6 accepted papers
Current Video-LLM approaches for Video Temporal Grounding (VTG) typically rely on direct timestamp generation from an unstructured visual-token stream, often resulting in brittle numerics and inconsistent boundaries. To address this, we propose Foresee-to-Ground (F2G), a framework that enforces a ve…
3D object detection from LiDAR point cloud data is important for autonomous driving systems. Recent two-stage 3D object detectors struggle to achieve satisfactory performance due to limitations in proposal quality, stemming from the degradation of geometric detail information in the generated propos…
Jigsaw puzzle solving remains difficult because models must reconcile local fragment cues with global structure. Most prior work leans solely on visual signals (edge or texture coherence) and rarely exploits natural-language descriptions, which are especially helpful for puzzles with eroded gaps. We
Few-shot object counting and detection aim to count objects along with their bounding boxes specified by exemplar bounding boxes. Current mainstream methods predict density maps by applying similarity between exemplar and image features to get counting results and detecting peak points from the dens…
Video Individual Counting (VIC) aims to predict the number of unique individuals in a single video. Existing methods learn representations based on trajectory labels for individuals which are annotation-expensive. To provide a more realistic reflection of the underlying practical challenge we introd…
Crowd counting is a difficult task because of the diversity of scenes. Most of the existing crowd counting methods adopt complex structures with massive backbones to enhance the generalization ability. Unfortunately, the performance of existing methods on large-scale data sets is not satisfactory. I…