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Found 13 papers, 9 papers with code
S3Editor: A Sparse Semantic-Disentangled Self-Training Framework for Face Video Editing
S3Editor is model-agnostic and compatible with various editing approaches.
The Efficiency Spectrum of Large Language Models: An Algorithmic Survey
The rapid growth of Large Language Models (LLMs) has been a driving force in transforming various domains, reshaping the artificial general intelligence landscape.
SEED-Bench-2: Benchmarking Multimodal Large Language Models
Multimodal large language models (MLLMs), building upon the foundation of powerful large language models (LLMs), have recently demonstrated exceptional capabilities in generating not only texts but also images given interleaved multimodal inputs (acting like a combination of GPT-4V and DALL-E 3).
PELA: Learning Parameter-Efficient Models with Low-Rank Approximation
This allows for direct and efficient utilization of the low-rank model for downstream fine-tuning tasks.
SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension
Based on powerful Large Language Models (LLMs), recent generative Multimodal Large Language Models (MLLMs) have gained prominence as a pivotal research area, exhibiting remarkable capability for both comprehension and generation.
Mining Conditional Part Semantics with Occluded Extrapolation for Human-Object Interaction Detection
Human-Object Interaction Detection is a crucial aspect of human-centric scene understanding, with important applications in various domains.
EVD Surgical Guidance with Retro-Reflective Tool Tracking and Spatial Reconstruction using Head-Mounted Augmented Reality Device
The head surface is reconstructed using depth data for spatial registration, avoiding fixing tracking targets rigidly on the patient's skull.
What Makes for Good Visual Tokenizers for Large Language Models?
In our benchmark, which is curated to evaluate MLLMs visual semantic understanding and fine-grained perception capabilities, we discussed different visual tokenizers pre-trained with dominant methods (i. e., DeiT, CLIP, MAE, DINO), and observe that: i) Fully/weakly supervised models capture more semantics than self-supervised models, but the gap is narrowed by scaling up the pre-training dataset.
Text to Point Cloud Localization with Relation-Enhanced Transformer
To overcome these two challenges, we propose a unified Relation-Enhanced Transformer (RET) to improve representation discriminability for both point cloud and natural language queries.
Chairs Can be Stood on: Overcoming Object Bias in Human-Object Interaction Detection
To quantitatively study the object bias problem, we advocate a new protocol for evaluating model performance.
Distance Matters in Human-Object Interaction Detection
2) Insufficient number of distant interactions in benchmark datasets results in under-fitting on these instances.
Relation-aware Compositional Zero-shot Learning for Attribute-Object Pair Recognition
The concept module generates semantically meaningful features for primitive concepts, whereas the visual module extracts visual features for attributes and objects from input images.
Multi-source Distilling Domain Adaptation
Deep neural networks suffer from performance decay when there is domain shift between the labeled source domain and unlabeled target domain, which motivates the research on domain adaptation (DA).
Domain Adaptation
Multi-Source Unsupervised Domain Adaptation
