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Found 10 papers, 5 papers with code
Reasoning Paths with Reference Objects Elicit Quantitative Spatial Reasoning in Large Vision-Language Models
Inspired by this observation, we develop a zero-shot prompting technique, SpatialPrompt, that encourages VLMs to answer quantitative spatial questions using reference objects as visual cues.
Can Feedback Enhance Semantic Grounding in Large Vision-Language Models?
We find that if prompted appropriately, VLMs can utilize feedback both in a single step and iteratively, showcasing the potential of feedback as an alternative technique to improve grounding in internet-scale VLMs.
Towards Good Practices for Efficiently Annotating Large-Scale Image Classification Datasets
This is expensive, and guaranteeing the quality of the labels is a major challenge.
Emergent Road Rules In Multi-Agent Driving Environments
For autonomous vehicles to safely share the road with human drivers, autonomous vehicles must abide by specific "road rules" that human drivers have agreed to follow.
Watch-And-Help: A Challenge for Social Perception and Human-AI Collaboration
In this paper, we introduce Watch-And-Help (WAH), a challenge for testing social intelligence in agents.
Synthesizing Environment-Aware Activities via Activity Sketches
In order to learn to perform activities from demonstrations or descriptions, agents need to distill what the essence of the given activity is, and how it can be adapted to new environments.
Multi-view to Novel view: Synthesizing Novel Views with Self-Learned Confidence
We address the task of multi-view novel view synthesis, where we are interested in synthesizing a target image with an arbitrary camera pose from given source images.
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Show, Adapt and Tell: Adversarial Training of Cross-domain Image Captioner
The domain critic assesses whether the generated sentences are indistinguishable from sentences in the target domain.
Tactics of Adversarial Attack on Deep Reinforcement Learning Agents
In the strategically-timed attack, the adversary aims at minimizing the agent's reward by only attacking the agent at a small subset of time steps in an episode.
Leveraging Video Descriptions to Learn Video Question Answering
Then, a large number of candidate QA pairs are automatically generated from descriptions rather than manually annotated.
