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Works219 from public data
- A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection1,570
A unified deep neural network, denoted the multi-scale CNN (MS-CNN), is proposed for fast multi- scale object detection, which is learned end-to-end, by optimizing a multi-task loss.
- CODA-Prompt: COntinual Decomposed Attention-Based Prompting for Rehearsal-Free Continual Learning672
This work proposes to learn a set of prompt components which are assembled with input- Conditioned weights to produce input-conditioned prompts, resulting in a novel attention-based end-to-end key-query scheme.
- BlockDrop: Dynamic Inference Paths in Residual Networks554
BlockDrop, an approach that learns to dynamically choose which layers of a deep network to execute during inference so as to best reduce total computation without degrading prediction accuracy, is introduced.
- SpotTune: Transfer Learning Through Adaptive Fine-Tuning525
In SpotTune, given an image from the target task, a policy network is used to make routing decisions on whether to pass the image through the fine-tuned layers or the pre-trained layers, which outperforms the traditional fine- Tuning approach on 12 out of 14 standard datasets.
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- A Broader Study of Cross-Domain Few-Shot Learning436
The proposed Broader Study of Cross-Domain Few-Shot Learning (BSCD-FSL) benchmark, consisting of image data from a diverse assortment of image acquisition methods, demonstrates that state-of-art meta- learning methods are surprisingly outperformed by earlier meta-learning approaches, and all meta- Learning methods underperform in relation to simple fine-tuning.
- Cross-Domain Image Retrieval with a Dual Attribute-Aware Ranking Network431
This work proposes a Dual Attribute-aware Ranking Network (DARN) for retrieval feature learning, consisting of two sub-networks, one for each domain, whose retrieval feature representations are driven by semantic attribute learning.
- Fully-Adaptive Feature Sharing in Multi-Task Networks with Applications in Person Attribute Classification410
Evaluation on person attributes classification tasks involving facial and clothing attributes suggests that the models produced by the proposed method are fast, compact and can closely match or exceed the state-of-the-art accuracy from strong baselines by much more expensive models.
- Image ranking and retrieval based on multi-attribute queries393
This work proposes a principled approach for multi-attribute retrieval which explicitly models the correlations that are present between the attributes in the vocabulary, and integrates ranking and retrieval within the same formulation.
- RepMet: Representative-Based Metric Learning for Classification and Few-Shot Object Detection386
This work proposes a new method for DML that simultaneously learns the backbone network parameters, the embedding space, and the multi-modal distribution of each of the training categories in that space, in a single end-to-end training process.
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- Targeted Supervised Contrastive Learning for Long-Tailed Recognition281
Targeted supervised contrastive learning (TSC) is proposed, which improves the uniformity of the feature distribution on the hypersphere, and achieves state-of-the-art performance on long-tailed recognition tasks.
- Revisiting RCNN: On Awakening the Classification Power of Faster RCNN272
The potential of detector classification power is demonstrated by a simple, effective, and widely-applicable Decoupled Classification Refinement (DCR) network and failure cases of state-of-the-art detectors are analyzed.
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- Differential Treatment for Stuff and Things: A Simple Unsupervised Domain Adaptation Method for Semantic Segmentation256
The reason why the current adversarial loss is often unstable in minimizing the distribution discrepancy is revealed and it is shown that the method can help ease this issue by minimizing the most similar stuff and instance features between the source and the target domains.
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- Jointly Optimize Data Augmentation and Network Training: Adversarial Data Augmentation in Human Pose Estimation226
The key idea is to design a generator that competes against a discriminator that explores weaknesses of the discriminators, while the discriminator learns from hard augmentations to achieve better performance.
- Diversity in Faces.222
Diversity in Faces (DiF) provides a data set of one million annotated human face images for advancing the study of facial diversity, and believes that by making the extracted coding schemes available on a large set of faces, can accelerate research and development towards creating more fair and accurate facial recognition systems.
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- AR-Net: Adaptive Frame Resolution for Efficient Action Recognition188
A novel approach, called AR-Net (Adaptive Resolution Network), that selects on-the-fly the optimal resolution for each frame conditioned on the input for efficient action recognition in long untrimmed videos.
- Everything at Once – Multi-modal Fusion Transformer for Video Retrieval173
This work presents a multi-modal, modality agnostic fusion transformer that learns to exchange information between multiple modalities, such as video, audio, and text, and integrate them into a fused representation in a joined multi- modal embedding space.
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 2026-10-16. 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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