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Found 13 papers, 6 papers with code
CARIS: Context-Augmented Referring Image Segmentation
Technically, CARIS develops a context-aware mask decoder with sequential bidirectional cross-modal attention to integrate the linguistic features with visual context, which are then aligned with pixel-wise visual features.
Learning Neural Implicit Surfaces with Object-Aware Radiance Fields
Then, we build the geometric correspondence between 2D planes and 3D meshes by rasterization, and project the estimated object regions into 3D explicit object surfaces by aggregating the object information across multiple views.
Learning Orthogonal Prototypes for Generalized Few-Shot Semantic Segmentation
POP builds a set of orthogonal prototypes, each of which represents a semantic class, and makes the prediction for each class separately based on the features projected onto its prototype.
Explaining Cross-Domain Recognition with Interpretable Deep Classifier
In this paper, we ask the question: how much each sample in source domain contributes to the network's prediction on the samples from target domain.
Lightweight and Progressively-Scalable Networks for Semantic Segmentation
In this paper, we thoroughly analyze the design of convolutional blocks (the type of convolutions and the number of channels in convolutions), and the ways of interactions across multiple scales, all from lightweight standpoint for semantic segmentation.
Silver-Bullet-3D at ManiSkill 2021: Learning-from-Demonstrations and Heuristic Rule-based Methods for Object Manipulation
This paper presents an overview and comparative analysis of our systems designed for the following two tracks in SAPIEN ManiSkill Challenge 2021: No Interaction Track: The No Interaction track targets for learning policies from pre-collected demonstration trajectories.
Motion-Focused Contrastive Learning of Video Representations
To this end, we compose a duet of exploiting the motion for data augmentation and feature learning in the regime of contrastive learning.
SeCo: Exploring Sequence Supervision for Unsupervised Representation Learning
In this paper, we compose a trilogy of exploring the basic and generic supervision in the sequence from spatial, spatiotemporal and sequential perspectives.
Transferring and Regularizing Prediction for Semantic Segmentation
In the view of extremely expensive expert labeling, recent research has shown that the models trained on photo-realistic synthetic data (e. g., computer games) with computer-generated annotations can be adapted to real images.
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Domain Adaptation
on SYNTHIA-to-Cityscapes
Night-time Scene Parsing with a Large Real Dataset
Although huge progress has been made on scene analysis in recent years, most existing works assume the input images to be in day-time with good lighting conditions.
Scheduled Differentiable Architecture Search for Visual Recognition
Moreover, we enlarge the search space of SDAS particularly for video recognition by devising several unique operations to encode spatio-temporal dynamics and demonstrate the impact in affecting the architecture search of SDAS.
Customizable Architecture Search for Semantic Segmentation
As a result, our CAS is able to search an optimized architecture with customized constraints.
Fully Convolutional Adaptation Networks for Semantic Segmentation
The recent advances in deep neural networks have convincingly demonstrated high capability in learning vision models on large datasets.
