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- DREAM: Efficient Dataset Distillation by Representative Matching130
A novel matching strategy named as Dataset distillation by REpresentAtive Matching (DREAM), where only representative original images are selected for matching, which is able to be easily plugged into popular dataset distillation frameworks and reduce the distilling iterations by more than 8 times without performance drop.
- InfoBatch: Lossless Training Speed Up by Unbiased Dynamic Data Pruning117
InfoBatch is a novel framework aiming to achieve lossless training acceleration by unbiased dynamic data pruning that randomly prunes a portion of less informative samples based on the loss distribution and rescales the gradients of the remaining samples to approximate the original gradient.
- MSINet: Twins Contrastive Search of Multi-Scale Interaction for Object ReID99
This work proposes a novel Twins Contrastive Mechanism (TCM) to provide more appropriate supervision for ReID architecture search, reduces the category overlaps between the training and validation data, and assists NAS in simulating real-world ReID training schemes.
- Efficient Dataset Distillation via Minimax Diffusion98
This work designs additional minimax criteria in the generative training to enhance these facets for the generated images of diffusion models and presents a theoretical model of the process as hierarchical diffusion control demonstrating the flexibility of the diffusion process to target these criteria without jeopardizing the faithfulness of the sample to the desired distribution.
- Dataset Quantization69
DQ is the first method that can successfully distill large-scale datasets such as ImageNet-1k with a state-of-the-art compression ratio and can be trained with negligible or no performance drop for both vision tasks as well as language tasks.
- SoccerNet 2022 Challenges Results42
The SoccerNet 2022 challenges were the second annual video understanding challenges organized by the SoccerNet team, and tasks (3-6) were novel, including their underlying data and annotations.
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- An Empirical Study of Vehicle Re-Identification on the AI City Challenge40
The solution for the Track2 in AI City Challenge 2021 (AICITY21) is introduced, a vehicle re-identification (ReID) task with both the real-world data and synthetic data, and unsupervised domain-adaptive (UDA) training, post-processing, model ensembling.
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- Summarizing Stream Data for Memory-Constrained Online Continual Learning35
This work proposes to Summarize the knowledge from the Stream Data (SSD) into more informative samples by distilling the training characteristics of real images through maintaining the consistency of training gradients and relationship to the past tasks.
- Finer-CAM: Spotting the Difference Reveals Finer Details for Visual Explanation30
Finer-CAM is proposed, a method that retains CAM’s efficiency while achieving precise localization of discriminative regions and allows adjustable comparison strength, enabling users to selectively highlight coarse object contours or fine discriminative details.
- Taming Diffusion for Dataset Distillation with High Representativeness24
This paper systematically investigate issues present in current diffusion-based dataset distillation methods, including inaccurate distribution matching, distribution deviation with random noise, and separate sampling, and proposes D^3HR, a novel diffusion-based framework to generate distilled datasets with high representativeness.
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- CoreInfer: Accelerating Large Language Model Inference with Semantics-Inspired Adaptive Sparse Activation18
This paper proposes the concept of sentence-wise core neurons, which refers to the subset of neurons most critical for a given sentence, and empirically demonstrates its effectiveness, and introduces CoreInfer, an MLP-free adaptive sparse activation inference method based on sentence-level prediction.
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- Can pre-trained models assist in dataset distillation?16
This work systematically study different options in PTMs, revealing that increasing model diversity enhances the performance of synthetic datasets and significantly improves the cross-architecture generalization over baseline DD methods.
- Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis15
Extensive empirical studies on a dozen datasets from various domains validate the superior interpretation capability of Prompt-CAM, requiring only a modification to the prediction head of Visual Prompt Tuning (VPT), in stark contrast to other interpretable methods that require designing specific models and training processes.
- An efficient global representation constrained by Angular Triplet loss for vehicle re-identification14
A simple Angular Triplet loss is introduced on the basis of analysis of different feature representations constrained by softmax loss and triplet loss, which can cooperate with softmax consistently and be seen as an effective baseline for vehicle re-identification task.
- Group Distributionally Robust Dataset Distillation with Risk Minimization13
Drawing inspiration from distributionally robust optimization, this work introduces an algorithm that combines clustering with the minimization of a risk measure on the loss to conduct DD and demonstrates its effective generalization and robustness across subgroups through numerical experiments.
- Stochastic style perturbation modelling for visible-Infrared person re-Identification with severely modality imbalance12
The Co-Modality Balance Learning (CMBL) framework is introduced, designed to recalibrate the balance in cross-modality learning and enhance the extraction of discriminative features, and the novel Class-Aware Contrast Similarity Learning (CACS) strategy, which capitalizes on latent feature consistency to boost intra-class compactness and inter-class separation.
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- HIERAMP: Coarse-to-Fine Autoregressive Amplification for Generative Dataset Distillation9
Across popular dataset distillation benchmarks, HIERAMP consistently improves validation performance without explicitly optimizing global proximity, demonstrating the importance of semantic amplification for effective dataset distillation.
- DREAM+: Efficient Dataset Distillation by Bidirectional Representative Matching9
A novel matching strategy called Dataset Distillation by Bidirectional REpresentAtive Matching (DREAM+), which selects representative original images for bidirectional matching, is proposed, which significantly reduces the number of distillation iterations by more than 15 times without affecting performance.
- Unsupervised Domain Adaptive Person Re-Identification via Intermediate Domains9
This work is the first to apply intermediate domains to unsupervised domain adaptive person re-identification problems and achieves a state-of-the-art performance.
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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