Search Results for author:
Found 20 papers, 7 papers with code
ProSwitch: Knowledge-Guided Instruction Tuning to Generate Professional and Non-Professional Styled Text
Large Language Models (LLMs) have demonstrated efficacy in various linguistic applications, including text summarization and controlled text generation.
Triad: A Framework Leveraging a Multi-Role LLM-based Agent to Solve Knowledge Base Question Answering
We evaluated the performance of our framework using three benchmark datasets, and the results show that our framework outperforms state-of-the-art systems on the LC-QuAD and YAGO-QA benchmarks, yielding F1 scores of 11. 8% and 20. 7%, respectively.
Triple Correlations-Guided Label Supplementation for Unbiased Video Scene Graph Generation
Video-based scene graph generation (VidSGG) is an approach that aims to represent video content in a dynamic graph by identifying visual entities and their relationships.
Improving Reference-based Distinctive Image Captioning with Contrastive Rewards
A recent DIC method proposes to generate distinctive captions by comparing the target image with a set of semantic-similar reference images, i. e., reference-based DIC (Ref-DIC).
Zero-shot Visual Relation Detection via Composite Visual Cues from Large Language Models
To dynamically fuse different cues, we further introduce a chain-of-thought method that prompts LLMs to generate reasonable weights for different visual cues.
TreePrompt: Learning to Compose Tree Prompts for Explainable Visual Grounding
In this paper, we argue that their poor interpretability is attributed to the holistic prompt generation and inference process.
Learning Combinatorial Prompts for Universal Controllable Image Captioning
To this end, we propose a novel prompt-based framework for CIC by learning Combinatorial Prompts, dubbed as ComPro.
Citation Trajectory Prediction via Publication Influence Representation Using Temporal Knowledge Graph
In this paper, we propose CTPIR, a new citation trajectory prediction framework that is able to represent the influence (the momentum of citation) of either new or existing publications using the history information of all their attributes.
Label Semantic Knowledge Distillation for Unbiased Scene Graph Generation
To this end, we propose a novel model-agnostic Label Semantic Knowledge Distillation (LS-KD) for unbiased SGG.
Rethinking the Evaluation of Unbiased Scene Graph Generation
Current Scene Graph Generation (SGG) methods tend to predict frequent predicate categories and fail to recognize rare ones due to the severe imbalanced distribution of predicates.
Rethinking the Reference-based Distinctive Image Captioning
Unfortunately, reference images used by existing Ref-DIC works are easy to distinguish: these reference images only resemble the target image at scene-level and have few common objects, such that a Ref-DIC model can trivially generate distinctive captions even without considering the reference images.
Explicit Image Caption Editing
Given an image and a reference caption, the image caption editing task aims to correct the misalignment errors and generate a refined caption.
Classification-Then-Grounding: Reformulating Video Scene Graphs as Temporal Bipartite Graphs
To this end, we propose a new classification-then-grounding framework for VidSGG, which can avoid all the three overlooked drawbacks.
Natural Language Video Localization with Learnable Moment Proposals
Given an untrimmed video and a natural language query, Natural Language Video Localization (NLVL) aims to identify the video moment described by the query.
Instance-wise or Class-wise? A Tale of Neighbor Shapley for Concept-based Explanation
Deep neural networks have demonstrated remarkable performance in many data-driven and prediction-oriented applications, and sometimes even perform better than humans.
Empower Distantly Supervised Relation Extraction with Collaborative Adversarial Training
In this paper, we propose collaborative adversarial training to improve the data utilization, which coordinates virtual adversarial training (VAT) and adversarial training (AT) at different levels.
Deep Learning for Weakly-Supervised Object Detection and Object Localization: A Survey
With the success of deep neural networks in object detection, both WSOD and WSOL have received unprecedented attention.
VL-NMS: Breaking Proposal Bottlenecks in Two-Stage Visual-Language Matching
In this paper, we argue that these methods overlook an obvious \emph{mismatch} between the roles of proposals in the two stages: they generate proposals solely based on the detection confidence (i. e., query-agnostic), hoping that the proposals contain all instances mentioned in the text query (i. e., query-aware).
Boundary Proposal Network for Two-Stage Natural Language Video Localization
State-of-the-art NLVL methods are almost in one-stage fashion, which can be typically grouped into two categories: 1) anchor-based approach: it first pre-defines a series of video segment candidates (e. g., by sliding window), and then does classification for each candidate; 2) anchor-free approach: it directly predicts the probabilities for each video frame as a boundary or intermediate frame inside the positive segment.
SCA-CNN: Spatial and Channel-wise Attention in Convolutional Networks for Image Captioning
Existing visual attention models are generally spatial, i. e., the attention is modeled as spatial probabilities that re-weight the last conv-layer feature map of a CNN encoding an input image.
