Multiple Object Tracking
on KITTI Test (Online Methods)
Learn to Predict Sets Using Feed-Forward Neural Networks
In our formulation we define a likelihood for a set distribution represented by a) two discrete distributions defining the set cardinally and permutation variables, and b) a joint distribution over set elements with a fixed cardinality.
Approximating the Permanent by Sampling from Adaptive Partitions
Computing the permanent of a non-negative matrix is a core problem with practical applications ranging from target tracking to statistical thermodynamics.
JRDB: A Dataset and Benchmark of Egocentric Robot Visual Perception of Humans in Built Environments
We present JRDB, a novel egocentric dataset collected from our social mobile manipulator JackRabbot.
Meta Learning with Differentiable Closed-form Solver for Fast Video Object Segmentation
This paper tackles the problem of video object segmentation.
CVPR19 Tracking and Detection Challenge: How crowded can it get?
Standardized benchmarks are crucial for the majority of computer vision applications.
Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression
By incorporating this generalized $IoU$ ($GIoU$) as a loss into the state-of-the art object detection frameworks, we show a consistent improvement on their performance using both the standard, $IoU$ based, and new, $GIoU$ based, performance measures on popular object detection benchmarks such as PASCAL VOC and MS COCO.
Learning Pairwise Relationship for Multi-object Detection in Crowded Scenes
As the post-processing step for object detection, non-maximum suppression (GreedyNMS) is widely used in most of the detectors for many years.
TrackerBots: Software in the Loop Study of Quad-Copter Robots for Locating Radio-tags in a 3D Space
We investigate the problem of tracking and planning for a UAV in a task to locate multiple radio-tagged wildlife in a three-dimensional (3D) setting in the context of our TrackerBots research project.
Deep Auto-Set: A Deep Auto-Encoder-Set Network for Activity Recognition Using Wearables
Automatic recognition of human activities from time-series sensor data (referred to as HAR) is a growing area of research in ubiquitous computing.
