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Found 15 papers, 8 papers with code
Natural Language Inference Improves Compositionality in Vision-Language Models
Compositional reasoning in Vision-Language Models (VLMs) remains challenging as these models often struggle to relate objects, attributes, and spatial relationships.
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 Synthetic Data for Visual Grounding
This paper extensively investigates the effectiveness of synthetic training data to improve the capabilities of vision-and-language models for grounding textual descriptions to image regions.
Grounding Language Models for Visual Entity Recognition
We introduce AutoVER, an Autoregressive model for Visual Entity Recognition.
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.
Going Beyond Nouns With Vision & Language Models Using Synthetic Data
We contribute Synthetic Visual Concepts (SyViC) - a million-scale synthetic dataset and data generation codebase allowing to generate additional suitable data to improve VLC understanding and compositional reasoning of VL models.
Ranked #69 on
Visual Reasoning
on Winoground
CODA-Prompt: COntinual Decomposed Attention-based Prompting for Rehearsal-Free Continual Learning
Our experiments show that we outperform the current SOTA method DualPrompt on established benchmarks by as much as 4. 5% in average final accuracy.
On the Transferability of Visual Features in Generalized Zero-Shot Learning
Generalized Zero-Shot Learning (GZSL) aims to train a classifier that can generalize to unseen classes, using a set of attributes as auxiliary information, and the visual features extracted from a pre-trained convolutional neural network.
ConStruct-VL: Data-Free Continual Structured VL Concepts Learning
This leads to reasoning mistakes, which need to be corrected as they occur by teaching VL models the missing SVLC skills; often this must be done using private data where the issue was found, which naturally leads to a data-free continual (no task-id) VL learning setting.
SimVQA: Exploring Simulated Environments for Visual Question Answering
By exploiting 3D and physics simulation platforms, we provide a pipeline to generate synthetic data to expand and replace type-specific questions and answers without risking the exposure of sensitive or personal data that might be present in real images.
Evolving Image Compositions for Feature Representation Learning
This paper proposes PatchMix, a data augmentation method that creates new samples by composing patches from pairs of images in a grid-like pattern.
Curriculum Labeling: Revisiting Pseudo-Labeling for Semi-Supervised Learning
Pseudo-labeling works by applying pseudo-labels to samples in the unlabeled set by using a model trained on the combination of the labeled samples and any previously pseudo-labeled samples, and iteratively repeating this process in a self-training cycle.
Drill-down: Interactive Retrieval of Complex Scenes using Natural Language Queries
We show that using multiple rounds of natural language queries as input can be surprisingly effective to find arbitrarily specific images of complex scenes.
Moviescope: Large-scale Analysis of Movies using Multiple Modalities
Film media is a rich form of artistic expression.
Chat-crowd: A Dialog-based Platform for Visual Layout Composition
In this paper we introduce Chat-crowd, an interactive environment for visual layout composition via conversational interactions.
