Search Results for author:
Found 8 papers, 3 papers with code
CRIS: CLIP-Driven Referring Image Segmentation
In addition, we present text-to-pixel contrastive learning to explicitly enforce the text feature similar to the related pixel-level features and dissimilar to the irrelevances.
Contrastive Learning
Generalized Referring Expression Segmentation
+3
Overfitting the Data: Compact Neural Video Delivery via Content-aware Feature Modulation
Internet video delivery has undergone a tremendous explosion of growth over the past few years.
Open-Vocabulary Segmentation with Unpaired Mask-Text Supervision
With this unpaired mask-text supervision, we propose a new weakly-supervised open-vocabulary segmentation framework (Uni-OVSeg) that leverages confident pairs of mask predictions and entities in text descriptions.
Contextual Graph Reasoning Networks
Graph Reasoning has shown great potential recently in modeling long-range dependencies, which are crucial for various computer vision tasks.
Exploring Set Similarity for Dense Self-supervised Representation Learning
By considering the spatial correspondence, dense self-supervised representation learning has achieved superior performance on various dense prediction tasks.
MSR: Making Self-supervised learning Robust to Aggressive Augmentations
With the training going on, the model begins to overfit noisy pairs.
BEV-SAN: Accurate BEV 3D Object Detection via Slice Attention Networks
In order to find them, we further propose a LiDAR-guided sampling strategy to leverage the statistical distribution of LiDAR to determine the heights of local slices.
IDEAL: Influence-Driven Selective Annotations Empower In-Context Learners in Large Language Models
However, since the prompts need to be sampled from a large volume of annotated examples, finding the right prompt may result in high annotation costs.
