Trending Research
SAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking with Motion-Aware Memory
The Segment Anything Model 2 (SAM 2) has demonstrated strong performance in object segmentation tasks but faces challenges in visual object tracking, particularly when managing crowded scenes with fast-moving or self-occluding objects.
Ranked #1 on
Visual Object Tracking
on GOT-10k
EchoMimicV2: Towards Striking, Simplified, and Semi-Body Human Animation
Recent work on human animation usually involves audio, pose, or movement maps conditions, thereby achieves vivid animation quality.
ShowUI: One Vision-Language-Action Model for GUI Visual Agent
showlab/showui
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In this work, we develop a vision-language-action model in digital world, namely ShowUI, which features the following innovations: (i) UI-Guided Visual Token Selection to reduce computational costs by formulating screenshots as an UI connected graph, adaptively identifying their redundant relationship and serve as the criteria for token selection during self-attention blocks; (ii) Interleaved Vision-Language-Action Streaming that flexibly unifies diverse needs within GUI tasks, enabling effective management of visual-action history in navigation or pairing multi-turn query-action sequences per screenshot to enhance training efficiency; (iii) Small-scale High-quality GUI Instruction-following Datasets by careful data curation and employing a resampling strategy to address significant data type imbalances.
OminiControl: Minimal and Universal Control for Diffusion Transformer
In this paper, we introduce OminiControl, a highly versatile and parameter-efficient framework that integrates image conditions into pre-trained Diffusion Transformer (DiT) models.
Star Attention: Efficient LLM Inference over Long Sequences
Inference with Transformer-based Large Language Models (LLMs) on long sequences is both costly and slow due to the quadratic complexity of the self-attention mechanism.
Mooncake: A KVCache-centric Disaggregated Architecture for LLM Serving
Compared to the baseline method, Mooncake can achieve up to a 525% increase in throughput in certain simulated scenarios while adhering to SLOs.
Unpacking DPO and PPO: Disentangling Best Practices for Learning from Preference Feedback
High-quality preference data leads to improvements of up to 8% in instruction following and truthfulness.
One Diffusion to Generate Them All
Experimental results demonstrate competitive performance across tasks in both generation and prediction such as text-to-image, multiview generation, ID preservation, depth estimation and camera pose estimation despite relatively small training dataset.
Marco-o1: Towards Open Reasoning Models for Open-Ended Solutions
aidc-ai/marco-o1
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Currently OpenAI o1 sparks a surge of interest in the study of large reasoning models (LRM).
StableAnimator: High-Quality Identity-Preserving Human Image Animation
Francis-Rings/StableAnimator
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During inference, we propose a novel Hamilton-Jacobi-Bellman (HJB) equation-based optimization to further enhance the face quality.
