Simple Open-Vocabulary Object Detection

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GLaMM: Pixel Grounding Large Multimodal Model (2023, methodology): “For Level-1, we use Tag2Text [14] and RAM [58] for image tagging, Co-DETR [62], EVAv02 [7], OWL-ViT [33], and POMP [40] for object localization, GRiT [48] and GPT4RoI [57] for attribute generation, and MiDAS [39] for depth estimation.”
Sigmoid Loss for Language Image Pre-Training (2023, background): “Follow-up works show that contrastively pre-trained models produce good representations for ﬁne-tuning [49, 16], linear regression [23], object detection [30], semantic segmentation [31] and video tasks [53].”
Zero-Shot Robotic Manipulation with Pretrained Image-Editing Diffusion Models (2023, methodology): “We re-implement MOO by using a large language model to extract the manipulation targets from the unstructured language labels in BridgeData V2, then OWL-ViT [46] to extract 2D bounding boxes for these objects.”
A Survey on Multimodal Large Language Models for Autonomous Driving (2023, background): “Additionally, they can adapt to changing road conditions with a level of understanding that closely resembles human intuition.”
Directly Fine-Tuning Diffusion Models on Differentiable Rewards (2023, neutral): “Figure 23 shows the ﬁne-tuning progress for the diffusion prompt “a bowl of fruit” with OWL-ViT query “strawberries.””