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A large-scale car dataset for fine-grained categorization and verification
TLDR
This paper presents an on-going effort in collecting a large-scale dataset, “CompCars”, that covers not only different car views, but also their different internal and external parts, and rich attributes, and demonstrates a few important applications exploiting the dataset.
View on IEEE
YouTube-VOS: A Large-Scale Video Object Segmentation Benchmark
TLDR
A new large-scale video object segmentation dataset called YouTube Video Object Segmentation dataset (YouTube-VOS) is built which aims to establish baselines for the development of new algorithms in the future.
View PDF on arXiv
Slimmable Neural Networks
TLDR
This work presents a simple and general method to train a single neural network executable at different widths, permitting instant and adaptive accuracy-efficiency trade-offs at runtime, and demonstrates better performance of slimmable models compared with individual ones across a wide range of applications.
View PDF on arXiv
YouTube-VOS: Sequence-to-Sequence Video Object Segmentation
TLDR
This work builds a new large-scale video object segmentation dataset called YouTube Video Object Segmentation dataset (YouTube-VOS) and proposes a novel sequence-to-sequence network to fully exploit long-term spatial-temporal information in videos for segmentation.
View PDF on arXiv
Efficient Video Object Segmentation via Network Modulation
TLDR
This work proposes a novel approach that uses a single forward pass to adapt the segmentation model to the appearance of a specific object and is 70× faster than fine-tuning approaches and achieves similar accuracy.
View on IEEE
Streamlined Dense Video Captioning
TLDR
A novel dense video captioning framework is proposed, which models temporal dependency across events in a video explicitly and leverages visual and linguistic context from prior events for coherent storytelling.
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Dense Captioning with Joint Inference and Visual Context
TLDR
A new model pipeline based on two novel ideas, joint inference and context fusion, is proposed, which achieves state-of-the-art accuracy on Visual Genome for dense captioning with a relative gain of 73% compared to the previous best algorithm.
View on IEEE
Revealing Structural Differences between Alkaline and Kraft Lignins by HSQC NMR
Technical lignins, mostly generated as byproducts from the pulping industry, are highly abundant aromatic sources. However, they are underutilized due to their complexity, as well as structural…
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The essential role of hippocampal alpha6 subunit-containing GABAA receptors in maternal separation stress-induced adolescent depressive behaviors
View on Elsevier
Context-Aware Zero-Shot Recognition
TLDR
This work aims to understand the identity of the novel objects in an image surrounded by the known objects using the inter-object relation prior, and leverage the visual context and the geometric relationships between all pairs of objects in a single image, and capture the information useful to infer unseen categories.
View PDF on arXiv
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