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Found 9 papers, 2 papers with code
Infant Agent: A Tool-Integrated, Logic-Driven Agent with Cost-Effective API Usage
Despite the impressive capabilities of large language models (LLMs), they currently exhibit two primary limitations, \textbf{\uppercase\expandafter{\romannumeral 1}}: They struggle to \textbf{autonomously solve the real world engineering problem}.
Robin3D: Improving 3D Large Language Model via Robust Instruction Tuning
RIG generates two key instruction data: 1) the Adversarial Instruction-following data, which features mixed negative and positive samples to enhance the model's discriminative understanding.
Interpolating Video-LLMs: Toward Longer-sequence LMMs in a Training-free Manner
In this paper, we first identify the primary challenges in interpolating Video-LLMs: (1) the video encoder and modality alignment projector are fixed, preventing the integration of additional frames into Video-LLMs, and (2) the LLM backbone is limited in its content length capabilities, which complicates the processing of an increased number of video tokens.
SegVG: Transferring Object Bounding Box to Segmentation for Visual Grounding
Specifically, we propose the Multi-layer Multi-task Encoder-Decoder as the target grounding stage, where we learn a regression query and multiple segmentation queries to ground the target by regression and segmentation of the box in each decoding layer, respectively.
Visual Grounding with Attention-Driven Constraint Balancing
Building upon this, we further propose a novel framework named Attention-Driven Constraint Balancing (AttBalance) to optimize the behavior of visual features within language-relevant regions.
ACTRESS: Active Retraining for Semi-supervised Visual Grounding
Building upon this, ACTRESS consists of an active sampling strategy and a selective retraining strategy.
Intent3D: 3D Object Detection in RGB-D Scans Based on Human Intention
To achieve detection based on human intention, it relies on humans to observe the scene, reason out the target that aligns with their intention ("pillow" in this case), and finally provide a reference to the AI system, such as "A pillow on the couch".
On the Faithfulness of Vision Transformer Explanations
In contrast, our proposed SaCo offers a reliable faithfulness measurement, establishing a robust metric for interpretations.
Token Transformation Matters: Towards Faithful Post-hoc Explanation for Vision Transformer
To incorporate the influence of token transformation into interpretation, we propose TokenTM, a novel post-hoc explanation method that utilizes our introduced measurement of token transformation effects.
