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Found 13 papers, 4 papers with code
UniCombine: Unified Multi-Conditional Combination with Diffusion Transformer
With the rapid development of diffusion models in image generation, the demand for more powerful and flexible controllable frameworks is increasing.
PixelPonder: Dynamic Patch Adaptation for Enhanced Multi-Conditional Text-to-Image Generation
Recent advances in diffusion-based text-to-image generation have demonstrated promising results through visual condition control.
Understanding Zero-shot Rare Word Recognition Improvements Through LLM Integration
In this study, we investigate the integration of a large language model (LLM) with an automatic speech recognition (ASR) system, specifically focusing on enhancing rare word recognition performance.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
ChartKG: A Knowledge-Graph-Based Representation for Chart Images
We present four cases to illustrate how our knowledge-graph-based representation can model the detailed visual elements and semantic relations in charts, and further demonstrate how our approach can benefit downstream applications such as semantic-aware chart retrieval and chart question answering.
Mamba-YOLO-World: Marrying YOLO-World with Mamba for Open-Vocabulary Detection
However, its performance is hindered by its neck feature fusion mechanism, which causes the quadratic complexity and the limited guided receptive fields.
Distilling Long-tailed Datasets
It reduces the distance between the student and the biased expert trajectories and prevents the tail class bias from being distilled to the synthetic dataset.
PTQ4DiT: Post-training Quantization for Diffusion Transformers
SSC extends this approach by dynamically adjusting the balanced salience to capture the temporal variations in activation.
Seed Optimization with Frozen Generator for Superior Zero-shot Low-light Enhancement
In this work, we observe that the generators, which are pre-trained on massive natural images, inherently hold the promising potential for superior low-light image enhancement against varying scenarios. Specifically, we embed a pre-trained generator to Retinex model to produce reflectance maps with enhanced detail and vividness, thereby recovering features degraded by low-light conditions. Taking one step further, we introduce a novel optimization strategy, which backpropagates the gradients to the input seeds rather than the parameters of the low-light enhancement model, thus intactly retaining the generative knowledge learned from natural images and achieving faster convergence speed.
QuEST: Low-bit Diffusion Model Quantization via Efficient Selective Finetuning
We empirically verify that our approach modifies the activation distribution and provides meaningful temporal information, facilitating easier and more accurate quantization.
Learning Gradient Fields for Scalable and Generalizable Irregular Packing
In this work, we delve deeper into a novel machine learning-based approach that formulates the packing problem as conditional generative modeling.
Leveraging Angular Information Between Feature and Classifier for Long-tailed Learning: A Prediction Reformulation Approach
Deep neural networks still struggle on long-tailed image datasets, and one of the reasons is that the imbalance of training data across categories leads to the imbalance of trained model parameters.
Ranked #21 on
Long-tail Learning
on CIFAR-10-LT (ρ=10)
Learning Calibrated Uncertainties for Domain Shift: A Distributionally Robust Learning Approach
We propose a framework for learning calibrated uncertainties under domain shifts, where the source (training) distribution differs from the target (test) distribution.
Distributionally Robust Learning for Unsupervised Domain Adaptation
This formulation motivates the use of two neural networks that are jointly trained --- a discriminative network between the source and target domains for density-ratio estimation, in addition to the standard classification network.
