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Found 6 papers, 5 papers with code
Aligning Modalities in Vision Large Language Models via Preference Fine-tuning
This procedure is not perfect and can cause the model to hallucinate - provide answers that do not accurately reflect the image, even when the core LLM is highly factual and the vision backbone has sufficiently complete representations.
How Many Unicorns Are in This Image? A Safety Evaluation Benchmark for Vision LLMs
Different from prior studies, we shift our focus from evaluating standard performance to introducing a comprehensive safety evaluation suite, covering both out-of-distribution (OOD) generalization and adversarial robustness.
Holistic Analysis of Hallucination in GPT-4V(ision): Bias and Interference Challenges
To bridge this gap, we introduce a new benchmark, namely, the Bias and Interference Challenges in Visual Language Models (Bingo).
Analyzing and Mitigating Object Hallucination in Large Vision-Language Models
Large vision-language models (LVLMs) have shown remarkable abilities in understanding visual information with human languages.
Bright Channel Prior Attention for Multispectral Pedestrian Detection
The method uses the V-channel of the HSV image of the thermal image as an attention map to trigger the unsupervised auto-encoder for visible light images, which gradually emphasizes pedestrian features across layers.
Deep Multi-View Subspace Clustering with Anchor Graph
To significantly reduce the complexity, we construct an anchor graph with small size for each view.
