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Found 5 papers, 5 papers with code
Groma: Localized Visual Tokenization for Grounding Multimodal Large Language Models
We introduce Groma, a Multimodal Large Language Model (MLLM) with grounded and fine-grained visual perception ability.
Recognize Any Regions
Understanding the semantics of individual regions or patches within unconstrained images, such as in open-world object detection, represents a critical yet challenging task in computer vision.
CoDet: Co-Occurrence Guided Region-Word Alignment for Open-Vocabulary Object Detection
CoDet then leverages visual similarities to discover the co-occurring objects and align them with the shared concept.
Ranked #2 on
Open Vocabulary Object Detection
on LVIS v1.0
(using extra training data)
EGC: Image Generation and Classification via a Diffusion Energy-Based Model
Learning image classification and image generation using the same set of network parameters is a challenging problem.
Rethinking Resolution in the Context of Efficient Video Recognition
Our key finding is that the major cause of degradation is not information loss in the down-sampling process, but rather the mismatch between network architecture and input scale.
