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Found 10 papers, 5 papers with code
Emergent Open-Vocabulary Semantic Segmentation from Off-the-shelf Vision-Language Models
From image-text pairs, large-scale vision-language models (VLMs) learn to implicitly associate image regions with words, which prove effective for tasks like visual question answering.
Frustratingly Simple but Effective Zero-shot Detection and Segmentation: Analysis and a Strong Baseline
Methods for object detection and segmentation often require abundant instance-level annotations for training, which are time-consuming and expensive to collect.
Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting
Models are tasked with the prediction of future motion for "scored actors" in each scenario and are provided with track histories that capture object location, heading, velocity, and category.
Iterative Scene Graph Generation
In this work, we propose a novel framework for scene graph generation that addresses this limitation, as well as introduces dynamic conditioning on the image, using message passing in a Markov Random Field.
Segmentation-grounded Scene Graph Generation
Our framework is agnostic to the underlying scene graph generation method and address the lack of segmentation annotations in target scene graph datasets (e. g., Visual Genome) through transfer and multi-task learning from, and with, an auxiliary dataset (e. g., MS COCO).
What-If Motion Prediction for Autonomous Driving
Forecasting the long-term future motion of road actors is a core challenge to the deployment of safe autonomous vehicles (AVs).
UniT: Unified Knowledge Transfer for Any-shot Object Detection and Segmentation
Weakly-supervised approaches draw on image-level labels to build detectors/segmentors, while zero/few-shot methods assume abundant instance-level data for a set of base classes, and none to a few examples for novel classes.
AttentionRNN: A Structured Spatial Attention Mechanism
Visual attention mechanisms have proven to be integrally important constituent components of many modern deep neural architectures.
Improving Distantly Supervised Relation Extraction using Word and Entity Based Attention
Relation extraction is the problem of classifying the relationship between two entities in a given sentence.
Faster K-Means Cluster Estimation
We propose a fast heuristic to overcome this bottleneck with only marginal increase in MSE.
