6 accepted papers
While Large Vision-Language Models (LVLMs) achieves remarkable success, hallucinations remain a significant barrier to their reliable deployment. Recent studies primarily attribute these defects to cross-modal attention imbalances, with most solutions focusing on re-weighting visual tokens or suppre…
Missing modalities present a fundamental challenge in multimodal models, often causing catastrophic performance degradation. Our observations suggest that this fragility stems from an imbalanced learning process, where the model develops an implicit preference for certain modalities, leading to the…
To identify objects beyond predefined categories, open-vocabulary aerial object detection (OVAD) leverages the zero-shot capabilities of visual-language models (VLMs) to generalize from base to novel categories. Existing approaches typically utilize self-learning mechanisms with weak text supervisio
The mainstream approach for correcting distortions in wide-angle images typically involves a cascading process of rectification followed by rectangling. These tasks address distorted image content and irregular boundaries separately, using two distinct pipelines. However, this independent optimizati…
Generative models are gaining momentum in both academic and industrial applications driven by the availability of large-scale datasets, especially in tasks involving Image-to-Image Translation. Meanwhile, poor human perception of nighttime environment has led to a demand for translation from night-v…
Obtaining three-dimensional information, especially the z-axis depth information, is crucial for robotic micromanipulation. Due to the unavailability of depth sensors such as lidars in micromanipulation setups, traditional depth acquisition methods such as depth from focus or depth from defocus dire…