Search Results for author: Daqi Liu

Found 12 papers, 3 papers with code

Self-Supervised Multi-Frame Neural Scene Flow

no code implementations24 Mar 2024 Dongrui Liu, Daqi Liu, Xueqian Li, Sihao Lin, Hongwei Xie, Bing Wang, Xiaojun Chang, Lei Chu

Neural Scene Flow Prior (NSFP) and Fast Neural Scene Flow (FNSF) have shown remarkable adaptability in the context of large out-of-distribution autonomous driving.

Autonomous Driving Scene Flow Estimation

Globally Optimal Event-Based Divergence Estimation for Ventral Landing

1 code implementation27 Sep 2022 Sofia McLeod, Gabriele Meoni, Dario Izzo, Anne Mergy, Daqi Liu, Yasir Latif, Ian Reid, Tat-Jun Chin

This is achieved by estimating divergence (inverse TTC), which is the rate of radial optic flow, from the event stream generated during landing.

Doubly Reparameterized Importance Weighted Structure Learning for Scene Graph Generation

no code implementations22 Jun 2022 Daqi Liu, Miroslaw Bober, Josef Kittler

As a structured prediction task, scene graph generation, given an input image, aims to explicitly model objects and their relationships by constructing a visually-grounded scene graph.

Graph Generation Scene Graph Generation +2

Importance Weighted Structure Learning for Scene Graph Generation

no code implementations14 May 2022 Daqi Liu, Miroslaw Bober, Josef Kittler

Scene graph generation is a structured prediction task aiming to explicitly model objects and their relationships via constructing a visually-grounded scene graph for an input image.

Graph Generation Scene Graph Generation +2

Asynchronous Optimisation for Event-based Visual Odometry

no code implementations2 Mar 2022 Daqi Liu, Alvaro Parra, Yasir Latif, Bo Chen, Tat-Jun Chin, Ian Reid

Event cameras open up new possibilities for robotic perception due to their low latency and high dynamic range.

Event-based vision Visual Odometry

Constrained Structure Learning for Scene Graph Generation

no code implementations27 Jan 2022 Daqi Liu, Miroslaw Bober, Josef Kittler

As a structured prediction task, scene graph generation aims to build a visually-grounded scene graph to explicitly model objects and their relationships in an input image.

Graph Generation Scene Graph Generation +2

Neural Belief Propagation for Scene Graph Generation

no code implementations10 Dec 2021 Daqi Liu, Miroslaw Bober, Josef Kittler

Scene graph generation aims to interpret an input image by explicitly modelling the potential objects and their relationships, which is predominantly solved by the message passing neural network models in previous methods.

Graph Generation Scene Graph Generation

Spatiotemporal Registration for Event-based Visual Odometry

2 code implementations CVPR 2021 Daqi Liu, Alvaro Parra, Tat-Jun Chin

The state-of-the-art method of contrast maximisation recovers the motion from a batch of events by maximising the contrast of the image of warped events.

Motion Estimation Visual Odometry

Topological Sweep for Multi-Target Detection of Geostationary Space Objects

no code implementations21 Mar 2020 Daqi Liu, Bo Chen, Tat-Jun Chin, Mark Rutten

In this paper, we propose a novel multi-target detection technique based on topological sweep, to find GEO objects from a short sequence of optical images.

object-detection Object Detection

Visual Semantic Information Pursuit: A Survey

no code implementations13 Mar 2019 Daqi Liu, Miroslaw Bober, Josef Kittler

Since it helps to enhance the accuracy and the consistency of the resulting interpretation, visual context reasoning is often incorporated with visual perception in current deep end-to-end visual semantic information pursuit methods.

Graph Generation object-detection +5

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