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Works109 from public data
- Weakly Supervised Deep Detection Networks851
This paper proposes a weakly supervised deep detection architecture that modifies one such network to operate at the level of image regions, performing simultaneously region selection and classification.
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- Dataset Condensation with Distribution Matching507
This work proposes a simple yet effective method that synthesizes condensed images by matching feature distributions of the synthetic and original training images in many sampled embedding spaces and significantly reduces the synthesis cost while achieving comparable or better performance.
- 452
- Efficient Parametrization of Multi-domain Deep Neural Networks391
This paper proposes to consider universal parametric families of neural networks, which still contain specialized problem-specific models, but differing only by a small number of parameters, and shows that these universal parametrization are very effective for transfer learning, where they outperform traditional fine-tuning techniques.
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- RenderDiffusion: Image Diffusion for 3D Reconstruction, Inpainting and Generation217
RenderDiffusion is presented, the first diffusion model for 3D generation and inference, trained using only monocular 2D supervision, that generates and renders an intermediate three-dimensional representation of a scene in each denoising step and allows for 2D inpainting to edit 3D scenes.
- Unsupervised Learning of Object Landmarks by Factorized Spatial Embeddings173
This paper proposes a novel unsupervised approach that can discover and learn landmarks in object categories, thus characterizing their structure, and shows that the learned landmarks establish meaningful correspondences between different object instances in a category without having to impose this requirement explicitly.
- Cross-domain Few-shot Learning with Task-specific Adapters170
This work proposes to learn task- specific weights from scratch directly on a small support set, in contrast to dynamically estimating them, and shows that task-specific weights through parametric adapters in matrix form with residual connections to multiple intermediate layers of a backbone network significantly improves the per-formance of the state-of-the-art models in the Meta-Dataset benchmark with minor additional cost.
- CAFE: Learning to Condense Dataset by Aligning Features165
This paper proposes a novel scheme to Condense dataset by Aligning FEatures (CAFE), which explicitly attempts to preserve the real-feature distribution as well as the discriminant power of the resulting synthetic set, lending itself to strong generalization capability to various architectures.
- Weakly Supervised Object Detection with Posterior Regularization133
A method based on features extracted from a Convolutional Neural Network and latent SVM that can represent and exploit the presence of multiple object instances in an image that outperforms the state-of-the-art in weakly-supervised object detection and object classification on the Pascal VOC 2007 dataset.
- Universal Representation Learning from Multiple Domains for Few-shot Classification117
URL is presented, which learns a single set of universal visual representations by distilling knowledge of multiple domain-specific networks after co-aligning their features with the help of adapters and centered kernel alignment, and shows that the universal representations can be further refined for previously unseen domains by an efficient adaptation step.
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- Synthesizing Informative Training Samples with GAN112
This paper proposes a novel method to synthesize Informative Training samples with GAN (IT-GAN), which freezes a pre-trained GAN model and learns the informative latent vectors that correspond to informative training samples.
- 100
- Knowledge Distillation for Multi-task Learning95
This work proposes a knowledge distillation based method that can optimize a multi-task learning model in a more balanced way and achieve better overall performance.
- 88
- Unsupervised Learning of Landmarks by Descriptor Vector Exchange71
A new perspective on the equivariance approach is developed by noting that dense landmark detectors can be interpreted as local image descriptors equipped with invariance to intra-category variations, and proposing a direct method to enforce such an invariance in the standard equivariant loss.
- Learning Multiple Dense Prediction Tasks from Partially Annotated Data64
This paper proposes a multi-task training procedure that successfully leverages task relations to supervise its multi- task learning when data is partially annotated, and learns to map each task pair to a joint pairwise task-space which enables sharing information between them in a computationally efficient way through another network conditioned on task pairs.
- Continual Representation Learning for Biometric Identification55
This paper proposes a new continual learning setting, namely "continual representation learning", which focuses on learning better representation in a continuous way, and demonstrates that existing CL methods can improve the representation in the new setting.
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- Few‐shot Learning of Homogeneous Human Locomotion Styles49
This paper proposes a transfer learning approach for adapting a learned neural network to characters that move in different styles from those on which the original neural network is trained, and introduces a canonical polyadic tensor decomposition to reduce the amount of parameters required for learning from each new style.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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