Search Results for author:
Found 10 papers, 4 papers with code
Seeing Motion at Nighttime with an Event Camera
In this work, we present a novel nighttime dynamic imaging method with an event camera.
JSTR: Joint Spatio-Temporal Reasoning for Event-based Moving Object Detection
In spatial reasoning stage, we project the compensated events into the same image coordinate, discretize the timestamp of events to obtain a time image that can reflect the motion confidence, and further segment the moving object through adaptive threshold on the time image.
Bring Event into RGB and LiDAR: Hierarchical Visual-Motion Fusion for Scene Flow
In this work, we bring the event as a bridge between RGB and LiDAR, and propose a novel hierarchical visual-motion fusion framework for scene flow, which explores a homogeneous space to fuse the cross-modal complementary knowledge for physical interpretation.
DACO: Towards Application-Driven and Comprehensive Data Analysis via Code Generation
We construct the DACO dataset, containing (1) 440 databases (of tabular data) collected from real-world scenarios, (2) ~2k query-answer pairs that can serve as weak supervision for model training, and (3) a concentrated but high-quality test set with human refined annotations that serves as our main evaluation benchmark.
Exploring the Common Appearance-Boundary Adaptation for Nighttime Optical Flow
In appearance adaptation, we employ the intrinsic image decomposition to embed the auxiliary daytime image and the nighttime image into a reflectance-aligned common space.
UFineBench: Towards Text-based Person Retrieval with Ultra-fine Granularity
Firstly, we construct a new \textbf{dataset} named UFine6926.
Advancing Transformer's Capabilities in Commonsense Reasoning
In this work, we aim to bridge the gap by introducing current ML-based methods to improve general purpose pre-trained language models in the task of commonsense reasoning.
Unsupervised Hierarchical Domain Adaptation for Adverse Weather Optical Flow
In motion adaptation, we utilize the flow consistency knowledge to align the cross-domain optical flows into a motion-invariance common space, where the optical flow from clean weather is used as the guidance-knowledge to obtain a preliminary optical flow for adverse weather.
Unsupervised Cumulative Domain Adaptation for Foggy Scene Optical Flow
To handle the practical optical flow under real foggy scenes, in this work, we propose a novel unsupervised cumulative domain adaptation optical flow (UCDA-Flow) framework: depth-association motion adaptation and correlation-alignment motion adaptation.
Closing the Loop: Joint Rain Generation and Removal via Disentangled Image Translation
Existing deep learning-based image deraining methods have achieved promising performance for synthetic rainy images, typically rely on the pairs of sharp images and simulated rainy counterparts.
