Search-TTA: A Multimodal Test-Time Adaptation Framework for Visual Search in the Wild (2505.11350v1)

Abstract: To perform autonomous visual search for environmental monitoring, a robot may leverage satellite imagery as a prior map. This can help inform coarse, high-level search and exploration strategies, even when such images lack sufficient resolution to allow fine-grained, explicit visual recognition of targets. However, there are some challenges to overcome with using satellite images to direct visual search. For one, targets that are unseen in satellite images are underrepresented (compared to ground images) in most existing datasets, and thus vision models trained on these datasets fail to reason effectively based on indirect visual cues. Furthermore, approaches which leverage large Vision LLMs (VLMs) for generalization may yield inaccurate outputs due to hallucination, leading to inefficient search. To address these challenges, we introduce Search-TTA, a multimodal test-time adaptation framework that can accept text and/or image input. First, we pretrain a remote sensing image encoder to align with CLIP's visual encoder to output probability distributions of target presence used for visual search. Second, our framework dynamically refines CLIP's predictions during search using a test-time adaptation mechanism. Through a feedback loop inspired by Spatial Poisson Point Processes, gradient updates (weighted by uncertainty) are used to correct (potentially inaccurate) predictions and improve search performance. To validate Search-TTA's performance, we curate a visual search dataset based on internet-scale ecological data. We find that Search-TTA improves planner performance by up to 9.7%, particularly in cases with poor initial CLIP predictions. It also achieves comparable performance to state-of-the-art VLMs. Finally, we deploy Search-TTA on a real UAV via hardware-in-the-loop testing, by simulating its operation within a large-scale simulation that provides onboard sensing.

Search-TTA: A Multimodal Test-Time Adaptation Framework for Visual Search in the Wild

In the paper "Search-TTA: A Multimodal Test-Time Adaptation Framework for Visual Search in the Wild," the authors introduce a critical advancement in the application of autonomous visual search (AVS) techniques by leveraging multimodal inputs via a test-time adaptation framework that improves search accuracy and efficiency in dynamic environments, such as ecological monitoring with UAVs. This framework, named Search-TTA, addresses inherent challenges found in AVS tasks which predominantly involve outdoor domains where targets cannot always be directly identified from coarse satellite imagery.

Key Contributions

The primary innovation of this work is the development of Search-TTA, which integrates text and image inputs through a multimodal approach to refine vision-LLM (VLM) predictions during real-time operations. Specifically, this is achieved by interfacing CLIP-based satellite image encoders that are dynamically adjusted using test-time adaptation mechanisms inspired by Spatial Poisson Point Processes. Key contributions highlighted include:

1. Alignment of Representations: The paper outlines the alignment of satellite imagery encoders with the vision encoders present in CLIP through patch-level contrastive learning. This facilitates the generation of score maps which can effectively translate cross-modality inputs into navigational cues for AVS agents.
2. Novel Feedback Loop: The introduction of a feedback loop allows for the updating and refinement of initial probability distributions, thereby eliminating inaccuracies that arise from static predictions in VLMs. The uncertainty-driven weighting scheme further stabilizes gradient updates, especially in the early stages of search.
3. Large-scale Dataset for Ecological Monitoring: The authors curated a substantial satellite image dataset tagged with coordinates of multiple unseen taxonomic targets, enabling the validation of the framework and ensuring broad applicability across various ecological taxa.

Numerical Results and Comparisons

The authors' empirical evaluation on both simulated and real-world UAV platforms demonstrated a substantial improvement in AVS planner performance by up to 9.7%, particularly in environments where initial predictions by CLIP were poor. When tested against state-of-the-art VLMs and existing AVS frameworks, the Search-TTA exhibited competitive performance, underscoring its robustness in handling diverse input modalities.

Key numerical outcomes include:

• Enhanced planner performance across different search strategies, such as reinforcement learning and information surfing, with consistent gains reflected in both bottom percentile target discovery and reduced RMSE of prediction maps.
• Demonstrated zero-shot generalization capabilities, where text-based queries can also refine VLM predictions without requiring additional encoder fine-tuning.

Implications and Future Directions

The implications of Search-TTA are notable both in practical AVS application scenarios and theoretical advances in adaptive AI systems. Practically, this framework opens up possibilities for more efficient environmental monitoring, disaster response, and resource exploration where real-time adaptability to unseen inputs and conditions is crucial. Theoretically, this work paves the way for future research into integrating multimodal inputs in large VLMs with real-time test-time adaptation, addressing challenges such as catastrophic forgetting and exploring additional sensory modalities.

Future advancements may involve scaling the framework within larger VLM architectures, further enhancing its real-time adaptive capabilities, and exploring its application to multi-target search processes. Moreover, incorporating diverse input modalities such as sound or thermal imagery could further augment search effectiveness in complex and occluded environments.

In conclusion, Search-TTA represents a significant step forward in autonomous search methodologies by seamlessly combining multimodal learning, adaptive feedback mechanisms, and robust dataset utilization to enhance the accuracy and efficiency of visual search operations in the wild.