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- 428
- Evo-ViT: Slow-Fast Token Evolution for Dynamic Vision Transformer317
Evo-ViT is presented, a self-motivated slow-fast token evolution approach for vision transformers that can accelerate vanilla transformers of both flat and deep-narrow structures from the very beginning of the training process.
- VITA: Towards Open-Source Interactive Omni Multimodal LLM204
VITA, the first-ever open-source Multimodal Large Language Model (MLLM) adept at simultaneous processing and analysis of Video, Image, Text, and Audio modalities, and meanwhile has an advanced multimodal interactive experience is introduced.
- Asymmetric Co-Teaching for Unsupervised Cross-Domain Person Re-Identification172
An asymmetric co-teaching framework, which resists noisy labels by cooperating two models to select data with possibly clean labels for each other, which can consistently benefit most clustering based methods, and boost the state-of-the-art adaptation accuracy.
- Toward an Expert Level of Lung Cancer Detection and Classification Using a Deep Convolutional Neural Network166
The three-dimensional convolutional neural network described in this article demonstrated both high sensitivity and high specificity in classifying pulmonary nodules regardless of diameters as well as superiority compared with manual assessment.
- 159
- High-Dimensional Dense Residual Convolutional Neural Network for Light Field Reconstruction153
This work formulate light field super-resolution (LFSR) as tensor restoration and develop a learning framework based on a two-stage restoration with 4-dimensional (4D) convolution, which allows the model to learn the features capturing the geometry information encoded in multiple adjacent views.
- Temporal Modulation Network for Controllable Space-Time Video Super-Resolution130
A Temporal Modulation Network (TMNet) to interpolate arbitrary intermediate frame(s) with accurate high-resolution reconstruction and demonstrates that the TMNet outperforms previous STVSR methods.
- Removing the Background by Adding the Background: Towards Background Robust Self-supervised Video Representation Learning115
Self-supervised learning has shown great potentials in improving the video representation ability of deep neural networks by getting supervision from the data itself, but some of the current methods tend to cheat from the background, so this work proposes to remove the background impact by adding the background.
- Viewpoint-Aware Loss with Angular Regularization for Person Re-Identification106
This work proposes a novel approach to viewpoint-aware adaptive label smoothing regularization (VALSR) that assigns the adaptive soft label to feature representation and demonstrates that it outperforms the state-of-the-art supervised Re-ID methods.
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- Cantor: Inspiring Multimodal Chain-of-Thought of MLLM74
It is argued that converging visual context acquisition and logical reasoning is pivotal for tackling visual reasoning tasks and an innovative multimodal CoT framework, termed Cantor, characterized by a perception-decision architecture is proposed, demonstrating the efficacy of the proposed framework.
- Enhancing Unsupervised Video Representation Learning by Decoupling the Scene and the Motion73
This work proposes to decouple the scene and the motion (DSM) with two simple operations, so that the model attention towards the motion information is better paid and the impact of the scene is weakened while the temporal sensitivity of the network is further enhanced.
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- Mining Approximate Frequent Itemsets In the Presence of Noise: Algorithm and Analysis70
This paper developed and implemented an algorithm to mine AFIs that generalizes the level-wise enumeration of frequent itemsets by allowing noise, and proposes the noise-tolerant support threshold, a relaxed version of support, which varies with the length of the itemset and the noise threshold.
- Training-free Transformer Architecture Search68
Experimental results demonstrate that the TF- TAS achieves a competitive performance against the state-of-the-art manually or automatically design ViT architectures, and it promotes the searching efficiency in ViT search space greatly.
- Do Not Disturb Me: Person Re-identification Under the Interference of Other Pedestrians63
A novel deep network termed Pedestrian-Interference Suppression Network (PISNet) is presented, which leverages a Query-Guided Attention Block (QGAB) to enhance the feature of the target in the gallery, under the guidance of the query.
- Co-Salient Object Detection with Co-Representation Purification57
This paper proposes a Co-Representation Purification Purification (CoRP) method aiming at searching noise-free co-representation, and uses the prediction to iteratively reduce irrelevant embeddings in the co- representation.
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- Training-Free Group Relative Policy Optimization53
This work proposes Training-Free Group Relative Policy Optimization (Training-Free GRPO), a cost-effective solution that enhances LLM agent performance without any parameter updates and demonstrates that Training-Free GRPO, when applied to DeepSeek-V3.1-Terminus, significantly improves out-of-domain performance.
- NOH-NMS51
This work proposes \heatmapname (\heatmapnameshort ), which pinpoints the objects nearby each proposal with a Gaussian distribution, together with \nmsname, which dynamically eases the suppression for the space that might contain other objects with a high likelihood.
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- SinKD: Sinkhorn Distance Minimization for Knowledge Distillation41
The Sinkhorn Knowledge Distillation (SinKD) is proposed that exploits the Sinkhorn distance to ensure a nuanced and precise assessment of the disparity between teacher and student distributions and gets rid of sample-wise KD that restricts the perception of divergence in each teacher-student sample pair.
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 2026-10-14. 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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