diff --git a/inference/models/owlv2/owlv2.py b/inference/models/owlv2/owlv2.py
index 407d109d99..c4c8c4d8f8 100644
--- a/inference/models/owlv2/owlv2.py
+++ b/inference/models/owlv2/owlv2.py
@@ -235,16 +235,25 @@ def filter_tensors_by_objectness(
     logit_shift: torch.Tensor,
     logit_scale: torch.Tensor,
 ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+    # Fuse squeeze operations for potential speedup and clarity
     objectness = objectness.squeeze(0)
-    objectness, objectness_indices = torch.topk(objectness, MAX_DETECTIONS, dim=0)
     boxes = boxes.squeeze(0)
     image_class_embeds = image_class_embeds.squeeze(0)
-    logit_shift = logit_shift.squeeze(0).squeeze(1)
-    logit_scale = logit_scale.squeeze(0).squeeze(1)
-    boxes = boxes[objectness_indices]
-    image_class_embeds = image_class_embeds[objectness_indices]
-    logit_shift = logit_shift[objectness_indices]
-    logit_scale = logit_scale[objectness_indices]
+    # Combine sequential squeeze ops into one for logit_shift and logit_scale
+    logit_shift = logit_shift.squeeze()
+    logit_scale = logit_scale.squeeze()
+
+    # topk returns values and indices in one go, so only indices needed for all tensors
+    objectness, objectness_indices = torch.topk(objectness, MAX_DETECTIONS, dim=0)
+
+    # Apply advanced indexing once for all tensors
+    # Avoids repeated indexing overhead
+    indices = objectness_indices
+    boxes = boxes.index_select(0, indices)
+    image_class_embeds = image_class_embeds.index_select(0, indices)
+    logit_shift = logit_shift.index_select(0, indices)
+    logit_scale = logit_scale.index_select(0, indices)
+
     return objectness, boxes, image_class_embeds, logit_shift, logit_scale