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Found 8 papers, 4 papers with code
Learning Kernel-Modulated Neural Representation for Efficient Light Field Compression
Light field is a type of image data that captures the 3D scene information by recording light rays emitted from a scene at various orientations.
Towards Motion Forecasting with Real-World Perception Inputs: Are End-to-End Approaches Competitive?
In fact, conventional forecasting methods are usually not trained nor tested in real-world pipelines (e. g., with upstream detection, tracking, and mapping modules).
Learning-based Spatial and Angular Information Separation for Light Field Compression
Light fields are a type of image data that capture both spatial and angular scene information by recording light rays emitted by a scene from different orientations.
DNN Training Acceleration via Exploring GPGPU Friendly Sparsity
The training phases of Deep neural network~(DNN) consumes enormous processing time and energy.
CP-ViT: Cascade Vision Transformer Pruning via Progressive Sparsity Prediction
We explore the sparsity in ViT and observe that informative patches and heads are sufficient for accurate image recognition.
TransCenter: Transformers with Dense Representations for Multiple-Object Tracking
Methodologically, we propose the use of image-related dense detection queries and efficient sparse tracking queries produced by our carefully designed query learning networks (QLN).
Ranked #13 on
Multi-Object Tracking
on MOT20
(using extra training data)
How To Train Your Deep Multi-Object Tracker
In this paper, we bridge this gap by proposing a differentiable proxy of MOTA and MOTP, which we combine in a loss function suitable for end-to-end training of deep multi-object trackers.
Ranked #4 on
Multi-Object Tracking
on 2D MOT 2015
CANU-ReID: A Conditional Adversarial Network for Unsupervised person Re-IDentification
Unsupervised person re-ID is the task of identifying people on a target data set for which the ID labels are unavailable during training.
