Search Results for author:
Found 10 papers, 1 papers with code
CoReS: Orchestrating the Dance of Reasoning and Segmentation
We believe that the act of reasoning segmentation should mirror the cognitive stages of human visual search, where each step is a progressive refinement of thought toward the final object.
Dual Mean-Teacher: An Unbiased Semi-Supervised Framework for Audio-Visual Source Localization
Audio-Visual Source Localization (AVSL) aims to locate sounding objects within video frames given the paired audio clips.
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?
Relevant Intrinsic Feature Enhancement Network for Few-Shot Semantic Segmentation
To improve the semantic consistency of foreground instances, we propose an unlabeled branch as an efficient data utilization method, which teaches the model how to extract intrinsic features robust to intra-class differences.
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.
Fashion Focus: Multi-modal Retrieval System for Video Commodity Localization in E-commerce
Nowadays, live-stream and short video shopping in E-commerce have grown exponentially.
The Field-of-View Constraint of Markers for Mobile Robot with Pan-Tilt Camera
In the field of navigation and visual servo, it is common to calculate relative pose by feature points on markers, so keeping markers in camera's view is an important problem.
Multiple receptive fields and small-object-focusing weakly-supervised segmentation network for fast object detection
To address this problem, we propose a multiple receptive field and small-object-focusing weakly-supervised segmentation network (MRFSWSnet) to achieve fast object detection.
Multi-loss-aware Channel Pruning of Deep Networks
Particularly, we introduce an additional loss to encode the differences in the feature and semantic distributions within feature maps between the baseline model and the pruned one.
A novel channel pruning method for deep neural network compression
In order to accelerate the selection process, the proposed method formulates it as a search problem, which can be solved efficiently by genetic algorithm.
