8 accepted papers
Context-dependent (CD) tasks demand the model to have advanced visual understanding ability, such as recognizing camouflaged objects and medical lesions. Current CD methods rely heavily on pixel-level annotated training sets, neglecting issues from redundant samples and the high annotation costs. In
Real-world infrared imagery presents unique challenges for vision-language models due to the scarcity of aligned text data and domain-specific characteristics. Although existing methods have advanced the field, their reliance on synthetic infrared images generated through style transfer from visible…
Current camouflaged object detection methods predominantly follow discriminative segmentation paradigms and heavily rely on predefined categories present in the training data, limiting their generalization to unseen or emerging camouflage objects. This limitation is further compounded by the labor-i…
High-quality perception is crucial in autonomous driving and monitoring systems, where millimeter-wave radar and infrared cameras play important roles due to their robustness and reliability under harsh conditions. Both technologies can serve as low-cost supplements to optical image detection, impro…
Efficiently navigating to a specific kind of objects in an unknown environment is an important and challenging research topic in Embodied AI. Existing methods, such as end-to-end learning ones and modular ones still struggle at this task as they have poor efficiency, interpretability and/or generali
Existing paradigms of pushing the state of the art require exponentially more training data in many fields. Coreset selection seeks to mitigate this growing demand by identifying the most efficient subset of training data. In this paper, we delve into geometry-based coreset methods and preliminarily…
Integrated aerial Platforms (IAPs), comprising multiple aircrafts, are typically fully actuated and hold significant potential for aerial manipulation tasks. Differing from a multiple aerial swarm, the aircrafts within the IAP are interconnected, presenting promising opportunities for enhancing loca…
A more realistic object detection paradigm, Open-World Object Detection, has arised increasing research interests in the community recently. A qualified open-world object detector can not only identify objects of known categories, but also discover unknown objects, and incrementally learn to categor…