Search Results for author:
Found 17 papers, 12 papers with code
Uni-SMART: Universal Science Multimodal Analysis and Research Transformer
Scientific literature often includes a wide range of multimodal elements, such as molecular structure, tables, and charts, which are hard for text-focused LLMs to understand and analyze.
SciAssess: Benchmarking LLM Proficiency in Scientific Literature Analysis
Recent breakthroughs in Large Language Models (LLMs) have revolutionized natural language understanding and generation, igniting a surge of interest in leveraging these technologies in the field of scientific literature analysis.
MolTC: Towards Molecular Relational Modeling In Language Models
Molecular Relational Learning (MRL), aiming to understand interactions between molecular pairs, plays a pivotal role in advancing biochemical research.
Towards 3D Molecule-Text Interpretation in Language Models
Through 3D molecule-text alignment and 3D molecule-centric instruction tuning, 3D-MoLM establishes an integration of 3D molecular encoder and LM.
LLaRA: Large Language-Recommendation Assistant
Treating the "sequential behaviors of users" as a distinct modality beyond texts, we employ a projector to align the traditional recommender's ID embeddings with the LLM's input space.
BianQue: Balancing the Questioning and Suggestion Ability of Health LLMs with Multi-turn Health Conversations Polished by ChatGPT
Large language models (LLMs) have performed well in providing general and extensive health suggestions in single-turn conversations, exemplified by systems such as ChatGPT, ChatGLM, ChatDoctor, DoctorGLM, and etc.
MolCA: Molecular Graph-Language Modeling with Cross-Modal Projector and Uni-Modal Adapter
MolCA enables an LM (e. g., Galactica) to understand both text- and graph-based molecular contents via the cross-modal projector.
Ranked #4 on
Molecule Captioning
on ChEBI-20
Towards Dynamic and Small Objects Refinement for Unsupervised Domain Adaptative Nighttime Semantic Segmentation
Moreover, semantic segmentation models trained on daytime datasets often face difficulties in generalizing effectively to nighttime conditions.
National Origin Discrimination in Deep-learning-powered Automated Resume Screening
Many companies and organizations have started to use some form of AIenabled auto mated tools to assist in their hiring process, e. g. screening resumes, interviewing candi dates, performance evaluation.
SSCBench: Monocular 3D Semantic Scene Completion Benchmark in Street Views
Monocular scene understanding is a foundational component of autonomous systems.
3D Semantic Scene Completion
3D Semantic Scene Completion from a single 2D image
SGDViT: Saliency-Guided Dynamic Vision Transformer for UAV Tracking
The proposed method designs a new task-specific object saliency mining network to refine the cross-correlation operation and effectively discriminate foreground and background information.
Continuity-Aware Latent Interframe Information Mining for Reliable UAV Tracking
To address the above issues, this work proposes a novel framework with continuity-aware latent interframe information mining for reliable UAV tracking, i. e., ClimRT.
Direct 3D information fusion for depth of field enhancement in optical-resolution photoacoustic microscopy
In this work, a 3D information fusion algorithm based on 3D stationary wavelet transform and joint weighted evaluation optimization is proposed to fuse multi-focus photoacoustic data to achieve large-volumetric and high-resolution 3D imaging.
HighlightNet: Highlighting Low-Light Potential Features for Real-Time UAV Tracking
Pixel-level range mask is introduced to make HighlightNet more focused on the enhancement of the tracking object and regions without light sources.
Local Perception-Aware Transformer for Aerial Tracking
Specifically, with local-modeling to global-search mechanism, the proposed tracker replaces the global encoder by a novel local-recognition encoder.
Let Invariant Rationale Discovery Inspire Graph Contrastive Learning
Specifically, without supervision signals, RGCL uses a rationale generator to reveal salient features about graph instance-discrimination as the rationale, and then creates rationale-aware views for contrastive learning.
Ad2Attack: Adaptive Adversarial Attack on Real-Time UAV Tracking
Therefore, to help increase awareness of the potential risk and the robustness of UAV tracking, this work proposes a novel adaptive adversarial attack approach, i. e., Ad$^2$Attack, against UAV object tracking.
