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Found 19 papers, 12 papers with code
Evolving Symbolic 3D Visual Grounder with Weakly Supervised Reflection
3D visual grounding (3DVG) is challenging because of the requirement of understanding on visual information, language and spatial relationships.
SeqAfford: Sequential 3D Affordance Reasoning via Multimodal Large Language Model
In this paper, we introduce the Sequential 3D Affordance Reasoning task, which extends the traditional paradigm by reasoning from cumbersome user intentions and then decomposing them into a series of segmentation maps.
MALoRA: Mixture of Asymmetric Low-Rank Adaptation for Enhanced Multi-Task Learning
Parameter-Efficient Fine-Tuning (PEFT) methods like LoRA have significantly improved the adaptation of LLMs to downstream tasks in a resource-efficient manner.
GRUtopia: Dream General Robots in a City at Scale
Recent works have been exploring the scaling laws in the field of Embodied AI.
Delta-CoMe: Training-Free Delta-Compression with Mixed-Precision for Large Language Models
Motivated by the long-tail distribution of singular values in the delta weights, we propose a delta quantization approach using mixed-precision.
MiLoRA: Harnessing Minor Singular Components for Parameter-Efficient LLM Finetuning
It is observed that the minor matrix corresponds to the noisy or long-tail information, while the principal matrix contains important knowledge.
LoRA-Flow: Dynamic LoRA Fusion for Large Language Models in Generative Tasks
Most prior works on LoRA combination primarily rely on task-level weights for each involved LoRA, making different examples and tokens share the same LoRA weights.
StyleBART: Decorate Pretrained Model with Style Adapters for Unsupervised Stylistic Headline Generation
Stylistic headline generation is the task to generate a headline that not only summarizes the content of an article, but also reflects a desired style that attracts users.
DREAMWALKER: Mental Planning for Continuous Vision-Language Navigation
VLN-CE is a recently released embodied task, where AI agents need to navigate a freely traversable environment to reach a distant target location, given language instructions.
ETPNav: Evolving Topological Planning for Vision-Language Navigation in Continuous Environments
To develop a robust VLN-CE agent, we propose a new navigation framework, ETPNav, which focuses on two critical skills: 1) the capability to abstract environments and generate long-range navigation plans, and 2) the ability of obstacle-avoiding control in continuous environments.
Multilingual Sentence Transformer as A Multilingual Word Aligner
In this paper, we investigate whether multilingual sentence Transformer LaBSE is a strong multilingual word aligner.
Discovering the Real Association: Multimodal Causal Reasoning in Video Question Answering
For textual data, the model prefers the local phrase semantics which may deviate from the global semantics in long sentences.
Towards Versatile Embodied Navigation
With the emergence of varied visual navigation tasks (e. g, image-/object-/audio-goal and vision-language navigation) that specify the target in different ways, the community has made appealing advances in training specialized agents capable of handling individual navigation tasks well.
Counterfactual Cycle-Consistent Learning for Instruction Following and Generation in Vision-Language Navigation
Since the rise of vision-language navigation (VLN), great progress has been made in instruction following -- building a follower to navigate environments under the guidance of instructions.
PEARL: Parallelized Expert-Assisted Reinforcement Learning for Scene Rearrangement Planning
Scene Rearrangement Planning (SRP) is an interior task proposed recently.
Structured Scene Memory for Vision-Language Navigation
Recently, numerous algorithms have been developed to tackle the problem of vision-language navigation (VLN), i. e., entailing an agent to navigate 3D environments through following linguistic instructions.
Active Visual Information Gathering for Vision-Language Navigation
Vision-language navigation (VLN) is the task of entailing an agent to carry out navigational instructions inside photo-realistic environments.
Deep Single-View 3D Object Reconstruction with Visual Hull Embedding
The key idea of our method is to leverage object mask and pose estimation from CNNs to assist the 3D shape learning by constructing a probabilistic single-view visual hull inside of the network.
Transferring Objects: Joint Inference of Container and Human Pose
In the inference phase, given a scanned 3D scene with different object candidates and a dictionary of human poses, our approach infers the best object as a container together with human pose for transferring a given object.
