Remote sensing imagery, despite its broad applications in helping achieve Sustainable Development Goals and tackle climate change, has not yet benefited from the recent advancements of versatile, task-agnostic vision language models (VLMs). A key reason is that the large-scale, semantically diverse image-text dataset required for developing VLMs is still absent for remote sensing images. Unlike natural images, remote sensing images and their associated text descriptions cannot be efficiently collected from the public Internet at scale. In this work, we bridge this gap by using geo-coordinates to automatically connect open, unlabeled remote sensing images with rich semantics covered in OpenStreetMap, and thus construct SkyScript, a comprehensive vision-language dataset for remote sensing images, comprising 2.6 million image-text pairs covering 29K distinct semantic tags. 
With continual pre-training on this dataset, we obtain a VLM that surpasses baseline models with a 6.2% average accuracy gain in zero-shot scene classification across seven benchmark datasets. It also demonstrates the ability of zero-shot transfer for fine-grained object attribute classification and cross-modal retrieval. We hope this dataset can support the advancement of VLMs for various multi-modal tasks in remote sensing, such as open-vocabulary classification, retrieval, captioning, and text-to-image synthesis.

SkyScript: A Large and Semantically Diverse Vision-Language Dataset for Remote Sensing

We propose a novel approach that utilizes virtual reality (VR) and simulation environments to quantify the impact of visual impairments (VIs) on daily tasks, e.g., to what extent does glaucoma slow people down in wiping a table or chopping vegetables? We utilize clinical data from patients to develop visual field models, allowing VR to mimic the visual perception of VI patients as if it is seen through their eyes. Additionally, we leverage BEHAVIOR, the state-of-the-art simulation environment, to recreate a household environment for six daily activities. By measuring the disparity between the subjects' performance with and without VI, we can accurately quantify the impact of VIs. We further quantify the effects of VIs on body and eye movements, and model how movement strategies affect task performance under the influence of VIs. We hope our results can provide valuable insights into the challenges faced by individuals with VIs.

Quantifying the Effect of Visual Impairments on Daily Activities in Virtual, Interactive Environments

Virtual Reality (VR) presents a novel method to study the mind by generating dynamic, immersive environments for participants to engage with. However, the adoption of VR in cognitive science has been slow---for one, in order to use VR as a methodological tool, we need to verify that studies conducted in VR reflect the same assumptions that are common in existing experimental methods. Here, we investigate this question by drawing on existing empirical work in the domain of intuitive physics. In Experiment 1, we found that participants' judgments on a physical scene prediction task largely correlated with the same judgments collected using conventional methods. In Experiment 2, participants preferentially explored objects that violated core principles of physics, even engaging in distinct hypothesis-testing behaviors in accordance with the violation. In sum, these findings suggest that VR can be an exciting, novel method to study human behavior.

Intuitions about physical scenes and objects are preserved in Virtual Reality (VR)

We present a framework for learning useful subgoals that support efficient long-term planning to achieve novel goals.  At the core of our framework is a collection of rational subgoals (RSGs), which are essentially binary classifiers over the environmental states. RSGs can be learned from weakly-annotated data, in the form of unsegmented demonstration trajectories, paired with abstract task descriptions, which are composed of terms that are initially unknown to the agent (e.g., collect-wood then craft-boat then go-across-river). Our framework also discovers dependencies between RSGs, such as predicting "collect-wood" is a helpful subgoal for task "craft-boat". Given a goal description, the learned subgoals and the derived dependencies  facilitate off-the-shelf planning algorithms, such as A*and RRT, by setting helpful subgoals as waypoints to the planner. It significantly improves performance-time efficiency.

Learning Rational Subgoals from Demonstrations and Instructions

Human intelligence is beyond pattern recognition. From a single image, we’re able to explain what we see, reconstruct the scene in 3D, predict what’s going to happen, and plan our actions accordingly. In this talk, I will present our recent work on physical scene understanding—building versatile, data-efficient, and generalizable machines that learn to see, reason about, and interact with the physical world. The core idea is to exploit the generic, causal structure behind the world, including knowledge from computer graphics, physics, and language, in the form of approximate simulation engines, and to integrate them with deep learning. Here, deep learning plays two major roles: first, it learns to invert simulation engines for efficient inference; second, it learns to augment simulation engines for constructing powerful forward models. I’ll focus on a few topics to demonstrate this idea: building scene representation for both object geometry and physics; learning expressive dynamics models for planning and control; perception and reasoning beyond vision.

Learning to See the Physical World

Main Track - Computer Vision

technical paper

We are pleased to announce the Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI-24), which will be held in Vancouver, British Columbia at the Vancouver Convention Centre – West Building from 20-27 February, 2024.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-24 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

We expect for AAAI-24 to be an in-person conference – one author of all accepted papers will be expected to present work in person unless there are exceptional circumstances that prevent this.

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AAAI 2024

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. 

Poster Session 2

poster

The 45th Annual Meeting of the Cognitive Science Society will take place in Sydney, Australia as a modified hybrid conference. Presentations from the main-session room will be live-streamed every day for all events. This will include all invited talks and award sessions. Recordings of all CogSci 2023 sessions will be made available for on-demand viewing after the conference. 
The in-person component will be held at the International Convention Centre Sydney at Darling Harbour. The Conference will take place Wednesday July 26th – Saturday July 29th, 2023 (Wednesday, July 26th is the Workshops Day).

Join us!

 [**The Program at a Glance can be found here**](https://cognitivesciencesociety.org/wp-content/uploads/2023/07/COgSci-PAAG-V2.pdf)
 [**The Detailed Program can be found here**](https://online.fliphtml5.com/ebtyf/mswz/)
 

#### **CogSci 2023 Program Book**
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CogSci 2023

The 45th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

Poster Session 1

PRS: Planning/Scheduling and Learning

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
AAAI is providing you with a conference planner, which you can use to help organize your itinerary of activities. This includes talks to attend in person, talks to attend remotely, breaks with colleagues and your site seeing activities. To access this conference planner, please go to [https://aaai-2023.takemobi.io](https://aaai-2023.takemobi.io).

In order to access this site, you need to register. If you haven't already, please register [here](https://aaai.org/Conferences/AAAI-23/registration/).


AAAI 2023

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

Jiajun Wu

5

20

SHORT BIO

Presentations

SkyScript: A Large and Semantically Diverse Vision-Language Dataset for Remote Sensing

Quantifying the Effect of Visual Impairments on Daily Activities in Virtual, Interactive Environments

Intuitions about physical scenes and objects are preserved in Virtual Reality (VR)

Learning Rational Subgoals from Demonstrations and Instructions

Learning to See the Physical World

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