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- STM: SpatioTemporal and Motion Encoding for Action Recognition472
This work proposes a STM block, which contains a Channel-wise SpatioTemporal Module (CSTM) to present the spatiotemporal features and aChannel-wise Motion Module(CMM) to efficiently encode motion features, and replaces original residual blocks in the ResNet architecture with STM blcoks to form a simple yet effective STM network.
- Joint Domain Alignment and Discriminative Feature Learning for Unsupervised Deep Domain Adaptation261
Joint domain alignment and discriminative feature learning is proposed, both of which guarantee the domain invariant features with better intra-class compactness and inter-class separability.
- IFRNet: Intermediate Feature Refine Network for Efficient Frame Interpolation232
This work devise an efficient encoder-decoder based network, termed IFRNet, for fast in-termediate frame synthesizing, which first extracts pyramid features from given inputs, and then refines the bilateral in-Termediate flow fields together with a powerful intermedi-ate feature until generating the desired output.
- PortraitBooth: A Versatile Portrait Model for Fast Identity-Preserved Personalization96
Por-traitBooth leverages subject embeddings from aface recognition model for personalized image generation without fine-tuning and eliminates computational overhead and mitigates identity distortion, and introduces dynamic identity preservation strategy, which ensures close resemblance to the original image identity.
- Selective Transfer With Reinforced Transfer Network for Partial Domain Adaptation68
A reinforced transfer network (RTNet), which utilizes both high-level and pixel-level information for PDA problem, and is composed of a reinforced data selector (RDS) based on reinforcement learning (RL), which filters out the outlier source samples, and a domain adaptation model which minimizes the domain discrepancy in the shared label space.
- ColorFormer: Image Colorization via Color Memory Assisted Hybrid-Attention Transformer57
Experimental results show that the proposed automatic image colorization method via color memory assisted hybrid-attention transformer can generate more realistic and semantically matched color images compared with state-of-the-art methods.
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- NTIRE 2021 Challenge on Video Super-Resolution36
This paper presents evaluation results from two competition tracks as well as the proposed solutions to the NTIRE 2021 Challenge on Video Super-Resolution, and develops conventional video SR methods focusing on the restoration quality.
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- M2-CLIP: A Multimodal, Multi-task Adapting Framework for Video Action Recognition17
This work designs a multi-task decoder with a rich set of supervisory signals, including the original contrastive learning head, a cross-modal classification head, a cross-modal masked language modeling head, and a visual classification head that adeptly satisfies the need for strong supervised performance within a multimodal framework.
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- Learning Comprehensive Motion Representation for Action Recognition12
A Comprehensive Motion Representation (CMR) learning method for action recognition, which achieves competitive performance on Something-Something V1 & V2 and Kinetics-400, and outperforms the current state-of-the-art on the temporal reasoning datasets Something- something V1 and V2.
- NoiseBoost: Alleviating Hallucination with Noise Perturbation for Multimodal Large Language Models11
NoiseBoost is proposed, a broadly applicable and simple method for alleviating hallucinations for MLLMs through the integration of noise feature perturbations and pioneerly enables semi-supervised learning for MLLMs, unleashing the power of unlabeled data.
- Unsupervised domain adaptation with target reconstruction and label confusion in the common subspace10
- Dynamic Frame Interpolation in Wavelet Domain9
This work proposes a novel two-stage frame interpolation framework termed WaveletVFI and finds that embedding a classifier in the motion perception network to learn a dynamic threshold for each sample can achieve more computation reduction with almost no loss of accuracy.
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- DynamicControl: Adaptive Condition Selection for Improved Text-to-Image Generation6
This work proposes a novel framework, DynamicControl, which supports dynamic combinations of diverse control signals, allowing adaptive selection of different numbers and types of conditions, and demonstrates its superiority over existing methods in terms of controllability, generation quality and composability under various conditional controls.
- Multi-Level Adaptive Region of Interest and Graph Learning for Facial Action Unit Recognition5
A novel multi-level adaptive ROI and graph learning (MARGL) framework to tackle the problem of limited representation capacity of regional features in facial action unit (AU) recognition tasks and significantly outperforms the previous state-of-the-art methods.
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Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. 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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