285 research outputs found
FORB: A Flat Object Retrieval Benchmark for Universal Image Embedding
Image retrieval is a fundamental task in computer vision. Despite recent
advances in this field, many techniques have been evaluated on a limited number
of domains, with a small number of instance categories. Notably, most existing
works only consider domains like 3D landmarks, making it difficult to
generalize the conclusions made by these works to other domains, e.g., logo and
other 2D flat objects. To bridge this gap, we introduce a new dataset for
benchmarking visual search methods on flat images with diverse patterns. Our
flat object retrieval benchmark (FORB) supplements the commonly adopted 3D
object domain, and more importantly, it serves as a testbed for assessing the
image embedding quality on out-of-distribution domains. In this benchmark we
investigate the retrieval accuracy of representative methods in terms of
candidate ranks, as well as matching score margin, a viewpoint which is largely
ignored by many works. Our experiments not only highlight the challenges and
rich heterogeneity of FORB, but also reveal the hidden properties of different
retrieval strategies. The proposed benchmark is a growing project and we expect
to expand in both quantity and variety of objects. The dataset and supporting
codes are available at https://github.com/pxiangwu/FORB/.Comment: NeurIPS 2023 Datasets and Benchmarks Trac
Robustness for Space-Bounded Statistical Zero Knowledge
We show that the space-bounded Statistical Zero Knowledge classes SZK_L and NISZK_L are surprisingly robust, in that the power of the verifier and simulator can be strengthened or weakened without affecting the resulting class. Coupled with other recent characterizations of these classes [Eric Allender et al., 2023], this can be viewed as lending support to the conjecture that these classes may coincide with the non-space-bounded classes SZK and NISZK, respectively
Unsupervised Contrast-Consistent Ranking with Language Models
Language models contain ranking-based knowledge and are powerful solvers of
in-context ranking tasks. For instance, they may have parametric knowledge
about the ordering of countries by size or may be able to rank reviews by
sentiment. Recent work focuses on pairwise, pointwise, and listwise prompting
techniques to elicit a language model's ranking knowledge. However, we find
that even with careful calibration and constrained decoding, prompting-based
techniques may not always be self-consistent in the rankings they produce. This
motivates us to explore an alternative approach that is inspired by an
unsupervised probing method called Contrast-Consistent Search (CCS). The idea
is to train a probing model guided by a logical constraint: a model's
representation of a statement and its negation must be mapped to contrastive
true-false poles consistently across multiple statements. We hypothesize that
similar constraints apply to ranking tasks where all items are related via
consistent pairwise or listwise comparisons. To this end, we extend the binary
CCS method to Contrast-Consistent Ranking (CCR) by adapting existing ranking
methods such as the Max-Margin Loss, Triplet Loss, and Ordinal Regression
objective. Our results confirm that, for the same language model, CCR probing
outperforms prompting and even performs on a par with prompting much larger
language models
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels
Deep learning-based video salient object detection has recently achieved
great success with its performance significantly outperforming any other
unsupervised methods. However, existing data-driven approaches heavily rely on
a large quantity of pixel-wise annotated video frames to deliver such promising
results. In this paper, we address the semi-supervised video salient object
detection task using pseudo-labels. Specifically, we present an effective video
saliency detector that consists of a spatial refinement network and a
spatiotemporal module. Based on the same refinement network and motion
information in terms of optical flow, we further propose a novel method for
generating pixel-level pseudo-labels from sparsely annotated frames. By
utilizing the generated pseudo-labels together with a part of manual
annotations, our video saliency detector learns spatial and temporal cues for
both contrast inference and coherence enhancement, thus producing accurate
saliency maps. Experimental results demonstrate that our proposed
semi-supervised method even greatly outperforms all the state-of-the-art fully
supervised methods across three public benchmarks of VOS, DAVIS, and FBMS.Comment: ICCV2019, code is available at
https://github.com/Kinpzz/RCRNet-Pytorc
Rationally designed α-conotoxin analogues maintained analgesia activity and weakened side effects
A lack of specificity is restricting the further application of conotoxin from Conus bullatus (BuIA). In this study, an analogue library of BuIA was established and virtual screening was used, which identified high α7 nicotinic acetylcholine receptor (nAChR)-selectivity analogues. The analogues were synthesized and tested for their affinity to functional human α7 nAChR and for the regulation of intracellular calcium ion capacity in neurons. Immunofluorescence, flow cytometry, and patch clamp results showed that the analogues maintained their capacity for calcium regulation. The results of the hot-plate model and paclitaxel-induced peripheral neuropathy model indicated that, when compared with natural BuIA, the analgesia activities of the analogues in different models were maintained. To analyze the adverse effects and toxicity of BuIA and its analogues, the tail suspension test, forced swimming test, and open field test were used. The results showed that the safety and toxicity of the analogues were significantly better than BuIA. The analogues of BuIA with an appropriate and rational mutation showed high selectivity and maintained the regulation of Ca2+ capacity in neurons and activities of analgesia, whereas the analogues demonstrated that the adverse effects of natural α-conotoxins could be reduced
Research Progress of the Prevention and Treatment of Metabolic Diseases Based on Short Chain Fatty Acids
Short chain fatty acids (SCFAs) are a class of saturated fatty acids containing 1-6 carbon atoms, which are mainly produced by specific flora in the intestine through the fermentation of dietary fiber, and play an important role in maintaining the homeostasis of the intestinal environment. Recent studies have shown that SCFAs can regulate glucose and lipid metabolism, regulate energy balance, maintain the intestinal barrier and reduce inflammatory responses, eventually participating in the occurrence and development of metabolic diseases such as type 2 diabetes mellitus, obesity, lipid metabolic disorders and nonalcoholic fat liver disease through the above multiple pathways. This article summarizes the mechanism of SCFAs regulating metabolism and the research progress in the prevention and treatment of metabolic diseases, in order to provide more reference materials for the prevention and treatment of metabolic diseases
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