Pensieve: Retrospect-then-Compare Mitigates Visual Hallucination

Multi-modal Large Language Models (MLLMs) demonstrate remarkable success across various vision-language tasks. However, they suffer from visual hallucination, where the generated responses diverge from the provided image. Are MLLMs completely oblivious to accurate visual cues when they hallucinate? Our investigation reveals that the visual branch may simultaneously advocate both accurate and non-existent content. To address this issue, we propose Pensieve, a training-free method inspired by our observation that analogous visual hallucinations can arise among images sharing common semantic and appearance characteristics. During inference, Pensieve enables MLLMs to retrospect relevant images as references and compare them with the test image. This paradigm assists MLLMs in downgrading hallucinatory content mistakenly supported by the visual input. Experiments on Whoops, MME, POPE, and LLaVA Bench demonstrate the efficacy of Pensieve in mitigating visual hallucination, surpassing other advanced decoding strategies. Additionally, Pensieve aids MLLMs in identifying details in the image and enhancing the specificity of image descriptions.

3D Data Augmentation for Driving Scenes on Camera

Driving scenes are extremely diverse and complicated that it is impossible to collect all cases with human effort alone. While data augmentation is an effective technique to enrich the training data, existing methods for camera data in autonomous driving applications are confined to the 2D image plane, which may not optimally increase data diversity in 3D real-world scenarios. To this end, we propose a 3D data augmentation approach termed Drive-3DAug, aiming at augmenting the driving scenes on camera in the 3D space. We first utilize Neural Radiance Field (NeRF) to reconstruct the 3D models of background and foreground objects. Then, augmented driving scenes can be obtained by placing the 3D objects with adapted location and orientation at the pre-defined valid region of backgrounds. As such, the training database could be effectively scaled up. However, the 3D object modeling is constrained to the image quality and the limited viewpoints. To overcome these problems, we modify the original NeRF by introducing a geometric rectified loss and a symmetric-aware training strategy. We evaluate our method for the camera-only monocular 3D detection task on the Waymo and nuScences datasets. The proposed data augmentation approach contributes to a gain of 1.7% and 1.4% in terms of detection accuracy, on Waymo and nuScences respectively. Furthermore, the constructed 3D models serve as digital driving assets and could be recycled for different detectors or other 3D perception tasks.