Remove projections (1024 native), add satellite text, dual GatedFusion

Architecture changes:
- Removed proj_drone/proj_sat (1024→512): retrieval space is now
  DINOv3 native 1024-dim, no information loss from projection
- TextFusionMLP: 2304→1024→1024 (was 2304→768→512), shared between branches
- Gallery branch now uses satellite captions (L1/L2/L3) via shared TextFusionMLP
- Two separate GatedFusion gates: α_q (query) and α_g (gallery)
- For sat images without captions (~57%): gate passes image features through

Dataset changes:
- GTAUAVDataset now loads satellite captions from caption index
- collate_gtauav_batch includes sat_caption_l1/l2/l3

Training loop:
- Passes satellite captions to model forward
- Logs both gate_q and gate_g values

11.1M trainable / 734M total (1.51%)

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

src/datasets/gtauav_dataset.py

@@ -176,13 +176,20 @@ class GTAUAVDataset(Dataset):
             else:
                 continue  # No match, skip.
 
-            # Get captions.
+            # Get drone captions.
             cap_data = self.caption_index.get(drone_name)
             if cap_data is not None:
                 l1, l2, l3 = _parse_caption_levels(cap_data["output"])
             else:
                 l1 = l2 = l3 = _EMPTY_CAPTION
 
+            # Pre-parse satellite captions for all candidates.
+            sat_captions: dict[str, tuple[str, str, str]] = {}
+            for sat_name in sat_candidates:
+                sat_cap = self.caption_index.get(sat_name)
+                if sat_cap is not None:
+                    sat_captions[sat_name] = _parse_caption_levels(sat_cap["output"])
+
             self.entries.append({
                 "drone_name": drone_name,
                 "drone_dir": pair["drone_img_dir"],
@@ -192,6 +199,7 @@ class GTAUAVDataset(Dataset):
                 "caption_l1": l1,
                 "caption_l2": l2,
                 "caption_l3": l3,
+                "sat_captions": sat_captions,
             })
 
     def _load_image(self, directory: str, filename: str, transform: Callable | None = None) -> torch.Tensor:
@@ -222,18 +230,28 @@ class GTAUAVDataset(Dataset):
 
         sat_img = self._load_image(entry["sat_dir"], sat_name, self.sat_transform)
 
-        # Captions with optional dropout.
+        # Drone captions with optional dropout.
         if self.drop_caption_prob > 0 and self._rng.random() < self.drop_caption_prob:
             l1 = l2 = l3 = _EMPTY_CAPTION
         else:
             l1, l2, l3 = entry["caption_l1"], entry["caption_l2"], entry["caption_l3"]
 
+        # Satellite captions (empty string if not available).
+        sat_caps = entry["sat_captions"].get(sat_name)
+        if sat_caps is not None:
+            sat_l1, sat_l2, sat_l3 = sat_caps
+        else:
+            sat_l1 = sat_l2 = sat_l3 = _EMPTY_CAPTION
+
         return {
             "drone_img": drone_img,
             "sat_img": sat_img,
             "caption_l1": l1,
             "caption_l2": l2,
             "caption_l3": l3,
+            "sat_caption_l1": sat_l1,
+            "sat_caption_l2": sat_l2,
+            "sat_caption_l3": sat_l3,
             "pair_id": entry["drone_name"],
         }
 
@@ -248,5 +266,8 @@ def collate_gtauav_batch(
         "caption_l1": [b["caption_l1"] for b in batch],
         "caption_l2": [b["caption_l2"] for b in batch],
         "caption_l3": [b["caption_l3"] for b in batch],
+        "sat_caption_l1": [b["sat_caption_l1"] for b in batch],
+        "sat_caption_l2": [b["sat_caption_l2"] for b in batch],
+        "sat_caption_l3": [b["sat_caption_l3"] for b in batch],
         "pair_ids": [b["pair_id"] for b in batch],
     }