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Found 6 papers, 4 papers with code
T-Rex2: Towards Generic Object Detection via Text-Visual Prompt Synergy
Recognizing the complementary strengths and weaknesses of both text and visual prompts, we introduce T-Rex2 that synergizes both prompts within a single model through contrastive learning.
Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks
We introduce Grounded SAM, which uses Grounding DINO as an open-set object detector to combine with the segment anything model (SAM).
T-Rex: Counting by Visual Prompting
Guided by the visual feedback from T-Rex, users can also interactively refine the counting results by prompting on missing or falsely-detected objects.
Visual In-Context Prompting
In-context prompting in large language models (LLMs) has become a prevalent approach to improve zero-shot capabilities, but this idea is less explored in the vision domain.
Revisiting Scene Text Recognition: A Data Perspective
To this end, we consolidate a large-scale real STR dataset, namely Union14M, which comprises 4 million labeled images and 10 million unlabeled images, to assess the performance of STR models in more complex real-world scenarios.
Testing the martingale difference hypothesis in high dimension
In this paper, we consider testing the martingale difference hypothesis for high-dimensional time series.
