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- MovieChat: From Dense Token to Sparse Memory for Long Video Understanding685
The MovieChat achieves state-of-the-art performance in long video understanding, along with the released MovieChat-1K benchmark with 1K long video and 14K manual annotations for validation of the effectiveness of the effectiveness of the method.
- StableVideo: Text-driven Consistency-aware Diffusion Video Editing243
This paper develops a novel inter-frame propagation mechanism for diffusion video editing, which leverages the concept of layered representations to propagate the appearance information from one frame to the next.
- DiffFashion: Reference-Based Fashion Design With Structure-Aware Transfer by Diffusion Models68
This work presents a novel diffusion model-based unsupervised structure-aware transfer method, namely DiffFashion, which outperforms state-of-the-art baseline models, generating more realistic images in the fashion design task.
- Five A$^{+}$ Network: You Only Need 9K Parameters for Underwater Image Enhancement68
Thorough extensive experiments and comprehensive visual comparison, it is shown that FA$^{+}$Net outperforms previous approaches by obtaining state-of-the-art performance on multiple datasets while significantly reducing both parameter count and computational complexity.
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- Back to Optimization: Diffusion-based Zero-Shot 3D Human Pose Estimation51
This work proposes the Zero-shot Diffusion-based Optimization (ZeDO) pipeline for 3D HPE to solve the problem of cross-domain and in-the-wild 3D HPE, which achieves state-of-the-art (SOTA) performance on Human3.6M and even outperforms learning-based methods trained on 3DPW.
- Deep Vision Multimodal Learning: Methodology, Benchmark, and Trend48
The types of architectures used in multimodal learning, including feature extraction, modality aggregation, and multi-modality loss functions, are reviewed, and it is indicated that pretraining paradigm, unified multitask framework, missing and noisy modality, and multimodale task diversity could be the future trends and challenges in the deep vision multimodAL learning field.
- PAD: Personalized Alignment of LLMs at Decoding-Time36
Personalized Alignment at Decoding-time (PAD) is presented, a novel framework designed to align LLM outputs with diverse personalized preferences during the inference phase, eliminating the need for additional training.
- BabyVision: Visual Reasoning Beyond Language35
This work introduces BabyVision, a benchmark designed to assess core visual abilities independent of linguistic knowledge for Multimodal LLMs, and explores solving visual reasoning with generation models by proposing BabyVision-Gen and automatic evaluation toolkit.
- PoSynDA: Multi-Hypothesis Pose Synthesis Domain Adaptation for Robust 3D Human Pose Estimation33
The Multi-Hypothesis Pose Synthesis Domain Adaptation (PoSynDA) framework is presented, which simulates the 3D pose distribution in the target domain, filling the data diversity gap and paves the way for the practical application of 3D human pose estimation.
- An Empirical Study of GPT-4o Image Generation Capabilities31
An empirical study of GPT-4o's image generation capabilities is conducted, benchmarking it against leading open-source and commercial models and identifying promising directions for future unified generative models, emphasizing the role of architectural design and data scaling.
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- VersaT2I: Improving Text-to-Image Models with Versatile Reward23
VersaT2I is presented, a versatile training framework that can boost the performance with multiple rewards of any T2I model using the Low-Rank Adaptation (LoRA), and introduces a gating function to combine multiple quality aspects, which can avoid conflicts between different quality aspects.
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- PromptHaze: Prompting Real-world Dehazing via Depth Anything Model10
Extensive experiments on widely-used real-world dehazing benchmarks demonstrate the superiority of PromptHaze in recovering authentic backgrounds and fine details from various haze scenes, outperforming state-of-the-art methods across multiple quality metrics.
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- Sequential Affinity Learning for Video Restoration10
The proposed Sequential Affinity Learning Network (SALN), a novel approach for video restoration that eliminates inefficient temporal modeling operators and pixel-level feature alignment in the network architecture, is designed based on an affinity mechanism that establishes direct correspondences between the Query frame, degraded sequence, and restored frames in latent space.
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- An Efficient Multi-prior Hybrid Approach for Consistent 3D Generation from Single Images–
Experimental results demonstrate the approach generates more consistent and realistic 3D assets than previous state-of-the-art methods in visual quality and quantitative metrics.
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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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