366 research outputs found
Intelligent visual media processing: when graphics meets vision
The computer graphics and computer vision communities have been working closely together in recent
years, and a variety of algorithms and applications have been developed to analyze and manipulate the visual media
around us. There are three major driving forces behind this phenomenon: i) the availability of big data from the
Internet has created a demand for dealing with the ever increasing, vast amount of resources; ii) powerful processing
tools, such as deep neural networks, provide e�ective ways for learning how to deal with heterogeneous visual data;
iii) new data capture devices, such as the Kinect, bridge between algorithms for 2D image understanding and
3D model analysis. These driving forces have emerged only recently, and we believe that the computer graphics
and computer vision communities are still in the beginning of their honeymoon phase. In this work we survey
recent research on how computer vision techniques bene�t computer graphics techniques and vice versa, and cover
research on analysis, manipulation, synthesis, and interaction. We also discuss existing problems and suggest
possible further research directions
ConstGCN: Constrained Transmission-based Graph Convolutional Networks for Document-level Relation Extraction
Document-level relation extraction with graph neural networks faces a
fundamental graph construction gap between training and inference - the golden
graph structure only available during training, which causes that most methods
adopt heuristic or syntactic rules to construct a prior graph as a pseudo
proxy. In this paper, we propose , a novel graph
convolutional network which performs knowledge-based information propagation
between entities along with all specific relation spaces without any prior
graph construction. Specifically, it updates the entity representation by
aggregating information from all other entities along with each relation space,
thus modeling the relation-aware spatial information. To control the
information flow passing through the indeterminate relation spaces, we propose
to constrain the propagation using transmitting scores learned from the Noise
Contrastive Estimation between fact triples. Experimental results show that our
method outperforms the previous state-of-the-art (SOTA) approaches on the DocRE
dataset
When does In-context Learning Fall Short and Why? A Study on Specification-Heavy Tasks
In-context learning (ICL) has become the default method for using large
language models (LLMs), making the exploration of its limitations and
understanding the underlying causes crucial. In this paper, we find that ICL
falls short of handling specification-heavy tasks, which are tasks with
complicated and extensive task specifications, requiring several hours for
ordinary humans to master, such as traditional information extraction tasks.
The performance of ICL on these tasks mostly cannot reach half of the
state-of-the-art results. To explore the reasons behind this failure, we
conduct comprehensive experiments on 18 specification-heavy tasks with various
LLMs and identify three primary reasons: inability to specifically understand
context, misalignment in task schema comprehension with humans, and inadequate
long-text understanding ability. Furthermore, we demonstrate that through
fine-tuning, LLMs can achieve decent performance on these tasks, indicating
that the failure of ICL is not an inherent flaw of LLMs, but rather a drawback
of existing alignment methods that renders LLMs incapable of handling
complicated specification-heavy tasks via ICL. To substantiate this, we perform
dedicated instruction tuning on LLMs for these tasks and observe a notable
improvement. We hope the analyses in this paper could facilitate advancements
in alignment methods enabling LLMs to meet more sophisticated human demands.Comment: Under revie
Preserving Knowledge Invariance: Rethinking Robustness Evaluation of Open Information Extraction
The robustness to distribution changes ensures that NLP models can be
successfully applied in the realistic world, especially for information
extraction tasks. However, most prior evaluation benchmarks have been devoted
to validating pairwise matching correctness, ignoring the crucial measurement
of robustness. In this paper, we present the first benchmark that simulates the
evaluation of open information extraction models in the real world, where the
syntactic and expressive distributions under the same knowledge meaning may
drift variously. We design and annotate a large-scale testbed in which each
example is a knowledge-invariant clique that consists of sentences with
structured knowledge of the same meaning but with different syntactic and
expressive forms. By further elaborating the robustness metric, a model is
judged to be robust if its performance is consistently accurate on the overall
cliques. We perform experiments on typical models published in the last decade
as well as a popular large language model, the results show that the existing
successful models exhibit a frustrating degradation, with a maximum drop of
23.43 F1 score. Our resources and code are available at
https://github.com/qijimrc/ROBUST.Comment: Accepted by EMNLP 2023 Main Conferenc
4-[5-(4-Pyridyl)-1,3,4-oxadiazol-2-yl]pyridine N-oxide–isophthalic acid (1/1)
The title compound, C12H8N4O2·C8H6O4, was synthesized from 4-[5-(4-pyridyl)-1,3,4-oxadiazol-2-yl]pyridine N-oxide and isophthalic acid. The two molecules are linked through O—H⋯O and O—H⋯N hydrogen bonds. Weak intramolecular π–π interactions between the two hydrogen-bonded chains result in the formation a one-dimensional supramolecular curved tape (the face-to-face distance between the pyridine N-oxide ring and the benzene ring is 3.7 Å)
Model-enhanced Vector Index
Embedding-based retrieval methods construct vector indices to search for
document representations that are most similar to the query representations.
They are widely used in document retrieval due to low latency and decent recall
performance. Recent research indicates that deep retrieval solutions offer
better model quality, but are hindered by unacceptable serving latency and the
inability to support document updates. In this paper, we aim to enhance the
vector index with end-to-end deep generative models, leveraging the
differentiable advantages of deep retrieval models while maintaining desirable
serving efficiency. We propose Model-enhanced Vector Index (MEVI), a
differentiable model-enhanced index empowered by a twin-tower representation
model. MEVI leverages a Residual Quantization (RQ) codebook to bridge the
sequence-to-sequence deep retrieval and embedding-based models. To
substantially reduce the inference time, instead of decoding the unique
document ids in long sequential steps, we first generate some semantic virtual
cluster ids of candidate documents in a small number of steps, and then
leverage the well-adapted embedding vectors to further perform a fine-grained
search for the relevant documents in the candidate virtual clusters. We
empirically show that our model achieves better performance on the commonly
used academic benchmarks MSMARCO Passage and Natural Questions, with comparable
serving latency to dense retrieval solutions
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