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The performance of density functional theory with the correlation consistent basis sets.
Density functional theory has been used in combination with the correlation consistent and polarization consistent basis sets to investigate the structures and energetics for a series of first-row closed shell and several second-row molecules of potential importance in atmospheric chemistry. The impact of basis set choice upon molecular description has been examined, and irregular convergence of molecular properties with respect to increasing basis set size for several functionals and molecules has been observed. The possible reasons and solutions for this unexpected behavior including the effect of contraction and uncontraction, of the basis set diffuse sp basis functions, basis set superposition error (BSSE) and core-valence sets also have been examined
On a question of Drinfeld on the Weil representation I: the finite field case
Let F be a finite field of odd cardinality, and let G= GL2(F). The group G
\times G \times G acts on F^2 \otimes F^2 \otimes F^2 via symplectic
similitudes, and has a natural Weil representation. Answering a question
rasised by V. Drinfeld, we decompose that representation into irreducibles. We
also decompose the analogous representation of GL2(A), where A is a cubic
algebra over F.Comment: 29 pages. Comments welcom
Patch-based 3D Natural Scene Generation from a Single Example
We target a 3D generative model for general natural scenes that are typically
unique and intricate. Lacking the necessary volumes of training data, along
with the difficulties of having ad hoc designs in presence of varying scene
characteristics, renders existing setups intractable. Inspired by classical
patch-based image models, we advocate for synthesizing 3D scenes at the patch
level, given a single example. At the core of this work lies important
algorithmic designs w.r.t the scene representation and generative patch
nearest-neighbor module, that address unique challenges arising from lifting
classical 2D patch-based framework to 3D generation. These design choices, on a
collective level, contribute to a robust, effective, and efficient model that
can generate high-quality general natural scenes with both realistic geometric
structure and visual appearance, in large quantities and varieties, as
demonstrated upon a variety of exemplar scenes.Comment: 23 pages, 26 figures, accepted by CVPR 2023. Project page:
http://weiyuli.xyz/Sin3DGen
Rethinking Person Re-identification from a Projection-on-Prototypes Perspective
Person Re-IDentification (Re-ID) as a retrieval task, has achieved tremendous
development over the past decade. Existing state-of-the-art methods follow an
analogous framework to first extract features from the input images and then
categorize them with a classifier. However, since there is no identity overlap
between training and testing sets, the classifier is often discarded during
inference. Only the extracted features are used for person retrieval via
distance metrics. In this paper, we rethink the role of the classifier in
person Re-ID, and advocate a new perspective to conceive the classifier as a
projection from image features to class prototypes. These prototypes are
exactly the learned parameters of the classifier. In this light, we describe
the identity of input images as similarities to all prototypes, which are then
utilized as more discriminative features to perform person Re-ID. We thereby
propose a new baseline ProNet, which innovatively reserves the function of the
classifier at the inference stage. To facilitate the learning of class
prototypes, both triplet loss and identity classification loss are applied to
features that undergo the projection by the classifier. An improved version of
ProNet++ is presented by further incorporating multi-granularity designs.
Experiments on four benchmarks demonstrate that our proposed ProNet is simple
yet effective, and significantly beats previous baselines. ProNet++ also
achieves competitive or even better results than transformer-based competitors
CASE: Learning Conditional Adversarial Skill Embeddings for Physics-based Characters
We present CASE, an efficient and effective framework that learns
conditional Adversarial Skill Embeddings for physics-based characters. Our
physically simulated character can learn a diverse repertoire of skills while
providing controllability in the form of direct manipulation of the skills to
be performed. CASE divides the heterogeneous skill motions into distinct
subsets containing homogeneous samples for training a low-level conditional
model to learn conditional behavior distribution. The skill-conditioned
imitation learning naturally offers explicit control over the character's
skills after training. The training course incorporates the focal skill
sampling, skeletal residual forces, and element-wise feature masking to balance
diverse skills of varying complexities, mitigate dynamics mismatch to master
agile motions and capture more general behavior characteristics, respectively.
Once trained, the conditional model can produce highly diverse and realistic
skills, outperforming state-of-the-art models, and can be repurposed in various
downstream tasks. In particular, the explicit skill control handle allows a
high-level policy or user to direct the character with desired skill
specifications, which we demonstrate is advantageous for interactive character
animation.Comment: SIGGRAPH Asia 202
Semi-automatic Data Annotation System for Multi-Target Multi-Camera Vehicle Tracking
Multi-target multi-camera tracking (MTMCT) plays an important role in
intelligent video analysis, surveillance video retrieval, and other application
scenarios. Nowadays, the deep-learning-based MTMCT has been the mainstream and
has achieved fascinating improvements regarding tracking accuracy and
efficiency. However, according to our investigation, the lacking of datasets
focusing on real-world application scenarios limits the further improvements
for current learning-based MTMCT models. Specifically, the learning-based MTMCT
models training by common datasets usually cannot achieve satisfactory results
in real-world application scenarios. Motivated by this, this paper presents a
semi-automatic data annotation system to facilitate the real-world MTMCT
dataset establishment. The proposed system first employs a deep-learning-based
single-camera trajectory generation method to automatically extract
trajectories from surveillance videos. Subsequently, the system provides a
recommendation list in the following manual cross-camera trajectory matching
process. The recommendation list is generated based on side information,
including camera location, timestamp relation, and background scene. In the
experimental stage, extensive results further demonstrate the efficiency of the
proposed system.Comment: 9 pages, 10 figure
Exploring Fine-Grained Representation and Recomposition for Cloth-Changing Person Re-Identification
Cloth-changing person Re-IDentification (Re-ID) is a particularly challenging
task, suffering from two limitations of inferior identity-relevant features and
limited training samples. Existing methods mainly leverage auxiliary
information to facilitate discriminative feature learning, including
soft-biometrics features of shapes and gaits, and additional labels of
clothing. However, these information may be unavailable in real-world
applications. In this paper, we propose a novel FIne-grained Representation and
Recomposition (FIRe) framework to tackle both limitations without any
auxiliary information. Specifically, we first design a Fine-grained Feature
Mining (FFM) module to separately cluster images of each person. Images with
similar so-called fine-grained attributes (e.g., clothes and viewpoints) are
encouraged to cluster together. An attribute-aware classification loss is
introduced to perform fine-grained learning based on cluster labels, which are
not shared among different people, promoting the model to learn
identity-relevant features. Furthermore, by taking full advantage of the
clustered fine-grained attributes, we present a Fine-grained Attribute
Recomposition (FAR) module to recompose image features with different
attributes in the latent space. It can significantly enhance representations
for robust feature learning. Extensive experiments demonstrate that FIRe
can achieve state-of-the-art performance on five widely-used cloth-changing
person Re-ID benchmarks
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