Search Results for author:
Found 15 papers, 9 papers with code
Mitigating Dialogue Hallucination for Large Multi-modal Models via Adversarial Instruction Tuning
To precisely measure this, we first present an evaluation benchmark by extending popular multi-modal benchmark datasets with prepended hallucinatory dialogues generated by our novel Adversarial Question Generator, which can automatically generate image-related yet adversarial dialogues by adopting adversarial attacks on LMMs.
Jack of All Tasks, Master of Many: Designing General-purpose Coarse-to-Fine Vision-Language Model
In this work, we introduce VistaLLM, a powerful visual system that addresses coarse- and fine-grained VL tasks over single and multiple input images using a unified framework.
Supervised Masked Knowledge Distillation for Few-Shot Transformers
Vision Transformers (ViTs) emerge to achieve impressive performance on many data-abundant computer vision tasks by capturing long-range dependencies among local features.
DiGeo: Discriminative Geometry-Aware Learning for Generalized Few-Shot Object Detection
Generalized few-shot object detection aims to achieve precise detection on both base classes with abundant annotations and novel classes with limited training data.
TempCLR: Temporal Alignment Representation with Contrastive Learning
For long videos, given a paragraph of description where the sentences describe different segments of the video, by matching all sentence-clip pairs, the paragraph and the full video are aligned implicitly.
Weakly-Supervised Temporal Article Grounding
Specifically, given an article and a relevant video, WSAG aims to localize all ``groundable'' sentences to the video, and these sentences are possibly at different semantic scales.
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.
Multi-Modal Few-Shot Object Detection with Meta-Learning-Based Cross-Modal Prompting
Our approach is motivated by the high-level conceptual similarity of (metric-based) meta-learning and prompt-based learning to learn generalizable few-shot and zero-shot object detection models respectively without fine-tuning.
Few-Shot Object Detection with Fully Cross-Transformer
Inspired by the recent work on vision transformers and vision-language transformers, we propose a novel Fully Cross-Transformer based model (FCT) for FSOD by incorporating cross-transformer into both the feature backbone and detection head.
The Met Dataset: Instance-level Recognition for Artworks
Testing is primarily performed on photos taken by museum guests depicting exhibits, which introduces a distribution shift between training and testing.
Query Adaptive Few-Shot Object Detection with Heterogeneous Graph Convolutional Networks
Few-shot object detection (FSOD) aims to detect never-seen objects using few examples.
Partner-Assisted Learning for Few-Shot Image Classification
In this paper, we focus on the design of training strategy to obtain an elemental representation such that the prototype of each novel class can be estimated from a few labeled samples.
Meta Faster R-CNN: Towards Accurate Few-Shot Object Detection with Attentive Feature Alignment
To improve the fine-grained few-shot proposal classification, we propose a novel attentive feature alignment method to address the spatial misalignment between the noisy proposals and few-shot classes, thus improving the performance of few-shot object detection.
Task-Adaptive Negative Envision for Few-Shot Open-Set Recognition
In this paper, we instead propose task-adaptive negative class envision for FSOR to integrate threshold tuning into the learning process.
COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation
To combat COVID-19, both clinicians and scientists need to digest vast amounts of relevant biomedical knowledge in scientific literature to understand the disease mechanism and related biological functions.
