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Found 20 papers, 11 papers with code
MapGS: Generalizable Pretraining and Data Augmentation for Online Mapping via Novel View Synthesis
Online mapping reduces the reliance of autonomous vehicles on high-definition (HD) maps, significantly enhancing scalability.
BPO: Towards Balanced Preference Optimization between Knowledge Breadth and Depth in Alignment
Building on this, we further propose Balanced Preference Optimization (BPO), designed to dynamically augment the knowledge depth of each sample.
ShifCon: Enhancing Non-Dominant Language Capabilities with a Shift-based Contrastive Framework
Although fine-tuning Large Language Models (LLMs) with multilingual data can rapidly enhance the multilingual capabilities of LLMs, they still exhibit a performance gap between the dominant language (e. g., English) and non-dominant ones due to the imbalance of training data across languages.
EVA: An Embodied World Model for Future Video Anticipation
Yet, applying the world model for accurate video prediction is quite challenging due to the complex and dynamic intentions of the various scenes in practice.
Enhancing Online Road Network Perception and Reasoning with Standard Definition Maps
Furthermore, we show that the introduction of the SD maps leads to a reduction of the number of parameters in the perception and reasoning task by leveraging SD map graphs while improving the overall performance.
CoCoGesture: Toward Coherent Co-speech 3D Gesture Generation in the Wild
Here, we construct the audio ControlNet through a trainable copy of our pre-trained diffusion model.
SemVecNet: Generalizable Vector Map Generation for Arbitrary Sensor Configurations
In response to this challenge, we propose a modular pipeline for vector map generation with improved generalization to sensor configurations.
Balancing Speciality and Versatility: a Coarse to Fine Framework for Supervised Fine-tuning Large Language Model
In an overall evaluation of both speciality and versatility, CoFiTune consistently outperforms baseline methods across diverse tasks and model scales.
Understanding Multimodal Deep Neural Networks: A Concept Selection View
The multimodal deep neural networks, represented by CLIP, have generated rich downstream applications owing to their excellent performance, thus making understanding the decision-making process of CLIP an essential research topic.
Incremental Residual Concept Bottleneck Models
Concept Bottleneck Models (CBMs) map the black-box visual representations extracted by deep neural networks onto a set of interpretable concepts and use the concepts to make predictions, enhancing the transparency of the decision-making process.
A Question-centric Multi-experts Contrastive Learning Framework for Improving the Accuracy and Interpretability of Deep Sequential Knowledge Tracing Models
However, the inherent black-box nature of deep learning techniques often poses a hurdle for teachers to fully embrace the model's prediction results.
Improving Low-Resource Knowledge Tracing Tasks by Supervised Pre-training and Importance Mechanism Fine-tuning
Knowledge tracing (KT) aims to estimate student's knowledge mastery based on their historical interactions.
Multi-level Contrastive Learning for Script-based Character Understanding
In this work, we tackle the scenario of understanding characters in scripts, which aims to learn the characters' personalities and identities from their utterances.
Bridging the Gap: Deciphering Tabular Data Using Large Language Model
This research marks the first application of large language models to table-based question answering tasks, enhancing the model's comprehension of both table structures and content.
Assisting Language Learners: Automated Trans-Lingual Definition Generation via Contrastive Prompt Learning
The standard definition generation task requires to automatically produce mono-lingual definitions (e. g., English definitions for English words), but ignores that the generated definitions may also consist of unfamiliar words for language learners.
Robust Human Identity Anonymization using Pose Estimation
Many outdoor autonomous mobile platforms require more human identity anonymized data to power their data-driven algorithms.
Fine-grained Contrastive Learning for Definition Generation
Recently, pre-trained transformer-based models have achieved great success in the task of definition generation (DG).
BLCU-ICALL at SemEval-2022 Task 1: Cross-Attention Multitasking Framework for Definition Modeling
This paper describes the BLCU-ICALL system used in the SemEval-2022 Task 1 Comparing Dictionaries and Word Embeddings, the Definition Modeling subtrack, achieving 1st on Italian, 2nd on Spanish and Russian, and 3rd on English and French.
Multitasking Framework for Unsupervised Simple Definition Generation
We demonstrate that the framework can generate relevant, simple definitions for the target words through automatic and manual evaluations on English and Chinese datasets.
Probabilistic Semantic Mapping for Urban Autonomous Driving Applications
Recent advancements in statistical learning and computational abilities have enabled autonomous vehicle technology to develop at a much faster rate.
