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Found 15 papers, 6 papers with code
Feedback-Driven Vision-Language Alignment with Minimal Human Supervision
Vision-language models (VLMs) have demonstrated remarkable potential in integrating visual and linguistic information, but their performance is often constrained by the need for extensive, high-quality image-text training data.
Be More Specific: Evaluating Object-centric Realism in Synthetic Images
In this work, we define a new standard for assessing object-centric realism that follows a shape-texture breakdown and proposes the first object-centric realism evaluation dataset for synthetic images.
Reparameterized Multi-Resolution Convolutions for Long Sequence Modelling
Global convolutions have shown increasing promise as powerful general-purpose sequence models.
NITO: Neural Implicit Fields for Resolution-free Topology Optimization
We introduce Neural Implicit Topology Optimization (NITO), a novel approach to accelerate topology optimization problems using deep learning.
From Concept to Manufacturing: Evaluating Vision-Language Models for Engineering Design
Large language models have demonstrated impressive capabilities in enabling this shift.
Constraining Generative Models for Engineering Design with Negative Data
Our negative-data generative model (NDGM) formulation easily outperforms classic models, generating 1/6 as many constraint-violating samples using 1/8 as much data in certain problems.
Diffusing the Optimal Topology: A Generative Optimization Approach
To address these issues, we propose a Generative Optimization method that integrates classic optimization like SIMP as a refining mechanism for the topology generated by a deep generative model.
Unifying Molecular and Textual Representations via Multi-task Language Modelling
Here, we propose the first multi-domain, multi-task language model that can solve a wide range of tasks in both the chemical and natural language domains.
Ranked #6 on
Molecule Captioning
on ChEBI-20
Just Mix Once: Worst-group Generalization by Group Interpolation
Advances in deep learning theory have revealed how average generalization relies on superficial patterns in data.
Accelerating Material Design with the Generative Toolkit for Scientific Discovery
With the growing availability of data within various scientific domains, generative models hold enormous potential to accelerate scientific discovery.
Few-Shot Diffusion Models
At test time, the model is able to generate samples from previously unseen classes conditioned on as few as 5 samples from that class.
SCHA-VAE: Hierarchical Context Aggregation for Few-Shot Generation
In few-shot learning the model is trained on data from many sets from distributions sharing some underlying properties such as sets of characters from different alphabets or objects from different categories.
Real-time Classification from Short Event-Camera Streams using Input-filtering Neural ODEs
INODE is trained like a standard RNN, it learns to discriminate short event sequences and to perform event-by-event online inference.
No Representation without Transformation
To explicitly demonstrate the effect of these higher order objects, we show that the inferred latent transformations reflect interpretable properties in the observation space.
Learning Common Representation from RGB and Depth Images
We propose a new deep learning architecture for the tasks of semantic segmentation and depth prediction from RGB-D images.
