Deep multimodal fusion for semantic image segmentation: A survey
Recent advances in deep learning have shown excellent performance in various scene
understanding tasks. However, in some complex environments or under challenging …
understanding tasks. However, in some complex environments or under challenging …
GeoAI for large-scale image analysis and machine vision: recent progress of artificial intelligence in geography
GeoAI, or geospatial artificial intelligence, has become a trending topic and the frontier for
spatial analytics in Geography. Although much progress has been made in exploring the …
spatial analytics in Geography. Although much progress has been made in exploring the …
Open-vocabulary panoptic segmentation with text-to-image diffusion models
We present ODISE: Open-vocabulary DIffusion-based panoptic SEgmentation, which unifies
pre-trained text-image diffusion and discriminative models to perform open-vocabulary …
pre-trained text-image diffusion and discriminative models to perform open-vocabulary …
Lisa: Reasoning segmentation via large language model
Although perception systems have made remarkable advancements in recent years they still
rely on explicit human instruction or pre-defined categories to identify the target objects …
rely on explicit human instruction or pre-defined categories to identify the target objects …
Convolutions die hard: Open-vocabulary segmentation with single frozen convolutional clip
Open-vocabulary segmentation is a challenging task requiring segmenting and recognizing
objects from an open set of categories in diverse environments. One way to address this …
objects from an open set of categories in diverse environments. One way to address this …
Oneformer: One transformer to rule universal image segmentation
Abstract Universal Image Segmentation is not a new concept. Past attempts to unify image
segmentation include scene parsing, panoptic segmentation, and, more recently, new …
segmentation include scene parsing, panoptic segmentation, and, more recently, new …
Block-nerf: Scalable large scene neural view synthesis
Abstract We present Block-NeRF, a variant of Neural Radiance Fields that can represent
large-scale environments. Specifically, we demonstrate that when scaling NeRF to render …
large-scale environments. Specifically, we demonstrate that when scaling NeRF to render …
Panoptic neural fields: A semantic object-aware neural scene representation
We present PanopticNeRF, an object-aware neural scene representation that decomposes
a scene into a set of objects (things) and background (stuff). Each object is represented by a …
a scene into a set of objects (things) and background (stuff). Each object is represented by a …
Masked-attention mask transformer for universal image segmentation
Image segmentation groups pixels with different semantics, eg, category or instance
membership. Each choice of semantics defines a task. While only the semantics of each task …
membership. Each choice of semantics defines a task. While only the semantics of each task …
Rethinking range view representation for lidar segmentation
LiDAR segmentation is crucial for autonomous driving perception. Recent trends favor point-
or voxel-based methods as they often yield better performance than the traditional range …
or voxel-based methods as they often yield better performance than the traditional range …