254 research outputs found
Measurement of Mercury in Flue Gas Based on an Aluminum Matrix Sorbent
The measurement of total mercury in flue gas based on an economical aluminum matrix sorbent was developed in this paper. A sorbent trap consisted of three tubes was employed to capture Hg from flue gas. Hg trapped on sorbent was transferred into solution by acid leaching and then detected by CVAAS. Hg adsorbed on sorbent was recovered completely by leaching process. The 87.7% recovery of Hg in flue gas by tube 1 and tube 2 was obtained on the equipment of coal combustion and sampling in lab. In order to evaluate the ability to recover and accurately quantify Hg0 on the sorbent media, the analytical bias test on tube 3 spiked with Hg0 was also performed and got the average recovery of 97.1%. Mercury measurements based on this method were conducted for three coal-fired power plants in China. The mercury in coal is distributed into bottom ash, electrostatic precipitator (ESP) ash, wet flue gas desulfurization (WFGD) reactant, and flue gas, and the relative distribution varied depending on factors such as the coal type and the operation conditions of plants. The mercury mass balances of three plants were also calculated which were 91.6%, 77.1%, and 118%, respectively. The reliability of this method was verified by the Ontario Hydro (OH) method either in lab or in field
Online Video Instance Segmentation via Robust Context Fusion
Video instance segmentation (VIS) aims at classifying, segmenting and
tracking object instances in video sequences. Recent transformer-based neural
networks have demonstrated their powerful capability of modeling
spatio-temporal correlations for the VIS task. Relying on video- or clip-level
input, they suffer from high latency and computational cost. We propose a
robust context fusion network to tackle VIS in an online fashion, which
predicts instance segmentation frame-by-frame with a few preceding frames. To
acquire the precise and temporal-consistent prediction for each frame
efficiently, the key idea is to fuse effective and compact context from
reference frames into the target frame. Considering the different effects of
reference and target frames on the target prediction, we first summarize
contextual features through importance-aware compression. A transformer encoder
is adopted to fuse the compressed context. Then, we leverage an
order-preserving instance embedding to convey the identity-aware information
and correspond the identities to predicted instance masks. We demonstrate that
our robust fusion network achieves the best performance among existing online
VIS methods and is even better than previously published clip-level methods on
the Youtube-VIS 2019 and 2021 benchmarks. In addition, visual objects often
have acoustic signatures that are naturally synchronized with them in
audio-bearing video recordings. By leveraging the flexibility of our context
fusion network on multi-modal data, we further investigate the influence of
audios on the video-dense prediction task, which has never been discussed in
existing works. We build up an Audio-Visual Instance Segmentation dataset, and
demonstrate that acoustic signals in the wild scenarios could benefit the VIS
task
HR-Pro: Point-supervised Temporal Action Localization via Hierarchical Reliability Propagation
Point-supervised Temporal Action Localization (PSTAL) is an emerging research
direction for label-efficient learning. However, current methods mainly focus
on optimizing the network either at the snippet-level or the instance-level,
neglecting the inherent reliability of point annotations at both levels. In
this paper, we propose a Hierarchical Reliability Propagation (HR-Pro)
framework, which consists of two reliability-aware stages: Snippet-level
Discrimination Learning and Instance-level Completeness Learning, both stages
explore the efficient propagation of high-confidence cues in point annotations.
For snippet-level learning, we introduce an online-updated memory to store
reliable snippet prototypes for each class. We then employ a Reliability-aware
Attention Block to capture both intra-video and inter-video dependencies of
snippets, resulting in more discriminative and robust snippet representation.
For instance-level learning, we propose a point-based proposal generation
approach as a means of connecting snippets and instances, which produces
high-confidence proposals for further optimization at the instance level.
Through multi-level reliability-aware learning, we obtain more reliable
confidence scores and more accurate temporal boundaries of predicted proposals.
Our HR-Pro achieves state-of-the-art performance on multiple challenging
benchmarks, including an impressive average mAP of 60.3% on THUMOS14. Notably,
our HR-Pro largely surpasses all previous point-supervised methods, and even
outperforms several competitive fully supervised methods. Code will be
available at https://github.com/pipixin321/HR-Pro.Comment: 12 pages, 8 figure
Towards Robust Referring Video Object Segmentation with Cyclic Relational Consensus
Referring Video Object Segmentation (R-VOS) is a challenging task that aims
to segment an object in a video based on a linguistic expression. Most existing
R-VOS methods have a critical assumption: the object referred to must appear in
the video. This assumption, which we refer to as semantic consensus, is often
violated in real-world scenarios, where the expression may be queried against
false videos. In this work, we highlight the need for a robust R-VOS model that
can handle semantic mismatches. Accordingly, we propose an extended task called
Robust R-VOS, which accepts unpaired video-text inputs. We tackle this problem
by jointly modeling the primary R-VOS problem and its dual (text
reconstruction). A structural text-to-text cycle constraint is introduced to
discriminate semantic consensus between video-text pairs and impose it in
positive pairs, thereby achieving multi-modal alignment from both positive and
negative pairs. Our structural constraint effectively addresses the challenge
posed by linguistic diversity, overcoming the limitations of previous methods
that relied on the point-wise constraint. A new evaluation dataset,
R\textsuperscript{2}-Youtube-VOSis constructed to measure the model robustness.
Our model achieves state-of-the-art performance on R-VOS benchmarks,
Ref-DAVIS17 and Ref-Youtube-VOS, and also our
R\textsuperscript{2}-Youtube-VOS~dataset.Comment: iccv 2023, https://github.com/lxa9867/R2VO
Leveraging Multimodal Features and Item-level User Feedback for Bundle Construction
Automatic bundle construction is a crucial prerequisite step in various
bundle-aware online services. Previous approaches are mostly designed to model
the bundling strategy of existing bundles. However, it is hard to acquire
large-scale well-curated bundle dataset, especially for those platforms that
have not offered bundle services before. Even for platforms with mature bundle
services, there are still many items that are included in few or even zero
bundles, which give rise to sparsity and cold-start challenges in the bundle
construction models. To tackle these issues, we target at leveraging multimodal
features, item-level user feedback signals, and the bundle composition
information, to achieve a comprehensive formulation of bundle construction.
Nevertheless, such formulation poses two new technical challenges: 1) how to
learn effective representations by optimally unifying multiple features, and 2)
how to address the problems of modality missing, noise, and sparsity problems
induced by the incomplete query bundles. In this work, to address these
technical challenges, we propose a Contrastive Learning-enhanced Hierarchical
Encoder method (CLHE). Specifically, we use self-attention modules to combine
the multimodal and multi-item features, and then leverage both item- and
bundle-level contrastive learning to enhance the representation learning, thus
to counter the modality missing, noise, and sparsity problems. Extensive
experiments on four datasets in two application domains demonstrate that our
method outperforms a list of SOTA methods. The code and dataset are available
at https://github.com/Xiaohao-Liu/CLHE
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