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Found 14 papers, 11 papers with code
VisionLLM v2: An End-to-End Generalist Multimodal Large Language Model for Hundreds of Vision-Language Tasks
It not only allows flexible transmission of task information and gradient feedback between the MLLM and multiple downstream decoders but also effectively resolves training conflicts in multi-tasking scenarios.
Groma: Localized Visual Tokenization for Grounding Multimodal Large Language Models
We introduce Groma, a Multimodal Large Language Model (MLLM) with grounded and fine-grained visual perception ability.
Ranked #17 on
Natural Language Visual Grounding
on ScreenSpot
UniRef++: Segment Every Reference Object in Spatial and Temporal Spaces
We evaluate our unified models on various benchmarks.
InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks
However, the progress in vision and vision-language foundation models, which are also critical elements of multi-modal AGI, has not kept pace with LLMs.
Ranked #1 on
Zero-Shot Video Retrieval
on MSR-VTT-full
(using extra training data)
Exploring Transformers for Open-world Instance Segmentation
Open-world instance segmentation is a rising task, which aims to segment all objects in the image by learning from a limited number of base-category objects.
VisionLLM: Large Language Model is also an Open-Ended Decoder for Vision-Centric Tasks
We hope this model can set a new baseline for generalist vision and language models.
Multi-Level Contrastive Learning for Dense Prediction Task
We extend our pretext task to supervised pre-training, which achieves a similar performance to self-supervised learning.
Universal Instance Perception as Object Discovery and Retrieval
All instance perception tasks aim at finding certain objects specified by some queries such as category names, language expressions, and target annotations, but this complete field has been split into multiple independent subtasks.
Described Object Detection
Generalized Referring Expression Comprehension
+15
Segment Every Reference Object in Spatial and Temporal Spaces
In this work, we end the current fragmented situation and propose UniRef to unify the three reference-based object segmentation tasks with a single architecture.
Self-supervised Video Representation Learning with Motion-Aware Masked Autoencoders
As a result, our model can extract effectively both static appearance and dynamic motion spontaneously, leading to superior spatiotemporal representation learning capability.
Language as Queries for Referring Video Object Segmentation
Referring video object segmentation (R-VOS) is an emerging cross-modal task that aims to segment the target object referred by a language expression in all video frames.
Ranked #3 on
Referring Expression Segmentation
on A2D Sentences
(using extra training data)
Towards High-Quality Temporal Action Detection with Sparse Proposals
Towards high-quality temporal action detection, we introduce Sparse Proposals to interact with the hierarchical features.
Watch Only Once: An End-to-End Video Action Detection Framework
WOO takes a unified video backbone to simultaneously extract features for actor location and action classification.
SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving
We open-source the SMARTS platform and the associated benchmark tasks and evaluation metrics to encourage and empower research on multi-agent learning for autonomous driving.
