Generalization and Hallucination of Large Vision-Language Models through
  a Camouflaged Lens
Lv Tang Peng-Tao Jiang Zhihao Shen Hao Zhang Jinwei Chen Bo Li
Large Vision-Language Model (LVLM) has seen burgeoning development and
increasing attention recently. In this paper, we propose a novel framework,
camo-perceptive vision-language framework (CPVLF), to explore whether LVLM can
generalize to the challenging camouflaged object detection (COD) scenario in a
training-free manner. During the process of generalization, we find that due to
hallucination issues within LVLM, it can erroneously perceive objects in
camouflaged scenes, producing counterfactual concepts. Moreover, as LVLM is not
specifically trained for the precise localization of camouflaged objects, it
exhibits a degree of uncertainty in accurately pinpointing these objects.
Therefore, we propose chain of visual perception, which enhances LVLM's
perception of camouflaged scenes from both linguistic and visual perspectives,
reducing the hallucination issue and improving its capability in accurately
locating camouflaged objects. We validate the effectiveness of CPVLF on three
widely used COD datasets, and the experiments show the potential of LVLM in the
COD task.