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Found 11 papers, 7 papers with code
3D Diffuser Actor: Policy Diffusion with 3D Scene Representations
We marry diffusion policies and 3D scene representations for robot manipulation.
Diffusion-ES: Gradient-free Planning with Diffusion for Autonomous Driving and Zero-Shot Instruction Following
Diffusion-ES samples trajectories during evolutionary search from a diffusion model and scores them using a black-box reward function.
Diffusion-TTA: Test-time Adaptation of Discriminative Models via Generative Feedback
Our method, Diffusion-TTA, adapts pre-trained discriminative models such as image classifiers, segmenters and depth predictors, to each unlabelled example in the test set using generative feedback from a diffusion model.
CAST: Concurrent Recognition and Segmentation with Adaptive Segment Tokens
We are thus inspired to learn image recognition with hierarchical image segmentation based entirely on unlabeled images.
Unsupervised Hierarchical Semantic Segmentation with Multiview Cosegmentation and Clustering Transformers
We enforce spatial consistency of grouping and bootstrap feature learning with co-segmentation among multiple views of the same image, and enforce semantic consistency across the grouping hierarchy with clustering transformers between coarse- and fine-grained features.
Universal Weakly Supervised Segmentation by Pixel-to-Segment Contrastive Learning
Weakly supervised segmentation requires assigning a label to every pixel based on training instances with partial annotations such as image-level tags, object bounding boxes, labeled points and scribbles.
Contextual Image Parsing via Panoptic Segment Sorting
We aim to leverage the densely labeled task, image parsing, a. k. a panoptic segmentation, to learn a model that encodes and discovers object-centric context.
Adversarial Structure Matching for Structured Prediction Tasks
The structure analyzer is trained to maximize the ASM loss, or to emphasize recurring multi-scale hard negative structural mistakes among co-occurring patterns.
Adaptive Affinity Fields for Semantic Segmentation
Semantic segmentation has made much progress with increasingly powerful pixel-wise classifiers and incorporating structural priors via Conditional Random Fields (CRF) or Generative Adversarial Networks (GAN).
Ranked #55 on
Semantic Segmentation
on Cityscapes test
Multigrid Neural Architectures
Most critically, multigrid structure enables networks to learn internal attention and dynamic routing mechanisms, and use them to accomplish tasks on which modern CNNs fail.
Implicit Sparse Code Hashing
We address the problem of converting large-scale high-dimensional image data into binary codes so that approximate nearest-neighbor search over them can be efficiently performed.
