Trending Research
DoRA: Weight-Decomposed Low-Rank Adaptation
By employing DoRA, we enhance both the learning capacity and training stability of LoRA while avoiding any additional inference overhead.
A Survey on Deep Learning for Theorem Proving
Theorem proving is a fundamental aspect of mathematics, spanning from informal reasoning in mathematical language to rigorous derivations in formal systems.
TextHawk: Exploring Efficient Fine-Grained Perception of Multimodal Large Language Models
We conduct extensive experiments on both general and document-oriented MLLM benchmarks, and show that TextHawk outperforms the state-of-the-art methods, demonstrating its effectiveness and superiority in fine-grained document perception and general abilities.
RoadBEV: Road Surface Reconstruction in Bird's Eye View
ztsrxh/roadbev
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This paper uniformly proposes two simple yet effective models for road elevation reconstruction in BEV named RoadBEV-mono and RoadBEV-stereo, which estimate road elevation with monocular and stereo images, respectively.
NeuroNCAP: Photorealistic Closed-loop Safety Testing for Autonomous Driving
We present a versatile NeRF-based simulator for testing autonomous driving (AD) software systems, designed with a focus on sensor-realistic closed-loop evaluation and the creation of safety-critical scenarios.
LayoutLLM: Layout Instruction Tuning with Large Language Models for Document Understanding
AlibabaResearch/AdvancedLiterateMachinery
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The core of LayoutLLM is a layout instruction tuning strategy, which is specially designed to enhance the comprehension and utilization of document layouts.
HANet: A Hierarchical Attention Network for Change Detection With Bitemporal Very-High-Resolution Remote Sensing Images
Benefiting from the developments in deep learning technology, deep-learning-based algorithms employing automatic feature extraction have achieved remarkable performance on the change detection (CD) task.
Ranked #1 on
Change Detection
on GoogleGZ-CD
