Search Results for author:
Found 10 papers, 3 papers with code
BACON: Supercharge Your VLM with Bag-of-Concept Graph to Mitigate Hallucinations
This paper presents Bag-of-Concept Graph (BACON) to gift models with limited linguistic abilities to taste the privilege of Vision Language Models (VLMs) and boost downstream tasks such as detection, visual question answering (VQA), and image generation.
Understanding the Multi-modal Prompts of the Pre-trained Vision-Language Model
In this paper, we conduct a direct analysis of the multi-modal prompts by asking the following questions: $(i)$ How do the learned multi-modal prompts improve the recognition performance?
MomentDiff: Generative Video Moment Retrieval from Random to Real
Video moment retrieval pursues an efficient and generalized solution to identify the specific temporal segments within an untrimmed video that correspond to a given language description.
RA-CLIP: Retrieval Augmented Contrastive Language-Image Pre-Training
This process can be considered as an open-book exam: with the reference set as a cheat sheet, the proposed method doesn't need to memorize all visual concepts in the training data.
Vortex Pooling: Improving Context Representation in Semantic Segmentation
To be specific, our approach outperforms the previous state-of-the-art model named DeepLab v3 by 1. 5% on the PASCAL VOC 2012 val set and 0. 6% on the test set by replacing the Atrous Spatial Pyramid Pooling (ASPP) module in DeepLab v3 with the proposed Vortex Pooling.
Deep Descriptor Transforming for Image Co-Localization
Reusable model design becomes desirable with the rapid expansion of machine learning applications.
Deep Label Distribution Learning with Label Ambiguity
However, it is difficult to collect sufficient training images with precise labels in some domains such as apparent age estimation, head pose estimation, multi-label classification and semantic segmentation.
Ranked #1 on
Head Pose Estimation
on BJUT-3D
Dense CNN Learning with Equivalent Mappings
Large receptive field and dense prediction are both important for achieving high accuracy in pixel labeling tasks such as semantic segmentation.
Mask-CNN: Localizing Parts and Selecting Descriptors for Fine-Grained Image Recognition
Fine-grained image recognition is a challenging computer vision problem, due to the small inter-class variations caused by highly similar subordinate categories, and the large intra-class variations in poses, scales and rotations.
