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Found 13 papers, 3 papers with code
STT: Stateful Tracking with Transformers for Autonomous Driving
In this paper, we propose STT, a Stateful Tracking model built with Transformers, that can consistently track objects in the scenes while also predicting their states accurately.
Pedestrian Crossing Action Recognition and Trajectory Prediction with 3D Human Keypoints
Accurate understanding and prediction of human behaviors are critical prerequisites for autonomous vehicles, especially in highly dynamic and interactive scenarios such as intersections in dense urban areas.
Automatic Non-Linear Video Editing Transfer
We propose an automatic approach that extracts editing styles in a source video and applies the edits to matched footage for video creation.
Regularizing Generative Adversarial Networks under Limited Data
Recent years have witnessed the rapid progress of generative adversarial networks (GANs).
Ranked #1 on
Image Generation
on CIFAR-100
Text as Neural Operator: Image Manipulation by Text Instruction
In recent years, text-guided image manipulation has gained increasing attention in the multimedia and computer vision community.
RetrieveGAN: Image Synthesis via Differentiable Patch Retrieval
Image generation from scene description is a cornerstone technique for the controlled generation, which is beneficial to applications such as content creation and image editing.
Neural Design Network: Graphic Layout Generation with Constraints
The first module predicts a graph with complete relations from a graph with user-specified relations.
Beyond Synthetic Noise: Deep Learning on Controlled Noisy Labels
Due to the lack of suitable datasets, previous research has only examined deep learning on controlled synthetic label noise, and real-world label noise has never been studied in a controlled setting.
Ranked #12 on
Image Classification
on WebVision-1000
Synthetic vs Real: Deep Learning on Controlled Noise
Performing controlled experiments on noisy data is essential in thoroughly understanding deep learning across a spectrum of noise levels.
Pose Embeddings: A Deep Architecture for Learning to Match Human Poses
We present a method for learning an embedding that places images of humans in similar poses nearby.
Learning Class-to-Image Distance with Object Matchings
We conduct image classification by learning a class-toimage distance function that matches objects.
Kernel Latent SVM for Visual Recognition
Latent SVMs (LSVMs) are a class of powerful tools that have been successfully applied to many applications in computer vision.
Beyond Actions: Discriminative Models for Contextual Group Activities
We propose a discriminative model for recognizing group activities.
