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Works49 from public data
- VBench: Comprehensive Benchmark Suite for Video Generative Models2,062
VBench is a comprehensive benchmark suite that dissects “video generation quality” into specific, hierarchical, and disentangled dimensions, each with tailored prompts and evaluation methods, and investi-gate the gaps between video and image generation models.
- MetaFormer is Actually What You Need for Vision1,462
It is argued that MetaFormer is the key player in achieving superior results for recent transformer and MLP-like models on vision tasks, and calls for more future research dedicated to improving MetaFormer instead of focusing on the token mixer modules.
- An Attention Enhanced Graph Convolutional LSTM Network for Skeleton-Based Action Recognition941
A novel Attention Enhanced Graph Convolutional LSTM Network (AGC-LSTM) for human action recognition from skeleton data can not only capture discriminative features in spatial configuration and temporal dynamics but also explore the co-occurrence relationship between spatial and temporal domains.
- LaVie: High-Quality Video Generation with Cascaded Latent Diffusion Models426
The incorporation of simple temporal self-attentions, coupled with rotary positional encoding, adequately captures the temporal correlations inherent in video data and proves that the process of joint image-video fine-tuning plays a pivotal role in producing high-quality and creative outcomes.
- Skeleton-Based Action Recognition with Spatial Reasoning and Temporal Stack Learning412
A novel model with spatial reasoning and temporal stack learning (SR-TSL) for skeleton-based action recognition, which consists of a spatial reasoning network (SRN) and a temporal stacklearning network (TSLN).
- MetaFormer Baselines for Vision384
The capacity of MetaFormer is explored, again, by migrating the focus away from the token mixer design, and several baseline models under MetaFormer are introduced using the most basic or common mixers, and demonstrate their gratifying performance.
- FreeU: Free Lunch in Diffusion U-Net297
This paper proposes a simple yet effective method, termed “FreeU”, which enhances generation quality without additional training or finetuning, and can be readily integrated to ex-isting diffusion models, e.g., Stable Diffusion, DreamBooth and ControlNet.
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- Diffusion as Shader: 3D-aware Video Diffusion for Versatile Video Generation Control202
This paper introduces Diffusion as Shader (DaS), a novel approach that supports multiple video control tasks within a unified architecture that leverages 3D tracking videos as control inputs, making the video diffusion process inherently 3D-aware.
- Pose-Guided Multi-Granularity Attention Network for Text-Based Person Search197
A pose-guided multi-granularity attention network (PMA) is proposed, which employs pose information to learn latent semantic alignment between visual body part and textual noun phrase and Experimental results show that this approach outperforms the state-of-the-art methods by 15 % in terms of the top-1 metric.
- VideoBooth: Diffusion-based Video Generation with Image Prompts159
This paper proposes a feed-forward framework VideoBooth, a generalizable framework where a single model works for a wide range of image prompts with only feed-forward passes, and proposes to embed image prompts in a coarse-to-fine manner.
- FasterCache: Training-Free Video Diffusion Model Acceleration with High Quality127
A novel training-free strategy designed to accelerate the inference of video diffusion models with high-quality generation, and CFG-Cache which optimizes the reuse of conditional and unconditional outputs to further enhance inference speed without compromising video quality.
- Skeleton-based action recognition with hierarchical spatial reasoning and temporal stack learning network94
This work proposes a novel model with hierarchical spatial reasoning and temporal stack learning network (HSR-TSL) to explore the discriminative spatial and temporal features for human action recognition, which consists of a hierarchical spatial Reasoning Network (HSRN) and a temporal stacklearning network (TSLN).
- Notice of Removal: Semantic Prompt for Few-Shot Image Recognition89
This paper designs two complementary mechanisms to insert semantic prompts into the feature extractor, one is to enable the interaction between semantic prompts and patch embeddings along the spatial dimension via self-attention, another is to supplement visual features with the transformed semantic prompts along the channel dimension.
- Adversarial Self-supervised Learning for Semi-supervised 3D Action Recognition80
Adversarial Self-Supervised Learning (ASSL) is presented, a novel framework that tightly couples SSL and the semi-supervised scheme via neighbor relation exploration and adversarial learning to improve the discrimination capability of learned representations for 3D action recognition.
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- Mugs: A Multi-Granular Self-Supervised Learning Framework73
This work proposes an effective MUlti-Granular Self-supervised learning (Mugs) framework to explicitly learn multi-granular visual features and surpasses SoTAs on other tasks, e.g. transfer learning, detection and segmentation.
- Contrast-Reconstruction Representation Learning for Self-Supervised Skeleton-Based Action Recognition62
A novel Contrast-Reconstruction Representation Learning network (CRRL) that simultaneously captures postures and motion dynamics for unsupervised skeleton-based action recognition and demonstrates the promise of the method by outperforming state-of-the-art approaches.
- Vchitect-2.0: Parallel Transformer for Scaling Up Video Diffusion Models61
This work presents Vchitect-2.0, a parallel transformer architecture designed to scale up video diffusion models for large-scale text-to-video generation, and proposes a Memory-efficient Training framework that incorporates hybrid parallelism and other memory reduction techniques, enabling efficient training of long video sequences on distributed systems.
- Towards Language-Driven Video Inpainting via Multimodal Large Language Models48
This work introduces a new task - language-driven video inpainting, which uses natural language instructions to guide the inpainting process, integrating Multimodal Large Language Models to understand and execute complex language-based inpaintingrequests effectively.
- CoS: Chain-of-Shot Prompting for Long Video Understanding31
Chain-of-Shot prompting is proposed, to frame shot selection as test-time visual prompt optimisation, choosing shots adaptive to video understanding semantic task by optimising shots-task alignment.
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- LongVie: Multimodal-Guided Controllable Ultra-Long Video Generation22
LongVie, an end-to-end autoregressive framework for controllable long video generation, introduces two core designs to ensure temporal consistency: a unified noise initialization strategy that maintains consistent generation across clips, and global control signal normalization that enforces alignment in the control space throughout the entire video.
- Rethinking Cross-Modal Interaction in Multimodal Diffusion Transformers22
Temperature-Adjusted Cross-modal Attention (TACA), a parameter-efficient method that dynamically rebalances multimodal interactions through temperature scaling and timestep-dependent adjustment, significantly enhances text-image alignment on the T2I-CompBench benchmark with minimal computational overhead.
- FreeMorph: Tuning-Free Generalized Image Morphing with Diffusion Model19
This paper introduces FreeMorph, the first tuning-free method for image morphing that accommodates inputs with different semantics or layouts and proposes a guidance-aware spherical interpolation design that incorporates explicit guidance from the input images by modifying the self-attention modules, thereby addressing identity loss and ensuring directional transitions throughout the generated sequence.
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- Scaling Supervised Local Learning with Augmented Auxiliary Networks15
The proposed AugLocal method opens up a myriad of opportunities for training high-performance deep neural networks on resource-constrained platforms by effectively scale up to tens of local layers with a comparable accuracy to BP-trained networks while reducing GPU memory usage by around 40%.
- Pose-Guided Joint Global and Attentive Local Matching Network for Text-Based Person Search14
Experimental results show that the proposed pose-guided joint global and attentive local matching network (GALM) outperforms the state-of-the-art methods by 15 \% in terms of top-1 metric.
- Cascade Attention Network for Person Search: Both Image and Text-Image Similarity Selection.14
A cascade attention network (CAN) to progressively select from person image and text-image similarity to select the description-related similarity scores from those local similarities is proposed.
- RepVideo: Rethinking Cross-Layer Representation for Video Generation13
This paper proposes RepVideo, an enhanced representation framework for text-to-video diffusion models that enhances the ability to generate accurate spatial appearances, such as capturing complex spatial relationships between multiple objects, but also improves temporal consistency in video generation.
- FSAR: Federated Skeleton-based Action Recognition with Adaptive Topology Structure and Knowledge Distillation13
This paper pioneers a novel Federated Skeleton-based Action Recognition (FSAR) paradigm, which enables the construction of a globally generalized model without accessing local sensitive data, and introduces an Adaptive Topology Structure (ATS), separating generalization and personalization by learning a domain-invariant topology shared across clients and a domain-specific topology decoupled from global model aggregation.
- Exploring Semantic Attributes from A Foundation Model for Federated Learning of Disjoint Label Spaces13
A new Federated Zero-Shot Learning (FZSL) paradigm is formulated to learn mid-level semantic knowledge at multiple local clients with non-shared local data and cumulatively aggregate a globally generalised central model for deployment.
- Few-Shot Learning with Part Discovery and Augmentation from Unlabeled Images13
A novel part-based self-supervised representation learning scheme to learn transferable representations by maximizing the similarity of an image to its discriminative part and a part augmentation strategy by retrieving extra images from a base dataset is proposed.
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- One-to-More: High-Fidelity Training-Free Anomaly Generation with Attention Control2
A training-free few-shot anomaly generation method, namely O2MAG, which leverages the self-attention in One reference anomalous image to synthesize More realistic anomalies, supporting effective downstream anomaly detection and bridge the semantic gap between encoded anomaly text prompts and true anomaly semantics.
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- DiverseAR: Boosting Diversity in Bitwise Autoregressive Image Generation1
DiverseAR is proposed, a principled and effective method that enhances image diversity without sacrificing visual quality by introducing an adaptive logits distribution scaling mechanism that dynamically adjusts the sharpness of the binary output distribution during sampling, resulting in smoother predictions and greater diversity.
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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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