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20142024
most citedMinVIS: A Minimal Video Instance Segmentation Framework without Video-based Training

28 citations · 61 across the 16 of their papers we have counts for

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16 papers · 1 filter

cs.CV2024

Exploring Camera Encoder Designs for Autonomous Driving Perception

Barath Lakshmanan, Joshua Chen, Shiyi Lan +3

The cornerstone of autonomous vehicles (AV) is a solid perception system, where camera encoders play a crucial role. Existing works usually leverage pre-trained Convolutional Neura…

cs.CV2024

What is Point Supervision Worth in Video Instance Segmentation?

Shuaiyi Huang, De-An Huang, Zhiding Yu +5

Video instance segmentation (VIS) is a challenging vision task that aims to detect, segment, and track objects in videos. Conventional VIS methods rely on densely-annotated object…

cs.CV2024

LITA: Language Instructed Temporal-Localization Assistant

De-An Huang, Shijia Liao, Subhashree Radhakrishnan +4

There has been tremendous progress in multimodal Large Language Models (LLMs). Recent works have extended these models to video input with promising instruction following capabilit…

cs.CV2024

Improving Distant 3D Object Detection Using 2D Box Supervision

Zetong Yang, Zhiding Yu, Chris Choy +3

Improving the detection of distant 3d objects is an important yet challenging task. For camera-based 3D perception, the annotation of 3d bounding relies heavily on LiDAR for accura…

cs.CV20241 cited

T-Stitch: Accelerating Sampling in Pre-Trained Diffusion Models with Trajectory Stitching

Zizheng Pan, Bohan Zhuang, De-An Huang +5

Sampling from diffusion probabilistic models (DPMs) is often expensive for high-quality image generation and typically requires many steps with a large model. In this paper, we int…

cs.CV2024

Fully Attentional Networks with Self-emerging Token Labeling

Bingyin Zhao, Zhiding Yu, Shiyi Lan +4

Recent studies indicate that Vision Transformers (ViTs) are robust against out-of-distribution scenarios. In particular, the Fully Attentional Network (FAN) - a family of ViT backb…