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Found 9 papers, 7 papers with code
MobileVLM V2: Faster and Stronger Baseline for Vision Language Model
We introduce MobileVLM V2, a family of significantly improved vision language models upon MobileVLM, which proves that a delicate orchestration of novel architectural design, an improved training scheme tailored for mobile VLMs, and rich high-quality dataset curation can substantially benefit VLMs' performance.
MobileVLM : A Fast, Strong and Open Vision Language Assistant for Mobile Devices
We present MobileVLM, a competent multimodal vision language model (MMVLM) targeted to run on mobile devices.
PivotNet: Vectorized Pivot Learning for End-to-end HD Map Construction
Furthermore, to supervise the position and topology of the vectorized point predictions, we propose a dynamic vectorized sequence loss.
MachMap: End-to-End Vectorized Solution for Compact HD-Map Construction
This report introduces the 1st place winning solution for the Autonomous Driving Challenge 2023 - Online HD-map Construction.
End-to-End Vectorized HD-map Construction with Piecewise Bezier Curve
Vectorized high-definition map (HD-map) construction, which focuses on the perception of centimeter-level environmental information, has attracted significant research interest in the autonomous driving community.
GRM: Gradient Rectification Module for Visual Place Retrieval
Visual place retrieval aims to search images in the database that depict similar places as the query image.
DeFRCN: Decoupled Faster R-CNN for Few-Shot Object Detection
Few-shot object detection, which aims at detecting novel objects rapidly from extremely few annotated examples of previously unseen classes, has attracted significant research interest in the community.
Ranked #4 on
Few-Shot Object Detection
on MS-COCO (1-shot)
Learning Open Set Network with Discriminative Reciprocal Points
In this process, one of the key challenges is to reduce the risk of generalizing the inherent characteristics of numerous unknown samples learned from a small amount of known data.
Transductive Episodic-Wise Adaptive Metric for Few-Shot Learning
By solving the problem with its closed-form solution on the fly with the setup of transduction, our approach efficiently tailors an episodic-wise metric for each task to adapt all features from a shared task-agnostic embedding space into a more discriminative task-specific metric space.
