133 citations · 266 across the 30 of their papers we have counts for
9 papers · 1 filter
CLIP is Also an Efficient Segmenter: A Text-Driven Approach for Weakly Supervised Semantic Segmentation
Yuqi Lin, Minghao Chen, Wenxiao Wang +5
Weakly supervised semantic segmentation (WSSS) with image-level labels is a challenging task. Mainstream approaches follow a multi-stage framework and suffer from high training cos…
Self-supervised and Weakly Supervised Contrastive Learning for Frame-wise Action Representations
Minghao Chen, Renbo Tu, Chenxi Huang +3
Previous work on action representation learning focused on global representations for short video clips. In contrast, many practical applications, such as video alignment, strongly…
GD-MAE: Generative Decoder for MAE Pre-training on LiDAR Point Clouds
Honghui Yang, Tong He, Jiaheng Liu +5
Despite the tremendous progress of Masked Autoencoders (MAE) in developing vision tasks such as image and video, exploring MAE in large-scale 3D point clouds remains challenging du…
One-shot Implicit Animatable Avatars with Model-based Priors
Yangyi Huang, Hongwei Yi, Weiyang Liu +6
Existing neural rendering methods for creating human avatars typically either require dense input signals such as video or multi-view images, or leverage a learned prior from large…
Improving alignment of dialogue agents via targeted human judgements
Amelia Glaese, Nat McAleese, Maja Trębacz +31
We present Sparrow, an information-seeking dialogue agent trained to be more helpful, correct, and harmless compared to prompted language model baselines. We use reinforcement lear…
Exploring the Relationship between Architecture and Adversarially Robust Generalization
Aishan Liu, Shiyu Tang, Siyuan Liang +4
Adversarial training has been demonstrated to be one of the most effective remedies for defending adversarial examples, yet it often suffers from the huge robustness generalization…
Towards In-distribution Compatibility in Out-of-distribution Detection
Boxi Wu, Jie Jiang, Haidong Ren +7
Deep neural network, despite its remarkable capability of discriminating targeted in-distribution samples, shows poor performance on detecting anomalous out-of-distribution data. T…
Towards Efficient Adversarial Training on Vision Transformers
Boxi Wu, Jindong Gu, Zhifeng Li +3
Vision Transformer (ViT), as a powerful alternative to Convolutional Neural Network (CNN), has received much attention. Recent work showed that ViTs are also vulnerable to adversar…
WeakM3D: Towards Weakly Supervised Monocular 3D Object Detection
Liang Peng, Senbo Yan, Boxi Wu +3
Monocular 3D object detection is one of the most challenging tasks in 3D scene understanding. Due to the ill-posed nature of monocular imagery, existing monocular 3D detection meth…