318 research outputs found
Towards Optimal Discrete Online Hashing with Balanced Similarity
When facing large-scale image datasets, online hashing serves as a promising
solution for online retrieval and prediction tasks. It encodes the online
streaming data into compact binary codes, and simultaneously updates the hash
functions to renew codes of the existing dataset. To this end, the existing
methods update hash functions solely based on the new data batch, without
investigating the correlation between such new data and the existing dataset.
In addition, existing works update the hash functions using a relaxation
process in its corresponding approximated continuous space. And it remains as
an open problem to directly apply discrete optimizations in online hashing. In
this paper, we propose a novel supervised online hashing method, termed
Balanced Similarity for Online Discrete Hashing (BSODH), to solve the above
problems in a unified framework. BSODH employs a well-designed hashing
algorithm to preserve the similarity between the streaming data and the
existing dataset via an asymmetric graph regularization. We further identify
the "data-imbalance" problem brought by the constructed asymmetric graph, which
restricts the application of discrete optimization in our problem. Therefore, a
novel balanced similarity is further proposed, which uses two equilibrium
factors to balance the similar and dissimilar weights and eventually enables
the usage of discrete optimizations. Extensive experiments conducted on three
widely-used benchmarks demonstrate the advantages of the proposed method over
the state-of-the-art methods.Comment: 8 pages, 11 figures, conferenc
What Goes beyond Multi-modal Fusion in One-stage Referring Expression Comprehension: An Empirical Study
Most of the existing work in one-stage referring expression comprehension
(REC) mainly focuses on multi-modal fusion and reasoning, while the influence
of other factors in this task lacks in-depth exploration. To fill this gap, we
conduct an empirical study in this paper. Concretely, we first build a very
simple REC network called SimREC, and ablate 42 candidate designs/settings,
which covers the entire process of one-stage REC from network design to model
training. Afterwards, we conduct over 100 experimental trials on three
benchmark datasets of REC. The extensive experimental results not only show the
key factors that affect REC performance in addition to multi-modal fusion,
e.g., multi-scale features and data augmentation, but also yield some findings
that run counter to conventional understanding. For example, as a vision and
language (V&L) task, REC does is less impacted by language prior. In addition,
with a proper combination of these findings, we can improve the performance of
SimREC by a large margin, e.g., +27.12% on RefCOCO+, which outperforms all
existing REC methods. But the most encouraging finding is that with much less
training overhead and parameters, SimREC can still achieve better performance
than a set of large-scale pre-trained models, e.g., UNITER and VILLA,
portraying the special role of REC in existing V&L research
Towards Language-guided Visual Recognition via Dynamic Convolutions
In this paper, we are committed to establishing an unified and end-to-end
multi-modal network via exploring the language-guided visual recognition. To
approach this target, we first propose a novel multi-modal convolution module
called Language-dependent Convolution (LaConv). Its convolution kernels are
dynamically generated based on natural language information, which can help
extract differentiated visual features for different multi-modal examples.
Based on the LaConv module, we further build the first fully language-driven
convolution network, termed as LaConvNet, which can unify the visual
recognition and multi-modal reasoning in one forward structure. To validate
LaConv and LaConvNet, we conduct extensive experiments on four benchmark
datasets of two vision-and-language tasks, i.e., visual question answering
(VQA) and referring expression comprehension (REC). The experimental results
not only shows the performance gains of LaConv compared to the existing
multi-modal modules, but also witness the merits of LaConvNet as an unified
network, including compact network, high generalization ability and excellent
performance, e.g., +4.7% on RefCOCO+
RNA-seq-based digital gene expression analysis reveals modification of host defense responses by rice stripe virus during disease symptom development in Arabidopsis
DEGs involved in protein phosphorylation at 14 dpi. (XLSX 52 kb
- …