143 research outputs found
Unsupervised Domain Adaptation on Reading Comprehension
Reading comprehension (RC) has been studied in a variety of datasets with the
boosted performance brought by deep neural networks. However, the
generalization capability of these models across different domains remains
unclear. To alleviate this issue, we are going to investigate unsupervised
domain adaptation on RC, wherein a model is trained on labeled source domain
and to be applied to the target domain with only unlabeled samples. We first
show that even with the powerful BERT contextual representation, the
performance is still unsatisfactory when the model trained on one dataset is
directly applied to another target dataset. To solve this, we provide a novel
conditional adversarial self-training method (CASe). Specifically, our approach
leverages a BERT model fine-tuned on the source dataset along with the
confidence filtering to generate reliable pseudo-labeled samples in the target
domain for self-training. On the other hand, it further reduces domain
distribution discrepancy through conditional adversarial learning across
domains. Extensive experiments show our approach achieves comparable accuracy
to supervised models on multiple large-scale benchmark datasets.Comment: 8 pages, 6 figures, 5 tables, Accepted by AAAI 202
Collect-and-Distribute Transformer for 3D Point Cloud Analysis
Although remarkable advancements have been made recently in point cloud
analysis through the exploration of transformer architecture, it remains
challenging to effectively learn local and global structures within point
clouds. In this paper, we propose a new transformer architecture equipped with
a collect-and-distribute mechanism to communicate short- and long-range
contexts of point clouds, which we refer to as CDFormer. Specifically, we first
utilize self-attention to capture short-range interactions within each local
patch, and the updated local features are then collected into a set of proxy
reference points from which we can extract long-range contexts. Afterward, we
distribute the learned long-range contexts back to local points via
cross-attention. To address the position clues for short- and long-range
contexts, we also introduce context-aware position encoding to facilitate
position-aware communications between points. We perform experiments on four
popular point cloud datasets, namely ModelNet40, ScanObjectNN, S3DIS, and
ShapeNetPart, for classification and segmentation. Results show the
effectiveness of the proposed CDFormer, delivering several new state-of-the-art
performances on point cloud classification and segmentation tasks. The code is
available at \url{https://github.com/haibo-qiu/CDFormer}.Comment: Code is available at https://github.com/haibo-qiu/CDForme
Discovering Human-Object Interaction Concepts via Self-Compositional Learning
A comprehensive understanding of human-object interaction (HOI) requires
detecting not only a small portion of predefined HOI concepts (or categories)
but also other reasonable HOI concepts, while current approaches usually fail
to explore a huge portion of unknown HOI concepts (i.e., unknown but reasonable
combinations of verbs and objects). In this paper, 1) we introduce a novel and
challenging task for a comprehensive HOI understanding, which is termed as HOI
Concept Discovery; and 2) we devise a self-compositional learning framework (or
SCL) for HOI concept discovery. Specifically, we maintain an online updated
concept confidence matrix during training: 1) we assign pseudo-labels for all
composite HOI instances according to the concept confidence matrix for
self-training; and 2) we update the concept confidence matrix using the
predictions of all composite HOI instances. Therefore, the proposed method
enables the learning on both known and unknown HOI concepts. We perform
extensive experiments on several popular HOI datasets to demonstrate the
effectiveness of the proposed method for HOI concept discovery, object
affordance recognition and HOI detection. For example, the proposed
self-compositional learning framework significantly improves the performance of
1) HOI concept discovery by over 10% on HICO-DET and over 3% on V-COCO,
respectively; 2) object affordance recognition by over 9% mAP on MS-COCO and
HICO-DET; and 3) rare-first and non-rare-first unknown HOI detection relatively
over 30% and 20%, respectively. Code and models will be made publicly available
at https://github.com/zhihou7/HOI-CL.Comment: Technical Repor
Responsible Active Learning via Human-in-the-loop Peer Study
Active learning has been proposed to reduce data annotation efforts by only
manually labelling representative data samples for training. Meanwhile, recent
active learning applications have benefited a lot from cloud computing services
with not only sufficient computational resources but also crowdsourcing
frameworks that include many humans in the active learning loop. However,
previous active learning methods that always require passing large-scale
unlabelled data to cloud may potentially raise significant data privacy issues.
To mitigate such a risk, we propose a responsible active learning method,
namely Peer Study Learning (PSL), to simultaneously preserve data privacy and
improve model stability. Specifically, we first introduce a human-in-the-loop
teacher-student architecture to isolate unlabelled data from the task learner
(teacher) on the cloud-side by maintaining an active learner (student) on the
client-side. During training, the task learner instructs the light-weight
active learner which then provides feedback on the active sampling criterion.
To further enhance the active learner via large-scale unlabelled data, we
introduce multiple peer students into the active learner which is trained by a
novel learning paradigm, including the In-Class Peer Study on labelled data and
the Out-of-Class Peer Study on unlabelled data. Lastly, we devise a
discrepancy-based active sampling criterion, Peer Study Feedback, that exploits
the variability of peer students to select the most informative data to improve
model stability. Extensive experiments demonstrate the superiority of the
proposed PSL over a wide range of active learning methods in both standard and
sensitive protection settings.Comment: 15 pages, 8 figure
Deep Dictionary Learning with An Intra-class Constraint
In recent years, deep dictionary learning (DDL)has attracted a great amount
of attention due to its effectiveness for representation learning and visual
recognition.~However, most existing methods focus on unsupervised deep
dictionary learning, failing to further explore the category information.~To
make full use of the category information of different samples, we propose a
novel deep dictionary learning model with an intra-class constraint (DDLIC) for
visual classification. Specifically, we design the intra-class compactness
constraint on the intermediate representation at different levels to encourage
the intra-class representations to be closer to each other, and eventually the
learned representation becomes more discriminative.~Unlike the traditional DDL
methods, during the classification stage, our DDLIC performs a layer-wise
greedy optimization in a similar way to the training stage. Experimental
results on four image datasets show that our method is superior to the
state-of-the-art methods.Comment: 6 pages, 3 figures, 2 tables. It has been accepted in ICME202
Knowledge-Aware Federated Active Learning with Non-IID Data
Federated learning enables multiple decentralized clients to learn
collaboratively without sharing the local training data. However, the expensive
annotation cost to acquire data labels on local clients remains an obstacle in
utilizing local data. In this paper, we propose a federated active learning
paradigm to efficiently learn a global model with limited annotation budget
while protecting data privacy in a decentralized learning way. The main
challenge faced by federated active learning is the mismatch between the active
sampling goal of the global model on the server and that of the asynchronous
local clients. This becomes even more significant when data is distributed
non-IID across local clients. To address the aforementioned challenge, we
propose Knowledge-Aware Federated Active Learning (KAFAL), which consists of
Knowledge-Specialized Active Sampling (KSAS) and Knowledge-Compensatory
Federated Update (KCFU). KSAS is a novel active sampling method tailored for
the federated active learning problem. It deals with the mismatch challenge by
sampling actively based on the discrepancies between local and global models.
KSAS intensifies specialized knowledge in local clients, ensuring the sampled
data to be informative for both the local clients and the global model. KCFU,
in the meantime, deals with the client heterogeneity caused by limited data and
non-IID data distributions. It compensates for each client's ability in weak
classes by the assistance of the global model. Extensive experiments and
analyses are conducted to show the superiority of KSAS over the
state-of-the-art active learning methods and the efficiency of KCFU under the
federated active learning framework.Comment: 14 pages, 12 figure
Patch-Wise Point Cloud Generation: A Divide-and-Conquer Approach
A generative model for high-fidelity point clouds is of great importance in
synthesizing 3d environments for applications such as autonomous driving and
robotics. Despite the recent success of deep generative models for 2d images,
it is non-trivial to generate 3d point clouds without a comprehensive
understanding of both local and global geometric structures. In this paper, we
devise a new 3d point cloud generation framework using a divide-and-conquer
approach, where the whole generation process can be divided into a set of
patch-wise generation tasks. Specifically, all patch generators are based on
learnable priors, which aim to capture the information of geometry primitives.
We introduce point- and patch-wise transformers to enable the interactions
between points and patches. Therefore, the proposed divide-and-conquer approach
contributes to a new understanding of point cloud generation from the geometry
constitution of 3d shapes. Experimental results on a variety of object
categories from the most popular point cloud dataset, ShapeNet, show the
effectiveness of the proposed patch-wise point cloud generation, where it
clearly outperforms recent state-of-the-art methods for high-fidelity point
cloud generation
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