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Found 10 papers, 7 papers with code
DOT: A Distillation-Oriented Trainer
In this paper, we observe a trade-off between task and distillation losses, i. e., introducing distillation loss limits the convergence of task loss.
Vision Learners Meet Web Image-Text Pairs
In this work, given the excellent scalability of web data, we consider self-supervised pre-training on noisy web sourced image-text paired data.
A Simple Framework for Text-Supervised Semantic Segmentation
LoDA and SimSeg jointly ameliorate a vanilla CLIP to produce impressive semantic segmentation results.
Decoupled Knowledge Distillation
To provide a novel viewpoint to study logit distillation, we reformulate the classical KD loss into two parts, i. e., target class knowledge distillation (TCKD) and non-target class knowledge distillation (NCKD).
Discriminability-Transferability Trade-Off: An Information-Theoretic Perspective
This work simultaneously considers the discriminability and transferability properties of deep representations in the typical supervised learning task, i. e., image classification.
Contrastive Vision-Language Pre-training with Limited Resources
However, these works require a tremendous amount of data and computational resources (e. g., billion-level web data and hundreds of GPUs), which prevent researchers with limited resources from reproduction and further exploration.
ExchNet: A Unified Hashing Network for Large-Scale Fine-Grained Image Retrieval
Retrieving content relevant images from a large-scale fine-grained dataset could suffer from intolerably slow query speed and highly redundant storage cost, due to high-dimensional real-valued embeddings which aim to distinguish subtle visual differences of fine-grained objects.
BBN: Bilateral-Branch Network with Cumulative Learning for Long-Tailed Visual Recognition
Extensive experiments on four benchmark datasets, including the large-scale iNaturalist ones, justify that the proposed BBN can significantly outperform state-of-the-art methods.
Ranked #37 on
Long-tail Learning
on CIFAR-10-LT (ρ=10)
Deep Learning for Fine-Grained Image Analysis: A Survey
Among various research areas of CV, fine-grained image analysis (FGIA) is a longstanding and fundamental problem, and has become ubiquitous in diverse real-world applications.
RPC: A Large-Scale Retail Product Checkout Dataset
The main challenge of this problem comes from the large scale and the fine-grained nature of the product categories as well as the difficulty for collecting training images that reflect the realistic checkout scenarios due to continuous update of the products.
