3.5k citations · 10.5k across the 119 of their papers we have counts for
17 papers · 1 filter
MOLTR: Multiple Object Localisation, Tracking, and Reconstruction from Monocular RGB Videos
Kejie Li, Hamid Rezatofighi, Ian Reid
Semantic aware reconstruction is more advantageous than geometric-only reconstruction for future robotic and AR/VR applications because it represents not only where things are, but…
EvidentialMix: Learning with Combined Open-set and Closed-set Noisy Labels
Ragav Sachdeva, Filipe R. Cordeiro, Vasileios Belagiannis +2
The efficacy of deep learning depends on large-scale data sets that have been carefully curated with reliable data acquisition and annotation processes. However, acquiring such lar…
HM4: Hidden Markov Model with Memory Management for Visual Place Recognition
Anh-Dzung Doan, Yasir Latif, Tat-Jun Chin +1
Visual place recognition needs to be robust against appearance variability due to natural and man-made causes. Training data collection should thus be an ongoing process to allow c…
MOTChallenge: A Benchmark for Single-Camera Multiple Target Tracking
Patrick Dendorfer, Aljoša Ošep, Anton Milan +5
Standardized benchmarks have been crucial in pushing the performance of computer vision algorithms, especially since the advent of deep learning. Although leaderboards should not b…
How Trustworthy are Performance Evaluations for Basic Vision Tasks?
Tran Thien Dat Nguyen, Hamid Rezatofighi, Ba-Ngu Vo +3
This paper examines performance evaluation criteria for basic vision tasks involving sets of objects namely, object detection, instance-level segmentation and multi-object tracking…
Joint Learning of Social Groups, Individuals Action and Sub-group Activities in Videos
Mahsa Ehsanpour, Alireza Abedin, Fatemeh Saleh +3
The state-of-the art solutions for human activity understanding from a video stream formulate the task as a spatio-temporal problem which requires joint localization of all individ…
Socially and Contextually Aware Human Motion and Pose Forecasting
Vida Adeli, Ehsan Adeli, Ian Reid +2
Smooth and seamless robot navigation while interacting with humans depends on predicting human movements. Forecasting such human dynamics often involves modeling human trajectories…
Auto-Rectify Network for Unsupervised Indoor Depth Estimation
Jia-Wang Bian, Huangying Zhan, Naiyan Wang +3
Single-View depth estimation using the CNNs trained from unlabelled videos has shown significant promise. However, excellent results have mostly been obtained in street-scene drivi…
FroDO: From Detections to 3D Objects
Kejie Li, Martin Rünz, Meng Tang +8
Object-oriented maps are important for scene understanding since they jointly capture geometry and semantics, allow individual instantiation and meaningful reasoning about objects.…
MOT20: A benchmark for multi object tracking in crowded scenes
Patrick Dendorfer, Hamid Rezatofighi, Anton Milan +6
Standardized benchmarks are crucial for the majority of computer vision applications. Although leaderboards and ranking tables should not be over-claimed, benchmarks often provide…
Real-time Image Smoothing via Iterative Least Squares
Wei Liu, Pingping Zhang, Xiaolin Huang +3
Edge-preserving image smoothing is a fundamental procedure for many computer vision and graphic applications. There is a tradeoff between the smoothing quality and the processing s…
3D Gated Recurrent Fusion for Semantic Scene Completion
Yu Liu, Jie Li, Qingsen Yan +4
This paper tackles the problem of data fusion in the semantic scene completion (SSC) task, which can simultaneously deal with semantic labeling and scene completion. RGB images con…
Hyperspectral Classification Based on 3D Asymmetric Inception Network with Data Fusion Transfer Learning
Haokui Zhang, Yu Liu, Bei Fang +3
Hyperspectral image(HSI) classification has been improved with convolutional neural network(CNN) in very recent years. Being different from the RGB datasets, different HSI datasets…
Switchable Precision Neural Networks
Luis Guerra, Bohan Zhuang, Ian Reid +1
Instantaneous and on demand accuracy-efficiency trade-off has been recently explored in the context of neural networks slimming. In this paper, we propose a flexible quantization s…
Automatic Pruning for Quantized Neural Networks
Luis Guerra, Bohan Zhuang, Ian Reid +1
Neural network quantization and pruning are two techniques commonly used to reduce the computational complexity and memory footprint of these models for deployment. However, most e…
Depth Based Semantic Scene Completion with Position Importance Aware Loss
Yu Liu, Jie Li, Xia Yuan +4
Semantic Scene Completion (SSC) refers to the task of inferring the 3D semantic segmentation of a scene while simultaneously completing the 3D shapes. We propose PALNet, a novel hy…
Learn to Predict Sets Using Feed-Forward Neural Networks
Hamid Rezatofighi, Tianyu Zhu, Roman Kaskman +6
This paper addresses the task of set prediction using deep feed-forward neural networks. A set is a collection of elements which is invariant under permutation and the size of a se…