We show that classifiers trained with random region proposals achieve
state-of-the-art Open-world Object Detection (OWOD): they can not only maintain
the accuracy of the known objects (w/ training labels), but also considerably
improve the recall of unknown ones (w/o training labels). Specifically, we
propose RandBox, a Fast R-CNN based architecture trained on random proposals at
each training iteration, surpassing existing Faster R-CNN and Transformer based
OWOD. Its effectiveness stems from the following two benefits introduced by
randomness. First, as the randomization is independent of the distribution of
the limited known objects, the random proposals become the instrumental
variable that prevents the training from being confounded by the known objects.
Second, the unbiased training encourages more proposal explorations by using
our proposed matching score that does not penalize the random proposals whose
prediction scores do not match the known objects. On two benchmarks:
Pascal-VOC/MS-COCO and LVIS, RandBox significantly outperforms the previous
state-of-the-art in all metrics. We also detail the ablations on randomization
and loss designs. Codes are available at this https URL

使用随机提案训练的分类器达到了最先进的开放世界对象检测，RandBox 是一种基于快速 R-CNN 的架构，并使用我们提出的匹配分数来鼓励更多提案探索，该方法大大优于以往在所有指标上的最新技术。

随机框是开放世界目标检测器

Random Boxes Are Open-world Object Detectors

Grounding temporal video segments described in natural language queries
effectively and efficiently is a crucial capability needed in
vision-and-language fields. In this paper, we deal with the fast video temporal
grounding (FVTG) task, aiming at localizing the target segment with high speed
and favorable accuracy. Most existing approaches adopt elaborately designed
cross-modal interaction modules to improve the grounding performance, which
suffer from the test-time bottleneck. Although several common space-based
methods enjoy the high-speed merit during inference, they can hardly capture
the comprehensive and explicit relations between visual and textual modalities.
In this paper, to tackle the dilemma of speed-accuracy tradeoff, we propose a
commonsense-aware cross-modal alignment (CCA) framework, which incorporates
commonsense-guided visual and text representations into a complementary common
space for fast video temporal grounding. Specifically, the commonsense concepts
are explored and exploited by extracting the structural semantic information
from a language corpus. Then, a commonsense-aware interaction module is
designed to obtain bridged visual and text features by utilizing the learned
commonsense concepts. Finally, to maintain the original semantic information of
textual queries, a cross-modal complementary common space is optimized to
obtain matching scores for performing FVTG. Extensive results on two
challenging benchmarks show that our CCA method performs favorably against
state-of-the-arts while running at high speed. Our code is available at
this https URL.

本文提出一种基于常识感知的跨模态对齐框架，通过从语料库中提取结构化语义信息，设计跨模态交互模块获得桥接式视觉和文本特征，并将其整合到共同的空间中进行快速视频时间定位。在两个具有挑战性的数据集上，证明该方法能够以高速运行，且表现优于现有的方法。

基于常识的快速视频时间对齐细节文本对齐学习

Learning Commonsense-aware Moment-Text Alignment for Fast Video Temporal Grounding

Long text brings a big challenge to semantic matching due to their
complicated semantic and syntactic structures. To tackle the challenge, we
consider using prior knowledge to help identify useful information and filter
out noise to matching in long text. To this end, we propose a knowledge
enhanced hybrid neural network (KEHNN). The model fuses prior knowledge into
word representations by knowledge gates and establishes three matching channels
with words, sequential structures of sentences given by Gated Recurrent Units
(GRU), and knowledge enhanced representations. The three channels are processed
by a convolutional neural network to generate high level features for matching,
and the features are synthesized as a matching score by a multilayer
perceptron. The model extends the existing methods by conducting matching on
words, local structures of sentences, and global context of sentences.
Evaluation results from extensive experiments on public data sets for question
answering and conversation show that KEHNN can significantly outperform
the-state-of-the-art matching models and particularly improve the performance
on pairs with long text.

采用先验知识辅助在长文本中进行语义匹配，提出了一种知识增强混合神经网络（KEHNN），该模型通过知识门将先验知识融入单词表示中，并建立了三个匹配通道，最终实现对句子的局部和全局语境进行匹配的高检索性能。

知识增强的文本匹配混合神经网络

Knowledge Enhanced Hybrid Neural Network for Text Matching

The estimation of correspondences between two images resp. point sets is a
core problem in computer vision. One way to formulate the problem is graph
matching leading to the quadratic assignment problem which is NP-hard. Several
so called second order methods have been proposed to solve this problem. In
recent years hypergraph matching leading to a third order problem became
popular as it allows for better integration of geometric information. For most
of these third order algorithms no theoretical guarantees are known. In this
paper we propose a general framework for tensor block coordinate ascent methods
for hypergraph matching. We propose two algorithms which both come along with
the guarantee of monotonic ascent in the matching score on the set of discrete
assignment matrices. In the experiments we show that our new algorithms
outperform previous work both in terms of achieving better matching scores and
matching accuracy. This holds in particular for very challenging settings where
one has a high number of outliers and other forms of noise.

本文提出了一种新的超图匹配框架，使用张量块协同升级方法，以排除离群值和其他噪音形式，改善超图匹配的成绩和准确性。