Search Results for author:
Found 15 papers, 13 papers with code
Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models
We try to narrow the gap by mining the potential of VLMs for better performance and any-to-any workflow from three aspects, i. e., high-resolution visual tokens, high-quality data, and VLM-guided generation.
Ranked #8 on
Visual Question Answering
on MM-Vet
Decoupled Kullback-Leibler Divergence Loss
In this paper, we delve deeper into the Kullback-Leibler (KL) Divergence loss and observe that it is equivalent to the Doupled Kullback-Leibler (DKL) Divergence loss that consists of 1) a weighted Mean Square Error (wMSE) loss and 2) a Cross-Entropy loss incorporating soft labels.
Understanding Imbalanced Semantic Segmentation Through Neural Collapse
Based on our empirical and theoretical analysis, we point out that semantic segmentation naturally brings contextual correlation and imbalanced distribution among classes, which breaks the equiangular and maximally separated structure of neural collapse for both feature centers and classifiers.
Generalized Parametric Contrastive Learning
Based on theoretical analysis, we observe that supervised contrastive loss tends to bias high-frequency classes and thus increases the difficulty of imbalanced learning.
Ranked #5 on
Long-tail Learning
on iNaturalist 2018
Region Rebalance for Long-Tailed Semantic Segmentation
In this paper, we study the problem of class imbalance in semantic segmentation.
Ranked #21 on
Semantic Segmentation
on ADE20K
Rebalanced Siamese Contrastive Mining for Long-Tailed Recognition
Given the promising performance of contrastive learning, we propose Rebalanced Siamese Contrastive Mining (ResCom) to tackle imbalanced recognition.
Ranked #5 on
Long-tail Learning
on CIFAR-10-LT (ρ=10)
Parametric Contrastive Learning
In this paper, we propose Parametric Contrastive Learning (PaCo) to tackle long-tailed recognition.
Ranked #12 on
Long-tail Learning
on iNaturalist 2018
Improving Calibration for Long-Tailed Recognition
Motivated by the fact that predicted probability distributions of classes are highly related to the numbers of class instances, we propose label-aware smoothing to deal with different degrees of over-confidence for classes and improve classifier learning.
Ranked #16 on
Long-tail Learning
on CIFAR-10-LT (ρ=100)
ResLT: Residual Learning for Long-tailed Recognition
From this perspective, the trivial solution utilizes different branches for the head, medium, and tail classes respectively, and then sums their outputs as the final results is not feasible.
Ranked #20 on
Long-tail Learning
on iNaturalist 2018
Channel-Level Variable Quantization Network for Deep Image Compression
In this paper, we propose a channel-level variable quantization network to dynamically allocate more bitrates for significant channels and withdraw bitrates for negligible channels.
Expectation-Maximization Attention Networks for Semantic Segmentation
It is designed to compute the representation of each position by a weighted sum of the features at all positions.
Ranked #11 on
Semantic Segmentation
on COCO-Stuff test
ADA-Tucker: Compressing Deep Neural Networks via Adaptive Dimension Adjustment Tucker Decomposition
Furthermore, we propose that weight tensors in networks with proper order and balanced dimension are easier to be compressed.
Differentiable Linearized ADMM
Recently, a number of learning-based optimization methods that combine data-driven architectures with the classical optimization algorithms have been proposed and explored, showing superior empirical performance in solving various ill-posed inverse problems, but there is still a scarcity of rigorous analysis about the convergence behaviors of learning-based optimization.
Joint Sub-bands Learning with Clique Structures for Wavelet Domain Super-Resolution
To solve these problems, we propose the Super-Resolution CliqueNet (SRCliqueNet) to reconstruct the high resolution (HR) image with better textural details in the wavelet domain.
Convolutional Neural Networks with Alternately Updated Clique
In contrast to prior networks, there are both forward and backward connections between any two layers in the same block.
