Search Results for author:
Found 17 papers, 8 papers with code
LSTPrompt: Large Language Models as Zero-Shot Time Series Forecasters by Long-Short-Term Prompting
Time-series forecasting (TSF) finds broad applications in real-world scenarios.
ART$\boldsymbol{\cdot}$V: Auto-Regressive Text-to-Video Generation with Diffusion Models
Second, it preserves the high-fidelity generation ability of the pre-trained image diffusion models by making only minimal network modifications.
Beyond Hallucinations: Enhancing LVLMs through Hallucination-Aware Direct Preference Optimization
Multimodal large language models have made significant advancements in recent years, yet they still suffer from a common issue known as the "hallucination problem", in which the models generate textual descriptions that inaccurately depict or entirely fabricate content from associated images.
Performative Time-Series Forecasting
Time-series forecasting is a critical challenge in various domains and has witnessed substantial progress in recent years.
InternLM-XComposer: A Vision-Language Large Model for Advanced Text-image Comprehension and Composition
We propose InternLM-XComposer, a vision-language large model that enables advanced image-text comprehension and composition.
Ranked #9 on
Visual Question Answering (VQA)
on InfiMM-Eval
MLLM-DataEngine: An Iterative Refinement Approach for MLLM
Despite the great advance of Multimodal Large Language Models (MLLMs) in both instruction dataset building and benchmarking, the independence of training and evaluation makes current MLLMs hard to further improve their capability under the guidance of evaluation results with a relatively low human cost.
PINNsFormer: A Transformer-Based Framework For Physics-Informed Neural Networks
Physics-Informed Neural Networks (PINNs) have emerged as a promising deep learning framework for approximating numerical solutions to partial differential equations (PDEs).
Filler Word Detection with Hard Category Mining and Inter-Category Focal Loss
Filler words like ``um" or ``uh" are common in spontaneous speech.
TridentSE: Guiding Speech Enhancement with 32 Global Tokens
In this paper, we present TridentSE, a novel architecture for speech enhancement, which is capable of efficiently capturing both global information and local details.
Exploring Effective Knowledge Transfer for Few-shot Object Detection
For the high-shot regime, we propose to use the knowledge learned from ImageNet as guidance for the feature learning in the fine-tuning stage, which will implicitly align the distributions of the novel classes.
An Anchor-Free Detector for Continuous Speech Keyword Spotting
Continuous Speech Keyword Spotting (CSKWS) is a task to detect predefined keywords in a continuous speech.
RetrieverTTS: Modeling Decomposed Factors for Text-Based Speech Insertion
In the proposed paradigm, global and local factors in speech are explicitly decomposed and separately manipulated to achieve high speaker similarity and continuous prosody.
Zero-Shot Text-to-Speech for Text-Based Insertion in Audio Narration
Given a piece of speech and its transcript text, text-based speech editing aims to generate speech that can be seamlessly inserted into the given speech by editing the transcript.
VisDrone-CC2020: The Vision Meets Drone Crowd Counting Challenge Results
Crowd counting on the drone platform is an interesting topic in computer vision, which brings new challenges such as small object inference, background clutter and wide viewpoint.
General-Purpose Speech Representation Learning through a Self-Supervised Multi-Granularity Framework
This paper presents a self-supervised learning framework, named MGF, for general-purpose speech representation learning.
Beating the Standard Quantum Limit under Ambient Conditions with Solid-State Spins
However, it has not been realized in solid-state spin systems at ambient conditions, owing to its intrinsic complexity for the preparation and survival of pure and entangled quantum states.
Quantum Physics
A Flow Base Bi-path Network for Cross-scene Video Crowd Understanding in Aerial View
Drones shooting can be applied in dynamic traffic monitoring, object detecting and tracking, and other vision tasks.
