Visual questions are often ambiguous: the same image-question pair may admit multiple valid answers depending on which region is referenced.However, current Visual Question Answering (VQA) systems typically collapse this ambiguity, committing to a single interpretation during decoding and evaluation.In this work, we study visual question ambiguity from a grounded, region-centric perspective.We operationalize ambiguity as the existence of multiple distinct answer-supporting regions in an image, each independently yielding a valid answer.This formulation makes ambiguity observable without requiring exhaustive multi-answer annotations.Based on this definition, we conduct a systematic empirical study of state-of-the-art Visual Large Language Models (VLLMs).We find that, under default decoding, VLLMs consistently under-report ambiguity-even when multiple valid visual groundings are present.Importantly, probing model hidden states reveals that ambiguity-related signals are already encoded in their internal representations, despite not being reliably expressed in outputs.Finally, we show that selectively activating multi-focus answering based on these signals can recover additional valid answers while avoiding excessive hallucination.Together, our results suggest that ambiguity in VQA is not merely an annotation artifact or capability limitation, but a property that VLLMs internally recognize yet often fail to surface under standard decoding assumptions.

In structured light systems, the accuracy of measurement notably diminishes when assessing complex texture objects, especially encountering boundaries between various colors. To address this challenge, this paper meticulously analyzes and establishes an error model, elaborating the correlation between phase errors and the gradients of phase and gray-scale. Based on this analysis, a novel high-precision method is proposed for measuring complex texture objects via bidirectional fringe projection. This approach firstly leverages horizontal and vertical fringe projections to derive bidirectional phase information and calculates the angles between the tangent of the texture edges and the phase gradient. Subsequently, a refined temporal phase correction algorithm is formulated based on the epipolar matching algorithm and the devised error model, effectively mitigating numerical instability issues within the algorithm and significantly reducing errors of bidirectional phases. Ultimately, corrected point clouds are calculated based on bidirectional phases, and the obtained point clouds are merged to further diminish phase errors. Comparison experiments indicate that this method can reduce Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) by 65.74% and 67.75%, respectively. Compared to existing methods, it improves performance by 27.29% and 33.74%, respectively, demonstrating superior performance.

Clarification Question Generation (abbr., CQG) is crucial for ambiguous question answering. It produces the structured "clarification question" to reveal the intention and possible answers. Currently, the existing CQG approach is grounded on a pipeline mode, i.e., predicting possible answers first, and further performing CQG accordingly. This approach is applicable and obtains promising performance. However, it suffers from an unavoidable bottleneck that the distinctions among the answers are difficult to perceive, while such distinctions reveal significantly different intentions of questions. Without the ability of distinction perception and differentiation, the generator easily falls into the hallucination caused by distracting intentions. To address the issues, we construct an end-to-end CQG model using multitask learning. In particular, we propose an Answer-oriented Distinction Perception (ADP) approach to enhance the multi-task learning process. Specifically, ADP conducts comparison between a pair of possible answers, and instructs the Large Language Model (LLM) to summarize their distinction. We integrate ADP into the multi-task learning framework, progressively coupling it with possible answer generation and CQG to form different auxiliary tasks. The goal is to obtain the generalized distinction-aware CQG model. In our experiments, we use LLaMA3 as the backbone of CQG, and fine-tune it by multi-task learning. We leverage ChatGPT to produce the distinction descriptions among possible answers, and use them as observable evidence to fine-tune LLaMA3 for ADP. We evaluate our CQG model on the benchmark dataset CAmbigNQ. The test result shows that our ADP-based end-to-end CQG obtains substantial improvements compared to the pipeline CQG model. In addition, we apply our CQG model to the downstream ambiguous question answering task, and achieve an F1-score of 45.1% with an improvement of 5.9% at best (4.1% at worst).

3D reconstruction is a fundamental task in robotics and AI, providing a prerequisite for many related applications. Fringe projection profilometry is an efficient and non-contact method for generating 3D point clouds out of 2D images. However, during the actual measurement, it is inevitable to experiment with translucent objects, such as skin, marble, and fruit. Indirect illumination from these objects has substantially compromised the precision of 3D reconstruction via the contamination of 2D images. This paper presents a fast and accurate approach to correct for indirect illumination. The essential idea is to design a highly suitable network architecture founded on a precise error model that facilitates accurate error rectification. Initially, our method transforms the error generated by indirect illumination into a sine series. Based on this error model, the multilayer perceptron is more effective in error correction than traditional methods and convolutional neural networks. Our network was trained solely on simulated data but was tested on authentic images. Three sets of experiments, including two sets of comparison experiments, indicate that the designed network can efficiently rectify the error induced by indirect illumination.

Fringe projection profilometry is a widely used technique for 3D measurement due to its high accuracy and speed. However, the accuracy significantly decreases when measuring complex texture objects, especially in the junction of different colors. This paper analyzes the causes of errors resulting from complex textures and proposes a height compensation method to revise the error by employing a dual-projector structure. Moreover, the dual-projector is capable of acquiring a pair of errors with opposite signs, which can be utilized to calculate the accurate 3D information after determining the ratio of this pair of errors. Experiments provide significant improvement in measuring complex texture objects, demonstrating the proposed method's ability.