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- Learning Salient Boundary Feature for Anchor-free Temporal Action Localization329
This paper proposes the first purely anchor-free temporal localization method, which is both efficient and effective, and beats all anchor-based and actionness-guided methods with a remarkable margin on THUMOS14, achieving state-of-the-art results, and comparable ones on ActivityNet v1.3.
- TEINet: Towards an Efficient Architecture for Video Recognition275
The proposed TEINet can achieve a good recognition accuracy on these datasets but still preserve a high efficiency, and is able to capture temporal structure flexibly and effectively, but also efficient for model inference.
- 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.
- Fast Learning of Temporal Action Proposal via Dense Boundary Generator228
An efficient and unified framework to generate temporal action proposals named Dense Boundary Generator (DBG), which draws inspiration from boundary-sensitive methods and implements boundary classification and action completeness regression for densely distributed proposals.
- Learning by Analogy: Reliable Supervision From Transformations for Unsupervised Optical Flow Estimation218
This work twists the general unsupervised learning pipeline by running another forward pass with transformed data from augmentation, along with using transformed predictions of original data as the self-supervision signal, and introduces a lightweight network with multiple frames by a highly-shared flow decoder.
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- 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.
- NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results94
The NTIRE 2022 challenge was to super-resolve an input image with a magnification factor of ×4 based on pairs of low and corresponding high resolution images and the aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics.
- Sonic: Shifting Focus to Global Audio Perception in Portrait Animation87
A novel audio-driven paradigm is proposed, dubbed as Sonic, to shift focus on the exploration of global audio perception and outperform existing SOTA methodologies in terms of video quality, temporally consistency, lip synchronization precision, and motion diversity.
- 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.
- Temporal Distinct Representation Learning for Action Recognition35
This paper designs a sequential channel filtering mechanism, i.e., Progressive Enhancement Module (PEM), to excite the discriminative channels of features from different frames step by step to avoid repeated information extraction and achieves visible improvements over the best competitor by 2.4% and 1.3%, respectively.
- Towards One-step Causal Video Generation via Adversarial Self-Distillation17
This work proposes a distillation-based framework for efficient causal video generation that enables high-quality synthesis with extremely limited denoising steps and proposes a novel Adversarial Self-Distillation (ASD) strategy, which aligns the outputs of the student model's n-step denoising process with its (n+1)-step version at the distribution level.
- RealTalk: Real-time and Realistic Audio-driven Face Generation with 3D Facial Prior-guided Identity Alignment Network16
A novel generalized audio-driven framework RealTalk, which consists of an audio-to-expression transformer and a high-fidelity expression-to-face renderer, and a lightweight facial identity alignment (FIA) module which includes a lip-shape control structure and a face texture reference structure is proposed.
- Joint Learning Content and Degradation Aware Feature for Blind Super-Resolution16
The proposed Content and Degradation aware SR Network dubbed CDSR outperforms the existing UDP models and achieves competitive performance on PSNR and SSIM even compared with the state-of-the-art SKP methods.
- OracleFusion: Assisting the Decipherment of Oracle Bone Script with Structurally Constrained Semantic Typography12
A novel twostage semantic typography framework that outperforms state-of-the-art baseline models in terms of semantics, visual appeal, and glyph maintenance, and provides expertlike insights on unseen oracle characters, making it a valuable tool for advancing the decipherment of OBS.
- 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.
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- 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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- VTBench: Comprehensive Benchmark Suite Towards Real-World Virtual Try-on Models6
VTBench is a hierarchical benchmark suite that systematically decomposes virtual image try-on into hierarchical, disentangled dimensions, each equipped with tailored test sets and evaluation criteria, to foster the field of virtual try-on towards challenging real-world scenario.
- 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.
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- CustAny: Customizing Anything from A Single Example3
This paper proposes a novel pipeline to construct a large dataset of general objects and build the Multi-Category ID-Consistent (MC-IDC) dataset, and introduces Customizing Anything (CustAny), a zero-shot framework that maintains ID fidelity and supports flexible text editing for general objects.
- ArtWeaver: Advanced Dynamic Style Integration via Diffusion Model3
A novel framework dubbed StyleMaster is proposed for this task by leveraging pretrained Stable Diffusion (SD), which addresses previous problems such as insufficient style and inconsistent semantics, and introduces two objective functions to optimize the model together with denoising loss, which can further enhance semantic and style consistency.
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- When Preferences Diverge: Aligning Diffusion Models with Minority-Aware Adaptive DPO1
An Adaptive-DPO loss function is introduced which improves the DPO loss in two ways: enhancing the model's learning of majority labels while mitigating the negative impact of minority samples, paving the way for more effective training methodologies in image generation tasks.
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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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