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- Hongsheng LiSuggested from co-authorship
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Works108 from public data
- Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation1,235
This work proposes a new T2V generation setting—One-Shot Video Tuning, where only one text-video pair is presented, and introduces Tune-A-Video, which involves a tailored spatio-temporal attention mechanism and an efficient one-shot tuning strategy.
- YOLO-World: Real-Time Open-Vocabulary Object Detection1,134
YOLO-World is introduced, an innovative approach that enhances YOLO with open-vocabulary detection capabilities through vision-language modeling and pre-training on large-scale datasets and proposes a new Re-parameterizable Vision-Language Path Aggregation Network (RepVL-PAN) and region-text contrastive loss to facilitate the interaction between visual and linguistic information.
- SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension1,018
This work addresses the evaluation of generative comprehension in MLLMs as a preliminary step towards a comprehensive assessment ofGenerative models, by introducing a benchmark named SEED-Bench, which spans 12 evaluation dimensions including the comprehension of both the image and video modality.
- Self-paced Contrastive Learning with Hybrid Memory for Domain Adaptive Object Re-ID691
This work proposes a novel self-paced contrastive learning framework with hybrid memory that outperforms state-of-the-arts on multiple domain adaptation tasks of object re-ID and even boosts the performance on the source domain without any extra annotations.
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- SEED-X: Multimodal Models with Unified Multi-granularity Comprehension and Generation365
This work presents a unified and versatile foundation model, namely, SEED-X, which is able to model multi-granularity visual semantics for comprehension and generation tasks and demonstrates its effectiveness in handling real-world applications across various domains after instruction tuning.
- UniRepLKNet: A Universal Perception Large-Kernel ConvNet for Audio, Video, Point Cloud, Time-Series and Image Recognition365
Large kernels are the key to unlocking the exceptional performance of ConvNets in domains where they were originally not proficient, and the proposed model achieves state- of-the-art performance on time-series forecasting and audio recognition tasks even without modality-specific customization to the architecture.
- GPT4Tools: Teaching Large Language Model to Use Tools via Self-instruction340
The GPT4Tools based on self-instruct to enable open-source LLMs, such as LLaMA and OPT, to use tools, generates an instruction-following dataset by prompting an advanced teacher with various multi-modal contexts using the Low-Rank Adaptation (LoRA) optimization.
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- All in One: Exploring Unified Video-Language Pre-Training258
This work introduces an end-to-end VLP model, namely all-in-one Transformer, that embeds raw video and textual signals into joint representations using a unified backbone architecture and introduces a novel and effective token rolling operation to encode temporal representations from video clips in a non-parametric manner.
- Making LLaMA SEE and Draw with SEED Tokenizer227
SEED is introduced, an elaborate image tokenizer that empowers LLMs with the ability to SEE and Draw at the same time, and with SEED tokens, LLM is able to perform scalable multimodal autoregression under its original training recipe, i.e., next-word prediction.
- SmartEdit: Exploring Complex Instruction-Based Image Editing with Multimodal Large Language Models213
A Bidirectional Interaction Module (BIM) is proposed that enables comprehensive bidirectional information interactions between the input image and the MLLM output and demonstrates that a small amount of complex instruction editing data can effectively stimulate SmartEdit’ s editing capabilities for more complex instructions.
- Self-supervising Fine-Grained Region Similarities for Large-Scale Image Localization210
This work proposes to self-supervise image-to-region similarities in order to fully explore the potential of difficult positive images alongside their sub-regions and outperforms state-of-the-arts on the standard localization benchmarks and shows excellent generalization capability on multiple image retrieval datasets.
- SEED-Bench-2-Plus: Benchmarking Multimodal Large Language Models with Text-Rich Visual Comprehension156
SEED-Bench-2-Plus is introduced, a benchmark specifically designed for evaluating text-rich visual comprehension of Multimodal Large Language Models and a thorough evaluation involving 34 prominent MLLMs is conducted, to emphasize the current limitations of MLLMs in text-rich visual comprehension.
- Refining Pseudo Labels with Clustering Consensus over Generations for Unsupervised Object Re-identification154
This work proposes to properly estimate pseudo label similarities between consecutive training generations with clustering consensus and refine pseudo labels with temporally propagated and ensembled pseudo labels to improve classification with dynamically changing classes over generations.
- Planting a SEED of Vision in Large Language Model150
This study identifies two crucial principles for the architecture and training of SEED that effectively ease subsequent alignment with LLMs, and emphasizes the great potential of discrete visual tokens in versatile multimodal LLMs and the importance of proper image tokenizers in broader research.
- Bridging Video-text Retrieval with Multiple Choice Questions131
This work enables fine-grained video-text interactions while maintaining high efficiency for retrieval via a novel pretext task, dubbed as Multiple Choice Questions (MCQ), where a parametric module BridgeFormer is trained to answer the “questions” constructed by the text features via resorting to the video features.
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- Progressive Correspondence Pruning by Consensus Learning112
This work introduces a "pruning" block that lets us identify reliable candidates among the initial matches according to consensus scores estimated using local-to-global dynamic graphs and achieves progressive pruning by stacking multiple pruning blocks sequentially.
- DreamDiffusion: Generating High-Quality Images from Brain EEG Signals111
Quantitative and qualitative results demonstrate the effectiveness of the proposed DreamDiffusion method as a significant step towards portable and low-cost ``thoughts-to-image'', with potential applications in neuroscience and computer vision.
- SEED-Data-Edit Technical Report: A Hybrid Dataset for Instructional Image Editing106
A pretrained Multimodal Large Language Model (MLLM) that unifies comprehension and generation with SEED-Data-Edit is fine-tuneed, indicating the potential and effectiveness of SEED-Data-Edit in advancing the field of instructional image editing.
- ATP-LLaVA: Adaptive Token Pruning for Large Vision Language Models104
This work proposes ATP-LLaVA, a novel approach that adaptively determines instance-specific token pruning ratios for each LLM layer, and introduces an Adaptive Token Pruning (ATP) module, which computes the importance score and pruning threshold based on input instance adaptively.
- SEED-Bench-2: Benchmarking Multimodal Large Language Models103
This work categorizes the capabilities of MLLMs into hierarchical levels from $L_0 to $L-4 based on the modalities they can accept and generate, and proposes SEED-Bench-2, a comprehensive benchmark that evaluates the hierarchical capabilities ofMLLMs.
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Publication data from OpenAlex; 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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