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Found 25 papers, 11 papers with code
Distributional Dataset Distillation with Subtask Decomposition
What does a neural network learn when training from a task-specific dataset?
Perceptual Group Tokenizer: Building Perception with Iterative Grouping
In this paper, we propose the Perceptual Group Tokenizer, a model that entirely relies on grouping operations to extract visual features and perform self-supervised representation learning, where a series of grouping operations are used to iteratively hypothesize the context for pixels or superpixels to refine feature representations.
Ranked #24 on
Self-Supervised Image Classification
on ImageNet
Representation Learning
Self-Supervised Image Classification
+2
Unseen Image Synthesis with Diffusion Models
While the current trend in the generative field is scaling up towards larger models and more training data for generalized domain representations, we go the opposite direction in this work by synthesizing unseen domain images without additional training.
A Zero-Shot Language Agent for Computer Control with Structured Reflection
To perform a task, recent works often require a model to learn from trace examples of the task via either supervised learning or few/many-shot prompting.
Vision-Language Dataset Distillation
In this work, we design the first vision-language dataset distillation method, building on the idea of trajectory matching.
Remember the Past: Distilling Datasets into Addressable Memories for Neural Networks
We propose an algorithm that compresses the critical information of a large dataset into compact addressable memories.
Adaptive Appearance Rendering
Hence, we develop an approach based on intermediate representations of poses and appearance: our pose-guided appearance rendering network firstly encodes the targets' poses using an encoder-decoder neural network.
Evolving Graphical Planner: Contextual Global Planning for Vision-and-Language Navigation
The ability to perform effective planning is crucial for building an instruction-following agent.
BabyWalk: Going Farther in Vision-and-Language Navigation by Taking Baby Steps
To this end, we propose BabyWalk, a new VLN agent that is learned to navigate by decomposing long instructions into shorter ones (BabySteps) and completing them sequentially.
Take the Scenic Route: Improving Generalization in Vision-and-Language Navigation
In the Vision-and-Language Navigation (VLN) task, an agent with egocentric vision navigates to a destination given natural language instructions.
Policy Message Passing: A New Algorithm for Probabilistic Graph Inference
A general graph-structured neural network architecture operates on graphs through two core components: (1) complex enough message functions; (2) a fixed information aggregation process.
Continuous Graph Flow
In this paper, we propose Continuous Graph Flow, a generative continuous flow based method that aims to model complex distributions of graph-structured data.
Probabilistic Neural Programmed Networks for Scene Generation
In this paper we address the text to scene image generation problem.
Structured Label Inference for Visual Understanding
In this paper, we exploit this rich structure for performing graph-based inference in label space for a number of tasks: multi-label image and video classification and action detection in untrimmed videos.
Sparsely Aggregated Convolutional Networks
We explore a key architectural aspect of deep convolutional neural networks: the pattern of internal skip connections used to aggregate outputs of earlier layers for consumption by deeper layers.
Factorized Variational Autoencoders for Modeling Audience Reactions to Movies
Matrix and tensor factorization methods are often used for finding underlying low-dimensional patterns from noisy data.
Active Learning for Structured Prediction from Partially Labeled Data
We propose a general purpose active learning algorithm for structured prediction, gathering labeled data for training a model that outputs a set of related labels for an image or video.
Generic Tubelet Proposals for Action Localization
Our class-independent TPN outperforms other tubelet generation methods, and our unified temporal deep network achieves state-of-the-art localization results on all three datasets.
LabelBank: Revisiting Global Perspectives for Semantic Segmentation
We advocate that holistic inference of image concepts provides valuable information for detailed pixel labeling.
Recalling Holistic Information for Semantic Segmentation
We advocate that high-recall holistic inference of image concepts provides valuable information for detailed pixel labeling.
Hierarchical Deep Temporal Models for Group Activity Recognition
In order to model both person-level and group-level dynamics, we present a 2-stage deep temporal model for the group activity recognition problem.
A Hierarchical Deep Temporal Model for Group Activity Recognition
In group activity recognition, the temporal dynamics of the whole activity can be inferred based on the dynamics of the individual people representing the activity.
Learning Structured Inference Neural Networks with Label Relations
Images of scenes have various objects as well as abundant attributes, and diverse levels of visual categorization are possible.
Structure Inference Machines: Recurrent Neural Networks for Analyzing Relations in Group Activity Recognition
As a concrete example, group activity recognition involves the interactions and relative spatial relations of a set of people in a scene.
Ranked #5 on
Group Activity Recognition
on Collective Activity
Deep Structured Models For Group Activity Recognition
This paper presents a deep neural-network-based hierarchical graphical model for individual and group activity recognition in surveillance scenes.
