406 citations · 1.8k across the 130 of their papers we have counts for
121 papers · 1 filter
Learning Accurate Segmentation Purely from Self-Supervision
Zuyao You, Zuxuan Wu, Yu-Gang Jiang
Accurately segmenting objects without any manual annotations remains one of the core challenges in computer vision. In this work, we introduce Selfment, a fully self-supervised fra…
DCDM: Divide-and-Conquer Diffusion Models for Consistency-Preserving Video Generation
Haoyu Zhao, Yuang Zhang, Junqi Cheng +5
Recent video generative models have demonstrated impressive visual fidelity, yet they often struggle with semantic, geometric, and identity consistency. In this paper, we propose a…
Efficient-LVSM: Faster, Cheaper, and Better Large View Synthesis Model via Decoupled Co-Refinement Attention
Xiaosong Jia, Yihang Sun, Junqi You +5
Feedforward models for novel view synthesis (NVS) have recently advanced by transformer-based methods like LVSM, using attention among all input and target views. In this work, we…
LRANet++: Low-Rank Approximation Network for Accurate and Efficient Text Spotting
Yuchen Su, Zhineng Chen, Yongkun Du +3
End-to-end text spotting aims to jointly optimize text detection and recognition within a unified framework. Despite significant progress, designing an accurate and efficient end-t…
FreezeVLA: Action-Freezing Attacks against Vision-Language-Action Models
Xin Wang, Jie Li, Zejia Weng +9
Vision-Language-Action (VLA) models are driving rapid progress in robotics by enabling agents to interpret multimodal inputs and execute complex, long-horizon tasks. However, their…
StableAvatar: Infinite-Length Audio-Driven Avatar Video Generation
Shuyuan Tu, Yueming Pan, Yinming Huang +6
Current diffusion models for audio-driven avatar video generation struggle to synthesize long videos with natural audio synchronization and identity consistency. This paper present…
Multimodal Referring Segmentation: A Survey
Henghui Ding, Song Tang, Shuting He +3
Multimodal referring segmentation aims to segment target objects in visual scenes, such as images, videos, and 3D scenes, based on referring expressions in text or audio format. Th…
Multi-Prompt Progressive Alignment for Multi-Source Unsupervised Domain Adaptation
Haoran Chen, Zexiao Wang, Haidong Cao +2
Large Vision-Language Models like CLIP have become a powerful foundation for Unsupervised Domain Adaptation due to their strong zero-shot generalization. State-of-the-art methods t…
StableAnimator++: Overcoming Pose Misalignment and Face Distortion for Human Image Animation
Shuyuan Tu, Zhen Xing, Xintong Han +5
Current diffusion models for human image animation often struggle to maintain identity (ID) consistency, especially when the reference image and driving video differ significantly…
SimpleAR: Pushing the Frontier of Autoregressive Visual Generation through Pretraining, SFT, and RL
Junke Wang, Zhi Tian, Xun Wang +4
This work presents SimpleAR, a vanilla autoregressive visual generation framework without complex architecure modifications. Through careful exploration of training and inference o…
Enhancing Vision Foundation Models via Multimodal Continual Pre-Training
Yitong Chen, Lingchen Meng, Wujian Peng +4
Vision Foundation Models (VFMs) provide strong visual representations for a wide range of applications. In this work, we enhance prevailing VFMs through multimodal training, allowi…
Achieving More with Less: Additive Prompt Tuning for Rehearsal-Free Class-Incremental Learning
Haoran Chen, Ping Wang, Zihan Zhou +3
Class-incremental learning (CIL) enables models to learn new classes progressively while preserving knowledge of previously learned ones. Recent advances in this field have shifted…
Cross-Modal Transferable Image-to-Video Attack on Video Quality Metrics
Georgii Gotin, Ekaterina Shumitskaya, Anastasia Antsiferova +1
Recent studies have revealed that modern image and video quality assessment (IQA/VQA) metrics are vulnerable to adversarial attacks. An attacker can manipulate a video through prep…
Comprehensive Multi-Modal Prototypes are Simple and Effective Classifiers for Vast-Vocabulary Object Detection
Yitong Chen, Wenhao Yao, Lingchen Meng +3
Enabling models to recognize vast open-world categories has been a longstanding pursuit in object detection. By leveraging the generalization capabilities of vision-language models…
CreatiLayout: Siamese Multimodal Diffusion Transformer for Creative Layout-to-Image Generation
Hui Zhang, Dexiang Hong, Yitong Wang +4
Diffusion models have been recognized for their ability to generate images that are not only visually appealing but also of high artistic quality. As a result, Layout-to-Image (L2I…
INST-IT: Boosting Instance Understanding via Explicit Visual Prompt Instruction Tuning
Wujian Peng, Lingchen Meng, Yitong Chen +7
Large Multimodal Models (LMMs) have made significant breakthroughs with the advancement of instruction tuning. However, while existing models can understand images and videos at a…
StableAnimator: High-Quality Identity-Preserving Human Image Animation
Shuyuan Tu, Zhen Xing, Xintong Han +4
Current diffusion models for human image animation struggle to ensure identity (ID) consistency. This paper presents StableAnimator, the first end-to-end ID-preserving video diffus…
ForgerySleuth: Empowering Multimodal Large Language Models for Image Manipulation Detection
Zhihao Sun, Haoran Jiang, Haoran Chen +4
Multimodal large language models have unlocked new possibilities for various multimodal tasks. However, their potential in image manipulation detection remains unexplored. When dir…
REDUCIO! Generating 1K Video within 16 Seconds using Extremely Compressed Motion Latents
Rui Tian, Qi Dai, Jianmin Bao +5
Commercial video generation models have exhibited realistic, high-fidelity results but are still restricted to limited access. One crucial obstacle for large-scale applications is…
DreamMesh: Jointly Manipulating and Texturing Triangle Meshes for Text-to-3D Generation
Haibo Yang, Yang Chen, Yingwei Pan +5
Learning radiance fields (NeRF) with powerful 2D diffusion models has garnered popularity for text-to-3D generation. Nevertheless, the implicit 3D representations of NeRF lack expl…
Downstream Transfer Attack: Adversarial Attacks on Downstream Models with Pre-trained Vision Transformers
Weijie Zheng, Xingjun Ma, Hanxun Huang +2
With the advancement of vision transformers (ViTs) and self-supervised learning (SSL) techniques, pre-trained large ViTs have become the new foundation models for computer vision a…
GenRec: Unifying Video Generation and Recognition with Diffusion Models
Zejia Weng, Xitong Yang, Zhen Xing +2
Video diffusion models are able to generate high-quality videos by learning strong spatial-temporal priors on large-scale datasets. In this paper, we aim to investigate whether suc…
V3Det Challenge 2024 on Vast Vocabulary and Open Vocabulary Object Detection: Methods and Results
Jiaqi Wang, Yuhang Zang, Pan Zhang +31
Detecting objects in real-world scenes is a complex task due to various challenges, including the vast range of object categories, and potential encounters with previously unknown…
OmniTokenizer: A Joint Image-Video Tokenizer for Visual Generation
Junke Wang, Yi Jiang, Zehuan Yuan +3
Tokenizer, serving as a translator to map the intricate visual data into a compact latent space, lies at the core of visual generative models. Based on the finding that existing to…