Machine learning models often inadvertently learn and perpetuate biases present in training data, particularly concerning sensitive attributes like gender and race. While existing fair representation learning approaches attempt to address this issue, they face challenges in balancing information preservation with bias elimination. This paper addresses the challenge of algorithmic bias in machine learning models by proposing a novel idempotence-constrained fair representation learning framework. We introduce a two-stage architecture that effectively eliminates both explicit and implicit bias while maintaining model performance. The first stage employs an idempotent encoder to remove explicit bias and enhance feature robustness through adaptive adversarial training, while the second stage utilizes a privacy filtering unit to eliminate implicit bias and task-irrelevant features. Our framework optimizes four key objectives: task performance, information preservation, sensitive information elimination, and representation stability. Theoretical analysis demonstrates convergence guarantees and representation stability of our approach. Extensive experiments on benchmark datasets, including UCI Adult and Heritage Health, show that our method achieves state-of-the-art performance in both fairness and accuracy metrics.

Label-efficient LiDAR-based 3D object detection is currently dominated by weakly/semi-supervised methods. Instead of exclusively following one of them, we propose MixSup, a more practical paradigm simultaneously utilizing massive cheap coarse labels and a limited number of accurate labels for Mixed-grained Supervision. We start by observing that point clouds are usually textureless, making it hard to learn semantics. However, point clouds are geometrically rich and scale-invariant to the distances from sensors, making it relatively easy to learn the geometry of objects, such as poses and shapes. Thus, MixSup leverages massive coarse cluster-level labels to learn semantics and a few expensive box-level labels to learn accurate poses and shapes. We redesign the label assignment in mainstream detectors, which allows them seamlessly integrated into MixSup, enabling practicality and universality. We validate its effectiveness in nuScenes, Waymo Open Dataset, and KITTI, employing various detectors. MixSup achieves up to 97.31% of fully supervised performance, using cheap cluster annotations and only 10% box annotations. Furthermore, we propose PointSAM based on the Segment Anything Model for automated coarse labeling, further reducing the annotation burden. The code is available at https://github.com/BraveGroup/PointSAM-for-MixSup.

Gaze-following is a kind of research that requires locating where the person in the scene is looking automatically under the topic of gaze estimation. It is an important clue for understanding human intention, such as identifying objects or regions of interest to humans. However, a survey of datasets used for gaze-following tasks reveals defects in the way they collect gaze point labels. Manual labeling may introduce subjective bias and is labor-intensive, while automatic labeling with an eye-tracking device would alter the person's appearance. In this work, we introduce GFIE, a novel dataset recorded by a gaze data collection system we developed. The system is constructed with two devices, an Azure Kinect and a laser rangefinder, which generate the laser spot to steer the subject's attention as they perform in front of the camera. And an algorithm is developed to locate laser spots in images for annotating 2D/3D gaze targets and removing ground truth introduced by the spots. The whole procedure of collecting gaze behavior allows us to obtain unbiased labels in unconstrained environments semi-automatically. We also propose a baseline method with stereo field-of-view (FoV) perception for establishing a 2D/3D gaze-following benchmark on the GFIE dataset. Project page: https://sites.google.com/view/gfie.

This paper aims for high-performance offline LiDAR-based 3D object detection. We first observe that experienced human annotators annotate objects from a track-centric perspective. They first label objects in a track with clear shapes, and then leverage the temporal coherence to infer the annotations of obscure objects. Drawing inspiration from this, we propose a high-performance offline detector in a track-centric perspective instead of the conventional object-centric perspective. Our method features a bidirectional tracking module and a track-centric learning module. Such design allows our detector to infer and refine a complete track once the object is detected at a certain moment. We refer this characteristic to "onCe detecTed, neveR Lost" and name the proposed system CTRL. Extensive experiments demonstrate the remarkable performance of our method, surpassing the human-level annotating accuracy and previous state-of-the-art methods in the highly competitive Waymo Open Dataset leaderboard without model ensemble. The code is available at https://github.com/tusen-ai/SST.

As the perception range of LiDAR expands, LiDAR-based 3D object detection contributes ever-increasingly to the long-range perception in autonomous driving. Mainstream 3D object detectors often build dense feature maps, where the cost is quadratic to the perception range, making them hardly scale up to the long-range settings. To enable efficient long-range detection, we first propose a fully sparse object detector termed FSD. FSD is built upon the general sparse voxel encoder and a novel sparse instance recognition (SIR) module. SIR groups the points into instances and applies highly-efficient instance-wise feature extraction. The instance-wise grouping sidesteps the issue of the center feature missing, which hinders the design of the fully sparse architecture. To further enjoy the benefit of fully sparse characteristic, we leverage temporal information to remove data redundancy and propose a super sparse detector named FSD++. FSD++ first generates residual points, which indicate the point changes between consecutive frames. The residual points, along with a few previous foreground points, form the super sparse input data, greatly reducing data redundancy and computational overhead. We comprehensively analyze our method on the large-scale Waymo Open Dataset, and state-of-the-art performance is reported. To showcase the superiority of our method in long-range detection, we also conduct experiments on Argoverse 2 Dataset, where the perception range ([Formula: see text] m) is much larger than Waymo Open Dataset ([Formula: see text] m).

As an essential nonverbal cue, the human gaze reveals human intentions and plays a crucial role in human daily activities. Therefore, automatic detection of the person’s gaze target has drawn the interests of the computer vision community. This is useful not only for identifying whether children are attentive in class but also for locating items of interest to humans in retail settings. Existing gaze-following methods have only explored and exploited the scenes context and the head cues. Considering the significance of human-object interaction in understanding human intentions, we present the Visual-Spatial Graph and introduce a graph attention network to analyze the interaction probability between the human and elements in the scene. Then the interaction probability inferred from the visual-spatial information that is aggregated by the attention mechanism can be transformed into an interactive attention map that depicts the areas people care about. In addition, we construct a transformer as an encoder to integrate the features extracted by the scene and head pathways aiming to decode the gaze target. After introducing interactive attention, our proposed method achieves outstanding performance on two benchmarks: GazeFollow and VideoAttentionTarget. Our code is available athttps://github.com/nkuhzx/VSG-IA.