Search Results for author:
Found 10 papers, 5 papers with code
PropTest: Automatic Property Testing for Improved Visual Programming
We propose PropTest, a general strategy that improves visual programming by further using an LLM to generate code that tests for visual properties in an initial round of proposed solutions.
Learning from Models and Data for Visual Grounding
We introduce SynGround, a novel framework that combines data-driven learning and knowledge transfer from various large-scale pretrained models to enhance the visual grounding capabilities of a pretrained vision-and-language model.
Improved Visual Grounding through Self-Consistent Explanations
Vision-and-language models trained to match images with text can be combined with visual explanation methods to point to the locations of specific objects in an image.
SCoRD: Subject-Conditional Relation Detection with Text-Augmented Data
To solve this problem, we propose an auto-regressive model that given a subject, it predicts its relations, objects, and object locations by casting this output as a sequence of tokens.
Improving Visual Grounding by Encouraging Consistent Gradient-based Explanations
We propose a margin-based loss for tuning joint vision-language models so that their gradient-based explanations are consistent with region-level annotations provided by humans for relatively smaller grounding datasets.
Chair Segments: A Compact Benchmark for the Study of Object Segmentation
We aim for ChairSegments to be the equivalent of the CIFAR-10 dataset but for quickly designing and iterating over novel model architectures for segmentation.
Using Visual Feature Space as a Pivot Across Languages
Our work aims to leverage visual feature space to pass information across languages.
Training Deep Neural Networks with Partially Adaptive Momentum
Experiments on standard benchmarks show that our proposed algorithm can maintain fast convergence rate as Adam/Amsgrad while generalizing as well as SGD in training deep neural networks.
On the Convergence of Adaptive Gradient Methods for Nonconvex Optimization
In this paper, we provide a fine-grained convergence analysis for a general class of adaptive gradient methods including AMSGrad, RMSProp and AdaGrad.
Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks
Experiments on standard benchmarks show that our proposed algorithm can maintain a fast convergence rate as Adam/Amsgrad while generalizing as well as SGD in training deep neural networks.
