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Found 13 papers, 5 papers with code
Generation and Comprehension of Unambiguous Object Descriptions
We propose a method that can generate an unambiguous description (known as a referring expression) of a specific object or region in an image, and which can also comprehend or interpret such an expression to infer which object is being described.
Learning like a Child: Fast Novel Visual Concept Learning from Sentence Descriptions of Images
In particular, we propose a transposed weight sharing scheme, which not only improves performance on image captioning, but also makes the model more suitable for the novel concept learning task.
Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN)
In this paper, we present a multimodal Recurrent Neural Network (m-RNN) model for generating novel image captions.
CNN-RNN: A Unified Framework for Multi-label Image Classification
While deep convolutional neural networks (CNNs) have shown a great success in single-label image classification, it is important to note that real world images generally contain multiple labels, which could correspond to different objects, scenes, actions and attributes in an image.
Are You Talking to a Machine? Dataset and Methods for Multilingual Image Question Answering
The quality of the generated answers of our mQA model on this dataset is evaluated by human judges through a Turing Test.
Training and Evaluating Multimodal Word Embeddings with Large-scale Web Annotated Images
In this paper, we focus on training and evaluating effective word embeddings with both text and visual information.
Attention Correctness in Neural Image Captioning
Attention mechanisms have recently been introduced in deep learning for various tasks in natural language processing and computer vision.
Are You Talking to a Machine? Dataset and Methods for Multilingual Image Question
The quality of the generated answers of our mQA model on this dataset is evaluated by human judges through a Turing Test.
Learning From Weakly Supervised Data by The Expectation Loss SVM (e-SVM) algorithm
We propose a novel learning algorithm called \emph{expectation loss SVM} (e-SVM) that is devoted to the problems where only the ``positiveness" instead of a binary label of each training sample is available.
STINet: Spatio-Temporal-Interactive Network for Pedestrian Detection and Trajectory Prediction
For the Waymo Open Dataset, we achieve a bird-eyes-view (BEV) detection AP of 80. 73 and trajectory prediction average displacement error (ADE) of 33. 67cm for pedestrians, which establish the state-of-the-art for both tasks.
Multi-modal 3D Human Pose Estimation with 2D Weak Supervision in Autonomous Driving
3D human pose estimation (HPE) in autonomous vehicles (AV) differs from other use cases in many factors, including the 3D resolution and range of data, absence of dense depth maps, failure modes for LiDAR, relative location between the camera and LiDAR, and a high bar for estimation accuracy.
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.
