741 research outputs found
POVD: Fine-grained Visual-Text Prompt-Driven Self-Training for Open-Vocabulary Object Detection
Inspired by the success of visual-language methods (VLMs) in zero-shot
classification, recent works attempt to extend this line of work into object
detection by leveraging the localization ability of pre-trained VLMs and
generating pseudo labels for unseen classes in a self-training manner. However,
since the current VLMs are usually pre-trained with aligning sentence embedding
with global image embedding, the direct use of them lacks fine-grained
alignment for object instances, which is the core of detection. In this paper,
we propose a simple but effective Pretrain-adaPt-Pseudo labeling paradigm for
Open-Vocabulary Detection (POVD) that introduces a fine-grained visual-text
prompt adapting stage to enhance the current self-training paradigm with a more
powerful fine-grained alignment. During the adapting stage, we enable VLM to
obtain fine-grained alignment by using learnable text prompts to resolve an
auxiliary dense pixel-wise prediction task. Furthermore, we propose a visual
prompt module to provide the prior task information (i.e., the categories need
to be predicted) for the vision branch to better adapt the pretrained VLM to
the downstream tasks. Experiments show that our method achieves the
state-of-the-art performance for open-vocabulary object detection, e.g., 31.5%
mAP on unseen classes of COCO
Incorporating Neuro-Inspired Adaptability for Continual Learning in Artificial Intelligence
Continual learning aims to empower artificial intelligence (AI) with strong
adaptability to the real world. For this purpose, a desirable solution should
properly balance memory stability with learning plasticity, and acquire
sufficient compatibility to capture the observed distributions. Existing
advances mainly focus on preserving memory stability to overcome catastrophic
forgetting, but remain difficult to flexibly accommodate incremental changes as
biological intelligence (BI) does. By modeling a robust Drosophila learning
system that actively regulates forgetting with multiple learning modules, here
we propose a generic approach that appropriately attenuates old memories in
parameter distributions to improve learning plasticity, and accordingly
coordinates a multi-learner architecture to ensure solution compatibility.
Through extensive theoretical and empirical validation, our approach not only
clearly enhances the performance of continual learning, especially over
synaptic regularization methods in task-incremental settings, but also
potentially advances the understanding of neurological adaptive mechanisms,
serving as a novel paradigm to progress AI and BI together
Charge qubit dynamics in a double quantum dot coupled to phonons
The dynamics of charge qubit in a double quantum dot coupled to phonons is
investigated theoretically in terms of a perturbation treatment based on a
unitary transformation. The dynamical tunneling current is obtained explicitly.
The result is compared with the standard perturbation theory at Born-Markov
approximation. The decoherence induced by acoustic phonons is analyzed at
length. It is shown that the contribution from deformation potential coupling
is comparable to that from piezoelectric coupling in small dot size and large
tunneling rate case. A possible decoupling mechanism is predicted.Comment: 8 pages, 6 figure
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