11,684 research outputs found
Multiple Instance Curriculum Learning for Weakly Supervised Object Detection
When supervising an object detector with weakly labeled data, most existing
approaches are prone to trapping in the discriminative object parts, e.g.,
finding the face of a cat instead of the full body, due to lacking the
supervision on the extent of full objects. To address this challenge, we
incorporate object segmentation into the detector training, which guides the
model to correctly localize the full objects. We propose the multiple instance
curriculum learning (MICL) method, which injects curriculum learning (CL) into
the multiple instance learning (MIL) framework. The MICL method starts by
automatically picking the easy training examples, where the extent of the
segmentation masks agree with detection bounding boxes. The training set is
gradually expanded to include harder examples to train strong detectors that
handle complex images. The proposed MICL method with segmentation in the loop
outperforms the state-of-the-art weakly supervised object detectors by a
substantial margin on the PASCAL VOC datasets.Comment: Published in BMVC 201
Qubit-oscillator concatenated codes: decoding formalism & code comparison
Concatenating bosonic error-correcting codes with qubit codes can
substantially boost the error-correcting power of the original qubit codes. It
is not clear how to concatenate optimally, given there are several bosonic
codes and concatenation schemes to choose from, including the recently
discovered GKP-stabilizer codes [arXiv:1903.12615] that allow protection of a
logical bosonic mode from fluctuations of the mode's conjugate variables. We
develop efficient maximum-likelihood decoders for and analyze the performance
of three different concatenations of codes taken from the following set: qubit
stabilizer codes, analog/Gaussian stabilizer codes, GKP codes, and
GKP-stabilizer codes. We benchmark decoder performance against additive
Gaussian white noise, corroborating our numerics with analytical calculations.
We observe that the concatenation involving GKP-stabilizer codes outperforms
the more conventional concatenation of a qubit stabilizer code with a GKP code
in some cases. We also propose a GKP-stabilizer code that suppresses
fluctuations in both conjugate variables and that can be initialized using only
controlled-SUM and Hadamard gates, and formulate qudit versions of
GKP-stabilizer codes.Comment: 17 pages, 5 figure
MetaDreamer: Efficient Text-to-3D Creation With Disentangling Geometry and Texture
Generative models for 3D object synthesis have seen significant advancements
with the incorporation of prior knowledge distilled from 2D diffusion models.
Nevertheless, challenges persist in the form of multi-view geometric
inconsistencies and slow generation speeds within the existing 3D synthesis
frameworks. This can be attributed to two factors: firstly, the deficiency of
abundant geometric a priori knowledge in optimization, and secondly, the
entanglement issue between geometry and texture in conventional 3D generation
methods.In response, we introduce MetaDreammer, a two-stage optimization
approach that leverages rich 2D and 3D prior knowledge. In the first stage, our
emphasis is on optimizing the geometric representation to ensure multi-view
consistency and accuracy of 3D objects. In the second stage, we concentrate on
fine-tuning the geometry and optimizing the texture, thereby achieving a more
refined 3D object. Through leveraging 2D and 3D prior knowledge in two stages,
respectively, we effectively mitigate the interdependence between geometry and
texture. MetaDreamer establishes clear optimization objectives for each stage,
resulting in significant time savings in the 3D generation process. Ultimately,
MetaDreamer can generate high-quality 3D objects based on textual prompts
within 20 minutes, and to the best of our knowledge, it is the most efficient
text-to-3D generation method. Furthermore, we introduce image control into the
process, enhancing the controllability of 3D generation. Extensive empirical
evidence confirms that our method is not only highly efficient but also
achieves a quality level that is at the forefront of current state-of-the-art
3D generation techniques.Comment: arXiv admin note: text overlap with arXiv:2306.17843,
arXiv:2209.14988 by other author
Herb Target Prediction Based on Representation Learning of Symptom related Heterogeneous Network.
Traditional Chinese Medicine (TCM) has received increasing attention as a complementary approach or alternative to modern medicine. However, experimental methods for identifying novel targets of TCM herbs heavily relied on the current available herb-compound-target relationships. In this work, we present an Herb-Target Interaction Network (HTINet) approach, a novel network integration pipeline for herb-target prediction mainly relying on the symptom related associations. HTINet focuses on capturing the low-dimensional feature vectors for both herbs and proteins by network embedding, which incorporate the topological properties of nodes across multi-layered heterogeneous network, and then performs supervised learning based on these low-dimensional feature representations. HTINet obtains performance improvement over a well-established random walk based herb-target prediction method. Furthermore, we have manually validated several predicted herb-target interactions from independent literatures. These results indicate that HTINet can be used to integrate heterogeneous information to predict novel herb-target interactions
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