95 research outputs found
Research on the Construction of Ecological Teaching Mode of Oral English for Professional Degree Postgraduates
The construction of postgraduate oral English classroom has always been the focus and difficulty of postgraduate teaching. For a long time, the postgraduate oral English teaching has followed the college English general oral teaching curriculum system, which is out of touch with the needs of the society and postgraduates for English. There is no curriculum system for the characteristics and training objectives of postgraduates, and it cannot meet the requirements of cultivating postgraduates’ diversified oral communication skills. This paper studies and explores the ecological teaching mode of oral English for engineering masters from the perspective of educational ecological theory, and focuses on the importance of the concept of “supply” in the ecological teaching of graduate students’ oral English. Improve the level of oral English to meet the requirements of postgraduate academic and daily oral communication
Attributes-Guided and Pure-Visual Attention Alignment for Few-Shot Recognition
The purpose of few-shot recognition is to recognize novel categories with a
limited number of labeled examples in each class. To encourage learning from a
supplementary view, recent approaches have introduced auxiliary semantic
modalities into effective metric-learning frameworks that aim to learn a
feature similarity between training samples (support set) and test samples
(query set). However, these approaches only augment the representations of
samples with available semantics while ignoring the query set, which loses the
potential for the improvement and may lead to a shift between the modalities
combination and the pure-visual representation. In this paper, we devise an
attributes-guided attention module (AGAM) to utilize human-annotated attributes
and learn more discriminative features. This plug-and-play module enables
visual contents and corresponding attributes to collectively focus on important
channels and regions for the support set. And the feature selection is also
achieved for query set with only visual information while the attributes are
not available. Therefore, representations from both sets are improved in a
fine-grained manner. Moreover, an attention alignment mechanism is proposed to
distill knowledge from the guidance of attributes to the pure-visual branch for
samples without attributes. Extensive experiments and analysis show that our
proposed module can significantly improve simple metric-based approaches to
achieve state-of-the-art performance on different datasets and settings.Comment: An expanded version of the same-name paper accepted by AAAI-202
Design from Policies: Conservative Test-Time Adaptation for Offline Policy Optimization
In this work, we decouple the iterative bi-level offline RL (value estimation
and policy extraction) from the offline training phase, forming a non-iterative
bi-level paradigm and avoiding the iterative error propagation over two levels.
Specifically, this non-iterative paradigm allows us to conduct inner-level
optimization (value estimation) in training, while performing outer-level
optimization (policy extraction) in testing. Naturally, such a paradigm raises
three core questions that are not fully answered by prior non-iterative offline
RL counterparts like reward-conditioned policy: (q1) What information should we
transfer from the inner-level to the outer-level? (q2) What should we pay
attention to when exploiting the transferred information for safe/confident
outer-level optimization? (q3) What are the benefits of concurrently conducting
outer-level optimization during testing? Motivated by model-based optimization
(MBO), we propose DROP (design from policies), which fully answers the above
questions. Specifically, in the inner-level, DROP decomposes offline data into
multiple subsets, and learns an MBO score model (a1). To keep safe exploitation
to the score model in the outer-level, we explicitly learn a behavior embedding
and introduce a conservative regularization (a2). During testing, we show that
DROP permits deployment adaptation, enabling an adaptive inference across
states (a3). Empirically, we evaluate DROP on various tasks, showing that DROP
gains comparable or better performance compared to prior methods.Comment: NeurIPS 202
Beyond Reward: Offline Preference-guided Policy Optimization
This study focuses on the topic of offline preference-based reinforcement
learning (PbRL), a variant of conventional reinforcement learning that
dispenses with the need for online interaction or specification of reward
functions. Instead, the agent is provided with fixed offline trajectories and
human preferences between pairs of trajectories to extract the dynamics and
task information, respectively. Since the dynamics and task information are
orthogonal, a naive approach would involve using preference-based reward
learning followed by an off-the-shelf offline RL algorithm. However, this
requires the separate learning of a scalar reward function, which is assumed to
be an information bottleneck of the learning process. To address this issue, we
propose the offline preference-guided policy optimization (OPPO) paradigm,
which models offline trajectories and preferences in a one-step process,
eliminating the need for separately learning a reward function. OPPO achieves
this by introducing an offline hindsight information matching objective for
optimizing a contextual policy and a preference modeling objective for finding
the optimal context. OPPO further integrates a well-performing decision policy
by optimizing the two objectives iteratively. Our empirical results demonstrate
that OPPO effectively models offline preferences and outperforms prior
competing baselines, including offline RL algorithms performed over either true
or pseudo reward function specifications. Our code is available on the project
website: https://sites.google.com/view/oppo-icml-2023
VGDiffZero: Text-to-image Diffusion Models Can Be Zero-shot Visual Grounders
Large-scale text-to-image diffusion models have shown impressive capabilities
across various generative tasks, enabled by strong vision-language alignment
obtained through pre-training. However, most vision-language discriminative
tasks require extensive fine-tuning on carefully-labeled datasets to acquire
such alignment, with great cost in time and computing resources. In this work,
we explore directly applying a pre-trained generative diffusion model to the
challenging discriminative task of visual grounding without any fine-tuning and
additional training dataset. Specifically, we propose VGDiffZero, a simple yet
effective zero-shot visual grounding framework based on text-to-image diffusion
models. We also design a comprehensive region-scoring method considering both
global and local contexts of each isolated proposal. Extensive experiments on
RefCOCO, RefCOCO+, and RefCOCOg show that VGDiffZero achieves strong
performance on zero-shot visual grounding
CEIL: Generalized Contextual Imitation Learning
In this paper, we present \textbf{C}ont\textbf{E}xtual \textbf{I}mitation
\textbf{L}earning~(CEIL), a general and broadly applicable algorithm for
imitation learning (IL). Inspired by the formulation of hindsight information
matching, we derive CEIL by explicitly learning a hindsight embedding function
together with a contextual policy using the hindsight embeddings. To achieve
the expert matching objective for IL, we advocate for optimizing a contextual
variable such that it biases the contextual policy towards mimicking expert
behaviors. Beyond the typical learning from demonstrations (LfD) setting, CEIL
is a generalist that can be effectively applied to multiple settings including:
1)~learning from observations (LfO), 2)~offline IL, 3)~cross-domain IL
(mismatched experts), and 4) one-shot IL settings. Empirically, we evaluate
CEIL on the popular MuJoCo tasks (online) and the D4RL dataset (offline).
Compared to prior state-of-the-art baselines, we show that CEIL is more
sample-efficient in most online IL tasks and achieves better or competitive
performances in offline tasks.Comment: NeurIPS 202
Self-supervised Guided Hypergraph Feature Propagation for Semi-supervised Classification with Missing Node Features
Graph neural networks (GNNs) with missing node features have recently
received increasing interest. Such missing node features seriously hurt the
performance of the existing GNNs. Some recent methods have been proposed to
reconstruct the missing node features by the information propagation among
nodes with known and unknown attributes. Although these methods have achieved
superior performance, how to exactly exploit the complex data correlations
among nodes to reconstruct missing node features is still a great challenge. To
solve the above problem, we propose a self-supervised guided hypergraph feature
propagation (SGHFP). Specifically, the feature hypergraph is first generated
according to the node features with missing information. And then, the
reconstructed node features produced by the previous iteration are fed to a
two-layer GNNs to construct a pseudo-label hypergraph. Before each iteration,
the constructed feature hypergraph and pseudo-label hypergraph are fused
effectively, which can better preserve the higher-order data correlations among
nodes. After then, we apply the fused hypergraph to the feature propagation for
reconstructing missing features. Finally, the reconstructed node features by
multi-iteration optimization are applied to the downstream semi-supervised
classification task. Extensive experiments demonstrate that the proposed SGHFP
outperforms the existing semi-supervised classification with missing node
feature methods.Comment: Accepted by 48th IEEE International Conference on Acoustics, Speech,
and Signal Processing (ICASSP 2023
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