7 citations · 33 across the 13 of their papers we have counts for
12 papers · 1 filter
FineEdit: Fine-Grained Image Edit with Bounding Box Guidance
Haohang Xu, Lin Liu, Zhibo Zhang +3
Diffusion-based image editing models have achieved significant progress in real world applications. However, conventional models typically rely on natural language prompts, which o…
FineViT: Progressively Unlocking Fine-Grained Perception with Dense Recaptions
Peisen Zhao, Xiaopeng Zhang, Mingxing Xu +10
While Multimodal Large Language Models (MLLMs) have experienced rapid advancements, their visual encoders frequently remain a performance bottleneck. Conventional CLIP-based encode…
Betrayed by Attention: A Simple yet Effective Approach for Self-supervised Video Object Segmentation
Shuangrui Ding, Rui Qian, Haohang Xu +2
In this paper, we propose a simple yet effective approach for self-supervised video object segmentation (VOS). Our key insight is that the inherent structural dependencies present…
Semantic-Aware Generation for Self-Supervised Visual Representation Learning
Yunjie Tian, Lingxi Xie, Xiaopeng Zhang +6
In this paper, we propose a self-supervised visual representation learning approach which involves both generative and discriminative proxies, where we focus on the former part by…
Motion-aware Contrastive Video Representation Learning via Foreground-background Merging
Shuangrui Ding, Maomao Li, Tianyu Yang +5
In light of the success of contrastive learning in the image domain, current self-supervised video representation learning methods usually employ contrastive loss to facilitate vid…
Bag of Instances Aggregation Boosts Self-supervised Distillation
Haohang Xu, Jiemin Fang, Xiaopeng Zhang +5
Recent advances in self-supervised learning have experienced remarkable progress, especially for contrastive learning based methods, which regard each image as well as its augmenta…
Multi-dataset Pretraining: A Unified Model for Semantic Segmentation
Bowen Shi, Xiaopeng Zhang, Haohang Xu +4
Collecting annotated data for semantic segmentation is time-consuming and hard to scale up. In this paper, we for the first time propose a unified framework, termed as Multi-Datase…
Semi-supervised Contrastive Learning with Similarity Co-calibration
Yuhang Zhang, Xiaopeng Zhang, Robert. C. Qiu +3
Semi-supervised learning acts as an effective way to leverage massive unlabeled data. In this paper, we propose a novel training strategy, termed as Semi-supervised Contrastive Lea…
Seed the Views: Hierarchical Semantic Alignment for Contrastive Representation Learning
Haohang Xu, Xiaopeng Zhang, Hao Li +3
Self-supervised learning based on instance discrimination has shown remarkable progress. In particular, contrastive learning, which regards each image as well as its augmentations…
K-Shot Contrastive Learning of Visual Features with Multiple Instance Augmentations
Haohang Xu, Hongkai Xiong, Guo-Jun Qi
In this paper, we propose the -Shot Contrastive Learning (KSCL) of visual features by applying multiple augmentations to investigate the sample variations within individual inst…
FLAT: Few-Shot Learning via Autoencoding Transformation Regularizers
Haohang Xu, Hongkai Xiong, Guojun Qi
One of the most significant challenges facing a few-shot learning task is the generalizability of the (meta-)model from the base to the novel categories. Most of existing few-shot…
AETv2: AutoEncoding Transformations for Self-Supervised Representation Learning by Minimizing Geodesic Distances in Lie Groups
Feng Lin, Haohang Xu, Houqiang Li +2
Self-supervised learning by predicting transformations has demonstrated outstanding performances in both unsupervised and (semi-)supervised tasks. Among the state-of-the-art method…